Securities Markets: Decimal Pricing has Contributed to Lower	 
Trading Costs and a More Challenging Trading Environment	 
(31-MAY-05, GAO-05-535).					 
                                                                 
In early 2001, U.S. stock and option markets began quoting prices
in decimal increments rather than fractions of a dollar. At the  
same time, the minimum price increment, or tick size, was reduced
to a penny on the stock markets and to 10 cents and 5 cents on	 
the option markets. Although many believe that decimal pricing	 
has benefited small individual (retail) investors, concerns have 
been raised that the smaller tick sizes have made trading more	 
challenging and costly for large institutional investors,	 
including mutual funds and pension plans. In addition, there is  
concern that the financial livelihood of market intermediaries,  
such as the broker-dealers that trade on floor-based and	 
electronic markets, has been negatively affected by the lower	 
ticks, potentially altering the roles these firms play in the	 
U.S. capital market. GAO assessed the effect of decimal pricing  
on retail and institutional investors and on market		 
intermediaries. 						 
-------------------------Indexing Terms------------------------- 
REPORTNUM:   GAO-05-535 					        
    ACCNO:   A25477						        
  TITLE:     Securities Markets: Decimal Pricing has Contributed to   
Lower Trading Costs and a More Challenging Trading Environment	 
     DATE:   05/31/2005 
  SUBJECT:   Brokerage industry 				 
	     Comparative analysis				 
	     Cost analysis					 
	     Cost effectiveness analysis			 
	     Financial analysis 				 
	     Investment companies				 
	     Prices and pricing 				 
	     Stock exchanges					 
	     Stocks (securities)				 
	     Systems analysis					 
	     Transparency					 

******************************************************************
** This file contains an ASCII representation of the text of a  **
** GAO Product.                                                 **
**                                                              **
** No attempt has been made to display graphic images, although **
** figure captions are reproduced.  Tables are included, but    **
** may not resemble those in the printed version.               **
**                                                              **
** Please see the PDF (Portable Document Format) file, when     **
** available, for a complete electronic file of the printed     **
** document's contents.                                         **
**                                                              **
******************************************************************
GAO-05-535

                 United States Government Accountability Office

                     GAO Report to Congressional Requesters

May 2005

SECURITIES MARKETS

 Decimal Pricing Has Contributed to Lower Trading Costs and a More Challenging
                              Trading Environment

                                       a

GAO-05-535

[IMG]

May 2005

SECURITIES MARKETS

Decimal Pricing Has Contributed to Lower Trading Costs and a More Challenging
Trading Environment

                                 What GAO Found

Trading costs, a key measure of market quality, have declined
significantly for retail and institutional investors since the
implementation of decimal pricing in 2001. Retail investors now pay less
when they buy and receive more when they sell stock because of the
substantially reduced spreads- the difference between the best quoted
prices to buy or sell. GAO's analysis of data from firms that analyze
institutional investor trades indicated that trading costs for large
investors have also declined, falling between 30 to 53 percent. Further,
87 percent of the 23 institutional investor firms we contacted reported
that their trading costs had either declined or remained the same since
decimal pricing began. Although trading is less costly, the move to the
1-cent tick has reduced market transparency. Fewer shares are now
generally displayed as available for purchase or sale in U.S. markets.
However, large investors have adapted by breaking up large orders into
smaller lots and increasing their use of electronic trading technologies
and alternative trading venues.

Although conditions in the securities industry overall have improved
recently, market intermediaries, particularly exchange specialists and
NASDAQ market makers, have faced more challenging operating conditions
since 2001. From 2000 to 2004, the revenues of the broker-dealers acting
as New York Stock Exchange specialists declined over 50 percent, revenues
for firms making markets on NASDAQ fell over 70 percent, and the number of
firms conducting such activities shrank from almost 500 to about 260.
However, factors other than decimal pricing have also contributed to these
conditions, including the sharp decline in overall stock prices since
2000, increased electronic trading, and heightened competition from
trading venues.

Average Quoted Spreads Before and After Decimal Pricing Implemented in
Cents per Share, February 2000 through November 2004

Quoted spread (in cents) 25 NYSE 20

15

10

5 Feb. .uneAug.Oct.

                                      Dec.

                                      Jan.

.Apr

ug.A

Dec.Jan.

yMa

                                   Sept. Feb.

uneJ

                                      Oct.

.Mar

yl

Ju

.NoAprJ 0

2000 2001 2002 2003 2004 Sample week (year and month)

Source: GAO.

United States Government Accountability Office

Contents

  Letter

Background Results in Brief Investors' Trading Costs Have Declined Since
Decimalization, but

Reduced Market Transparency Has Caused Firms to Adopt New Trading
Strategies

Some Stock Intermediaries Have Experienced Lower Profits since
Decimalization, but Other Factors Have Contributed to the Declines

Decimal Pricing Has Had a Limited Impact on the Options Markets,

but Other Factors Have Helped Improve Market Quality Observations Agency
Comments

1 3 6

8

44

60 71 72

Appendixes                                                             
                Appendix I:             Scope and Methodology              74 
                                 Methodology for Assessing Impact on       74 
                                       Institutional Investors            
                             Methodology for Assessing Impact on Market    75 
                                           Intermediaries                 
                             Methodology for Assessing Impact on Options   76 
                                               Markets                    
               Appendix II:   Methodology for GAO Analysis of Trade and    77 
                                             Quotes Data                  
                               Measurement of Institutional Investors'    
              Appendix III:               Trading Costs in                
                              Basis Points Shows Decline since Decimal    
                                               Pricing                    
                                             Implemented                   96 
              Appendix IV:   Additional Analysis Using Trade and Quotes   106 
                                                Data                      
                Appendix V:    GAO Contacts and Staff Acknowledgments     110 
                                                                          111 
                                                                          116 

Tables	Table 1: Table 2: Table 3:

Table 4:

Average Quoted Spreads Before and After Decimalization,
2000-2004 (cents per share) 11
Average Effective Spreads Before and After
Decimalization, 2000-2004 (cents per share) 12
Volume-weighted Average Effective Spreads Before and
After Decimalization for Selected NYSE and NASDAQ
Stocks, by Market Capitalization (cents per share) 13
Institutional Investor Positions on Changes to Trading
Costs After Decimalization 28

                                    Contents

Table 5:	Price Change Volatility for NYSE and NASDAQ Stocks Before and
After Decimalization 31

Table 6:	Average Number of Shares Displayed at the Best Quoted Prices
Reported by NYSE and NASDAQ in Studies of Their Markets Before and After
Decimalization 32

Table 7: Average Trade Size for NYSE and NASDAQ, 1999-2004 (in shares) 37
Table 8: NYSE Specialist Firm Revenues and Profits, 1999-2004 (in millions
of dollars) 47 Table 9: NYSE Reported Trades, Average Daily Volume, and
Average Trade Size, 1999-2004 49

Table 10: NYSE Member Broker-Dealer Revenues from NASDAQ Market Making
Activities, 1999-2004 (in millions of dollars) 50

Table 11: NASDAQ Average Trade Size, and Average Daily Volume, 1999-2004
51 Table 12: Number of Specialist Firms Operating on Selected Stock
Markets, 1999-2004 51 Table 13: Consolidation among NASDAQ Market Makers,

1999-2004 52 Table 14: Pre- and Postdecimalization Sample Weeks 81 Table
15: Price Characteristics of NYSE-Listed and NASDAQ

Stocks 86 Table 16: Volume Characteristics of NYSE-Listed and NASDAQ
Stocks 88 Table 17: Average Quoted Spreads Before and After
Decimalization, 2000-2004 (basis points) 106 Table 18: Average Effective
Spreads Before and After Decimalization, 2000-2004 (basis points) 107

Figures	Figure 1: Figure 2: Figure 3:

Figure 4: Figure 5:

Average Quoted Spreads Before and After Decimalization (cents per share)
10 Total Trading Costs from a Trade Analytics Firm for NYSE and NASDAQ
Stocks, 1999-2003 (cents per share) 17 Trading Cost Components from a
Trade Analytics Firm for NYSE and NASDAQ Stocks, 2001 and 2003 (cents per
share) 19 Total Trading Costs from Two Trade Analytics Firms for NYSE,
1998-2004 (cents per share) 21 Total Trading Costs from Two Trade
Analytics Firms for NASDAQ Stocks, 1998-2004 (cents per share) 22

Contents

Figure 6:	Trading Cost Components from Two Trade Analytics
Firms for NYSE Stocks, 2001 and 2004 (cents per
share) 23

Figure 7:	Trading Cost Components from Two Trade Analytics
Firms for NASDAQ Stocks, 2001 and 2004 (cents per
share) 24

Figure 8:	Volume-weighted Average Number of Shares Displayed at
the Best Quoted Prices on the NYSE and NASDAQ Before
and After Decimalization, Sample Weeks from February
2000-November 2004 33

Figure 9: Proportion of Total Share Trading Volume NASDAQ and
NYSE Stocks by ECNs, 1996-2003 40
Figure 10: Securities Industry Total Revenues and Net Income,
1994-2004 46
Figure 11: NYSE Specialist Participation Rates, 1999-2004, in
Percent of Trades 48

Figure 12: Securities Industry Revenues and Net Income as
Compared to the Performance of the S&P 500 Stock
Index, 1994-2004 54

Figure 13: Number of IPOs and Dollars Raised, 1994-2004 58
Figure 14: Total Contract Trading Volumes for Stock Options, 2000-
2004 (Volume in millions) 63

Figure 15: Distribution of Average Daily Closing Prices for Full
Sample of Matching Stocks and 300 Matched-Pairs
Sample 87

Figure 16: Distribution of Average Daily Trading Volume for Full
Sample of Matching Stocks and 300 Matched-Pairs
Sample 89

Figure 17: Total Trading Costs from a Trade Analytics Firm for NYSE
and NASDAQ Stocks, 1999-2004 (basis points) 98
Figure 18: Trading Cost Components from One Trade Analytics Firm
for NYSE and NASDAQ, 2001-2003 (basis points) 99
Figure 19: Total Trading Costs from Two Trade Analytics Firms for
NYSE Stocks, 2001-2004 (basis points) 101
Figure 20: Total Trading Costs from Two Trade Analytics Firms for
NASDAQ Stocks, 2001-2004 (basis points) 102
Figure 21: Trading Cost Components from Two Trade Analytics
Firms for NYSE Stocks, 2001 and 2004 (basis points) 103

Figure 22: Trading Cost Components from Two Trade Analytics
Firms for NASDAQ Stocks, 2001 and 2004 (basis
points) 104

Figure 23: Quote Clustering After Decimalization, 2001-2004 108

Contents

Figure 24: Quote Clustering After Decimalization, by Sample Week,
2001-2004 109

Contents

Abbreviations

Amex American Stock Exchange
ATS alternative trading system
BOX Boston Options Exchange
bps basis points
CBOE Chicago Board Options Exchange
CQ Consolidated Quotes
CMS composite match score
ECN electronic communication network
FOCUS Financial and Operational Combined Uniform Single
GAO Government Accountability Office
IBM International Business Machines
IPO initial public offering
ISE International Securities Exchange
MMID market maker identification
mps messages per second
NASD National Association of Securities Dealers
NASDAQ National Association of Securities Dealers Automated

Quotations NBB national best bid NBO national best offer NBBO national
best bid and offer NMS National Market System NYSE New York Stock Exchange
OPRA Options Price Reporting Authority OTC over-the-counter OTCBB
over-the-counter bulletin board PCX Pacific Exchange Phlx Philadelphia
Stock Exchange SEC Securities and Exchange Commission SIA Securities
Industry Association TAQ Trade and Quote VWAP volume weighted average
price

This is a work of the U.S. government and is not subject to copyright
protection in the United States. It may be reproduced and distributed in
its entirety without further permission from GAO. However, because this
work may contain copyrighted images or other material, permission from the
copyright holder may be necessary if you wish to reproduce this material
separately.

A

United States Government Accountability Office Washington, D.C. 20548

May 31, 2005

The Honorable Michael Enzi United States Senate

The Honorable Rick Santorum United States Senate

With encouragement from Congress, in 2000 the Securities and Exchange
Commission (SEC) ordered U.S. stock and option markets to begin quoting
prices in decimal increments rather than fractions of a dollar.1 As U.S.
markets implemented decimal pricing in early 2001, they also reduced the
minimum price increment, or tick size, at which prices could be quoted.
The minimum tick on the stock markets generally fell from 1/16 of a dollar
to a penny and on the option markets from 1/8 and 1/16 of a dollar to 10
cents and 5 cents, respectively.2 The United States had been one of the
last countries to use fractions on its markets, and decimal pricing was
expected to simplify securities pricing for investors, help lower
investors' trading costs and align U.S. pricing standards with those of
other markets.

Many market participants and others who have observed the markets believe
that decimal pricing has benefited small retail investors seeking to buy
or sell a few hundred shares of stock.3 But concerns have been raised that
the smaller tick size has made trading more challenging and costly for
large institutional investors, including mutual funds and pension plans,
that trade large blocks of shares.4 In addition, concerns exist over
whether trading in 1-cent ticks has negatively affected the financial
livelihood of market intermediaries, such as the broker-dealers that trade
on floor-based

1A stock is a security that signifies an ownership position in a company.
An option contract provides the purchaser the right to buy (call) or sell
(put) a fixed amount of a given security at a specified price within a
limited period of time.

2For option contracts priced $3 and above, the tick size was reduced from
1/8 of a dollar (12.5 cents) to 10 cents, and the tick size for contracts
priced below $3 was reduced from 1/16 of a dollar (6.25 cents) to 5 cents.

3As used in this report, retail investors are individuals who buy or sell
securities for their own accounts.

4Institutional investors are entities, such as mutual funds, insurance
companies, pension plans, or charitable organizations, that invest on
behalf of themselves or others. Such investors typically have large pools
of assets and buy and sell securities in large quantities or blocks. The
stock markets classify block trades as those involving 10,000 shares or
more.

and electronic markets, potentially altering the roles these firms play in
the U.S. capital market.

This report responds to your February 12, 2004, request that we study the
impact of decimal pricing on the trading of U.S. stocks and options. As
agreed with your staffs, our objectives were to study the impact of
decimal pricing on (1) retail and institutional investors, (2) market
intermediaries, and (3) options market investors and intermediaries.

To determine the effect of decimalization on retail and institutional
investors in securities, we analyzed a comprehensive database of all
trades conducted on U.S. stock markets from February 2000 to November 2004
to identify changes to key characteristics of stock markets, such as
spreads, liquidity, trading volumes, and price volatility.5 We also
analyzed data on institutional investors' trading costs that were provided
by three trade analytics firms in order to identify trends in these costs
before and after decimalization. In addition, we reviewed relevant
academic, industry, and regulatory studies that address the effects of
decimal pricing on the stock markets. Finally, we interviewed almost 70
market participants, including securities traders, broker-dealers, and
institutional investors such as pension and mutual fund investment
managers, as well as representatives of regulatory agencies, stock
markets, electronic trading systems, and industry associations. To
determine decimalization's effect on intermediaries in U.S. stock markets,
we reviewed studies and data on market participants' revenue and
profitability and interviewed a variety of intermediaries, including
broker-dealers, market makers, regional and national exchange specialists,
and traders. We sought the perspectives of other market participants,
including representatives from regulatory agencies, stock markets,
industry associations, and institutional investors. To determine the
effect of decimal pricing and the tick size reductions on investors and
intermediaries in the options market, we reviewed studies by options
exchanges; interviewed representatives of all six U.S. options markets, as
well as broker-dealers and hedge funds that trade options; and reviewed
comment letters that SEC received on potential changes in options market
regulations. Appendixes I and II contain a full description of our scope
and methodology. We conducted our work in Baltimore, Boston, Chicago, Los
Angeles, New York, Philadelphia, San Francisco, and

5This analysis used the Trade and Quotes (TAQ) database maintained by the
New York Stock Exchange. The TAQ contains records of all trades and price
quotes from all U.S. exchanges and the NASDAQ Stock Market.

Washington, D.C., between May 2004 and May 2005 in accordance with
generally accepted government auditing standards.

Background	In implementing decimal pricing, regulators hoped to improve
the quality of U.S. stock and option markets. The quality of a market can
be assessed using various characteristics, but the trading costs that
investors incur when they execute orders are a key aspect of market
quality. Trading costs are generally measured differently for retail and
institutional investors. In addition to the commission charges to paid
broker-dealers that execute trades, the other primary trading cost for
retail investors, who typically trade no more than a few hundred shares at
a time, is measured by the spread, which is the difference between the
best quoted "bid" and "ask" prices that prevail at the time the order is
executed. The bid price is the best price at which market participants are
willing to buy shares, and the ask price is the best price at which market
participants are willing to sell shares.6 The spread represents the cost
of trading for small orders because if an investor buys shares at the ask
price and then immediately sells them at the bid price, the resulting loss
or cost is represented by the size of the spread.

Because institutional orders are generally much larger than retail orders
and completing one order can require multiple trades executed at varying
prices, spreads are not generally used to measure institutional investors'
trading costs. Instead, the components of trading costs for large
institutional investors, who often seek to buy or sell large blocks of
shares such as 50,000 or 1 million shares, include the order's market
impact, broker commissions paid, and exchange fees incurred, among other
things. An order's market impact is the extent to which the security
changes in price after the investor begins trading. For example, if the
price of a stock begins to rise in reaction to the increased demand after
an investor begins executing trades to complete a large order, the average
price at which the investor's total order is executed will be higher than
the stock's price would have been without the order.

6In general, for a given stock transaction the best bid (ask) price
represents the highest (lowest) price available from all U.S. market
venues providing quotes to sellers (buyers) of the stock. This is known as
the national best bid and offer, or NBBO.

In addition to trading costs, decimal pricing may have affected several
other aspects of market quality, including liquidity, transparency, and
price volatility.

Liquidity. Liquid markets have many buyers and sellers willing to trade
and have sufficient shares to execute trades quickly without markedly
affecting share prices. Generally, the more liquid the overall market or
markets for particular stocks are, the lower the market impact of any
individual orders. Small orders for very liquid stocks will have minimal
market impact and lower trading costs. However, larger orders,
particularly for less liquid stocks, can affect prices more and thus have
greater market impact and higher trading costs.

Transparency. When markets are transparent, the number and prices of
available shares are readily disclosed to all market participants, and
prices and volumes of executed trades are promptly disseminated. A key
factor that can affect market participants' perceptions of market
transparency is the volume of shares publicly displayed as available at
the best quoted bid and ask prices, as well as at points around these
prices-known as market depth. Markets with small numbers of shares
displayed in comparison to the size of investors' typical orders seem less
transparent to investors because they have less information that can help
them specify the price and size of their own orders so as to execute
trades with minimal trading costs.

Price volatility. Price volatility is a measure of the frequency of price
changes as well as a measure of the amount by which prices change over a
period of time. Highly volatile markets typically disadvantage investors
that execute trades with less certainty of the prices they will receive.
Conversely, market intermediaries, such as broker-dealers, can benefit
from highly volatile markets because they may be able to earn more revenue
from trading more frequently as prices rise and fall.

The trading that occurs on U.S. securities markets is facilitated by
brokerdealers that act as market intermediaries. These intermediaries
perform different functions depending on the type of trading that occurs
in each market. On markets that use centrally located trading floors to
conduct trading, such as the New York Stock Exchange (NYSE), trading
occurs primarily through certain broker-dealer firms that have been
designated as specialists for particular stocks. These specialists are
obligated to maintain fair and orderly markets by buying shares from or
selling shares to the other broker-dealers who present orders from
customers on the trading floor or through the electronic order routing
systems used by the exchange.

Interacting with the specialists on the trading floor are employees from
large broker-dealer firms that receive orders routed from these firms'
offices around the country. In addition, specialists receive orders from
staff from small, independent broker-dealer firms who work only on the
floor.

In contrast, trading of the stocks listed on the NASDAQ Stock Market
(NASDAQ), which does not have a central physical trading location, is
conducted through electronic systems operated by broker-dealers acting as
market makers or by alternative trading venues. For particular stocks,
market makers enter quotes indicating the prices at which these firms are
simultaneously willing to buy from or sell shares to other broker-dealers
into NASDAQ's electronic system. The NASDAQ system displays these quotes
to all other broker-dealers that are registered to trade on that market.
Much of the trading in NASDAQ stocks now also takes place in alternative
trading venues, including electronic communication networks (ECN), which
are registered as broker-dealers and electronically match the orders they
receive from their customers, much like an exchange.

At the same time that decimal pricing was being implemented, other changes
were also occurring in the marketplace. For example, in 1997, SEC enacted
new rules regarding how market makers and specialists must handle the
orders they received from their customers, including requiring firms to
display these orders to the market when their prices are better than those
currently offered by that broker.7 These rules facilitated the growth of
additional trading venues such as the ECNs, which compete with the
established markets, such as NYSE and NASDAQ, for trading volumes. The
increased use of computerized trading has also provided alternative
mechanisms for trading and reduced the role of specialists, market makers,
and other intermediaries in the trading process. In addition, after rising
significantly during the late 1990s, U.S. stock prices experienced several
years of declines, affecting trading costs and market intermediary
profits. Facing lower investment returns, institutional investors and
professional traders have focused more on reducing trading costs to
improve those returns. Regulators also began placing greater emphasis on
institutional

7Securities Exchange Act Release No. 37619A (Sept. 6, 1996), 61 FR 48290 (Sept.
                                   12, 1996).

investors' duty to obtain the best execution for their trades, further
increasing the pressure on these firms to better manage their trading
costs.8

Results in Brief 	Trading costs, a key measure of market quality, have
declined significantly for retail and institutional investors since the
implementation of decimal pricing in 2001. Retail investors are now able
to trade small orders that execute in one trade more cheaply as a result
of the substantially reduced spreads that prevail in the stock markets.
Data from firms that analyze institutional investors' trading costs and
academic studies also showed that trading costs for large investors have
also declined. Further, 20 of the 23 institutional investor firms we
contacted (representing about 31 percent of assets managed by the top 300
U.S. money management firms) reported that their trading costs had either
declined or remained the same since decimal pricing began. The extent to
which decimal pricing is responsible for these improvements is not clear
because other factors, including the multiyear downturn in stock prices
that began in 2000, may have also contributed to the reduced trading
costs. Although trading is less costly, the move to the 1-cent tick
appears to have reduced market transparency as the number of shares that
are generally displayed as available for purchase or sale in U.S. stock
markets shrank. In part, institutional investors became less willing to
display large orders to the markets because the 1-cent tick lowered the
financial risks for other traders seeking to "step ahead" of these larger
orders by entering orders priced just a penny better. Institutional
investors told us that they had adapted to these new conditions by
breaking up large orders into smaller lots and using electronic trading
technologies to execute these smaller orders in the markets. In addition,
they reported increasing their use of alternative trading venues, such as
ECNs and crossing networks that anonymously match large institutional
investor orders. Through these adaptations, institutional investors have
been able to continue executing large orders at reduced costs.

8For example, the Chartered Financial Analyst Institute, which sets
standards for investment professionals, issued guidelines on trade
management that emphasize the need for investment managers to seek to
achieve best execution for their clients. In addition, a top SEC
examination official noted in a speech in 2002 that firms should increase
their efforts to better ensure that the broker-dealers they use are
achieving the best executions for their trades.

Although investors appear to have benefited since decimal pricing began,
some market intermediaries have faced more challenging operating
conditions. Despite overall improving conditions in the securities
industry since 2001, broker-dealers acting as exchange specialists and
NASDAQ market makers have seen their profits fall, forcing some to merge
with other firms or to leave the industry. Between 2000 and 2004, the
exchange specialist broker-dealers that match investor orders and buy and
sell shares on the trading floors of various exchanges experienced reduced
revenues and profits. For example, in 2004 NYSE exchange specialists
reported aggregate revenues of $902 million, down by more than 50 percent
from the $2.1 billion such firms earned in 2000. Broker-dealers that make
markets in NASDAQ and other non-exchange listed stocks appear to have been
affected even more by the lower spreads and reductions in displayed
liquidity that have accompanied decimal pricing. According to data from
the Securities Industry Association, aggregate revenues for these firms
declined more than 70 percent between 2000 and 2004, falling from $9
billion to $2.5 billion. Since 2001, market intermediaries conducting
certain activities have consolidated. For example, the number of NYSE
specialist firms fell from 25 in 1999 to 7 in 2004, and the number of
NASDAQ market makers declined from almost 500 in 2000 to about 260 in
2004. However, factors other than decimal pricing have also contributed to
these conditions, including the sharp decline in overall stock prices
since 2000, reduced revenues from customers' increasing use of electronic
trading strategies, and heightened competition from ECNs and other
electronic trading venues. Market participants noted that these trends had
been in place before decimalization. We found that market intermediaries
had attempted to adapt to the new conditions by changing their business
practices. For example, NASDAQ market makers had begun charging
commissions on trades, broker-dealers had invested heavily in
technological trading devices and data management systems, and other firms
had reduced the sizes of their trading staffs. These conditions and the
perceived decline in displayed liquidity in U.S. stock markets has caused
a proposal to be made to conduct a pilot study of the use of higher
minimum ticks for stock trading. Such a pilot was favored by most of the
market intermediaries we contacted but by only about half of the
institutional investors interviewed, and some of those that were open to
testing larger tick sizes for trading saw them as being useful primarily
for less liquid stocks rather than for all stocks.

The effect of decimal pricing for options trading has been less
significant. In part, options markets were less affected because the tick
sizes that accompanied decimal pricing did not represent large changes
from those

previously in use. Nevertheless, the quality of U.S. options markets, as
measured by their trading costs, liquidity, and increased trading volumes,
has improved since 2001. However, options markets participants attributed
these improvements primarily to other changes, including the increased
competition arising from multilisting (the trading of options on the same
securities on multiple exchanges), which began in 1999, and the
establishment of new electronic exchanges and trading systems. Decimal
pricing's effect on options market intermediaries such as market makers
and specialists has been mixed, with market participants indicating that
floor-based firms have experienced declining revenues and profitability
and electronic-based firms are seeing increased trading revenues and
profitability. A 2004 SEC release sought industry comments on a range of
issues pertaining to options markets, including whether these markets
should use 1-cent ticks. However, officials of options exchanges and firms
we contacted and virtually all of those providing comments to SEC were
strongly opposed to lowering minimum price increments to one penny for
options. Many were concerned that penny ticks would generate large numbers
of price quote messages that would overwhelm the transmission and
processing capacity of the existing market and data vendor systems. They
also feared that lower intermediary revenues and more price points would
reduce liquidity in the options markets.

In their comments on a draft of this report, staff from SEC's Division of
Market Regulation and Office of Economic Analysis said that, overall, the
report accurately depicted conditions in the markets after the
implementation of decimal pricing.

  Investors' Trading Costs Have Declined Since Decimalization, but Reduced
  Market Transparency Has Caused Firms to Adopt New Trading Strategies

Trading costs for both retail and institutional investors fell after the
implementation of decimal pricing and the corresponding reduction in tick
size. While decimalization appears to have helped to lower these costs,
other factors-such as the multiyear downturn in stock prices-also likely
contributed to these cost reductions. Although trading costs and other
market quality measures improved after decimal pricing's implementation,
another measure-the transparency of U.S. stock markets-declined following
the reduction in tick size in 2001 because fewer shares were displayed as
available for trading. However, most market participants we interviewed
reported they have been able to continue to execute large orders by using
electronic trading tools to submit a larger volume of smaller orders and
making greater use of alternative trading venues.

    Decimal Pricing Reduced Trading Costs for Retail Investors

In ordering U.S. markets to convert to decimal pricing, SEC had several
goals.9 These included making securities pricing easier for investors to
understand and aligning U.S. markets' pricing conventions with those of
foreign securities markets. Decimalization appears to have succeeded in
meeting these goals. In addition, SEC hoped that decimal pricing would
result in lower investor trading costs, as lower tick sizes would spur
competition that would lead to reduced spreads. Narrower spreads benefit
retail investors because retail size orders generally execute in one trade
at one price. Prior to being ordered to implement decimal pricing, U.S.
stock markets had voluntarily reduced their minimum ticks from 1/8 to 1/16
of a dollar, and studies of these actions found that spreads declined as a
result.

Following decimalization and the implementation of the 1-cent tick in
2001, retail investor trading costs declined further as spreads were
narrowed even more substantially.10 To analyze the effects of decimal
pricing, we selected a sample of 300 pairs of NYSE-listed and NASDAQ
stocks with similar characteristics (like share price and trading
activity).11 We examined several weeks before and after the implementation
of decimal pricing and found that spreads declined after decimal prices
were implemented and remained low through 2004. Our study considered 12
weeklong sample periods from February 2000 to January 2001 (our
predecimalization period) and 12 weeklong sample periods from April 2001
through November 2004 (our postdecimalization period). As shown in figure
1, quoted spreads continued a steady decline on both NYSE and

9SEC, Division of Market Regulation: Order Directing the Exchanges and
NASD to Submit a Decimalization Implementation Plan, Exchange Act Release
No. 42360 (January 28, 2000), 65 Fed. Reg. 5003 (2000).

10Another component of small investors' trading costs is the commission
they pay to brokerdealers for executing their trades. The move to decimal
pricing was not expected to change retail commissions and thus have not
been included in our analysis of retail investor costs.

11For this analysis, we selected pairs of NYSE and NASDAQ stocks by
matching stocks with similar trading and stock characteristics. By
generating these pairs, we attempted to prevent our results from being
influenced by the differences between stocks' characteristics so as to
better isolate the impact of decimal pricing alone. By selecting pairs of
NYSE and NASDAQ stocks, our sample may be biased because the smallest
NASDAQ stocks are not generally comparable in characteristics to NYSE
stocks; this bias may tend to overstate the benefits of decimalization
such as reductions in spreads and thus caution should be used in
generalizing our results. However, our matched pairs also tended to
underrepresent stocks with higher daily trading volume, which likely would
bias our results toward understating spread reductions.

NASDAQ following the implementation of decimal pricing, falling to levels
well below those that existed before the conversion to decimal pricing.

  Figure 1: Average Quoted Spreads Before and After Decimalization (cents per
                                     share)

                            Quoted spread (in cents)

) Jan.(7-11) Dec.(10-14

                                       )

Feb.(7-11)

(20-24).Apr

2)(10-14)

(10-14)ug.A

une (19-23)

) Oct.(23-27

.No

                                       )

))(6-10) Jan.(22-26

Dec.(18-22

(23-27).Apr

4)

                                  Sept.(23-27)

8) Feb.(24-2

une (2-6)J

) Oct.(20-24

(8-12).Mar

                                 (19-23) (1-5)

y (6-10Ma

                                  Sept.(18-22

y (8-1

ug.(20-2 A

.No

.Mar

MaJ

                                      y y

luJ

luJ

                              2001 2002 2003 2004

Sample week (year, month, and days)

NYSE

Nasdaq

Source: GAO.

Note: The figure presents the average spread for the stocks in our sample
from a 5-day period (a trading week) in each of the above listed months.
Our sample weeks exclude any from February and March 2001 because not all
stocks were trading using decimal prices during the transition period. The
change in spread for each stock in this analysis was weighted by its
trading volume relative to the total trading volume. See appendix II for a
detailed explanation of the methodology for this analysis.

Our analysis of the TAQ data also found that quoted spreads declined for
stocks with varying levels of trading volume. As shown in table 1, quoted
spreads declined significantly after decimal pricing began for the most
actively traded stocks, those with medium levels of trading volume, and
also for those with the lowest amount of daily trading activity, with the

average quoted spread falling 73 percent for NYSE stocks and 68 percent
for NASDAQ stocks.

Table 1: Average Quoted Spreads Before and After Decimalization, 2000-2004
                               (cents per share)

NYSE quoted spread NASDAQ quoted spread

Average Average Stocks by average spread in cents Average spread in cents
Average daily volume of shares before spread in cents before spread in
cents traded decimals after decimals Percent change decimals after
decimals Percent change

                      High 14.93 2.77 -81% 12.95 2.74 -79%

Medium 14.94 3.78 -75 15.58 4.47

Low 16.25 5.26 -68 18.97 7.69

All stocks 15.39 4.18 -73 16.96 5.39

Source: GAO analysis of TAQ data.

Note: Quoted spreads in the table represent the volume-weighted average
quoted spread (i.e., stocks and weeks with more total trading volume have
greater weight) over 12 sample weeks during the predecimals period
(February 2000-January 2001) and 12 sample weeks during the postdecimals
period (April 2001-November 2004) for our sample of stocks. Stocks were
segregated by volume according to the following categories:

o 	High volume stocks were those in our sample of stocks with average
daily trading volumes exceeding 500,000 shares.

o 	Medium volume stocks were those in our sample of stocks with average
daily trading volumes between 100,000 and 499,999 shares.

o 	Low volume stocks were those in our sample of stocks with average daily
trading volumes of less than 100,000 shares.

Quoted spreads are time-weighted across quotes (quotes in effect longer
have greater weights) and volume-weighted across stocks (stocks with more
shares traded have greater weight).

While the quoted spread measure is useful for illustrative purposes, a
better measure of the cost associated with the bid-ask spread is the
effective spread, which is twice the difference between the price at which
an investor's trade is executed and the midpoint between the quoted bid
and ask prices that prevailed at the time the order was executed.12 Thus,
the effective spread measures the actual costs of trades occurring rather
than just the difference between the best quoted prices at the time of the
trade. As shown in table 2, effective spreads declined by 62 percent for
our NYSE

12For example, the effective spread for a trade executed for an investor
at a price of $10.03 for stock that was purchased when the bid-ask prices
were $10.01 (bid) and $10.03 (ask) would be 2 cents per share.

sample stocks and 59 percent for our NASDAQ sample stocks between the
periods after decimal pricing was implemented.

 Table 2: Average Effective Spreads Before and After Decimalization, 2000-2004
                               (cents per share)

NYSE effective spreads NASDAQ effective spreads

Average Average Stocks by average spread in cents Average spread in cents
Average daily volume of shares before spread in cents Percent before
spread in cents traded decimals after decimals change decimals after
decimals Percent change

                      High 14.85 4.71 -68% 15.85 4.86 -69%

Medium 13.17 4.95 -62 15.14 6.15

Low 12.86 6.37 -50 16.00 8.27

All stocks 13.36 5.05 -62 15.66 6.48

Source: GAO analysis of TAQ data.

Note: Effective quoted spreads (the difference between the price at which
a trade is executed and the midpoint between the prevailing quoted bid and
ask prices) in the table represent the volume-weighted average effective
spread (i.e., stocks and weeks with more total trading volume have greater
weight) over 12 sample weeks during the predecimals period (February
2000-January 2001) and 12 sample weeks during the postdecimals period
(April 2001-November 2004) for our sample of stocks. Stocks were
segregated by volume according to the following categories:

o 	High volume stocks were those in our sample of stocks with average
daily trading volumes exceeding 500,000 shares.

o 	Medium volume stocks were those in our sample of stocks with average
daily trading volumes between 100,000 and 499,999 shares.

o 	Low volume stocks were those in our sample of stocks with average daily
trading volumes of less than 100,000 shares.

In addition, several academic and industry studies found similar results.
For example, one academic study examined differences in trade execution
cost and market quality measures in 300 NYSE stocks and 300 NASDAQ stocks
(matched on market capitalization) for several weeks before decimal
pricing was fully implemented on NYSE stocks and after both markets
converted to decimal pricing. As shown in table 3, the study found that
average effective spreads declined by 41 percent for the NYSE stocks and
by 54 percent for the NASDAQ stocks from the predecimalization sample
period (January 8-26, 2001) to the postdecimalization sample period (April
9-August 31, 2001).13 As the table also shows, the study found that
spreads declined the most for NYSE stocks with the largest market
capitalizations and for NASDAQ stocks with the smallest market
capitalizations.14

Table 3: Volume-weighted Average Effective Spreads Before and After
Decimalization for Selected NYSE and NASDAQ Stocks, by Market
Capitalization (cents per share)

NYSE effective spreads NASDAQ effective spreads Stocks by market Before
decimals After decimals Percent Before decimals After decimals

                                    Percent

capitalization

                 (cents) (cents) change (cents) (cents) change

        Large           12.51    6.93      -45%     12.55     5.61       -55% 
       Medium           11.78    9.76      -17      14.76     8.97   
        Small           17.05   12.50      -27      18.89     7.56   
     All stocks         12.67    7.45      -41      12.66     5.78   

Source: Hendrik Bessembinder.

13Hendrik Bessembinder, "Trade Execution Costs and Market Quality after
Decimalization," Journal of Financial and Quantitative Analysis, vol. 38,
no. 4, 760.

14Market capitalization is a company's share price multiplied by the
number of shares outstanding.

Similar declines in spreads were also reported in studies that SEC
required the various markets to conduct as part of its order directing
them to implement decimal pricing. For example, in its impact study, NYSE
reported that share-weighted average effective spreads declined 43 percent
for all 2,466 NYSE-listed securities trading in the pre-and
postdecimalization sample periods the exchange selected.15 NASDAQ's study
found that effective spreads declined between its sample periods by an
average of 46 percent for the 4,766 NASDAQ securities that converted to
penny increments on April 9, 2001.16 In addition, an official at a major
U.S. stock market told us that all the research studies that he reviewed
on the impact of decimal pricing concluded that spreads narrowed overall
in response to the reduction in tick size.

Many market participants we interviewed also indicated that retail
investors benefited from the narrower spreads that followed decimalization
and the adoption of 1-cent ticks. For example, a representative of a firm
that analyzes trading activities of large investors told us that investors
trading 100 shares are better off following decimalization because small
trades can be executed at the now lower best quoted prices.
Representatives from two broker-dealers stated that the narrower spreads
that prevailed following decimalization meant that more money stayed with
the buyers and sellers of stock rather than going to market intermediaries
such as brokers-dealers and market makers. Furthermore, the chief
financial officer of a small broker-dealer told us that retail investors
had benefited from the adoption of the 1-cent tick because their orders
can generally be executed with one transaction at a single price unlike
those of institutional investors, which are typically larger than the
number of shares displayed as available at the best prices.

15New York Stock Exchange, Inc., Decimalization of Trading on the New York
Stock Exchange: A Report to the Securities and Exchange Commission,
September 7, 2001. For the predecimalization sample period, the NYSE used
the 19 trading days from August 1, 2000, through August 25, 2000. The
postdecimalization sample period is composed of the 21 trading days in
June 2001.

16The NASDAQ Stock Market, Inc., The Impact of Decimalization on the
NASDAQ Stock Market: Final Report to the SEC, June 11, 2001. The
predecimalization sample period NASDAQ used included the 2 weeks before
and the 2 weeks after the final date all NASDAQ securities had converted
to penny increments on April 9, 2001.

    Institutional Investors' Trading Costs Have Also Declined Since
    Decimalization

Analysis of the multiple sources of data that we collected generally
indicated that institutional investors' trading costs had declined since
decimal prices were implemented. We obtained data from three leading firms
that collect and analyze information about institutional investors'
trading costs. These trade analytics firms (Abel/Noser, Elkins/McSherry,
and Plexus Group) obtain trade data directly from institutional investors
and brokerage firms and then calculate trading costs, including market
impact costs, typically for the purpose of helping investors and traders
limit costs of trading.17 These firms also aggregate client data in order
to approximate total average trading costs for all their institutional
investor clients. Generally, the client base represented in these firms'
aggregate trade cost data is broad enough to be sufficiently
representative of all institutional investors. For example, officials at
one firm told us that its data captured 80 to 90 percent of all
institutional investors and covers trading for every stock listed on the
major U.S. stock markets.18 An official of a major U.S. stock market told
us that these firms are well regarded and that their information is
particularly informative because these firms measure costs from the point
the customer makes the decision to trade by using the price at which
stocks are trading at that time, which is data that exchanges and markets
generally do not have.

17ITG, another trade analytics firm, did not begin to measure
institutional investors' trading costs until January 2003, after the
implementation of decimal pricing and 1-cent ticks.

18Specifically, Abel/Noser captures data from about 50 large investment
management firms that in some years represent over 500 institutional
investors and well over 1,000 unique portfolio managers. In addition,
Abel/Noser claims its data represent nearly $3 trillion in principal
traded each year. Elkins/McSherry captures trade data from about 1,400
investment managers and 2,000 brokers worldwide, capturing about 20
percent of all dollars traded on NYSE and NASDAQ. The Plexus Group
collects data from money managers representing as many as 100
institutional investors.

Although these firms use different methodologies, their data uniformly
showed that costs had declined since decimal pricing was implemented. Our
analysis of data from the Plexus Group showed that costs declined on both
NYSE and NASDAQ in the 2 years after these markets converted to decimal
pricing. Plexus Group analyzes various components of institutional
investor trading costs, including the market impact of investors'
trading.19 Total trading costs declined by about 53 percent for NYSE
stocks, falling from about 33 cents per share in early 2001 to about 15.5
cents (fig. 2). For NASDAQ stocks, the decline was about 44 percent, from
about 25.7 cents to about 14.4 cents. The decline in trading costs, shown
in figure 2, began before both markets implemented decimal pricing,
indicating that causes other than decimal pricing were also affecting
institutional investors' trading during this period. An official from a
trade analytics firm told us that the spike in costs that preceded the
decimalization of NASDAQ stocks correlated to the pricing bubble that
technology sector stocks experienced in the late 1990s and early 2000s. An
official from another trade analytics firm explained that trading costs
increased during this time because when some stocks' prices would begin to
rise, other investors-called momentum investors-would also begin making
purchases and drive prices for these stocks up even faster. As a result,
other investors faced greater than usual market impact costs when also
trading these stocks. In general, trading during periods when stock prices
are either rapidly rising or falling can make trading very costly.

19To measure market impact costs, the Plexus Group compares a proprietary
benchmark stock price to the average price an investor receives. The
Plexus Group benchmark attempts to show the price at which the order for a
particular stock should be executed. The firm calculates this expected
price using trade data of its clients for the two quarters preceding the
date of the trade under study and takes into account variables such as
trade size, liquidity, and the direction of stock price movement.

Figure 2: Total Trading Costs from a Trade Analytics Firm for NYSE and
NASDAQ Stocks, 1999-2003 (cents per share)

Cents per share

80

70

60

50

40

30

20

10 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2

1999 2000 2001 2002 2003

Year and quarter

NYSE

Nasdaq

Source: GAO analysis of Plexus Group data.

Note: Data are reported quarterly. After a phase-in period, all NYSE
stocks were trading with decimal prices by January 29, 2001, and all
NASDAQ stocks were converted by April 9, 2001.

Cents Per Share Versus Basis Points

Institutional investors' trading costs are commonly measured in two units:
cents per share and basis points. Cents per share is an absolute measure
of cost based on executing a single share. Basis points-measured in
hundredths of a percentage point-show the absolute costs relative to the
stock's average share price. Costs reported in terms of basis points can
show changes resulting solely from changes in the level of stock prices.

In this section, we present our analysis of trade anlytics firms' data on
institutional investor trading costs in cents per share because the period
surrounding the U.S. markets' implementation of decimal pricing coincided
with a large decline in the overall prices of stocks. Therefore, we chose
to present data on trading costs in cents per share units as a way to
better isolate decimalization's impact. However, we also calculated these
same costs in basis points and present this analysis in appendix III.

Source: GAO.

According to our analysis of the Plexus Group data, market impact and
delays in submitting orders accounted for the majority of the decline in
trading costs for NYSE stocks and NASDAQ stocks.20 Together, the reduction
in these two cost components accounted for nearly 17 cents per share (or
about 96 percent) out of a total decline of about 17.6 cents per share on
NYSE. Delay costs declined about 11.2 cents per share in the 2 years
following the implementation of decimal pricing and 1-cent ticks on NYSE
and market impact costs declining by about 5.8 cents (fig. 3). An SEC
economist noted that declines in delay costs may reflect increased
efficiency on the part of institutional investors in trading rather than
changes in the markets themselves.

20Delay costs are market impact costs that occur between the time
institutional investors' portfolio managers direct their traders to buy or
sell stock and the moment these orders are released to brokers. The amount
that the stock's price changes during this period is the cost of delaying
the order. An order may be delayed for a number of reasons-for instance,
because it could affect prices in the market too much. See Plexus Group,
The Official Icebergs of Transaction Costs, Commentary #54, January 1998.

Figure 3: Trading Cost Components from a Trade Analytics Firm for NYSE and

NASDAQ Stocks, 2001 and 2003 (cents per share)

NYSE Nasdaq

Cents per share Cents per share

35 35

30 30

25 25

20 20

15 15

10 10

55

00 2001 2003 2001 2003

Year Year

Delay

Market impact

Commission

Source: GAO analysis of Plexus Group data.

Note: Data are from first quarter 2001 to second quarter 2003 for NYSE and
second quarter 2001 to second quarter 2003 for NASDAQ.

Figure 3 also shows that market impact and delay costs accounted for all
declines to total NASDAQ trading costs. For example, market impact and
delay costs declined about 14.1 cents per share between the second quarter
of 2001 and the second quarter of 2003. However, at the same time that
these cost components were improving, commission charges for NASDAQ stocks
were rising. As shown in figure 3, commissions that market intermediaries
charged for trading NASDAQ stocks increased about 2.8 cents per share from
second quarter of 2001 to second quarter of 2003. Industry representatives
told us these increases were the result of the broker-dealers that made
markets in NASDAQ stocks transitioning from trading as a principal, in
which a portion of the trade's final price included some compensation for
the market maker, to trading as an agent for the customer and charging an
explicit commission.21

Analysis of data from the other two trade analytics firms from whom we
obtained data, Elkins/McSherry and Abel/Noser, also indicated that
institutional investor trading costs declined following the decimalization
of U.S. stock markets in 2001. Because these two firms' methodologies do
not include measures of delay, which the Plexus Group data shows can be
significant, analysis of data from these two firms results in trading cost
declines of a lower magnitude than those indicated by the Plexus Group
data analysis. Nevertheless, the data we analyzed from Elkins/McSherry
showed total costs for NYSE stocks declined about 40 percent between the
first quarter of 2001 and year-end 2004 from about 11.5 cents per share to
about 6.9 cents per share. Analysis of Abel/Noser data indicated that
total trading costs for NYSE stocks declined about 30 percent, from 6.9
cents per share to 4.8 cents per share between year-end 2000 and 2004
(fig. 4).

21As principals, NASDAQ market makers had earned revenue from spreads by
buying shares at the bid price from investors and selling those same
shares to other investors at the higher ask price.

Figure 4: Total Trading Costs from Two Trade Analytics Firms for NYSE,
1998-2004 (cents per share)

Cents per share

                                       12

                                       10

                                       8

                                       6

                                       4

                                       2

                                       0

Q4Q1Q2 Q3Q4Q1 Q2 Q3Q4 Q1 Q2Q3 Q4 Q1Q2Q3 Q4Q1Q2 Q3 Q4Q1 Q2 Q3Q4

1998 1999 2000 2001 2002 2003 2004

Year and quarter

                        Elkins/McSherry (quarterly data)

                       Abel/Noser (averaged annual data)

Source: GAO analysis of Elkins/McSherry and Abel/Noser data.

Note: Elkins/McSherry data are quarterly from fourth quarter of 1998 and
the fourth quarter of 2004; Abel/Noser data are year-end totals for
1998-2004.

Our analysis of these firms' data also indicated that total trading costs
declined for NASDAQ stocks, which appeared to have declined even more
significantly than they did for NYSE stocks. For example, our analysis of
the Elkins/McSherry data showed that total trading costs for NASDAQ stocks
dropped by nearly 50 percent, from about 14.6 cents per share to about 7.4
cents per share, between the second quarter of 2001 when that market
decimalized and the end of 2004. Analysis of the Abel/Noser data indicated
that total trading costs declined about 46 percent for NASDAQ stocks
between the end of 2000 and 2004, falling from 8.7 cents per share to 4.7
cents per share (fig. 5).

Figure 5: Total Trading Costs from Two Trade Analytics Firms for NASDAQ
Stocks, 1998-2004 (cents per share) Cents per share

                                       20

                                       15

                                       10

                                       5

                                       0

Q4 Q1 Q2Q3Q4 Q1Q2Q3 Q4 Q1Q2 Q3 Q4Q1 Q2 Q3Q4Q1 Q2Q3Q4 Q1 Q2Q3 Q4

1998 1999 2000 2001 2002 2003 2004

Year and quarter

                        Elkins/McSherry (quarterly data)

                       Abel/Noser (averaged annual data)

Source: GAO analysis of Elkins/McSherry and Abel/Noser data.

Note: Elkins/McSherry data are quarterly from fourth quarter of 1998 and
the fourth quarter of 2004; Abel/Noser data are year-end totals for
1998-2004.

As our analysis of the Plexus Group data showed, the Elkins/McSherry and
Abel/Noser data also indicated that reductions to market impact costs
accounted for a vast proportion of overall reductions for NYSE stocks
(fig. 6).22 Analysis of the Elkins/McSherry data indicated that these
costs declined by 3.7 cents per share, accounting for about 80 percent of
the total fall in trading costs during this period. The 1.1 cent per share
reduction in

22These two firms analyze market impact costs by comparing their clients'
trades to the volume-weighted average price (VWAP) of the particular
stocks traded. The VWAP represents the average price at which a particular
stock traded on a specific trading day and is calculated by adding up the
dollars traded for every transaction (price times shares traded) and then
dividing by the total number of shares traded for the day. The closer an
investor's average price is to the VWAP, the lower the calculated market
impact costs.

 market impact costs identified in the Abel/Noser data represented over half of
the total trading cost reductions of 2.1 cents per share for NYSE stocks.

Figure 6: Trading Cost Components from Two Trade Analytics Firms for NYSE
Stocks, 2001 and 2004 (cents per share)

Elkins/McSherry Abel/Noser

Cents per share Cents per share

12 12

10 10

88

0.1

66

44

22

00 2001 2004 2001 2004

Year Year

Exchange fee

Market impact

Commission

Source: GAO analysis of Elkins/McSherry and Abel/Noser data.

Note: Abel/Noser does not account for exchange fees as a component of
trading cost. For Elkins/McSherry, we obtained first quarter 2001 data and
fourth quarter 2004. For Abel/Noser, we obtained data from the end of 2000
and 2004.

Reductions to market impact costs explained the entire decline to total
trading costs captured by the Elkins/McSherry and Abel/Noser data for
NASDAQ stocks, and the total declines would have been even larger had
commissions for these stocks not increased after 2001. Market impact costs
declined about 10.6 cents per share (about 78 percent) according to our
analysis of the Elkins/McSherry data, and 6.7 cents per share (about 87

percent) according to our analysis of the Abel/Noser data (fig. 7).
However, during this period, commissions charged on NASDAQ stock trades
included in these firms' data increased by more than 3 cents per share,
representing a more than threefold increase in commissions as measured by
Elkins/McSherry and a more than sixfold rise according to Abel/Noser.

Figure 7: Trading Cost Components from Two Trade Analytics Firms for
NASDAQ Stocks, 2001 and 2004 (cents per share)

Elkins/McSherry Abel/Noser

Cents per share Cents per share 15 15

12 12

99

0.1

66

33

00 2001 2004 2001 2004

Year Year

Exchange fee Market impact Commission Source: GAO analysis of
Elkins/McSherry and Abel/Noser data.

Note: Abel/Noser does not account for exchange fees as a component of
trading cost. For Elkins/McSherry, we obtained first quarter 2001 data and
fourth quarter 2004. For Abel/Noser, we obtained data from the end of 2000
and 2004.

Data from a fourth firm, ITG, which recently began measuring institutional
trading costs, also indicates that such costs have declined. This firm
began collecting data from its institutional clients in January 2003. Like
the other trade analytics firms, its data is similarly broad based,
representing about 100 large institutional investors and about $2 trillion
worth of U.S. stock trades. ITG's measure of institutional investor
trading cost is solely composed of market impact costs and does not
include explicit costs, such as commissions and fees, in its calculations.
Although changes in ITG's client base for its trade cost analysis service
prevented direct period to period comparisons, an ITG official told us
that its institutional investor clients' trading costs have been trending
lower since 2003.23

Academic Studies Generally In attempting to identify all relevant research
relating to the impact of

Showed Declining Costs for decimal pricing on institutional investors, we
found 15 academic studies

Institutional Investors	that discussed the impact of decimalization but
only 3 that specifically examined institutional investors' trading costs.
As of May 2005, none of these three studies had been published in an
academic journal. Two of these studies used direct measures of trading
costs, and the other used an indirect measure.24 Those that relied on more
direct measures of these costs found that these costs had declined since
the implementation of decimal pricing and 1-cent ticks. The first of these
studies analyzed more than 80,000 orders in over 1,600 NYSE-listed stocks
that were traded by 32 institutional investors.25 To measure the change in
trading costs after decimal pricing was implemented, this study used data
from one of the leading trade analytics firms and computed trading costs
over the period from November 28, 2000, to January 26, 2001 (before the
change to decimal pricing), and the period from January 30 to March 31,
2001 (after decimal pricing). The study found that institutional trading
costs appeared to have

23We do not present the specific analysis of ITG's data because the firm's
client base for its trade cost analysis grew significantly after it first
began offering this service, including the addition of some larger clients
with sophisticated trading operations that contributed to the overall
decline measured by the firm.

24According to an academic recognized as an expert in financial markets'
use of information technology, studies based on direct measurements of
institutional trading costs, such as data compiled by trade analytics
firms and exchanges, lead to more reliable calculations of trading costs
than do studies that rely on indirect determinants.

25Sugato Chakravarty, Venkatesh Panchapagesan, and Robert A. Wood. "Has
Decimalization Hurt Institutional Investors? An Investigation into Trading
Costs and Order Routing Practices of Buy-Side Institutions" (Unpublished
study: May 28, 2003).

declined by about 5 cents per share (or about 11 percent), falling from 44
cents per share to 39 cents per share after NYSE switched to 1-cent ticks.

The other study that used direct measures of institutional trading costs
examined the trading of over 1,400 NASDAQ stocks.26 The author of this
study obtained data on over 120,000 orders for NASDAQ stocks submitted by
institutional investors, which allowed her to calculate the costs of
trading orders of more than 9,999 shares before and after NASDAQ's
adoption of 1-cent ticks. Given the potentially large volume of order
data, the author studied three sample periods, each consisting of 5
trading days: February 1 through 8, 2001 (before decimalization), and June
18 through 22 and November 26 through 30, 2001 (after decimalization).
Trading costs in this study are measured as the difference between an
order's volumeweighted average execution price and a pre-execution
benchmark price, the opening midquote (the midpoint between the quoted bid
and ask prices). Using the opening midquote benchmark, the author found
that average trading costs for orders of 10,000 shares and above fell
about 19 cents per share (or about 49 percent), from about 39 cents per
share to about 20 cents per share during the 9 months or so after NASDAQ's
adoption of 1-cent ticks.

26Ingrid M. Werner, "Execution Quality for Institutional Orders Routed to
NASDAQ Dealers Before and After Decimals, Study Prepared for the Fisher
College of Business, The Ohio State University" (October 20, 2003).

Unlike the other two studies we identified, the third study reported that
costs for institutional investors had increased. However, this study
relied on an indirect measure of these costs for its analysis.27 To assess
the change in trading costs, the authors of this study examined a sample
of 265 mutual funds chosen from a database of mutual funds compiled by
Morningstar, an independent investment research firm. These firms were
selected using two criteria-investing predominantly in U.S. stocks and
having at least 90 percent of assets invested in stocks. However, the
study did not obtain these mutual funds' actual trading data but instead
attempted to identify costs by comparing the funds' daily returns (gain or
loss from the prior day's closing price) to the daily returns of a
synthetic benchmark for the periods before and after decimalization, from
April 17 through August 25, 2000, and from April 16 through August 24,
2001.28 After finding that the returns of actively managed mutual funds
were generally lower than the returns of the benchmark in the period after
decimals were introduced, the authors attributed the lower returns to
increases in the trading costs for these funds.

Although this is a plausible explanation for these funds' lower returns,
some of the market participants that we spoke with indicated that other
factors could also account for the results. For example, officials from a
large mutual fund company that had reviewed the study told us that the
lower returns may have resulted from the 3-year decline in stock prices in
the market. As the value of their assets decline, funds can report higher
expenses because their fixed operating costs correspondingly represent a
larger portion of a mutual fund's total costs, which would reduce reported
returns. In addition, an academic regarded as an expert in applying
technology to the financial markets noted that the lower returns could be
the result of many of the funds in the study's sample having similar
holdings that all performed more poorly than those in the benchmark
portfolio in the months following decimalization.

27Nicolas P.B. Bollen and Jeffrey A. Busse, "Tick Size, Trading Costs, and
Mutual Fund Performance" (Unpublished study: 2004).

28The authors constructed a synthetic benchmark that mimics the stock
holdings and expense ratios of the actual mutual funds they studied.
Because the benchmark portfolio has zero trading costs by construction,
the difference between the return on the benchmark and the actual funds
was the authors' measure of trading cost.

Institutional Investors Reported In addition to analyzing data from trade
analytics firms and academic Reduced or Level Trading Costs studies, we
interviewed 23 institutional investors that represented nearly after
Decimalization one-third of assets managed by a ranking of the 300 largest
money

managers.29 Representatives for 20 of these firms said that their trading
costs had fallen or stayed about the same since decimals were implemented
(table 4).

Table 4: Institutional Investor Positions on Changes to Trading Costs
After Decimalization

                            Institutional investors

                     Trading cost change      Number                  Percent 
                                Declined        15                        65% 
                               Increased         3         
                   Stayed about the same         5         
                                   Total        23                       100% 

Source: GAO.

Note: Percentages rounded to the nearest full number.

As shown in table 4, fifteen of these firms said that their trading costs
had declined since decimals were introduced. These firms included large
mutual fund companies, pension fund administrators, a hedge fund, and
smaller asset management firms, indicating that cost declines in our
sample were not limited solely to just larger firms with greater trading
resources. For example, a representative of a small money management firm
not ranked as one of the 300 largest noted that trading costs had
decreased since decimalization. In addition, the president of a hedge fund
that was ranked in the lower half of the rankings told us that his firm's
trading costs had declined significantly since 2001. As shown in the table
above, 5 of the 23 firms we interviewed said that their costs had remained
about the same since decimal pricing was implemented. For example,
representatives of one large mutual fund firm that measures its trading
costs internally as well as through a trade analytics firm told us that
their

29This ranking was published in Institutional Investor, vol. 38, no. 7
(July 2004). The firms we interviewed represented a broad cross section of
the institutional investor community, including representatives of the
four largest money managers in the United States in 2003, four large
public pension plan administrators, two large hedge funds, and other
large, midsize, and small money managers with assets under management
ranging from about $2 billion to $500 billion.

firm's transaction costs had not increased since decimal pricing was
introduced, but had trended down to flat. Three institutional investors
reported higher trading costs. One of these firms, a large mutual fund
manager, attributed the increases to heightened levels of volatility
following the reduction in tick size. For example, in his view, stock
prices tended to trade in a wider daily range since decimals were
implemented than they had before. The other two firms included a mutual
fund firm and a mid-size asset management firm, with officials from the
mutual fund noting that trading had become more involved and that
completing trades of similarly sized orders takes longer since the
conversion to decimal pricing.

In discussing institutional investors' views on their trading costs since
decimal pricing began, we found that the precision with which these firms
measured their trading costs varied. Many firms told us that they used
outside trade analytics firms, such as Abel/Noser, Elkins/McSherry, ITG,
and Plexus Group, to measure their transaction costs. Representatives of
some firms and a state pension plan administrator noted that their firms
used trade cost analysis tools from more than one trade analytics firm.
The head of trading for one firm said that his firm had been using a trade
analytics firm to measure their trading costs for 10 years. Some firms
said that they had developed in-house capabilities to measure their own
transaction costs. These systems appeared to vary in their levels of
sophistication. For example, representatives of a large money management
firm told us that they had developed a sophisticated cost measurement
system that shows them what a trade should cost before it is executed. The
system takes into account factors such as the executing broker and the
market venue where the trade executes. A managing partner of another firm
noted that it measures costs of completed trades in-house, including the
bid-ask spreads and the execution prices, and compares them to the
volume-weighted average price for trades it executes. Some money managers
told us that their firms did not measure their costs for trading. For
example, officials from one firm said that while not formally measuring
costs on their own, they sometimes were provided with data on the costs of
their trades from their own clients who use trade analytics firms to
evaluate the costs of using various money managers. Also, another state
pension plan administrator told us that while his organization does not
currently measure its trading costs, it plans to do so within the next 2
years.

    Volatility Has Also Improved since Decimal Pricing Began

In addition to lower spreads and reduced market impact costs, some market
participants noted that another measure of market quality-price
volatility-had also improved since decimal pricing was implemented.
According to some market participants, the smaller 1-cent ticks generally
slowed price movement in the markets and narrowed the range of prices at
which stocks trade over the course of time, such as a day. For example, a
noted expert on market microstructure told us that price volatility has
declined since the reduction in tick size because price changes occur in
smaller increments.30 Our own study of NYSE and NASDAQ stocks using TAQ
data showed that price volatility has declined since decimal pricing was
implemented. To assess the change in volatility for the stocks in our
sample, we calculated the percentage change in price for each one hour
increment (between 10 a.m. and 4 p.m.) each trading day. We also
calculated the percentage change in price for each stock that occurred
between 10 a.m. and 4 p.m. For each stock, we also calculated the standard
deviation of these percentage changes, which measures how widely the
individual price changes are dispersed around the average change, and
reported the median (that is the middle) standard deviation. As shown in
table 5, the volatility of the price changes in the stocks in our sample
decreased for both the hourly percentage change between 10 a.m. and 4 p.m.
each trading day and the percentage change from 10 a.m. to 4 p.m. each
trading day after decimal prices were implemented. These findings were in
agreement with a recently published academic study.31

30Market microstructure is the study of the process of how the trading of
securities affects prices, volumes and trader behavior.

31See Hendrik Bessembinder, "Trade Execution Costs."

Table 5: Price Change Volatility for NYSE and NASDAQ Stocks Before and
After Decimalization

Median standard deviation of price changes

NYSE stocks NASDAQ stocks

Price change Before After Before After period decimals decimals decimals
decimals

                         Hourly 1.00% 0.79% 0.97% 0.75%

From 10 a.m. to
4 p.m. 2.48 1.99 2.37

Source: GAO analysis of TAQ data.

Note: The median standard deviation in this table is based on the
continuously compounded percentage change in the quote midpoint for each
stock.

However, not all participants attributed the reduced price volatility to
decimal pricing. For example, a representative of a trade analytics firm
noted that with the Internet boom, investors increased their positions in
technology-sector stocks in a hurry and when the prices of these stocks
fell-which was coincident with the change to decimal pricing-investors
quickly reversed their positions. By selling quickly, these investors
incurred greater market impact costs. With the subsiding of this type of
trading activity in ensuing years, markets have become calmer, which has
made trading less costly.

    Despite Reduced Market Transparency for Large Orders, Institutional
    Investors Have Been Able to Complete Trades

Although some major elements of market quality-trading costs and
volatility-have improved since decimal pricing began, another market
quality element-transparency-appears to have been negatively affected. The
transparency of a market can depend on whether large numbers of shares are
publicly quoted as available to buy or sell. The various sources of data
we collected and analyzed indicated that after decimal pricing and the
1-cent tick were implemented in 2001, the volume of shares shown as
available for sale-or displayed depth-on U.S. stock markets declined
significantly. For example, studies required by SEC on the impact of
decimal pricing on trading, among other things, on U.S. markets showed
that the average number of shares displayed for trading on NYSE and NASDAQ
at the best quoted prices declined by about two-thirds between a

sample period before the markets converted to decimal pricing and a period
soon after the conversion took place (table 6).32

 Table 6: Average Number of Shares Displayed at the Best Quoted Prices Reported
 by NYSE and NASDAQ in Studies of Their Markets Before and After Decimalization

                          Average shares Average shares     
             Market     displayed before displayed after       Percent change 
              NYSEa                7,930              2,657              -67% 
            NASDAQb               13,974              4,539 

Source: GAO analysis of NYSE and NASDAQ data.

aAverages on NYSE are trade weighted. Averages are for all 2,466
NYSE-listed securities trading in both sample periods. NYSE's presample
period is August 1-25, 2000; its postsample period is June 2001.

bAverages are for 4,766 NASDAQ-listed securities that converted to decimal
pricing on April 9, 2001. Another 211 securities converted to decimal
pricing earlier. NASDAQ's pre sample period is the 2 weeks prior and 2
weeks after the conversion date.

In addition, our own study of 300 matched pairs of NYSE and NASDAQ stocks
found that the liquidity at the best quoted prices declined significantly.
According to our analysis, the average number of shares displayed at the
best quoted prices fell by 60 percent on NYSE and 34 percent on NASDAQ
over the nearly 5-year period between February 2000 and November 2004
(fig. 8). The greatest declines occurred around the time that the markets
converted to decimal pricing and 1-cent ticks. In its impact study, NASDAQ
attributed declines in the volume of shares displayed at the best prices
to the conversion to decimal pricing.

32New York Stock Exchange, Inc., Decimalization of Trading on the New York
Stock Exchange, A Report to the Securities and Exchange Commission, 9.
Also see NASDAQ Stock Market, Inc., The Impact of Decimalization on The
NASDAQ Stock Market: Final Report to the SEC, 33.

Figure 8: Volume-weighted Average Number of Shares Displayed at the Best
Quoted Prices on the NYSE and NASDAQ Before and After Decimalization,
Sample Weeks from February 2000-November 2004

Number of shares (in thousands) Feb.(7-11)

(20-24).Apr

2)(10-14)

(10-14)ug.A

une (19-23)

) Oct.(23-27

.No

                                       )

))(6-10) Jan.(22-26

Dec.(18-22

(23-27).Apr

4)

) Jan.(7-11) Dec.(10-14

                                 ) Sept.(23-27)

8) Feb.(24-2

une (2-6)J

) Oct.(20-24

(8-12).Mar

                           (19-23) (1-5) Sept.(18-22

y (6-10May (8-1

ug.(20-2 A

.No

.Mar

MaJ

                                      y y

luJ

luJ

2001 2002 2003 2004 Sample week (year, month, and days)

NYSE Nasdaq Source: GAO.

The amount of shares displayed as available for trading also declined at
prices away from the best quoted prices. For example, the SEC-mandated
NYSE impact study shows that the amount of shares displayed for trading
within about a dollar of the midpoint between the best quoted prices
generally declined to well under half of what it was when the tick size
was 1/16 of a dollar. NASDAQ's own impact study reported that the
cumulative amount of shares displayed for trading declined by about 37
percent within a fixed distance equal to twice the size of the average
quoted spread from the midpoint between the best quoted prices.33 This
decline in the volume of shares displayed across all prices-called market
depth-is particularly significant for institutional investors because they
are often executing large orders over multiple price points that are
sometimes inferior to the best quoted prices.

Various reasons can explain the reduced number of shares displayed at the
best prices. First, the amount of shares displayed for trading at the best
price likely declined because the decrease in the minimum tick size
created more prices at which orders could be displayed. The reduction in
tick size increased the number of price points per dollar at which shares
could be quoted from 16, under the previous minimum tick size of 1/16 of a
dollar, to 100. With more price points available to enter orders, some
traders that may have previously priced their orders in multiples of 1/16
to match the best quoted price may now instead be sending orders priced 1,
2, or 3 cents away from the best price, depending on their own trading
strategy. As a result, the volume of shares displayed as available at the
best price is lower as more shares are now distributed over nearby prices.

In addition to fewer shares displayed at the best price, displayed market
depth may also have declined because the reduction in tick size reduced
incentives to large-order investors to display their trading interest.
Since the implementation of penny ticks, market participants said that
displaying large orders is less advantageous than before because other
traders could now submit orders priced one penny better and execute these
orders ahead of the larger orders. This trading strategy, called "penny
jumping" or "stepping ahead," harms institutional investors that display
large orders

33NASDAQ used the average quoted spread from January 2001, before the
market converted to decimal pricing, to study the cumulative number of
shares that were displayed before and after decimalization.

and can increase their trading costs.34 For example, an investor wants to
purchase a large quantity of shares of a stock (e.g., 15,000 shares) and
submits an order to buy at a price of $10.00 (a limit order).35 Another
trader, seeing this large trading interest, submits a smaller limit order
(e.g., 100 shares) to buy the same stock at $10.01. This smaller order
will be executed against the first market order (which are orders executed
at the best price currently prevailing at the time they are presented for
execution) that arrives. As a result, the investor's larger order will go
unexecuted until that investor cancels its existing order at $10.00 and
resubmits it at a higher price. In this case, the investor's trading costs
increase due to price movements that occur in the process of completing a
large order (i.e., market impact).

The potential for stepping ahead has increased because in a 1-cent tick
environment the financial risk to traders stepping ahead of larger
displayed orders has been greatly reduced. For example, assume a trader
who steps ahead of a larger order offering to buy shares at $10.00 by
entering a limit order to buy 100 shares at a price of $10.01 is executed
against an incoming market order. However, if the price of the stock
appears to be ready to decline, such as when additional orders to sell are
entered with prices lower than $10.00, the trader who previously stepped
ahead can quickly enter an order to sell the 100 shares back to the large
investor whose order is displayed at $10.00. In such situations, the
trader's loss is only one penny per share, whereas in the past, traders
stepping ahead would have risked at least 1/16 of a dollar per share. Many
market participants we spoke to acknowledged that institutional investors
are reluctant to display large orders in the markets following the switch
to 1-cent ticks for fear that competing traders would improve the best
quoted prices by one penny and drive up prices to execute large orders.

34In a related matter, on April 6, 2005, the SEC Commission adopted
Regulation NMS (National Market System), which included a ban on
quotations in increments of less than one penny (known as subpenny
pricing) for stocks priced $1 and above. In prior GAO work, we found that
quoting in subpenny increments resulted in more instances of traders
"stepping ahead" of large limit orders. For additional information on
subpenny pricing, see GAO's testimony Securities Markets: Preliminary
Observations on the Use of Subpenny Pricing, GAO-04-968T (Washington,
D.C.: July 22, 2004).

35An order that specifies a particular price at which it can be executed
is called a limit order. Limit orders are required to be executed at the
specified price or better. Limit orders provide liquidity to markets.

The potential that the reduced tick size would increase the prevalence of
stepping ahead was acknowledged prior to decimal pricing's implementation.
For example, in 1997 a prominent academic researcher predicted that
problems with stepping ahead would increase following decimalization
because smaller price increments would make it easier (i.e., cheaper) for
professional traders to step in front of displayed orders and that this
would result in fewer shares being quoted and less transparency in the
markets.36 However, some market participants we interviewed acknowledged
that stepping ahead had been a problem before decimal pricing was
implemented. For example, representatives of a hedge fund told us they
were worried about getting stepped ahead of if they revealed their
interest to trade large amounts of a stock by entering limit orders with
large numbers of shares even when ticks were 1/8 and 1/16. An SEC staff
person told us that instances of orders being stepped ahead of has
increased since the penny tick was implemented, but he did not think that
it negated the benefits of decimal pricing overall.

Institutional Investors Have Although markets became less transparent
following decimalization,

Adjusted Their Trading Methods institutional investors and traders appear
to be able to execute large orders

to Continue Executing Large at a lower cost by adapting their trading
strategies and technologies. For

Orders	example, the academic study that studied around 120,000 large
orders submitted for NASDAQ stocks found that the average proportion of
total order size that was executed (filled) increased slightly from 78
percent before the change to decimal pricing to about 81 percent about 6
months following the change. Similarly, the study found the length of time
required to fill orders-measured from the time the order arrived at a
NASDAQ dealer to the time of the last completed trade-decreased from about
81 minutes before decimal pricing to about 78 minutes 6 months after.37
Eight of the institutional investment firms we contacted for this report
also provided information about their experiences in completing trades. Of
these, officials from seven of the eight told us that their fill rates had
either stayed about the same or had increased. An official at one firm
noted that the proportion of orders that were completely executed had
risen by as much as 10 percent in the period following decimal pricing's
introduction.

36Lawrence Harris, Decimalization: A Review of the Arguments and Evidence,
USC Working Paper (Los Angeles, Calif.: Apr. 3, 1997), i.

37Ingrid M. Werner, 17 and 26.

One of the ways that institutional investors have adapted their trading
strategies to continue trading large orders is to break up these orders
into a number of smaller lots. These smaller orders can more easily be
executed against the smaller number of shares displayed at the best
prices. In addition, not displaying their larger orders all at once
prevents other traders from stepping ahead. Evidence of this change in
investors' trading strategy is illustrated by the decline in the average
executed trade size on NYSE and NASDAQ. As table 7 shows, the average size
of trades executed on these markets has declined about 67 percent since
1999 on NYSE and by about 41 percent on NASDAQ.

Table 7: Average Trade Size for NYSE and NASDAQ, 1999-2004 (in shares)

                                                                      Percent 
                                                                       change 
                                                                        1999- 
            Market  1999    2000    2001    2002    2003      2004       2004 

                     NYSE 1,205 1,187 907 666 488 393 -67%

NASDAQ 808 693 782 735 580 477

                 Source: GAO analysis of NYSE and NASDAQ data.

With average trade size down, some market participants noted that at least
4 to 5 times as many trades are required to fill some large orders since
decimalization. For example, a representative of a large mutual fund
company said that his traders have always broken their funds' large orders
up into smaller lots so that they could trade without revealing their
activity to others in the marketplace. Before decimalization, completing
an order may have required 10 trades, but following the change to decimal
pricing a similar order might require as many as 200 smaller trades.
Referring to the increased difficulty of locating large blocks of shares
available for trading, one representative of a money management firm
stated that "decimalization changed the trading game from hunting
elephants to catching mice." In fact, the number of trades that NYSE
reported being executed on its market increased more than fourfold between
1999 and 2004, rising from about 169 million trades to about 933 million
trades.38

Institutional Investors To facilitate the trading of large orders while
minimizing market impact Increasingly Use Electronic costs, many market
participants said that they had increased their use of Trading and
Alternative Trading electronic trading techniques. Many of these
techniques involve

Venues

algorithmic execution strategies, which are computer-driven models that
segment larger orders into smaller ones and transmit these over specified
periods of time and trading venues. The simplest algorithms may just break
a large order into smaller pieces and route these to whichever exchange or
alternative trading system offers the best price. Institutional investors
often obtain these algorithms as part of systems offered by broker-dealers
and third-party vendors. They may also develop them using their own staff
and integrate them into the desktop order management systems they use to
help conduct their trading.

One of the primary purposes of using these algorithmic trading systems is
to conduct trading in a way that prevents other traders from learning that
a large buyer or seller is active in the market. Institutional investors
want tools that allow them to trade more anonymously to reduce the extent
to

38Data on the volume of trades executed on NASDAQ for this period was not
comparable to that from NYSE because trades in NASDAQ stocks were
increasingly being executed outside this market. The declining trading
volumes being reported by NASDAQ were the result of alternative trading
venues, such as ECNs, executing increasing portions of volume in NASDAQ
shares but reporting these trades outside the NASDAQ trade reporting
system. For example, trades executed by the Island ECN were previously
reported to NASDAQ and were included in NASDAQ's total trading volume
statistics. However, in 2002 Island began reporting its trades instead
through the Cincinnati Stock Exchange (now called the National Stock
Exchange), which caused a reduction of over 20 percent in trades that
NASDAQ reported as being executed within its market.

which others can profit at their expense, such as when other traders,
realizing that a large buyer is active, also buy shares, which quickly
causes prices to rise, in hopes of selling these now more expensive shares
to this large buyer. Several market participants told us that the
anonymity that algorithms provide reduces the potential for other traders
to learn that a large buyer or seller is active in the market (known as
information leakage), thus reducing the likely market impact of executing
the entire order.

The use of these tools is growing. A 2004 survey conducted by The Tabb
Group, a financial markets' consulting firm, of more than 50 head and
senior traders at institutional investor firms reported that over 60
percent of these firms were using algorithmic trading vehicles.39 The
report noted that this widespread adoption rate was higher than
anticipated. Many of the market participants we contacted also told us
they were actively using algorithms in their trading activities and those
that were not currently using algorithms generally indicated that they
planned to begin using them in their trading strategies in the near
future. In its report, The Tabb Group predicted that algorithmic trading
will grow by almost 150 percent over the next 2 years.

To locate the additional shares available for trading that are otherwise
not displayed, institutional investors are also increasingly using
alternative trading venues outside the primary markets, such as NYSE and
NASDAQ, to execute their large orders at lower cost. For example,
institutional investors are conducting increasing portions of their
trading on ECNs. Originally, ECNs were broker-dealers that operated as
real-time electronic trading markets by allowing their customers to enter
orders for stocks and obtain executions automatically when the prices of
the orders entered matched those of orders entered by other customers.
Recently, ECNs have entered into formal associations with existing stock
exchanges.40

39See The Tabb Group, Institutional Equity Trading in America: A Buy-Side
Perspective. (Westborough, Mass.: April 2004), 32.

40For example, in 2001 SEC approved the establishment of the Archipelago
Exchange as the stock trading facility of the Pacific Exchange. See SEC,
PCX Rulemaking: Order Approving Proposed Rule Change by the Pacific
Exchange, Inc., as Amended, and Notice of Filing and Order Granting
Accelerated Approval to Amendment Nos. 4 and 5 Concerning the
Establishment of the Archipelago Exchange as the Equities Trading Facility
of PCX Equities, Inc., Exchange Act Release No. 44983 (October 25, 2001),
66 Fed. Reg. 55225 (2001).

Use of ECNS has been a growing trend. According to The Tabb Group, 88
percent of the institutional investor firms it surveyed responded that
they traded using ECNs. Furthermore, a 2004 survey by Institutional
Investor magazine asked the trading staff of institutional investor firms
to identify their preferred venues for executing stock trades. The survey
reported that three of the top five trading venues for institutional stock
trade execution were ECNs.41 According to data we obtained from a
financial markets consulting firm, the share of ECN trading in NASDAQ and
NYSE stocks has increased between 1996 and 2003. For example, ECN trading
volume increased from about 9 percent of all NASDAQ trading in 1996 to
about 40 percent of total NASDAQ trading volume in 2003 (fig. 9).

Figure 9: Proportion of Total Share Trading Volume NASDAQ and NYSE Stocks
by ECNs, 1996-2003

Percentage

                    1996 1997 1998 1999 2000 2001 2002 2003

Year

                                      NYSE

                                     Nasdaq

              Source: GAO analysis of Celent Communications data.

41Justin Shack, "The Orders of Battle," Institutional Investor, vol. 38,
no. 11, November 2004, 82.

The percent of trading volume for NYSE stocks conducted through ECNs has
also increased, though to a much lesser degree than has these
organizations' trading in NASDAQ stocks. According to some market
participants, ECNs have been less successful in gaining greater market
share in NYSE stocks because of rules that result in most orders being
sent to that exchange. For example, one regulation-the trade through rule-
requires that broker-dealers send orders to the venue offering the best
price, and in most cases NYSE has the best quoted price for its listed
stocks. However, in a report issued by a financial market consulting firm,
ECN officials called the trade through rule anticompetitive because the
rule fails to acknowledge that some investors value the certainty and
speed of execution more than they do price. They noted that under current
rules, the NYSE specialists have as long as 30 seconds to decide whether
to execute an order sent to them or take other actions. During this time,
market participants told us that the price of the stock can change and
their order may not be executed or will be executed at an undesirable
price. On April 6, 2005, SEC approved Regulation NMS (National Market
System) which, among other things, limits the applicability of trade
through requirements to quotes that are immediately accessible.42

42Regulation NMS was originally proposed for public comment in February
2004. Exchange Act Release No. 49325 (Feb. 26, 2004), 69 Fed. Reg. 11126
(2004). The SEC extended the period for comment and issued a supplemental
release regarding Regulation NMS in May 2004. Exchange Act Release No.
49749 (May 20, 2004), 69 Fed. Reg. 30142 (2004). SEC reproposed a revised
Regulation NMS in December 2004. Exchange Act Release No. 50870 (Dec. 16,
2004), 69 Fed. Reg. 77424 (2004). Changes to the trade through rule (now
known as the Order Protection Rule) are to be implemented for a limited
number of stocks beginning April 10, 2006, and for all National Market
System stocks by June 12, 2006.

Institutional investors we spoke with highlighted anonymity, speed, and
the quality of the prices they receive as reasons for their increased use
of ECNs. The respondents to The Tabb Group survey indicated that their
firms used ECNs to reduce market impact costs and to take advantage of
lower fee structures. Many market participants we interviewed and studies
we reviewed also indicated that trading using ECNs lowered institutional
trading costs. According to market participants we interviewed,
decimalization accelerated technology innovation, which they believe has
been significant in reducing trading costs primarily by providing a means
for investors to directly access the markets and reducing the need for
intermediation. However, many acknowledged that increasing use of ECNs has
been a growing trend since 1997, when SEC implemented rule changes that
allowed ECNs to better compete against NASDAQ market makers.43

Other alternative trading venues that institutional investors are
increasingly using to execute their large orders are block trading
platforms operated by broker-dealers called crossing networks. These
networks are operated by brokers such as ITG, Liquidnet, and Pipeline
Trading Systems. Crossing networks generally provide an anonymous venue
for institutional investors to trade large blocks of stock (including
orders involving tens or hundreds of thousands of shares) directly with
other institutional investors. For example, one crossing network
integrates its software with the investor's desktop order management
system so that all of the investor's orders are automatically submitted to
this crossing network in an effort to identify a match with another
institutional investor. Once a match is identified, the potential buyer
and seller are notified, at which time they negotiate the number of shares
and price at which a trade would occur. The heads of stock trading for two
large money management firms told us an advantage of using crossing
networks is that they minimize market impact costs by allowing investors
to trade in large blocks without disclosing their trading interests to
others in the markets. Also, the chief executive officer of a crossing
network noted that the absence of market intermediaries in the negotiation
of trades on crossing networks provides the customers' traders with the
ability to control the price and quantity of their executions. However, we
were told that crossing networks may not be the preferred

43These rules include the Limit Order Display Rule (SEC Rule 11Ac1-4) and
the Quote Rule (SEC Rule 11Ac1-1). Rule 11Ac1-4 mandated that public limit
orders for all NASDAQ securities should be reflected in the best bid and
offer disseminated by that market. Rule 11Ac1-1, states that market makers
may not post one quote on NASDAQ and a different quote on an alternative
quote dissemination system (i.e., ECN). These rules are known as the Order
Handling Rules.

strategy for all kinds of institutional orders because orders remain
unexecuted if a natural match cannot be found.

Crossing networks are gaining in prominence among institutional investors
as a destination of choice for trading large quantities of stock.
According to The Tabb Group's survey of head and senior traders, 70
percent of all firms reported using crossing networks.44 In Institutional
Investor's 2004 survey, Liquidnet, a crossing network established in 2002,
ranked second on the list of institutional investors' favorite venues for
trade executions.45

Despite advances in electronic trading technologies that give
institutional investors increased access to markets, some institutional
investors continue to use full-service brokers to locate natural sources
of liquidity as they did before decimal pricing began. According to
institutional investor officials we interviewed, with fewer shares
displayed as available for trading and reductions in average trade size,
they are more patient about the time required to completely execute (fill)
large orders using brokers in this way. In addition, some noted they
increasingly use NYSE floor brokers to facilitate the trading of large
orders in less-liquid stocks, explaining that floor brokers have
information advantages in the current market structure that help to
minimize adverse price changes.

Market Conditions May Also In addition to increased use of electronic
trading, overall market conditions Have Helped Lower Institutional also
likely helped lower trading costs for institutional investors. For
Investors' Trading Costs example, prices on U.S. stock markets began a
multiyear downturn around

2000. As stock prices declined, asset managers faced increased pressure to
manage costs and boost investment returns. Representatives of all four
leading firms we interviewed that analyze institutional investors' trading
activity noted that the declining market that persisted after the
implementation of decimal pricing also had led to reduced costs.
Representatives of two of these trade analytics firms noted specifically
that institutional buyers and sellers appeared more cost sensitive as a
result of the 3-year declining stock market, which caused investment
returns to decline substantially. This increased the incentive for
institutional investors to take actions to lower their trading costs as a
way to offset some of the reduced market returns.

44The Tabb Group, Institutional Equity Trading, 29. 45Shack, "Orders of
Battle," 82.

  Some Stock Intermediaries Have Experienced Lower Profits since Decimalization,
  but Other Factors Have Contributed to the Declines

Although overall securities industry profits have returned to levels
similar to those in the past, some market intermediaries, particularly
those brokerdealers acting as exchange specialists and NASDAQ market
makers, have been significantly affected by the implementation of decimal
pricing. Between 2000 and 2004, exchange specialists and NASDAQ market
makers generally saw their revenues and profits from stock trading fall,
forcing some smaller market intermediaries out of the market. Decimal
pricing was not the only force behind these declines, however. Sharp
declines in the overall level of prices in the stock market, the growing
use of trading strategies that bypass active intermediary involvement, and
heightened competition from ECNs and other electronic trading venues have
affected revenues and profits. We found that intermediaries were adapting
to the new conditions by changing their business practices-for example, by
investing in electronic trading devices and data management systems,
reducing the size of their trading staffs, or changing how they priced
their services. In response to the negative conditions that some believe
exist in U.S. stock markets, a proposal has been made to conduct a pilot
test of the use of a higher minimum tick for trading. Many of the market
intermediaries but fewer than half of the institutional investors we
contacted favored this move.

    Conditions in the Overall Securities Industry Appear to Be Improving

The business environment for the securities industry as a whole, which saw
reduced revenues after 2000, appears to be improving. The Securities
Industry Association (SIA), which represents the broker-dealers holding
the majority of assets in the securities industry, has compiled data on
all of its member broker-dealers that have conducted business with public
customers in the United States over the last 25 years.46 As shown in
figure 10, the data SIA compiles are derived from filings broker-dealers
are required to make with the SEC and detail, among other things, revenues
and expenses for market activities such as trading in stocks, debt
securities, and options and managing assets.47 SIA's 2004 data show that
industry revenues of $237 billion, while down from the height of the bull
market in 2000, are now similar to revenues earned before the
unprecedented gains of 2000.48 In addition, the industry's total pretax
net income of $24.0 billion in 2003 and $20.7 billion in 2004 represent
some of the highest levels of pretax industry profits of the past 25
years.

46SIA has approximately 600 members. SIA members include most of the
largest U.S. brokerdealers.

47These filings are the Financial and Operational Combined Uniform Single
(FOCUS) reports.

48A bull market is a market in which stock prices rise over a sustained
period of time.

Figure 10: Securities Industry Total Revenues and Net Income, 1994-2004
Dollars in billions

350 300 250 200 150 100 50 0 '80 '81 '82 '83 '84 '85 '86 '87 '88 '89 '90
'91 '92 '93 '94 '95 '96 '97 '98 '99 '00 '01 '02 '03 '04

Year

Total revenue

Net income

Source: Securities Industry Association.

Further, our review indicated these improved industry conditions are not
only the result of improved performance among the largest firms. By
examining the trend in this data after excluding the results for the 25
largest broker-dealers, the revenue and net-income trend for the remaining
firms revealed the same pattern of improvement.

    Decimalization Has Negatively Affected Exchange Specialists

Despite these improvements, some market intermediaries, such as stock
exchange specialists, have been negatively affected by the shift to
decimal pricing. Stock exchange specialists buy or sell shares from their
own accounts when insufficient demand exists to match orders from public
customers directly. The lower spreads that have prevailed since decimal
pricing have reduced the income that exchange specialists can earn from
this activity. In addition, the number of shares displayed as being
available for purchase or sale has declined, leaving specialist firms with
less information about market trends and thus less ability to trade
profitably.49 According to NYSE data, between 2000 and 2004 aggregate NYSE
specialist revenues declined by more than 50 percent, falling from $2.1
billion to $902 million (table 8).

Table 8: NYSE Specialist Firm Revenues and Profits, 1999-2004 (in millions
of dollars)

                     Category 1999 2000 2001 2002 2003 2004

                 Revenues $1,566 $2,136 $1,776 $1,645 $987 $902

                After-tax profits $476 $708 $414 $397 $3a $(38)b

Source: GAO analysis of NYSE data.

aResult reflects the booking of approximately $147 million in fines that
NYSE specialist firms paid to settle charges with SEC and NYSE for trading
violations.

bResult reflects the booking of approximately $109 million in fines that
NYSE specialist firms paid to settle charges with SEC and NYSE for trading
violations.

Further, since decimal pricing began, the extent to which specialist firms
participate in trades on their own exchanges has been low, falling below
predecimalization levels. The participation rate shows the percentage of
the total shares traded represented by trades conducted by specialists as
part of their obligation to purchase shares when insufficient demand
exists or sell shares when insufficient numbers of shares are being
offered. After climbing during the first year decimal pricing was
implemented, the percentage of trades on NYSE in which NYSE specialists
participated declined from 15.1 percent in 2001 to 10.2 percent in 2004
(fig. 11).

49Lawrence Harris and Venkatesh Panchapagesan, "The Information Content of
the Limit Order Book: Evidence from NYSE Specialist Trading Decisions,"
Journal of Financial Markets, Vol. 8 (2005).

Figure 11: NYSE Specialist Participation Rates, 1999-2004, in Percent of
Trades

Participation rate (percentage)

100

90

80

70

60

50

40

30

20

Sum of shares 10 NYSE's specialist purchased and sold =

participation rate 2 x total volume

0 1999 2000 2001 2002 2003 2004 Year

Source: NYSE Fact Book.

Note: These percentages are calculated by dividing the total number of
shares that specialists trade by twice the total volume of the trading on
the exchange to reflect that specialists are usually only either buying
shares or selling shares as part of a trade with other customers.

The trend toward smaller order sizes and more trade executions that have
accelerated since the introduction of decimal pricing (as discussed
earlier in this report) has also impacted the operating expenses of
exchange specialists. The average trade execution size on the NYSE dropped
from 1,205 shares per execution in 1999 to 393 shares per execution in
2004, so that specialists now generally process more trades to execute
orders than they did before decimal pricing began. This trend toward
greater numbers of executions, which many market participants indicated
was exacerbated by decimal pricing, has required exchange specialists to
absorb additional processing costs and make related investments in more
robust data management and financial reporting tools. For example, each
trade that is submitted for clearance and settlement carries a fee, paid
to the National

Securities Clearing Corporation, of between $0.0075 to $0.15 per trade.50
Several smaller regional exchange specialist firms we spoke with
highlighted these kinds of increased operating costs as significant to
their ability to continue profitable operations. Additionally, a floor
brokerage firm we spoke with said that other charges had contributed to
its declining operating performance. These charges included those from
clearing firms, which typically charge in the range of $0.20 cents per 100
shares to process trades, and execution fees from exchange specialists
related to the processing of more trades and typically paid by floor
brokers.

As shown in table 9 below, average trade size has declined over the past 6
years as the number of executions on NYSE has risen. As the table shows,
volumes have remained relatively consistent since 2002, even though
exchange specialists and floor brokers have seen their revenue and profits
decline during this period.

Table 9: NYSE Reported Trades, Average Daily Volume, and Average Trade
Size, 1999-2004

                      Category  1999    2000    2001    2002    2003     2004 
                Total reported                                         
           trades (in millions                                         
                    of shares)   169     221     339     546     723      933 

Average daily share volume (in millions of shares) 809 1,042 1,240 1,441
1,398 1,457

Average trade size

(number of shares) 1,205 1,187 907 666 488 393

Source: NYSE Fact Book.

50This fee is one charged for trade recording and is assessed at $0.0025
per share in the trade with a minimum charge of $0.0075 and the maximum of
$0.15. For example, a trade executed for 10,000 shares would be charged
the maximum of $0.15. However, if this trade is broken into two executions
of 5,000 shares each, each trade would be charged $0.125, or a total of
$0.25, illustrating how more trades could lead to higher clearing costs.

    Broker-dealers Revenues from NASDAQ Activities Have Also Fallen since
    Decimal Pricing Began

Decimal pricing has also generally negatively affected the profitability
of firms that make markets in NASDAQ stocks. Traditionally, these firms
earned revenue by profitably managing their inventories of shares and
earning the spread between the prices at which they bought and sold
shares. With the reduced bid-ask spreads and declines in displayed
liquidity that have accompanied decimal pricing, the ability of
broker-dealers to profitably make markets in NASDAQ stocks has been
significantly adversely affected. For example, an official from one firm
said that penny spreads had severely curtailed the amount of revenues that
market makers could earn from their traditional principal trading. Table
10 presents SIA data on all NYSE members, which SIA indicates is often
used as a proxy for the entire industry. As the table shows, these firms'
revenues from NASDAQ market making activities, after rising between 1999
and 2000, declined about 73 percent between 2000 and 2004, falling from
nearly $9 billion to about $2.5 billion.

Table 10: NYSE Member Broker-Dealer Revenues from NASDAQ Market Making
Activities, 1999-2004 (in millions of dollars)

                     Category 1999 2000 2001 2002 2003 2004

               Revenues $6,786 $8,994 $4,648 $2,742 $2,385 $2,462

Source: SIA Databank.

Firms acting as NASDAQ market makers have also seen their operating
expenses rise since decimal pricing began. Officials at one broker-dealer
said that because the average trade size is smaller, market makers now
generally process more trades to execute the same volume. This increase in
the number of executions has required NASDAQ market makers to absorb
additional processing and clearing costs. Additionally, the increased
number of executions associated with decimal pricing has required some
NASDAQ market makers to increase their investments in information
technology systems. Table 11 shows the reduced average order size on the
NASDAQ market over the past 6 years.

    Table 11: NASDAQ Average Trade Size, and Average Daily Volume, 1999-2004

                         Category  1999    2000    2001   2002   2003    2004 
               Average trade size                                      
               (number of shares)   808     693    782    735    580   
             Average daily volume                                      
          (in millions of shares)  1,071   1,752  1,923  1,754  1,702   1,808 
                  Source: NASDAQ.                                      

    Declining Intermediary Profits Have Accelerated Industry Consolidation

Declining revenues and increased operating expenses since the
implementation of decimal pricing have encouraged some firms to merge with
other entities and forced other smaller market intermediaries out of the
market, accelerating a trend toward consolidation among stock exchange
specialists and NASDAQ market makers. Generally, to date, two developments
have contributed to the decline in the number of specialists: acquisitions
of smaller firms by larger entities and, on the regional exchanges,
smaller specialist firms and proprietorships leaving the business. As
shown in table 12, the number of specialist firms operating on various
floor-based stock exchanges has declined significantly in recent years.

Table 12: Number of Specialist Firms Operating on Selected Stock Markets,
1999- 2004

                           Number of specialist firms

Market 1999

Boston Stock Exchange 16

NYSE 25

Philadelphia Stock
Exchange 20 3

Sources: Boston Stock Exchange, NYSE, and Philadelphia Stock Exchange.

The number of firms that make markets on NASDAQ has similarly declined.
Between 2000, when 491 firms were acting as NASDAQ market makers, and
2004, the number of firms making markets in NASDAQ stocks declined to
258-a drop of more than 47 percent. According to an industry association
official, NASDAQ market-making activity is increasingly not a stand-alone
profitable business activity with firms but instead is conducted

to support other lines of business. For example, an official of a
brokerdealer that makes markets in NASDAQ stocks told us that his firm has
made no profits on its market-making operations in the last 3 years but
continues the activity in order to present itself as a full-service firm
to customers.

Although fewer firms are now acting as market makers, the overall NASDAQ
market has not necessarily been affected. Since 2000, the number of stocks
traded on NASDAQ has declined from 4,831 to 3,295, potentially reducing
the need for market makers. In addition, some firms that continue to make
markets have expanded the number of stocks in which they are active. For
example, one large broker-dealer expanded its market-making activities
from 500 stocks to more than 1,500. A NASDAQ official told us that with
reduced numbers of stocks being traded, the average number of market
makers per stock has increased since decimal pricing began. As shown in
table 13, our analysis of data from NASDAQ indicated that although the
number of NASDAQ market makers has declined, the number of firms making
markets in the top 100 most active NASDAQ stocks actually grew between
1999 and 2004.

Table 13: Consolidation among NASDAQ Market Makers, 1999-2004

                                      1999  2000   2001   2002   2003    2004 
            Number of NASDAQ market                                    
                             makers    528  491    459    384    316      258 
          Market makers per top 100                                    
                      NASDAQ stocks     23     25     28     32     29 

Source: NASDAQ.

Improved technology has likely helped market makers increase their ability
to make markets in more stocks. An official at one market maker we spoke
with explained that his firm had invested in systems that automatically
update the firm's price quotes across multiple stocks when overall market
prices change, allowing the firm to manage the trading of more stocks with
the same or fewer staff. The use of such technology helps explain why the
number of market makers per stock has not fallen as the overall number of
market-making firms has declined.

    Other Factors Have Contributed to Declining Intermediary Revenues and
    Profits

Although decimal pricing affected market intermediaries' operations, the
changes in these firms' revenues, profits, and viability are not
exclusively related to the reduction in the minimum tick size. One major
impact on firms' revenues since 2000 has been the sharp multiyear decline
in overall stock market prices. Securities industry revenues have
historically been correlated with the performance of U.S. stock markets
(fig. 12). After 5 consecutive years of returns exceeding 10 percent,
prices on U.S. stock markets began declining in March 2000, and these
losses continued until January 2003. The performance record for U.S.
stocks during this period represents some of the poorest investment
returns for U.S. stocks over the last 75 years. Because intermediary
revenues tend to be correlated with broader stock market returns, as
measured by the Standard & Poor's 500 (S&P 500) Stock Index, many market
observers we spoke with told us that the 3-year down market, which
coincided with the transition to decimal pricing, contributed to reduced
intermediary revenues and profits.

Figure 12: Securities Industry Revenues and Net Income as Compared to the
Performance of the S&P 500 Stock Index, 1994- 2004

Dollars in billions

S&P 500

350 1,500

1,400

300 1,300

1,200

250 1,100

1,000

900 200 800

700 150 600

500 100 400

300

50 200

100

           00 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

Year

Total revenue

Net income

S&P 500

Source: Securities Industry Association.

The widespread emergence of technology-driven trading techniques, such as
algorithmic trading models, has also reportedly affected market
intermediaries negatively. These new techniques allow institutional
investors, which account for the bulk of stock trading volume, to execute
trades with less active intermediary involvement. Although only
brokerdealers can legally submit trades for execution on U.S. stock
markets, broker-dealers are reportedly only charging around 1 cent per
share to transmit orders sent electronically as part of algorithmic
trading models, an amount that represents much less revenue than the
standard commission of around 5 cents per share for orders broker-dealers
execute using their own trading systems and staff. Market intermediaries'
revenues are also reduced by institutional investors increasing use of
alternative execution venues such as crossing networks to execute trades.
The commissions these venues charge are less than those of traditional
broker-dealers, specialists, and market makers. Several market observers
said that because

crossing networks and algorithmic trading solutions divert order flow from
and create price competition for traditional broker-dealers, their
increased use is a probable factor in the reduced profitability of
exchange specialists, floor brokers, and NASDAQ market makers.

The increasing use of ECNs also has also likely reduced the revenues
earned by market intermediaries. Several market participants we spoke with
told us that the increased number of executions on ECNs, such as Bloomberg
Tradebook, Brut, and INET, has reduced the profits of exchange
specialists, floor brokers, and NASDAQ market makers. ECN executions are
done on an agency/commission basis, typically in the range of 1 to 3 cents
per share, compared with traditional broker-dealer execution fees of
approximately 5 cents per share. As a result, the activities that lower
investors trading costs can result in lower revenues for market
intermediaries. However, market participants noted that institutional
investors' use of electronic trading technologies and ECNs had been
increasing even before decimal pricing was implemented.

    Brokerage Firms Have Made Adjustments to Business Activities and Personnel
    Levels

We found that in response to the changes brought about by decimal pricing
and particularly to changes in institutional investors' trading behavior,
many stock market intermediaries had adapted their business operations by
making investments in technology to improve trading tools and data
management systems, reducing the size of their trading staffs, and
changing the pricing and mix of services they offer. Most exchange
specialists, floor brokers, NASDAQ market makers, and the broker-dealer
staff that trade stocks listed on the exchanges we spoke with had made
investments in new technology since the implementation of decimal pricing.
For example, some NASDAQ market makers and listed traders were
increasingly using aggregation software to locate pools of liquidity
instead of relying on telephone contacts with other broker-dealers as they
had in the past. Several intermediaries were also using algorithmic
trading solutions more frequently to execute routine customer orders,
allowing more time for their staff to work on more complex transactions or
the trading of less liquid stocks.

Other intermediary firms have responded to the more challenging business
environment since 2000 by reducing the size of their trading staffs. Most
stock broker-dealer firms we spoke with employed fewer human traders in
2004 than they had before 2001. Senior traders at the firms we spoke with
cited reduced profits and the increased number of electronic and automated
executions as the primary reasons for the reductions in the

number of traders they employed. Consequently, although trades executed by
broker-dealers using computer-generated algorithms typically generated
lower revenues from commissions than traditional executions, the reduced
salary and overhead costs associated with employing fewer traders, we were
told, had made it easier for some broker-dealers to maintain viable stock
trading operations.

We also found that market intermediaries were adapting to the new business
environment by modifying the pricing and mix of the services they offered.
For example, instead of trading as principals, using their own capital to
purchase or sell shares for customers, many NASDAQ market makers have
begun acting as agents that match such orders to other orders in the
market. Like ECNs, these market makers charge commissions to match buy and
sell orders. The agency/commission model provides the benefit of reduced
risk for NASDAQ market makers because they were using less of their own
capital to conduct trading activity. However, market participants told us
that this activity may not generally be as profitable for market makers as
traditional principal/dealer trading operations. Other firms had attempted
to diversify or broaden their service offerings. For example, a NYSE floor
brokerage firm we spoke with was attempting to make up for lost revenues
by developing a NASDAQ market-making function.

Some firms were also expanding into other product lines. For example, one
large NASDAQ market maker we spoke with was attempting to make up for
declining stock trading revenue by becoming a more active market maker in
other over-the-counter stocks outside those traded on NASDAQ's National
Market System, including those sold on the Over-the-Counter Bulletin Board
(OTCBB) market, which trades stocks of companies whose market valuations,
earnings, or revenues are not large enough to qualify them for listing on
a national securities market like NYSE or NASDAQ.51 These stocks often
trade with higher spreads on a percentage basis than do the stocks listed
on the national exchanges. Finally, other firms had moved staff and other
resources formerly used to trade stocks to support the trading of other
instruments, such as corporate bonds, credit derivatives, or energy
futures.

51Over-the-counter stocks are those not listed on exchanges. NASDAQ's
National Market System includes the largest, most actively traded stocks.

    Decimal Pricing Did Not Appear to Affect Businesses' Ability to Raise
    Capital

The willingness and ability of broker-dealers to assist companies with
raising capital in U.S. markets also does not appear to have diminished as
a result of decimal pricing. Broker-dealers, acting as investment banks,
help American businesses raise funds for operations through sales of stock
and bonds and other securities to investors. After the initial public
offering (IPO), such securities can be traded among investors in the
secondary markets on the stock exchanges and other trading venues.52
Several market observers had voiced concerns that the reduced displayed
liquidity and declining ability of market makers to profit from trading
could reduce the liquidity for newly issued and less active stocks. In
turn, this loss of liquidity could make it more difficult for firms to
raise capital. We found that in 2002 and 2003, U.S. stock underwriting
activity was down significantly from recent years (fig. 13). However, as
figure 13 shows, although stock IPOs are down from record levels of the
bull market of the late 1990s, 247 companies offered stock to the public
for the first time in 2004-up from the 2002 and 2003 levels of 86 and 85
companies, respectively. Additionally, stock underwriting activity
measured in dollars rose to $47.9 billion in 2004, a level consistent with
activity in the late 1990s.

52An IPO is the first sale of stock by a private company to the general
public. This process is often called "going public" and represents the
primary market. The secondary market for stocks is the market where
securities are traded after they are initially offered in the primary
market.

Figure 13: Number of IPOs and Dollars Raised, 1994-2004

Number of issues

Dollars in billions

                                     1,000

                                      800

                                      600

                                      400

                                      200

0

80

70

60

50

40

30

20

10

0 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 Year

Number of companies issuing new stock for the first time (initial public
offerings)

Dollar amounts raised

Source: Securities Industry Association.

Of the market participants that we spoke with, most did not believe that
decimal pricing had affected companies' ability to raise capital in U.S
markets, noting that underwriting activity is primarily related to
investors' overall demand for stocks. More IPOs generally occur during
periods with strong economic growth and good stock market performance.
Institutional investors we spoke with noted that the poor growth of the
U.S. economy after 2000 and the associated uncertainty about future
business conditions had contributed more than decimal pricing to the
reduced level of new stock issues in 2002 and 2003. Others cited the new
Sarbanes-Oxley Act corporate governance and disclosure requirements, which
can increase the costs of being a public company, as a factor that may be
discouraging some firms that otherwise would have to sought to raise
capital from filing an IPO. However, one broker-dealer official said that
his firm was less willing to help small companies raise capital because of
its reduced ability since decimal pricing began to profitably make a
market in the new firm's stock after its IPO.

    Proposed Pilot for Higher Minimum TickSize Receives Mixed Responses

In response to the drop in displayed liquidity and other negative
conditions that some believe to exist in the U.S. stock markets, a
proposal has been made to conduct a pilot that would test the use of a
higher minimum tick for trading, but opinions among the various market
participants we spoke with were mixed. The proposal, which was put forth
by a senior official at one NYSE specialist firm, calls on SEC to oversee
a pilot program that would test a 5-cent tick on 200 to 300 NYSE stocks
across all markets. The purpose of the pilot program would be to provide
SEC with information it could use to decide whether larger-sized ticks
improve market quality in U.S. stock markets.

Proponents believe that larger ticks would address some of the perceived
negative conditions such as the reduction in displayed liquidity brought
about with the change to penny ticks. For example, some proponents
anticipate that investors would be more willing to display large orders
because larger tick sizes would increase the financial risk of stepping
ahead for other traders. Some also expected that market intermediaries
would be more willing to trade in less liquid stocks because of the
increased potential to profit from larger spreads. Some proponents of a
pilot program believed 5-cent ticks would also increase the cost
efficiency, speed, and simplicity of execution for large-order investors,
especially in less liquid stocks. Most of the market intermediaries we
spoke with supported the proposed 5-cent pilot for stocks. Opinions from
the representatives of the markets we spoke with were more mixed, with
officials from floor-based exchanges supporting the pilot, while officials
from two of the electronic markets we spoke with did not support a change
and officials from two others supporting the pilot under the belief that
larger ticks would benefit less liquid stocks.

Of the 23 institutional investors we talked with, 10 indicated support for
a proposed 5-cent pilot, 9 did not see a need for such a pilot, and 4 were
indifferent or had no opinion. Of those institutional investors who did
not see the need to conduct a pilot, most indicated that 5-cent ticks
would not increase liquidity in the markets because the negative
conditions that are attributed to decimal pricing are more the result of
the inefficiencies they believed existed in markets that rely on executing
trades manually rather than using technology to execute them
automatically. In addition, officials at several firms noted that such a
pilot is unnecessary because institutional investors have already adjusted
to penny ticks. For example, an official of a very large institutional
investment firm noted that the challenges of locating sufficient numbers
of shares for trading large orders had already been solved with advances
in electronic trading and crossing networks.

Some of these investors were also concerned that conducting such a pilot
could have negative consequences. For example, one firm noted that having
different ticks for different stocks could potentially confuse investors.
Also, a trade association official noted that mandating that some stocks
trade only in 5-cent ticks could be viewed as a form of price fixing,
particularly for highly liquid stocks that were already trading
efficiently using a 1-cent tick. An official from a financial markets
consulting and research firm noted that if a pilot program were to occur,
NASDAQ stocks should be included; this would better isolate the effects of
a larger tick size on market quality factors since NYSE appears to be
undergoing changes towards a more electronic marketplace, potentially
making it more difficult to interpret the study's results.

In addition, some of the 10 institutional investors that supported a pilot
of nickel-sized ticks indicated that they saw such ticks as being useful
primarily for less-liquid stocks that generally have fewer shares
displayed for trading, including smaller capitalized stocks. These
proponents told us that 5-cent ticks might increase displayed liquidity
for such stocks. In addition, they stated that 5-cent ticks could provide
financial incentive for intermediaries to increase their participation in
the trading of such stocks, including providing greater compensation for
market makers and specialists to commit more capital to facilitate
large-order trades. Many also anticipated a reduction in stepping ahead
since it would become more costly to do so. SEC staff that we asked about
the pilot told us that conducting such a test did not appear to be
warranted because, to date, the benefits of penny pricing-most notably the
reduction in trading costs through narrower spreads-seem clearly to
justify the costs. They also noted that penny pricing does not, and is not
designed to, establish the optimal spread in a particular security, which
will be driven by market forces.

  Decimal Pricing Has Had a Limited Impact on the Options Markets, but Other
  Factors Have Helped Improve Market Quality

Decimal pricing in U.S. options markets has generally had a more limited
impact on the options market than it has on the stock market. Although
various measures of market quality, including trading costs and liquidity,
have improved in U.S. options markets, factors other than decimal pricing
are believed to be the primary contributors. First, the tick size
reductions adopted for options trading were less dramatic than those
adopted in the stock markets. Second, other factors, including increased
competition among exchanges to list the same options, the growing use of
electronic trading, and a new system that electronically links the various
markets, were seen as being more responsible for improvement in U.S.
options

markets. Options market intermediaries such as market makers and
specialists have had mixed experiences since decimal pricing began, with
floor-based firms facing declining revenues and profitability and
electronicbased firms seeing increased trading revenues and profitability.
As part of a concept release on a range of issues pertaining to the
options markets, SEC has sought views on reducing tick sizes further in
the options markets by lowering them from the current 5 and 10 cents to
one penny. Options market participants were generally strongly opposed to
such a move for a variety of reasons, including the possibility that the
number of quotes could increase dramatically, overwhelming information
systems, and the potential for reduced displayed liquidity.

    The Shift to Decimal Pricing Did Not Reduce Tick Sizes for Options as Much
    as for Stocks

One reason that decimal pricing's impact on options markets was not seen
as significant was that the tick size reductions for options market were
not as large as those adopted for the stock markets. Options markets had
previously used a minimum tick size of 1/8 of a dollar (12.5 cents) for
options contracts priced at $3 and more and a tick size of 1/16 of a
dollar (6.25 cents) for options priced at less than $3. After decimal
pricing came into effect, these tick sizes fell to 10 cents and 5 cents,
respectively-a decrease of 20 percent. This decline was far less than the
84 percent reduction in tick size in the stock market, where the bid-ask
spread dropped from 1/16 of a dollar to 1 cent.

Studies done by four options exchanges in 2001 to assess the impact of
decimal prices on, among other factors, options contract bid-ask spreads
did not find that decimal pricing had any significant effect on the
spreads for options.53 Most market participants shared this view. For
example, an official of a large market-making firm stated that
decimalization in the options market was "a small ripple in a huge pond."

    Although Decimal Pricing Not Significant, Key Measures of Options Markets'
    Quality Have Improved

Although decimal pricing's impact was not seen as significant, various
measures used to assess market quality have shown improvements in U.S.
options markets in recent years. Unlike for stocks, data on trading costs
in options markets was not generally available. For example, we could not
identify any trade analytics or other firms that collected and analyzed
data for options trading. However, some market participants we interviewed
indicated that bid-ask spreads, which represent a measure of cost of
trading in options markets, have narrowed since the 1990s. In addition,
the studies done by SEC and others also indicated that spreads have
declined for options markets.

In addition to lower trading costs, liquidity, which is another measure
that could be used to assess the quality of the options market, has
improved since decimal pricing was implemented. According to industry
participants we interviewed, liquidity in the options market has increased
since 2001. They noted that trading volumes (which can be an indicator of
liquidity) had reached historic levels and that many new liquidity
providers, such as hedge funds and major securities firms, had entered the
market. As shown in figure 14, options trading volumes have grown
significantly (61 percent) since 2000, rising from about 673 million
contracts to an all-time high of 1.08 billion contracts in 2004.

53The four option exchanges whose studies we obtained include the American
Stock Exchange, Chicago Board Options Exchange, Pacific Exchange, and
Philadelphia Stock Exchange.

Figure 14: Total Contract Trading Volumes for Stock Options, 2000-2004
(Volume in millions)

Contract trading volume (in millions)

1,200

                                    1,083.6

1,000

800

600

400

200

0 2000 2001 2002 2003 2004 Year

Source: GAO analysis of Options Clearing Corporation data.

However, some market participants noted that the implementation of decimal
pricing in the stock markets had negatively affected options traders.
According to these participants, the reduced number of shares displayed in
the underlying stock markets and quote flickering in stock prices had made
buying and selling shares in the stock markets and determining an accurate
price for the underlying stocks more difficult.54 As a result, options
traders' and market makers' attempts to hedge the risks of their options
positions by trading in the stock markets had become more challenging and
costly.

54Quotes "flicker" on trading information screens when the prices of
underlying stocks are changing too rapidly.

    Factors Other Than Decimal Pricing Have Been Credited with Improving the
    Quality of Options Markets

Market participants attributed the improvements in market quality for U.S.
options markets not to decimal pricing but to other developments,
including the practice of listing options contracts on more than one
exchange (multilisting), the growing use of electronic exchanges, and the
development of electronic linkages among markets. These developments have
increased competition in these markets. Multilisting, one of the most
significant changes, created intense competition among U.S. options
markets.55 Although SEC had permitted multilistings since the early 1990s,
the options exchanges had generally tended not to list options already
being actively traded on another exchange, but began doing so more
frequently in August 1999.56 According to an SEC study, in August 1999, 32
percent of stock options were traded on more than one exchange, and that
percentage rose steadily to 45 percent in September 2000. The study also
showed that the percentage of total options volume traded on only one
exchange fell from 61 percent to 15 percent during the same period. Almost
all actively traded stock options are now listed on more than one U.S.
options exchange.

55The first multiple listing of an options contract occurred in February
1976 when the Chicago Board Options Exchange multilisted options on the
stock of the Boise Cascade Corporation, which had previously been listed
only by the Philadelphia Stock Exchange.

56In September 2000, both the Department of Justice and SEC reached a
settlement with the American Stock Exchange, Chicago Board Options
Exchange, Pacific Exchange, and Philadelphia Stock Exchange with respect
to alleged anticompetitive activities and the failure to adequately
enforce compliance with their own rules.

Multilisting has been credited with increasing price competition among
exchanges and market participants. The SEC study examined, among other
things, how multiple listings impacted pricing and spreads in the options
market and found that the heightened competition had produced significant
economic benefits to investors in the form of lower quoted and effective
spreads.57 The study looked at 1-week periods, beginning with August 9
through 13, 1999 (a benchmark period prior to widespread multilisting of
actively traded options), and ending with October 23 through 27, 2000 (a
benchmark period during which the actively traded options in the study
were listed on more than one exchange). During this period, the average
quoted spreads for the most actively traded stock options declined 8
percent. Quoted spreads across all options exchanges over this same period
showed a much more dramatic change, declining approximately 38 percent.
The actual transaction costs that investors paid for their options
executions, as measured by effective spreads, also declined, falling 19
percent for options priced below $20 and 35 percent for retail orders of
50 contracts or less. Several academic studies also showed results
consistent with SEC's findings that bid-ask spreads had declined since the
widespread multiple listing of the most active options.58

The introduction of the first all-electronic options exchange in 2000 also
increased competition in the options markets. Traditionally, trading on
U.S. options markets had occurred on the floors of the various exchanges.
On the new International Securities Exchange (ISE), which began operations
in May 2000, multiple (i.e., competing) market makers and specialists can
submit separate quotes on a single options contract electronically. The
quotes are then displayed on the screens of other market makers and at the
facilities of broker-dealers with customers interested in trading options,
enhancing competition for customer orders. ISE also introduced the
practice of including with its quotes the number of contracts available at

57SEC, Office of Compliance Inspections and Examinations and Office of
Economic Analysis, Special Study: Payment for Order Flow and
Internalization in the Options Markets, December 2000. The quoted spread
is the difference between the displayed bid and ask prices and generally
measures retail trading costs, since retail investors typically conduct
transactions at these prices. The effective spread measures the trading
cost relative to the midpoint of the quoted spread at the time the trade
occurred. The lower the effective spread, the lower the cost to investors.

58See Patrick De Fontnouvelle, Raymond P.H. Fishe, and Jeffrey H. Harris,
"The Behavior of Bid-Ask Spreads and Volume in Options Markets During the
Competition for Listings in 1999," The Journal of Finance, vol. 58, no. 6,
(December 2003); Battalio, Robert, Brian Hatch and Robert Jennings,
"Toward a National Market System for U.S. Exchange-Listed Stock Options,"
The Journal of Finance, vol. 59, no. 2, (April 2004).

the quoted price. According to market participants, the additional
information benefited retail and institutional investors by providing them
with better information on the depth of the market and the price at which
an order was likely to be executed. Finally, ISE allowed customers to
execute trades in complete anonymity and attracted additional sources of
liquidity by allowing market makers to access its market remotely.

In response, the four floor-based options exchanges-the American Stock
Exchange, Chicago Board Options Exchange (CBOE), the Pacific Exchange
(PCX), and the Philadelphia Stock Exchange-also began including the number
of available contracts with their quotations and offering electronic
trading systems in addition to their existing floor-based trading model.59
Another new entrant, the Boston Options Exchange (BOX) (an affiliate of
the Boston Stock Exchange) also began all-electronic operations in 2004.
The result has been increased quote competition among markets and their
participants that has helped to further narrow spreads and has opened
markets to a wide range of new liquidity providers, including
broker-dealers, institutional firms, and hedge funds.

Electronic linkages were first introduced to U.S. options markets in 2003,
offering the previously unavailable opportunity to route orders among all
the registered options exchanges. In January 2003, SEC announced that the
options markets had implemented the intermarket linkage plan, so that U.S.
options exchanges could electronically route orders and messages to one
another. The new linkages further increased competition in the options
industry and made the markets more efficient, largely by giving brokers,
dealers, and investors' better access to displayed market information.
According to SEC and others, as a result of this development investors can
now receive the best available prices across all options exchanges,
regardless of the exchange to which an order was initially sent.
Intermarket linkages are as essential to the effective functioning of the
options markets as they are to the functioning of the stock markets and
will further assist in establishing a national options market system.

59These systems include the American Stock Exchange's ANTE, the CBOE's
Hybrid Trading System, the Pacific Exchange's PCX Plus, and the
Philadelphia Stock Exchange's XL. The two electronic-based option
exchanges are BOX and ISE.

    The Impact on Options Market Intermediaries Varied since Decimal Pricing
    Began

Decimal pricing and other changes in options markets appear to have
affected the various types of market intermediaries differently.
Representatives of firms that trade primarily on floor-based exchanges
told us that their revenues and profits from market making had fallen
while their expenses had increased. For example, one options specialist
said that his firm's profitability had declined on a per-option basis and
was now back to pre-1995 levels. However, he noted that the cost of
technology to operate in today's market had increased substantially and
that adverse market conditions and increased competition were more
responsible for his firm's financial conditions than were decimal prices.

The increasingly competitive and challenging environment has also led to
continued consolidation among firms that trade on the various options
exchange floors. According to data from one floor-based options exchange,
the number of market intermediaries active on its market declined
approximately 22 percent between 2000 and 2004. Market intermediaries and
exchange officials we spoke with noted in particular that the smaller
broker-dealer firms that trade options and sometimes have just one or two
employees had been the most affected, with many either merging with other
firms or going out of business because of their inability to compete in
the new trading environment.

In contrast, the introduction of electronic exchanges and expanded
opportunities for electronic trading at other exchanges has been
beneficial for some market intermediaries. Officials of some
broker-dealers that trade options electronically told us that their firms'
operations had benefited from the increased trading volume and the
efficiency of electronic trading. The officials added that other firms,
such as large financial institutions, had increased their participation in
the options marketplace. They also noted that the availability of
electronic trading systems and the inherent economies of scale associated
with operating such systems had attracted new marketplace entrants,
including some hedge funds and major securities firms. For example,
representatives of ISE and several brokerdealers told us that the ability
to trade electronically had encouraged several large broker-dealers that
were not previously active in options markets to begin acting as market
makers on that exchange. These firms, they explained, were able to enter
into the options markets because making markets electronically is less
expensive than investing in the infrastructure and staff needed to support
such operations on a trading floor. According to market participants we
spoke with, these new entrants appeared to have provided increased
competition and positively affected spreads, product innovation, and
liquidity in the options industry.

    Options Market Participants Oppose Lower Minimum Ticks for the Options
    Industry

In 2004, SEC issued a concept release that sought public comments on
options-related issues that have emerged since the multiple listing of
options began in 1999, including whether the markets should reduce the
minimum tick sizes for options from 5 and 10 cents to 1-cent increments.60
According to the release, SEC staff believed that penny pricing in the
options market would improve the efficiency and competitiveness of options
trading, as it has in the markets for stocks, primarily by tightening
spreads. If lower ticks did lead to narrower spreads for options prices,
investors trading costs would likely similarly decline. As of May 2004,
SEC has received and reviewed comments on the concept release but has
taken no further action.

All of the options exchanges and virtually all of the options firms we
spoke with, as well as 15 of the 16 organizations and individuals that
submitted public comments on SEC's 1-cent tick size proposal, were opposed
to quoting options prices in increments lower than those currently in use
(10 and 5 cents, depending on the price of an options contract). One of
the primary reasons for this opposition was that trading options contracts
in 1cent increments would significantly increase quotation message
traffic, potentially overwhelming the capacity of the existing systems
that process options quotes and disrupting the dissemination of market
data. For any given stock, hundreds of different individual options
contracts can be simultaneously trading, with each having a different
strike price (the specified price at which the holder can buy or sell
underlying stock) and different expiration date.61 Because options are
contracts that provide their holders with the right to either buy or sell
a particular stock at the specified strike price, an option's value and
therefore its price also changes as the

60Competitive Developments in the Options Markets, Exchange Act Release
No. 49175 (Feb. 3, 2004), 69 Fed. Reg. 6124 (2004). In this release, SEC
also sought comments on a variety of issues, including payment for order
flow, internalization, and specialist participation guarantees. Payment
for order flow is an arrangement under which a broker is paid to route its
customer orders to a particular market for execution. Internalization
occurs when a brokerage firm fills a customer's order from the broker's
own inventory of securities without exposing the order to the market.
Specialist participation guarantees offer these intermediaries a
percentage of the order flow from a particular options exchange for
providing liquidity, depth, and continuity in that market.

61An actively-traded stock like International Business Machines (IBM) may
have thousands of options available for trading. For example, if IBM's
stock price is around $100, options granting the right to buy or sell the
stock are likely trading with strike prices of $90, $95, $100, $105, $110,
etc., and each of these prices will have separate options expiration dates
(months). Simultaneously, trading will also be occurring in both call
options and put options using the same strike prices and expiration
months.

underlying stock's price changes. If options were priced in pennies,
market participants said that thousands of new option price quotes could
be generated because prices would need to adjust more rapidly to remain
accurate than they do using nickel or dime increments.

Markets and market participants also expressed concerns that penny pricing
would exacerbate an already existing problem for the industry- ensuring
that the information systems used to process and transmit price quotations
to market participants have adequate capacity. The quotes generated by
market makers on the various markets are transmitted by the systems
overseen by the Options Price Reporting Authority (OPRA). The OPRA system
has been experiencing message capacity issues for several years. In terms
of the number of messages per second (mps) that can be processed, the OPRA
system had a maximum mps of 3,000 in January 2000. Since then, the
processing and transmission capacity of the system has had to be expanded
significantly to accommodate the growth in options' quoting volumes, and
as of April 2005, the OPRA system was capable of processing approximately
160,000 mps. Prior to the implementation of decimal pricing in 2001,
similar concerns about the impact on message traffic volumes were also
raised for stocks, but the magnitude of the anticipated increases were
much larger for options.

To address the capacity constraints in the options market systems thus
far, the administrators of the OPRA system have tried to reduce quotation
traffic by having the options exchanges engage in quote mitigation. Quote
mitigation requires the exchanges to agree to prioritize their own quotes
and trade report message volumes so that the amount of traffic submitted
does not exceed a specified percentage of the system's total capacity. As
of April 2005, the OPRA administrators were limiting the volume of
messages that exchanges were able to transmit to just 88,000 mps based on
requests from the six options exchanges.

Two market participants that commented on SEC's proposal noted that with
options market data continuing to grow at a phenomenal rate each year,
OPRA would have to continue increasing its current message capacity to
meet ongoing demand. If penny quoting were to create even faster growth in
the total number of price quotes generated, market participants indicated
that options exchanges, market data vendors, and broker-dealers would need
to spend substantial sums of money on operational and technological
improvements to their capacity and communication systems in order to
handle the increased amounts of market data. These costs, they said, would
likely be passed on to investors.

Another reason that market participants objected to lowering tick sizes
for options trading was that doing so would likely reduce market
intermediaries' participation in the markets. Because these intermediaries
make their money from the spreads between the bid and offer prices,
narrower spreads that would likely accompany penny ticks would also reduce
these intermediaries' revenues and profits. This, in turn, would reduce
these firms' ability and willingness to provide liquidity, especially for
options that are traded less frequently. According to the commenters on
the proposal and the participants we contacted, intermediaries would
likely become reluctant to provide continuous two-sided markets (e.g.,
offering both to buy and sell options simultaneously) to facilitate
trading, since profit potential would be limited by the 80 percent or more
reduction in tick size. And because the 1-cent tick could increase the
chance of other traders stepping ahead of an order, such intermediaries
could become reluctant to display large orders. With the options markets
having hundreds of options for one underlying stock, market intermediaries
would likely quote fewer numbers of contracts, which would further reduce
displayed liquidity, and market transparency.

Market participants also raised other concerns about trading in penny
ticks for options. For example, they worried that option prices quoted in
1-cent increments would change in price too rapidly, resulting in more
quote "flickering." They also noted that the options market could
experience some of the other negative effects that have occurred in the
stock markets, including increasing instances of stepping ahead by other
traders.

SEC staff responsible for options markets oversight told us that they
would like to see tick sizes reduced in the options markets as a means of
lowering costs to investors. They acknowledged that the benefits of such
tick size reductions would have to be balanced with the likely
accompanying negative impacts. SEC staff responsible for options markets
oversight told us that they would like to see tick sizes reduced in the
options markets as a means of lowering costs to investors. They
acknowledged that the benefits of such tick size reductions would have to
be balanced with the likely accompanying negative impacts. They noted that
recent innovations permit a small amount of trading in pennies and that
continued innovation and technological advances may lead to approaches
more favorable to investors without substantial negative effects.

Observations	In advocating decimal pricing, Congress and SEC expected to
make stock and options pricing easier for the average investor to
understand and reduce trading costs, particularly for retail investors,
from narrower bidask spreads. These goals appear to have been met.
Securities priced in dollars and cents are clearly more understandable,
and the narrower spreads that have accompanied this change have made
trading less costly for retail investors. Although the resulting trading
environment has become more challenging for institutional investors, they
too appear to have benefited from generally lower trading costs since
decimal pricing was implemented. In response to the reduced displayed
market depth, institutional investors are splitting larger orders into
smaller lots to reduce the market impact of their trading and accelerating
their adoption of electronic trading technologies and alternative trading
venues. As a result of these adaptations, institutional investors have
been able to continue to trade large numbers of shares and at even less
total cost than before.

However, since decimal pricing was introduced, the activities performed by
some market intermediaries have become less profitable. Decimal prices
have adversely affected broker-dealers' ability to earn revenues and
profits from their stock trading activities. But one of the goals of
decimal pricing was to lower the artificially established tick size, and
thus the loss of revenue for market intermediaries that had benefited from
this price constraint was a natural outcome. Various other factors,
including institutional investors' adoption of electronic technologies
that reduce the need for direct intermediation, can also explain some of
market intermediaries' reduced revenues. Nevertheless, the depressed
financial condition of some intermediaries would be of more concern if
conditions were also similarly negative for investors, which we found was
not the case.

In response to the changes since decimal pricing began, a proposal has
been made to conduct a pilot program to test higher tick sizes. This
program would provide regulators with data on the impacts, both positive
and negative, of such trading. However, given that many investors and
market intermediaries have made considerable efforts to adapt their
trading strategies and invest in technologies that allow them to be
successful in the penny tick trading environment, the need for increased
tick sizes appears questionable.

Although decimal pricing has been a less significant development in U.S.
options markets, other factors, such as new entrants and the increased use

of electronic trading and linkages, have served to improve the quality of
these markets. SEC's proposal to further reduce tick sizes in the options
markets has been met with widespread opposition from industry
participants, and many of the concerns market participants raised,
including the potential for significant increases in quote traffic and
less displayed liquidity, appear to have merit. The magnitude of these
potential impacts appears larger than those that accompanied the
implementation of penny ticks for stocks. As a result, it is not clear
that additional benefits of the narrower spreads that could accompany
mandated tick size reductions would be greater than the potentially
negative impacts and increased costs arising from greatly increased quote
processing traffic.

Agency Comments	We provided a draft of this report to SEC for comments and
we received oral comments from staff in SEC's Division of Market
Regulation and Office of Economic Analysis. Overall, these staff said that
our report accurately depicted conditions in the markets after the
implementation of decimal pricing. They also provided various technical
comments that we incorporated where appropriate.

As agreed with your offices, unless you publicly announce its contents
earlier, we plan no further distribution of this report until 30 days
after the date of this report. At that time, we will send copies of this
report to the Chairman and Ranking Minority Member, Subcommittee on
Securities and Investments, Senate Committee on Banking, Housing, and
Urban Affairs. We will also send copies of this report to the Chairman,
SEC. We will make copies available to others upon request. This report
will also be available at no charge on GAO's Web site at
http://www.gao.gov.

Please contact me at (202) 512-8678 if you or your staff have any
questions concerning this report. Contact points for our Offices of
Congressional Relations and Public Affairs may be found on the last page
of this report. Key contributors to this report are listed in appendix V.

Richard J. Hillman Director, Financial Markets and Community Investment

Appendix I

                             Scope and Methodology

To determine the impact of decimal pricing on retail investors, we
analyzed data from a database of trades and quotes from U.S. stock markets
between February 2000 and November 2004. Appendix II contains a detailed
methodology of this analysis. Using this data, we selected a sample of
stocks traded on the New York Stock Exchange (NYSE) and the NASDAQ Stock
Market (NASDAQ) and calculated how the trading in these stocks had changed
between a 1-year period before and an almost 4-year period after decimal
pricing began. As part of this analysis, we examined the changes in
spreads on these stocks (the relevant measure of trading costs for retail
investors). We also undertook steps to assess the reliability of the data
in the TAQ database by performing a variety of error checks on the data
and using widely accepted methods for removing potential errors from data
to ensure its reliability. Based on these discussions, we determined that
these data were sufficiently reliable for our purposes. We also reviewed
market and academic studies of decimal pricing's impact on spreads. In
addition, we interviewed officials from over 30 broker-dealers, the
Securities and Exchange Commission (SEC), NASD, two academics, and five
alternative trading venues, eight stock markets, four trade analytics
firms, a financial markets consulting and research firm, and four industry
trade groups.

  Methodology for Assessing Impact on Institutional Investors

To analyze the impact of decimal pricing on institutional investors, we
obtained and analyzed institutional trading cost data from three leading
trade analytics firms-Plexus Group, Elkins/McSherry, and Abel/Noser-
spanning from the first quarter of 1999 through second quarter of 2003
from the Plexus Group and from the fourth quarter of 1998 to the end of
2004 from Elkins/McSherry and Abel/Noser-to determine how trading costs
for institutional investors responded to decimalization. These firms' data
do not include costs for trades that do not fully execute. To address this
issue, we interviewed institutional investors on their experiences in
filling large orders. We also undertook steps to assess the reliability of
the trade analytics firms' data by interviewing their staffs about the
steps the firms follow to ensure the accuracy of their data. Based on
these discussions, we determined that these data were sufficiently
reliable for our purposes.

To identify all relevant research that had been conducted on the impact of
decimal pricing on institutional investors' trading costs, we searched
public and private academic and general Internet databases and spoke with
academics, regulators, and market participants. We identified 15 academic
studies that met our criteria for scope and methodological considerations.

                        Appendix I Scope and Methodology

Of these, 3 addressed trading costs for institutional investors and 12
addressed trading costs for retail investors.

To determine the impact of pricing on investors' ability to trade, we
interviewed roughly 70 judgmentally selected agencies and firms, including
representatives of 23 institutional investors with assets under management
ranging from $2 billion to more than $1 trillion. The assets being managed
by these 23 firms represented 31 percent of the assets under management by
the largest 300 money managers in 2003. In addition, we also discussed the
impact on intuitional investors during our interviews with brokerdealers,
securities regulators, academics, and alternative trading venues, stock
exchanges, trade analytics firms, a financial market consulting and
research firm, and industry trade groups.

  Methodology for Assessing Impact on Market Intermediaries

To assess the impact of decimal pricing on stock market intermediaries, we
obtained data on the revenues of the overall securities industry from the
Securities Industry Association (SIA). SIA's revenue data come from the
reports that each broker-dealer conducting business with public customers
is required to file with SEC-the Financial and Operational Combined
Uniform Single (FOCUS) reports. We used these data to analyze the trend in
revenues for the industry as a whole as well as to identify the revenues
associated with making markets in NASDAQ stocks. In addition, we obtained
data on the specialist broker-dealer revenues and participation rates and
on executed trade sizes from NYSE. For the number of specialist firms
participating on U.S. markets, we sought data from NYSE and the other
exchanges, including the American Stock Exchange (Amex), the Boston Stock
Exchange, the Chicago Stock Exchange, the Pacific Exchange (PCX), and the
Philadelphia Stock Exchange (Phlx). We obtained data on the number of
market makers and the trend in executed trade size from NASDAQ. We
discussed how these organizations ensure the reliability of their data
with officials from the organizations where relevant and determined that
their data were sufficiently reliable for our purposes. We also discussed
the impact of decimals on market intermediaries during our interviews with
officials from broker-dealers, securities regulators, alternative trading
venues, stock exchanges, trade analytics firms, a financial market
consulting and research firm, and industry trade groups, as well as
experts from academia.

                        Appendix I Scope and Methodology

  Methodology for Assessing Impact on Options Markets

To determine the impact of decimal pricing on the options markets, both
investors and intermediaries, we reviewed studies that four U.S. options
exchanges, including Amex, Chicago Board Options Exchange (CBOE), PCX, and
Phlx, submitted to SEC in 2001 on the impact of decimalization on their
markets. We also performed literature searches on the Internet for
academic and other studies that examined the impact of decimal pricing on
options markets. In addition, we also attempted to identify any sources or
organizations that collected and analyzed options trading costs.

To determine the impact on intermediaries, we interviewed officials of all
six U.S. options exchanges, including Amex, Boston Options Exchange, CBOE,
International Securities Exchange, PCX, and Phlx, and various market
participants (an independent market maker, designated primary market
makers, specialists, a floor broker, hedge funds and a retail investor
firm) to ascertain their perspectives on the impact of the conversion to
decimalization on them, investors, and the markets.

To determine the potential impact of reducing the minimum price tick in
the options markets to a penny, we interviewed officials from the option
exchanges and market participants. We also reviewed all comment letters
that SEC had received on its concept release discussing potential changes
in options market regulation, including lowering the minimum tick size in
the options markets to a penny. We reviewed those letters posted on SEC's
Web site as of May 4, 2005. Sixteen of these letters specifically
commented on the penny-pricing proposal.

Appendix II

Methodology for GAO Analysis of Trade and Quotes Data

To assess the impact of decimal pricing, one of the activities we
performed was to analyze data from the New York Stock Exchange (NYSE)
Trade and Quote (TAQ) database spanning the 5-year period between February
2000 (before the conversion to decimal pricing) and November 2004 (after
the adoption of decimal pricing) to determine how trading costs for retail
investors changed and how various market statistics changed, such as the
average number of shares displayed at the best prices before and after
decimalization. Although maintained by NYSE, this database includes all
trades and quotes that occurred on the various exchanges and the NASDAQ
Stock Market (NASDAQ). Using this database, we performed an event-type
study analyzing the behavior of trading cost and market quality variables
for NYSE and NASDAQ stocks in pre- and postdecimalization environments.1
For each of our sample stocks, we used information on each recorded trade
and quote (that is, intraday trade and quote data) for each trading day in
our sample period. We generally followed the methods found in two recently
published academic studies that examined the impact of decimalization on
market quality and trade execution costs.2 In particular, we analyzed the
pre-and postdecimalization behavior of several trading cost and market
quality variables, including various bid-ask spread measures and price
volatility, and we also analyzed quote and trade execution price
clustering across NYSE and NASDAQ environments. We generally presented our
results on an average basis for sample stocks in a given market in the
pre-and postdecimalization periods; in some cases we separated sample
stocks into groups based on their average daily trading

1Throughout, "decimalization" reflects the transition from fractional
pricing (that is, pricing generally in sixteenths of a dollar) to decimal
pricing (that is, pricing in round cents) and, more significantly, the 84
percent reduction in the minimum price increment, or tick, from
one-sixteenth of a dollar to 1 cent. Decimalization was fully implemented
on the NYSE on January 29, 2001, but not until April 9, 2001, on NASDAQ.
An event study is the analytical framework used to measure the economic
effect of an event, such as the transition from fractional to decimal
pricing.

2Hendrik Bessimbinder, 2003, "Trade Execution Costs and Market Quality
after Decimalization," Journal of Financial and Quantitative Analysis
38(4), 747-777; and K. Chung, B. Van Ness, and R. Van Ness, 2004, "Trading
Costs and Quote Clustering on the NYSE and NASDAQ after Decimalization,"
Journal of Financial Research 27(3), 309-328. Bessimbinder (2003)
performed an event study analysis of the impact of decimalization on
several trade execution cost and market quality measures using a sample of
NYSE-listed and NASDAQ stocks in a predecimalization period and a
postdecimalization period. While not an event study, Chung et al. (2004)
focused on the differences in several trade execution cost and market
quality measures between a sample of NYSE-listed and NASDAQ stocks in the
month after decimalization; they also analyzed quote clustering, which is
the tendency for quotes to "cluster" at certain price points, such as
nickels and dimes.

Appendix II
Methodology for GAO Analysis of Trade and
Quotes Data

volume and reported our results so that any differences across stock
characteristics could be observed.3

Our analysis was based on intraday trade and quote data from the TAQ
database, which includes all trade and quote data (but not order
information) for all NYSE-listed and NASDAQ stocks, among others. TAQ data
allowed us to study variables that are based on trades and quotes but did
not allow us to study any specific effects on or make any inferences
regarding orders or institutional trading costs.4

Our data consisted of trade and quote activity for all stocks listed on
NYSE, NASDAQ, and the American Stock Exchange (Amex) from February 1,
2000, through November 30, 2004, excluding the month of September 2001. We
focused on NYSE-listed and NASDAQ issues, as is typical in the literature,
since the potential sample size from eligible Amex stocks tends to be much
smaller. Our analysis compared 300 matched NYSE and NASDAQ stock pairs
over the 12 months prior to decimalization and 12 months selected from the
period spanning April 2001 through November

3Stocks were grouped by volume according to the following categories:

o 	High volume stocks were those in our sample of stocks with average
daily trading volumes exceeding 500,000 shares (the maximum was less than
1.6 million shares).

o 	Medium volume stocks were those in our sample of stocks with average
daily trading volumes between 100,000 and 499,999 shares.

o 	Low volume stocks were those in our sample of stocks with average daily
trading volumes of less than 100,000 shares.

4For example, since an order can often be filled through a number of trade
executions, and use of TAQ data implicitly assumes that each trade record
reflects a unique order that is filled, our analysis failed to address any
impact of a change in how orders are filled and the costs associated with
this.

                                  Appendix II
                   Methodology for GAO Analysis of Trade and
                                  Quotes Data

2004.5 In constructing our sample period, we omitted the months of
February and March 2001 from consideration, because not all stocks were
trading using decimal prices during the transition period.

Because there were a host of concurrent factors impacting the equities
markets around the time of and since the transition to decimal pricing, it
is unlikely that any of our results can be attributed solely to
decimalization. Any determination of statistically significant differences
in pre-and postdecimalization trading cost and market quality variables
was likely due to the confluence of decimalization and these other
factors.

Determining the Sample Period Determining the best sample period presented
a challenge because

for Our Analysis	decimalization was implemented at different times on NYSE
and NASDAQ. The transition to decimal pricing was completed on NYSE on
January 29, 2001, while on NASDAQ it was completed on April 9, 2001. In
addition, there were selected decimalization pilots on NYSE and NASDAQ
prior to full decimalization on each. Researchers who have analyzed the
transition to decimal pricing have generally divided up the pre-and
postdecimalization sample periods differently depending on the particular
focus of their research.6 Relatively short sample periods too close to the
transition might suffer from unnatural transitory effects related to the
learning process in a new trading environment, while sample periods
farther from the implementation date or longer in scope might suffer from
the influence of confounding factors. Analyses comparing different months
before and after decimalization (e.g., December 2000 versus May 2001)

5Since there are important structural differences between the NYSE and
NASDAQ markets and the stocks listed on each, a general analysis of the
effect of an event on both markets could yield biased results if the stock
samples are not chosen carefully. For this reason, researchers analyzing
the impact of decimalization on the NYSE and NASDAQ usually employ a
matched-pairs analysis. For our analysis, a matched pair consisted of one
NYSElisted stock and one NASDAQ stock (among all NASDAQ stocks) that
provided the closest match to it in terms of characteristics related to
trading activity, such as share price and average daily trading volume,
which are generally thought to explain variation in bid-ask spreads, among
other things. By matching the stocks on these characteristics, a
matchedpairs analysis attempts to isolate the effect of an event on the
different markets by considering how it affects groups of analogous
stocks.

6Bessimbinder (2003) separated the pre and postdecimalization periods as
the 3 weeks before January 29, 2001, and from April 9, 2001 through August
31, 2001. Chung et al. (2004) considered only May 2001, as their focus was
not on a pre-versus postdecimalization comparison. Other studies, both
from researchers and exchanges, examining decimalization often selected a
1-month or shorter period sometime shortly before decimalization and an
analogous period sometime after decimalization for comparison.

Appendix II
Methodology for GAO Analysis of Trade and
Quotes Data

might suffer from seasonal influences. We extended the current body of
research, which includes studies by academic and industry researchers,
exchanges and markets, and regulators, by including more recent time
periods in our analysis, providing an expanded view of the trend in trade
execution cost and market quality variables since 2000. However, to the
extent that the influence of other factors introduced by expanding the
sample window outweighed any influence of decimalization on trade cost and
market quality measures, our results should be interpreted with caution.

Our sample period spanned February 2000 through November 2004 (table 14).
The predecimalization period included February 1, 2000, through January
19, 2001, and the postdecimalization period included April 23, 2001,
through November 5, 2004, excluding September 2001 (due to the effects of
the September 11 terrorist attacks). We selected one week from each month,
allowing for monthly five-trading day comparisons that avoided holidays
and options expiration days as well controlling for seasonality issues.7
Our predecimalization period consisted of a 1-week sample from each of the
12 months and our postdecimalization period consisted of twelve 1-week
sample periods excerpted from April 2001 through November 2004, excluding
the month of September 2001.

7Despite the two "event dates" for the NYSE and NASDAQ, our analysis
incorporated calendar-period comparisons rather than event-time
comparisons (for example, 1 month following decimalization on the NYSE
compared with 1 month following decimalization on NASDAQ). We believed
that it was reasonable to assume that the lag time between full
decimalization on the NYSE and NASDAQ would not lead to any sizeable
learning discrepancies between the markets since NASDAQ market
participants were able to observe NYSE activity over this period.

Appendix II
Methodology for GAO Analysis of Trade and
Quotes Data

       Table 14: Pre- and Postdecimalization Sample Weeks Day of the week

      Period    Year           Month Monday Tuesday Wednesday Thursday Friday 
Predecimals  2000                    February 7 8 9 10                     
                2000                    March 20 21 22 23                     
                2000                    April 10 11 12 13                     
                2000                      May 8 9 10 11                       
                2000                     June 19 20 21 22                     
                2000                     July 10 11 12 13                     
                2000                    August 21 22 23 24                    
                2000                  September 18 19 20 21                   
                2000                   October 23 24 25 26                    
                2000                     November 6 7 8 9                     
                2000                   December 18 19 20 21                   
                2001                   January 22 23 24 25                    
Postdecimals 2001                    April 23 24 25 26                     
                2001                    August 20 21 22 23                    
                2001                   December 10 11 12 13                   
                2002                                      January 7 8 9 10 11 
                2002                                           May 6 7 8 9 10 
                2002                                 September 23 24 25 26 27 
                2003                                  February 24 25 26 27 28 
                2003                                           June 2 3 4 5 6 
                2003                                   October 20 21 22 23 24 
                2004                                       March 8 9 10 11 12 
                2004                                      July 19 20 21 22 23 
                2004                                       November 1 2 3 4 5 
                                           Source: GAO.                       
                         Note: The sample weeks selected avoided holidays and 
                                  partial trading days either before or after 
                          holidays, as well as other noted trading stoppages, 
                            options expiration days (the third Friday of each 
                      month), and end of quarter days, all of which may lead  
                                   to unusual trading activity.               

                                  Appendix II
                   Methodology for GAO Analysis of Trade and
                                  Quotes Data

Generating the Sample of Stocks for Our Analysis

Generally following the methods used by other researchers, we generated
our list by including only common shares of domestic companies that were
active over our period of interest and that were not part of
decimalization pilot programs in effect before January 29, 2001.
Specifically, we excluded preferred stocks, warrants, lower class common
shares (for example, Class B and Class C shares), as well as NASDAQ stocks
with five-letter symbols not representing Class A shares.8 We then
eliminated from consideration stocks with average share prices that were
below $5 or above $150 over the February 2000 through December 2000
period. We also eliminated stocks for which there were no recorded trades
on 10 percent or more of the trading days, to ensure sufficient data,
leaving us with 981 NYSE-listed and 1,361 NASDAQ stocks in the potential
sample universe. Our stock samples for the analysis ultimately consisted
of 300 matched pairs of NYSE-listed and NASDAQ stocks.

8NASDAQ stock symbols are four to five letters in length. A fifth letter
in a NASDAQ stock symbol indicates, among other things, share class or
unusual circumstances such as bankruptcy or delayed SEC filing.

                                  Appendix II
                   Methodology for GAO Analysis of Trade and
                                  Quotes Data

Generating the Matched Pairs	The NYSE-listed and NASDAQ stocks were
matched on variables that are generally thought to help explain interstock
differences in spreads. To the extent that our matching samples of
NYSE-listed and NASDAQ stocks had similar attributes, any differences in
spreads between the groups should have been due to reasons other than
these attributes. The attributes we considered were (1) share price, (2)
share price volatility, (3) number of trades, and (4) trade size.9 For the
matching procedure, daily data from February 2000 through December 2000
were used and averages were taken over this sample period. Share price was
measured by the mean value of the daily closing price and volatility by
the average of the logarithm of the high-low intraday price range. The
number of trades was measured by the average daily number of trades, and
average trade size was measured as the average daily trading volume.10
These factors have different measurement units, implying that they could
not be directly converted into a single measure of similarity. To develop
a combined measure of similarity we first had to standardize the measures
of all factors so that their average values and differences in their
averages were measured on comparable scales. Once standardized measures of
averages and differences were developed, we were able to sum the four
measurements into a total measure of similarity and identify matched pairs
of stocks. Comparability was assured because all averages and differences
were divided by the standard deviation of the measure of each factor on
the NYSE.

Our matching algorithm was similar to those described in Chung et al.
(2004) and Van Ness et al. (2001). To obtain a matching sample of NYSE and
NASDAQ stocks, we first calculated the following combined measure of
similarity-the composite match score (CMS)-for each NYSE stock using our
entire sample of NASDAQ stocks. The CMS is defined as

9While Bessimbinder (2003) used only market capitalization as the matching
criterion, Chung et al. (2004) used five stock attributes-share price,
number of trades, trade size, return volatility, and market
capitalization. In the absence of market capitalization data, we followed
Van Ness et al. (2001) and used four variables. Researchers have generally
found that overall results are similar regardless of the matching
variables used.

10The reported number of trades on NYSE is not directly comparable to that
reported on NASDAQ due to interdealer trading on NASDAQ. NASDAQ volume has
been estimated to be exaggerated by 30 percent to 50 percent relative to
NYSE volume. As with Chung et al. (2004), we counterbalance the
discrepancy by including trades in NYSE-listed stocks that occur outside
of the NYSE, reflecting activity at regional exchanges and elsewhere,
rather that incorporating an "inflation factor."

Appendix II
Methodology for GAO Analysis of Trade and
Quotes Data

                                       2

Please refer to the PDF version of this document 
(http://www.gao.gov/cgi-bin/getrpt?GAO-05-535) for the formula defining 
the CMS,

in which the superscripts N and T refer to NYSE and NASDAQ,

NT

respectively, and Yv and Yv represent one of the four stock attributes for
each-in which i denotes the NYSE stock and j denotes the NASDAQ stock
being matched. In the matching algorithm, each of the attributes v was
weighted equally. Unlike the matching algorithms in the two aforementioned
papers, we divided each stock attribute difference by the sample standard
deviation of that attribute for the entire NYSE sample-

N

denoted as sYv -in order to create unit less measures that were normalized
relative to the overall NYSE attributes.

Ultimately, for each NYSE stock we selected the NASDAQ stock with the
smallest CMS. Chung et al. (2004) used a sequential matching algorithm as
is common in the literature. To start, they considered an NYSE stock and
computed its CMS with all NASDAQ stocks; they matched that NYSE stock to
the NASDAQ stock with the lowest CMS. Then they considered the next NYSE
stock, but the NASDAQ stock that matched the prior NYSE stock was no
longer considered among the possible universe of matches for this or any
subsequent NYSE stock. The outcome of this type of algorithm is path
dependent-the order in which the NYSE stocks are taken influences the
ultimate list of unique matches. We employed another method that avoided
this path dependence-ensuring an optimal match for each stock-but also
allowed for the possibility of duplicate, nonunique NASDAQ matches. For
the 981 NYSE-listed stocks, there were 293

                                  Appendix II
                   Methodology for GAO Analysis of Trade and
                                  Quotes Data

NASDAQ stocks that provided the best matches.11 We chose the 300 best CMS
matched pairs, which consisted of 300 NYSE and 186 unique NASDAQ stocks.12
Of these 186 NASDAQ stocks, 114 were best matches for one NYSE-listed
stock, 45 were best matches for two NYSE-listed stocks, 19 were best
matches for three NYSE-listed stocks, 5 were best matches for four
NYSE-listed stocks, 1 was a best match for five NYSE-listed stocks, and 2
were best matches for seven NYSE-listed stocks. In the subsequent
analysis, each NASDAQ stock was weighted according to the number of best
matches it yielded. For example, if a NASDAQ stock provided the best match
for two NYSE-listed stocks, it was counted twice in the overall averages
for NASDAQ.

Characteristics of Our Sample The pairings resulting from the CMS
minimization algorithm were well

Stocks

matched. The average share price for the 300 NYSE-listed (NASDAQ matching)
stocks was $19.66 ($19.56), the average daily volume was 132,404
(127,107), the average number of trades per day was 121 (125), and the
measure of daily volatility was 0.018 (0.018). In terms of average share
price, the 300 matching-pair stocks were fairly representative of the full
sample of matching stocks, as well as of the potential sample universe of
stocks, as illustrated in table 15 and figure 15. However, the resulting
matched-pairs sample tended to have more lower-priced stocks.

11Of the 293 NASDAQ matches, 127 were best matches for one NYSE-listed
stock, 64 were best matches for two NYSE-listed stocks, 28 were best
matches for three, 13 were best matches for four, 16 were best matches for
five, 7 were best matches for six, 11 were best matches for seven, 1 was a
best match for eight, 6 were best matches for nine, 3 were best matches
for ten, and 17 were best matches for 11 to 26 NYSE stocks.

12Relative to the marginal cost in terms of computing resources and
analysis time, the marginal benefit of increasing the number of matched
pairs was limited, as the top 400 (500) matched pairs consisted of 400
(500) NYSE stocks and 215 (235) NASDAQ matching stocks.

Appendix II
Methodology for GAO Analysis of Trade and
Quotes Data

  Table 15: Price Characteristics of NYSE-Listed and NASDAQ Stocks NYSE NASDAQ

               Potential universe of stocks                  
                                     Number              981            1,361 
               Average (median) share price  $29.20 (24.07)    $25.27 (17.54) 
                   Percent priced below $25              52%              65% 
                   Percent priced below $50              87%              89% 
             Full sample of matching stocks                  
                                     Number              981              263 
               Average (median) share price  $29.20 (24.07)    $26.60 (22.27) 
                   Percent priced below $25              52%              52% 
                   Percent priced below $50              87%              91% 
          300 matched-pair sample of stocks                  
                                     Number              300       186 unique 
               Average (median) share price  $19.66 (16.55)    $19.56 (15.94) 
                   Percent priced below $25              75%              74% 
                   Percent priced below $50              96%              98% 

Source: GAO analysis of TAQ data.

Note: Share price was measured as the average daily closing price from
February 2000 through December 2000. Of the 186 NASDAQ stocks, 114 were
best matches for one NYSE-listed stock and the remainder were best matches
for multiple NYSE-listed stocks. In the analysis, each NASDAQ stock was
weighted according to the number of best matches it yielded.

                                  Appendix II
                   Methodology for GAO Analysis of Trade and
                                  Quotes Data

Figure 15: Distribution of Average Daily Closing Prices for Full Sample of
Matching Stocks and 300 Matched-Pairs Sample

Frequency 200

150

100

50

0 >0 >5 >10 >15 >20 >25 >30 >35 >40 >45 >50 >55 >60 >65 >70 >75 >80 >85
>90 >95 to to to to to to to to to to to to to to to to to to to 5
101520253035404550556065707580859095

Price range

NYSE -- All Sample Stocks (981)
Nasdaq -- All Sample Matches (293 Unique)
NYSE -- 300 Matched Pairs
Nasdaq -- 300 Matched Pairs (186 Unique)
Source: GAO analysis of TAQ data.

Note: Share price was measured as the average daily closing price from
February 2000 through December 2000. There were 981 NYSE-listed stocks and
293 matching NASDAQ stocks in the all matching stocks sample. There were
300 NYSE-listed and 300 (186 unique) matching NASDAQ stocks in the matched
pairs sample. Each NASDAQ stock was weighted according to the number of
best matches it yielded.

In terms of average daily trading volume, the matched-pairs sample
underrepresented higher-volume stocks, which likely biased our results
toward reporting larger spreads (see table 16 and fig. 16).

                                  Appendix II
                   Methodology for GAO Analysis of Trade and
                                  Quotes Data

 Table 16: Volume Characteristics of NYSE-Listed and NASDAQ Stocks NYSE NASDAQ

      Potential universe of stocks                         
                 Number                                981              1,361 
      Average (median) daily volume     689,811 (190,070)   607,687 (125,627) 
      Percent below 150,000 shares                     45%                54% 
      Percent below 500,000 shares                     69%                82% 
     Full sample of matching stocks                        
                 Number                                981 
      Average (median) daily volume     689,811 (190,070)   511,980 (185,787) 
      Percent below 150,000 shares                     45%                48% 
      Percent below 500,000 shares                     69%                75% 
    300 matched-pair sample of stocks                      
                 Number                                300 
      Average (median) daily volume     132,404 (74,188)     127,107 (73,204) 
      Percent below 150,000 shares                     73%                75% 
      Percent below 500,000 shares                     97%                97% 

Source: GAO analysis of TAQ data.

Note: Volume was measured as the average daily trading volume from
February 2000 through December 2000. Of the 186 NASDAQ stocks, 114 were
best matches for one NYSE-listed stock and the remainder were best matches
for multiple NYSE-listed stocks. In the analysis, each NASDAQ stock was
weighted according to the number of best matches it yielded.

                                  Appendix II
                   Methodology for GAO Analysis of Trade and
                                  Quotes Data

Figure 16: Distribution of Average Daily Trading Volume for Full Sample of
                  Matching Stocks and 300 Matched-Pairs Sample

Frequency 10 20 40

 60 80 100 150 200 250 300 350 400 450 500 550 600 650 700 750 800 850 900 950
                                     1,000

Volume range (in thousands)

                        NYSE -- All Sample Stocks (981)

                   Nasdaq -- All Sample Matches (293 Unique)

                           NYSE -- 300 Matched Pairs

                    Nasdaq -- 300 Matched Pairs (186 Unique)

Source: GAO analysis of TAQ data.

Note: Volume was measured as the average daily trading volume from
February 2000 through December 2000. There were 981 NYSE-listed stocks and
293 matching NASDAQ stocks in the all matching stocks sample. There were
300 NYSE-listed and 300 (186 unique) matching NASDAQ stocks in the matched
pairs sample. Each NASDAQ stock was weighted according to the number of
best matches it yielded.

Filtering and Manipulation of Once we had defined our stock sample, to
undertake the subsequent

                                     >1,00

                              Trade and Quote Data

analysis we first had to filter the trades and quotes data for each sample
stock, which involved discarding records with TAQ-labeled errors (such as
canceled trade records and quote records identified with trading halts),
identifying and removing other potentially erroneous quotation and trade
records (such as stale quotes or trade or quote prices that appeared
aberrant), as well as simply confining the data to records between 9:30
a.m. and 4 p.m. We also had to determine the national best bid and offer
quotes in effect at any given moment from all quoting market venues-the
NBBO quotation. In general, for a given stock the best bid (offer)
represents the

Appendix II
Methodology for GAO Analysis of Trade and
Quotes Data

highest (lowest) price available from all market venues providing quotes
to sellers (buyers) of the stock.

The NBBO quotes data for a given stock were used to compute quoted bidask
spreads, quote sizes, and share prices, as well as intraday price
volatility for that stock on a daily basis. They were also used
independently to document any quote clustering activity in that stock. The
trades' data for a particular stock were used to analyze daily price
ranges and trade execution price clustering. For each stock, the trades
and NBBO quotes data were used to compute effective bid-ask spreads, which
rely on both quotes and trades data.

The TAQ Consolidated Quotes (CQ) file covers most activity in major U.S.
market centers but does not include foreign market centers. A record in
the CQ file represents a quote update originating in one of the included
market centers: Amex, the Boston Stock Exchange, the Chicago Stock
Exchange, electronic communication networks (ECN) and alternative trading
systems (ATS), NASDAQ, the National Stock Exchange, NYSE, the Pacific
Stock Exchange, and the Philadelphia Stock Exchange.13 It does not per se
establish a comprehensive marketwide NBBO quote, however. A quote update
consists of a bid price and the number of shares for which that price is
valid and an offer price and the number of shares for which that price is
valid. In general, a quote update reflects quote additions or
cancellations. The record generally establishes the best bid and offer
prevailing in a given market center. Normally, a quote from a market
center is regarded as firm and valid until it is superseded by a new quote
from that center-that is, a quote update from a market center supersedes
that market center's previous quotes and establishes its latest, binding
quotes.

Specifying the NBBO involved determining the best bid and offer quotes
available-at a particular instant, the most recent valid bids and offers
posted by all market centers were compared and the highest bid and the

13In the TAQ CQ file, NASDAQ dealers and ECNs are collectively classified
under "T" as the source market for quotations for NYSE-listed issues. The
market maker identification (MMID) data field provides an additional
classification layer among NASDAQ dealers and ECNs. For example, "TRIM"
denotes "Trimark," a NASDAQ dealer, while "BRUT" denotes the BRUT ECN.
"CAES" is the acronym for "Computer Assisted Execution System," which is a
NASDAQ system that allows its members to quote NYSE-listed stocks. The
National Securities Clearing Corporation provides a listing of NASDAQ
market makers and their MMIDs in the Member Directory at www.nscc.com. The
Boston Stock Exchange, the Chicago Stock Exchange, the National Stock
Exchange, the Pacific Stock Exchange, and the Philadelphia Stock Exchange
are regional exchanges.

                                  Appendix II
                   Methodology for GAO Analysis of Trade and
                                  Quotes Data

lowest offer were selected as the NBBO quotes. The national best bid (NBB)
and national best offer (NBO) are not necessarily from the same market
center or posted concurrently, and the bid and offer sizes can be
different. Bessimbinder (2003) outlined a general method for determining
the NBBO. First, the best bid and offer in effect for NYSE-listed stocks
among individual NASDAQ dealers (as indicated by the MMID data field) was
assessed and designated as the NASDAQ bid and offer. Then, the best bid
and offer in effect across the NYSE, the five regional exchanges, and
NASDAQ were determined and designated as the NBBO quotations for
NYSE-listed stocks. For NASDAQ stocks, quote records from NASDAQ market
makers reflect the best bid and offer across these participants
(collectively classified as "T" in the TAQ data). Competing quotes are
issued from other markets (e.g., the Pacific Stock Exchange) as well as
NASDAQ's SuperMontage Automated Display Facility, which reflects the
quotes from most ECNs. We required additional details in constructing the
NBBO, since quote records from competing market makers and market centers
can have concurrent time stamps and there can be multiple quotes from the
same market center recorded with the same time stamp. Moreover, identical
bid or offer prices can be quoted by multiple market makers. To address
these complications, we relied on language offered in SEC's Regulation NMS
proposal, which defined the NBBO by ranking all such identical bids or
offers first by size (giving the highest ranking to the bid or offer
associated with the largest size) and then by time (giving the highest
ranking to the bid or offer received first in time). In our algorithm, the
NBB (NBO) is located by comparing the existing bids (offers) from all
venues. The NBBO is updated with each instance of a change in the NBB or
NBO.

General Analysis Techniques	Each NBBO quotation was weighted by its
duration (i.e., the time for which it was effective) and used to compute a
sample week time-weighted average NBBO quotation for the relevant market,
which was reported on a volume-weighted (relative to total sample market
trading volume) basis. Ultimately, these averages were compared across
markets and across pre and postdecimalization periods. The same general
techniques were used in computing effective spreads, which were determined
by comparing trade executions with NBBO quotations. For analysis of trades
data (e.g., in computing price ranges), a simple average over all stocks
in a given market was computed. In analyzing volatility, intraday returns
were measured for each stock based on continuously compounded percentage
changes in quotation midpoints, which were recorded between 10 a.m. and 4
p.m. The standard deviation of the intraday returns was then computed for
each stock, and the cross-sectional median across all stocks was taken. In

                                  Appendix II
                   Methodology for GAO Analysis of Trade and
                                  Quotes Data

Measuring Trade Execution Costs and Other Market Quality Components with
TAQ Data

Calculating Quoted Bid-Ask Spreads as a Simple Measure of Trading Costs

assessing clustering, the frequencies of trades and quotes at pennies,
nickels, dimes, and quarters were determined for each market on an
aggregate basis.

In reporting any differences between the pre-and postdecimalization sample
periods in the trade execution cost and market quality measures that we
analyzed, statistical significance was assessed based on crosssectional
variation in the stock-specific means. With the exception of volatility
measures, statistical significance was assessed using a standard ttest for
equality of means. Since average volatility measures do not conform well
to the t-distribution, median volatility was reported for each market and
the Wilcoxon rank sum test used to assess equality.

TAQ data allowed us to study variables that are based on trades and quotes
but did not allow us to study any specific effects on or make any
inferences regarding orders or institutional trading costs. This is an
important limitation because the transition to decimal pricing may have
impacted retail traders, whose generally smaller orders tend to be
executed in a single trade, differently than institutional traders. Use of
TAQ data implicitly assumes that each trade record reflects a unique order
that is filled, so our analysis failed to address any impact of a change
in how orders are filled and the costs associated with this. We reported
the preand postdecimalization behavior of quoted bid-ask spreads and
effective spreads. Beyond measures of trade execution cost, market quality
is multidimensional. Possible adverse effects of decimalization on market
quality included increased trade execution costs for large traders,
increased commissions to offset smaller bid-ask spreads, slower order
handling and trade executions, decreased market depth, and increased price
volatility. The TAQ data allowed measurement of quotation sizes and price
volatility, which we reported. We also analyzed quote clustering, which
reflects any unusual frequency with which prices tend to bunch at
multiples of nickels, for example. We generally presented our results on
an average basis for a given market in the pre-and postdecimalization
periods; we also reported the results for sample stocks grouped by average
daily trading volume.

Average pre-and postdecimalization bid-ask spreads were calculated in
cents per share and basis points (that is, the spread in cents relative to
the NBBO midpoint) using the NBBO quote prices. The average spread was
obtained in the following way. First, each NBBO quote for a given stock
was weighted by the elapsed time before it was updated-its duration-on a
given day of a sample week relative to the total duration of all NBBO

                                  Appendix II
                   Methodology for GAO Analysis of Trade and
                                  Quotes Data

Calculating Effective Bid-Ask Spreads as a Better Measure of Trading Costs

Measuring Quotation Sizes

Measuring Intraday Return Volatility

quotes for that stock in that sample week. Next, the duration-weighted
average over the five trading days in that sample period for that stock
was used to compute the average across all stocks in a given market for
that week; ultimately, a volume-weighted average was computed. For the
twelve-sample week period, a volume-weighted average was also computed.

The effective bid-ask spread-how close the execution price of a trade is
relative to the quote midpoint-is generally considered to be the most
relevant measure of trade execution cost, as it allows measurement of
trades that execute at prices not equal to the bid or ask. In keeping with
standard practice, we measured the effective spread for a trade as twice
the absolute difference between the price at which a trade was executed
and the midpoint of the contemporaneous NBBO quote. Suppose for example
that the NBB is $20.00 and the NBO is $20.10, so that the NBBO midpoint is
$20.05. If a trade executes at a price of $20.05 then the effective spread
is zero because the trade executed at the midpoint of the spread- the
buyer of the stock paid $0.05 per share less than the ask price, while the
seller received $0.05 per share more than the bid price. If a trade
executes at $20.02 with the same NBBO prices, the effective spread is
$0.06-the buyer of the stock paid $0.08 per share less than the ask price,
while the seller received $0.02 per share more than the bid price.
Effective spreads were computed in cents per share and in basis points.

Smaller quote sizes could reflect a decrease in liquidity supply, which in
turn could be associated with increased volatility. The size of each NBBO
quote was weighted by its duration and used to compute a volumeweighted
average over each sample week as well as across all sample weeks.

A reduction in the tick size could lead to a decline in liquidity supply,
which in turn could create more volatile prices. Intraday returns were
measured for each stock based on continuously compounded percentage
changes in quotation midpoints, which were recorded on an hourly basis
between 10 a.m. and 4 p.m. The continuously compounded return over 6
hours, from 10 a.m. to 4 p.m., was also computed. The standard deviation
(a measure of dispersion around the average) of the intraday returns was
then computed for each stock, and the cross-sectional median (the middle
of the distribution) was taken over all stocks in a given market.

                                  Appendix II
                   Methodology for GAO Analysis of Trade and
                                  Quotes Data

Measuring Daily Price Range

Measuring Trade and Quote Clustering

Efforts to Assess Reliability of TAQ Data

As another measure of price volatility, we also considered how a stock's
daily price range (i.e., the highest and lowest prices at which trades
were executed) may have changed following the implementation of decimal
pricing, as the claim has been made that prices have been moving to a
greater degree during the day after decimalization. We computed the
equalweighted average of each stock's daily price range and then computed
the average over all stocks in a given market. To account for potentially
varying price levels across the pre and postdecimalization sample periods,
we computed the price range in both cents per share as well as relative to
the midpoint of the first NBBO quote for each day.

Decimalization provides a natural experiment to test whether market
participants prefer to trade or quote at certain prices when their choices
are unconstrained by regulation. Theory suggests that if price discovery
is uniform, realized trades should not cluster at particular prices. The
existence of price clustering following decimalization could suggest a
fundamental psychological bias by investors for round numbers and that
there may be only minor differences between the transactions prices that
would prevail under a tick size of 5 cents relative to those observed
under decimal pricing.14 For quotes, according to competing hypotheses in
the literature, clustering may be due to dealer collusion, or it may
simply be a natural phenomenon-as protection against informed traders, as
compensation for holding inventory, or to minimize negotiation costs.15
For our analysis, we computed the frequency of trade executions and quotes
across the range of price points, but we did not attempt to determine the
causes of any clustering.

Consistent with generally accepted government auditing standards, we
assessed the reliability of computer-processed data that support our
findings. To assess the reliability of TAQ data, we performed a variety of
error checks on data from a random sample of stocks and dates. This
involved comparing aggregated intraday data with summary daily data,
scanning for outliers and missing data. In addition, since the TAQ
database is in widespread use by researchers and has been for several
years, we were able to employ additional methods for discarding
potentially erroneous data records following widely accepted methods
(e.g., we

14This was explored in a working paper, D. Ikenberry and J. Weston, 2003,
"Clustering in U.S. Stock Prices after Decimalization."

15This was explored in Chung et al. (2004).

Appendix II
Methodology for GAO Analysis of Trade and
Quotes Data

discarded quotation information in which a price or size was reported as
negative). We assessed the reliability of our analysis of the TAQ data by
performing several executions of the programs using identical and slight
modifications of the program coding. Program logs were also generated and
reviewed for errors.

Appendix III

Measurement of Institutional Investors' Trading Costs in Basis Points Shows
Decline since Decimal Pricing Implemented

As discussed in the body of this report, institutional investors' trading
costs are commonly measured in cents per share and basis points (bps).
Cents per share is an absolute measure of cost based on executing a single
share. Basis points-measured in hundredths of a percentage point-show the
absolute costs relative to the stock's average share price. For example,
for a stock with a share price of $20, a transaction cost of $.05 would be
0.25 percent or 25 bps. Costs reported in terms of basis points can show
changes resulting solely from changes in the level of stock prices-if the
price of the $20 stock falls to $18, the $.05 transaction cost would now
be almost 0.28 percent or 28 bps. However, many organizations track costs
using basis points, and in this appendix we present the results of our
institutional trading cost analysis in basis points.

Analysis of the multiple sources of data that we collected generally
indicated that institutional investors' trading costs had declined since
decimal prices were implemented. Specifically, NYSE converted to decimal
pricing on January 29, 2001, and NASDAQ completed its conversion on April
9, 2001. We obtained data from three leading firms that collect and
analyze information about institutional investors' trading costs. These
trade analytics firms (Abel/Noser, Elkins/McSherry, and Plexus Group)
obtain trade data directly from institutional investors and brokerage
firms and calculate trading costs, including market impact costs (the
extent to which the security changes in price after the investor begins
trading), typically for the purpose of helping investors and traders limit
costs of trading.1 These firms also aggregate client data so as to
approximate total average trading costs for all institutional investors.
Generally, the client base represented in aggregate trade cost data is
sufficiently broad based that the firm's aggregate cost data can be used
to make generalizations about the institutional investor industry.

Although utilizing different methodologies, the data from the firms that
analyze institutional investor trading costs uniformly showed that costs
had declined since decimal pricing was implemented. Our analysis of data
from the Plexus Group showed that costs declined on both NYSE and NASDAQ
during the 2 year period after these markets converted to decimal pricing.
Plexus Group uses a methodology that analyzes various components of
institutional investor trading costs, including the market impact of
investors' trading.2 Total trading costs declined by about 32

1ITG, another trade analytics firm, did not begin to measure institutional
investors' trading costs until January 2003, after the implementation of
decimal pricing and 1-cent ticks.

Appendix III
Measurement of Institutional Investors'
Trading Costs in Basis Points Shows Decline
since Decimal Pricing Implemented

percent for NYSE stocks, falling from about 82 bps to 56 bps (fig. 17).
For NASDAQ stocks, the decline was about 25 percent, from about 102 bps to
about 77 bps. As can be seen in figure 17, the decline in trading costs
began before both markets implemented decimal pricing, which indicates
that other causes, such as the 3-year declining stock market, in addition
to decimal pricing, were also affecting institutional investors' trading
during this period. An official from a trade analytics firm told us that
the spike in costs that preceded the decimalization of NASDAQ stocks
correlated to the pricing bubble that technology sector stocks experienced
in the late 1990s and early 2000s. An official from another trade
analytics firm explained that trading costs increased during this time
because when some stocks' prices would begin to rise, other
investors-called momentum investors-would begin making purchases and cause
prices for these stocks to move up even faster. As a result, other
investors faced greater than usual market impact costs when also trading
these stocks. In general, trading during periods when stock prices are
either rapidly rising or falling can make trading very costly.

2To measure market impact costs, the Plexus Group compares a proprietary
benchmark stock price to the average price an investor receives. The
Plexus Group benchmark attempts to show the price at which the order for a
particular stock should be executed. The firm calculates this expected
price using trade data of its clients for the two quarters preceding the
date of the trade under study, and takes into account variables such as
trade size, liquidity, and the direction of stock price movement.

                                  Appendix III
                    Measurement of Institutional Investors'
                  Trading Costs in Basis Points Shows Decline
                       since Decimal Pricing Implemented

Figure 17: Total Trading Costs from a Trade Analytics Firm for NYSE and
NASDAQ Stocks, 1999-2004 (basis points) Basis points

                                      150

                                      120

                                       90

                                       60

                                       30

Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2

1999 2000 2001 2002 2003

Year and quarter

NYSE

Nasdaq

Source: GAO analysis of Plexus Group data.

Note: Data are reported quarterly. After a phase-in period, all NYSE
stocks were trading with decimal prices by January 29, 2001, and all
NASDAQ stocks were converted by April 9, 2001.

According to our analysis of the Plexus Group data, all of the decline in
trading costs for NYSE stocks and NASDAQ stocks were caused by decreases
in the costs resulting from market impact and delay for orders.3 Together,
the reduction in these two components accounted for 29.1 bps or all of
total decline, with delay costs representing 20.6 bps (or about 71
percent) in the approximately 2 years following the implementation of

3Delay costs are a type of market impact cost that occur between the time
institutional investors' portfolio managers direct their traders to buy or
sell stock and the moment these orders are released to brokers. The amount
that the stock's price changes during this period is the cost of delaying
the order. An order may be delayed for a number of reasons-for instance,
because it could affect prices in the market too much. Plexus Group, The
Official Icebergs of Transaction Costs, Commentary #54, January 1998.

Appendix III
Measurement of Institutional Investors'
Trading Costs in Basis Points Shows Decline
since Decimal Pricing Implemented

decimal pricing and 1-cent ticks on the NYSE. However, commissions
increased 3 bps, which led total trading costs to decline 26.1 bps (fig.
18).

Figure 18: Trading Cost Components from One Trade Analytics Firm for NYSE
and NASDAQ, 2001-2003 (basis points)

NYSE Nasdaq

Basis points Basis points

                                      120

                                      100

                                       80

                                       60

                                       40

                                       20

                                       0

120

100

80

60

40

20

0 2001 2003 2001 2003 Year Year

Delay
Market impact
Commission

Source: GAO analysis of Plexus Group data.

Note: Data are from first quarter 2001 to second quarter 2003 for NYSE and
second quarter 2001 to second quarter 2003 for NASDAQ.

Figure 18 also shows that market impact and delay costs account for all
declines to total NASDAQ trading costs. For example, market impact and
delay costs declined 40.9 bps between the second quarter of 2001 and the
second quarter of 2003. However, overall trading costs declined by only
24.4 bps, which is 16.5 bps less than declines in market impact and delay
costs. According to Plexus Group data, overall costs would have declined
further if not for increases to commission costs for NASDAQ stocks, the

Appendix III
Measurement of Institutional Investors'
Trading Costs in Basis Points Shows Decline
since Decimal Pricing Implemented

only cost component that increased after NASDAQ converted to decimal
pricing and 1-cent ticks. As shown in figure 18, commissions that market
intermediaries charged for trading NASDAQ stocks increased 16.5 bps from
the second quarter of 2001 to the second quarter of 2003. Industry
representatives told us these increases reflect the evolution of the
NASDAQ brokerage industry from trading as principals, in which the
compensation earned by market makers was embedded in the final trade
price, to that of an agency brokerage model, in which broker-dealers
charge explicit commissions to represent customer orders in the
marketplace.4

Analysis of data from the other two trade analytics firms from which we
obtained data, Elkins/McSherry and Abel/Noser, also indicated that
institutional investor trading costs varied but declined following the
decimalization of U.S. stock markets in 2001. Because these two firms'
methodologies do not include measures of delay, which the Plexus Group
data shows can be significant, analysis of data from these two firms
results in trading cost declines of a lower magnitude than those indicated
by the Plexus Group data analysis. Nevertheless, the data we analyzed from
Elkins/McSherry showed total costs for NYSE stocks declined about 20
percent between the first quarter of 2001 and year-end 2004 from about 29
bps to about 24 bps. Analysis of Abel/Noser data indicated that total
trading costs for NYSE stocks declined 25 percent from 20 bps to 15 bps
between year-end 2000 and 2004 (fig. 19).

4For example, NASDAQ market makers previously could earn revenue trading
as principals by buying shares at the bid price from investors and selling
those shares to other investors at the higher ask price, thus earning the
difference or spread amount as compensation.

                                  Appendix III
                    Measurement of Institutional Investors'
                  Trading Costs in Basis Points Shows Decline
                       since Decimal Pricing Implemented

Figure 19: Total Trading Costs from Two Trade Analytics Firms for NYSE
Stocks, 2001-2004 (basis points)

Basis points

35

30

25

20

15

10

5

0 Q4 Q1 Q2Q3Q4 Q1Q2Q3 Q4 Q1Q2 Q3 Q4Q1 Q2 Q3Q4Q1 Q2Q3Q4 Q1 Q2Q3 Q4

1998 1999 2000 2001 2002 2003 2004

Year and quarter

                        Elkins/McSherry (quarterly data)

                       Abel/Noser (averaged annual data)

Source: GAO analysis of Elkins/McSherry and Abel/Noser data.

Note: Elkins/McSherry data are quarterly from fourth quarter of 1998 and
the fourth quarter of 2004; Abel/Noser data are year-end totals for
1998-2004.

Our analysis of these firms' data also indicated that total trading costs
declined in basis points for NASDAQ stocks or were flat. For example, our
analysis of the Elkins/McSherry data showed that total trading costs for
NASDAQ stocks dropped by roughly 13 percent, from about 38 bps to about 32
bps between the second quarter of 2001 when that market decimalized to
year-end 2004. Analysis of the Abel/Noser data indicated that total
trading costs increased nearly 5 percent for NASDAQ stocks during that
period, increasing from 21 bps to 22 bps (fig. 20). This increase in
trading cost can possibly be explained by the approximately 50 percent
decline in average share price over the period.

                                  Appendix III
                    Measurement of Institutional Investors'
                  Trading Costs in Basis Points Shows Decline
                       since Decimal Pricing Implemented

Figure 20: Total Trading Costs from Two Trade Analytics Firms for NASDAQ
Stocks, 2001-2004 (basis points) Basis points

                                       50

                                       40

                                       30

                                       20

                                       10

                                       0

Q4 Q1 Q2Q3Q4 Q1Q2Q3 Q4 Q1Q2 Q3 Q4Q1 Q2 Q3Q4Q1 Q2Q3Q4 Q1 Q2Q3 Q4

1998 1999 2000 2001 2002 2003 2004

Year and quarter

                        Elkins/McSherry (quarterly data)

                       Abel/Noser (averaged annual data)

Source: GAO analysis of Elkins/McSherry and Abel/Noser data.

Note: Elkins/McSherry data are quarterly from fourth quarter of 1998 and
the fourth quarter of 2004; Abel/Noser data are year-end totals for
1998-2004.

Similar to Plexus Group data analysis, our analysis of the Elkins/McSherry
and Abel/Noser data also indicated that reductions to market impact costs
accounted for a vast proportion of overall reductions for NYSE stocks
(fig. 21).5 Analysis of the Elkins/McSherry data indicated that by
declining 7.6 bps during this period, reduced market impact accounted for
95 percent of total cost trading declines. The 3 bps reduction in market
impact costs

5These two firms analyze market impact costs by comparing their clients'
trades to the volume-weighted average price (VWAP) of the particular
stocks traded. The VWAP represents the average price at which a particular
stock traded on a specific trading day and is calculated by weighting each
trade's price according to the proportion of shares of a specific stock it
represents on a given day. The closer an investor's average price is to
the VWAP, the lower the calculated market impact costs.

Appendix III
Measurement of Institutional Investors'
Trading Costs in Basis Points Shows Decline
since Decimal Pricing Implemented

identified in the Abel/Noser data represented the entire total trading
cost reductions for NYSE stocks.

Figure 21: Trading Cost Components from Two Trade Analytics Firms for NYSE
Stocks, 2001 and 2004 (basis points)

Elkins/McSherry Abel/Noser

Basis points Basis points

                                       30

                                       25

                                       20

                                       15

                                       10

                                       5

                                      0 30

                                       25

                                      0.2

20

15

10

5

0 2001 2004 2001 2004 Year Year

Exchange fee Market impact Commission

Source: GAO analysis of Elkins/McSherry and Abel/Noser data.

Note: Abel/Noser does not account for exchange fees as a component of
trading cost. For Elkins/McSherry, we obtained first quarter 2001 data and
fourth quarter 2004. For Abel/Noser, we obtained data from the end of 2000
and 2004.

Reductions to market impact costs explain virtually the entire decline to
total trading costs captured by the Elkins/McSherry data for NASDAQ stocks
and all of the Abel/Noser data for NASDAQ stocks. For Elkins/McSherry and
Abel/Noser, such costs would have produced even larger total declines had
commissions for such stocks not increased since 2001. Market impact costs
declined 22.3 bps (about 64 percent) according

Appendix III
Measurement of Institutional Investors'
Trading Costs in Basis Points Shows Decline
since Decimal Pricing Implemented

to our analysis of the Elkins/McSherry data and 14 bps (about 74 percent)
according to analysis of the Abel/Noser data (fig. 22). However, during
this period, commissions charged on NASDAQ stock trades included in these
firms' data increased by 16.9 bps, marking approximately a sixfold
increase in commissions as measured by Elkins/McSherry and by 15 bps or
about a fifteenfold increase according to Abel/Noser.

Figure 22: Trading Cost Components from Two Trade Analytics Firms for
NASDAQ Stocks, 2001 and 2004 (basis points)

Elkins/McSherry Abel/Noser

Basis points Basis points 40 35 30 25 20 15 10 5 0

                                     40 35

                                      0.2

30

25

20

15

10

5

0 2001 2004 2001 2004 Year Year

Exchange fee Market impact Commission

Source: GAO analysis of Elkins/McSherry and Abel/Noser data.

Note: Abel/Noser does not account for exchange fees as a component of
trading cost. For Elkins/McSherry, we obtained first quarter 2001 data and
fourth quarter 2004. For Abel/Noser, we obtained data from the end of 2000
and 2004.

Appendix III
Measurement of Institutional Investors'
Trading Costs in Basis Points Shows Decline
since Decimal Pricing Implemented

Data from a fourth firm, ITG, which recently began measuring institutional
trading costs, also indicates that such costs have declined. This firm
began collecting data from its institutional clients in January 2003. Like
the other trade analytics firms, its data is similarly broad based,
representing about 100 large institutional investors and about $2 trillion
worth of U.S. stock trades. ITG's measure of institutional investor
trading cost is solely composed of market impact costs and does not
include explicit costs, such as commissions and fees, in its calculations.
Although changes in ITG's client base for its trade cost analysis service
prevented direct period to period comparisons, an ITG official told us
that its institutional investor clients' trading costs have been trending
lower since 2003.6

6We do not present the specific analysis of ITG's data because the firm's
client base for its trade cost analysis grew significantly after it first
began offering this service, including the addition of some larger clients
with sophisticated trading operations that contributed to the overall
decline measured by the firm.

Appendix IV

Additional Analysis Using Trade and Quotes Data

As part of our analysis of the Trade and Quotes database, we also examined
how quoted and effective spreads changed as a percentage of stock prices
and also examined whether the extent to which quotes clustered on
particular prices changed since decimal pricing began. In addition to
measuring spreads in cents per share, spreads are also frequently measured
in basis points, which are 1/100 of a percent. We found that spreads
generally declined when measured in basis points similar to our analysis
measured in cents. Reporting spreads in basis points potentially accounts
for changes in the general price level of our sample stocks, which could
impact our results reported in cents per share. We found that both quoted
and effective spreads generally declined when measured relative to quote
midpoints as they did when measured simply in cents (see tables 17 and
18).

  Table 17: Average Quoted Spreads Before and After Decimalization, 2000-2004
                                 (basis points)

NYSE quoted spread NASDAQ quoted spread

Average Average Average Average Stocks by average spread in basis spread
in basis spread in basis spread in basis daily volume of shares points
before points after points before points after traded decimals decimals
Percent change decimals decimals Percent change

                       High 49.3 16.0 -68% 40.9 13.0 -68%

Medium 71.8 19.5 -73 72.4 22.4

Low 125.7 32.6 -74 127.9 36.6

All stocks 78.4 25.1 -68 82.0 27.2

Source: GAO analysis of TAQ data.

Note: Quoted spreads in the table represent the volume-weighted average
quoted spread (i.e., stocks and weeks with more total trading volume have
greater weight) as a percentage of the midpoint of the prevailing quotes
over 12 sample weeks during the predecimals period (February 2000-January
2001) and 12 sample weeks during the postdecimals period (April
2001-November 2004) for our sample of stocks. Stocks were segregated by
volume according to the following categories:

o 	High volume stocks were those in our sample of stocks with average
daily trading volumes exceeding 500,000 shares.

o 	Medium volume stocks were those in our sample of stocks with average
daily trading volumes between 100,000 and 499,999 shares.

o 	Low volume stocks were those in our sample of stocks with average daily
trading volumes of less than 100,000 shares.

                                  Appendix IV
                   Additional Analysis Using Trade and Quotes
                                      Data

 Table 18: Average Effective Spreads Before and After Decimalization, 2000-2004
                                 (basis points)

NYSE effective spreads NASDAQ effective spreads

Average Average Average Average Stocks by average spread in basis spread
in basis spread in basis spread in basis daily volume of shares points
before points after points before points after traded decimals decimals
Percent change decimals decimals Percent change

                       High 47.8 29.4 -38% 51.1 25.3 -51%

Medium 61.8 26.5 -57 70.8 30.4

Low 99.4 38.3 -61 112.4 39.0

All stocks 65.3 29.4 -55 73.4 32.5

Source: GAO analysis of TAQ data.

Note: Effective quoted spreads (the difference between the price at which
a trade is executed and the midpoint between the prevailing quoted bid and
ask prices) in the table represent the volume-weighted average effective
spread (i.e., stocks and weeks with more total trading volume have greater
weight) as a percentage of the midpoint of the prevailing quotes over 12
sample weeks during the predecimals period (February 2000-January 2001)
and 12 sample weeks during the postdecimals period (April 2001-November
2004) for our sample of stocks. Stocks were segregated by volume according
to the following categories:

o 	High volume stocks were those in our sample of stocks with average
daily trading volumes exceeding 500,000 shares.

o 	Medium volume stocks were those in our sample of stocks with average
daily trading volumes between 100,000 and 499,999 shares.

o 	Low volume stocks were those in our sample of stocks with average daily
trading volumes of less than 100,000 shares.

We also analyzed the extent to which quote and trade execution prices
cluster at particular price points, a phenomenon known as clustering.
Clustering, particularly on multiples of nickels, dimes, and quarters, has
been well documented by various researchers, and various reasons are cited
to explain why all possible price points are not used with equal
frequency. We extended the general body of research to include how
clustering may have changed after decimalization, but we do not attempt to
explain its causes. We generally found that prices tend to cluster on
certain price points-especially on nickel, dime, and quarter multiples-but
this tendency has been lessening over time. We provide examples of
clustering in national best bid quote prices recorded for our sample of
NYSE-listed stocks, but the same general features were found in national
best offer quote and trade execution prices for both NYSE-listed and
Nasdaq stocks. Figure 23 illustrates quote price clustering (using
national best bid prices) over our entire postdecimalization sample
period, which included 12

                                  Appendix IV
                   Additional Analysis Using Trade and Quotes
                                      Data

sample weeks from April 2001 through November 2004. Prices are observed
generally clustering at nickel increments.

Figure 23: Quote Clustering After Decimalization, 2001-2004 Percentage
frequency 2.0

1.5

1.0

.5

0 .00 .05 .10 .15 .20 .25 .30 .35 .40 .45 .50 .55 .60 .65 .70 .75 .80 .85
.90 .95 .99

                              Decimal price quote

Source: GAO analysis of TAQ data.

Notes: Quote clustering in the figure represents the frequency with which
each national best bid quote price point, from zero cents to 99 cents, was
used by all of the NYSE-listed stocks from our matchedpairs sample over
the 12 sample weeks during the postdecimals period (April 2001-November
2004). While not included in this appendix, similar results were generally
obtained for both NYSE-listed and Nasdaq stocks using national best offer
quote and trade execution prices.

We also analyzed how clustering may have changed over time. Using the same
data as above, we separated the data by sample week. Our results,
displayed in figure 24, depict a general decline in the use of price
increment multiples of a nickel. This may suggest that traders have been
adapting their strategies to the penny environment and are becoming
increasingly comfortable with using various price points, which may be a
result of the increased use of electronic trading. It may also be the case
that traders are making use of the finer price grid to gain execution
priority.

                                  Appendix IV
                   Additional Analysis Using Trade and Quotes
                                      Data

Figure 24: Quote Clustering After Decimalization, by Sample Week,
2001-2004

Percentage frequency 25

20

15

10

5

0 April August December January May September February June October March
July November (21-27) (20-24) (10-14) (7-11) (6-10) (23-27) (24-28) (2-6)
(20-24) (8-12) (19-23) (1-5)

2001 2002 2003 2004

Sample week (year, month, and days)

Decimal price with the following ending

$.x0 $.x5 $.x1 $.x6 $.x2 $.x7 $.x3 $.x8 $.x4 $.x9 Source: GAO analysis of
TAQ data.

Notes: Quote clustering in the figure represents the frequency with which
each national best bid quote price point, from zero cents to 99 cents, was
used by all of the NYSE-listed stocks from our matchedpairs sample over
the 12 sample weeks during the postdecimals period (April 2001-November
2004). The notation y.x0 indicates any price for which the second decimal
place is a zero (e.g., $5.20); similarly, the notation y.x9 indicates any
price for which the second decimal place is a nine (e.g., $5.29). While
not included in this appendix, similar results were generally obtained for
both NYSElisted and Nasdaq stocks using national best offer quote and
trade execution prices.

Appendix V

                     GAO Contacts and Staff Acknowledgments

GAO Contacts Richard J. Hillman, (202) 512-8678

Staff 	In addition to the individuals named above, Cody Goebel, Emily
Chalmers, Jordan Corey, Joe Hunter, Austin Kelly, Mitchell Rachlis, Carl
Ramirez,

Acknowledgments	Omyra Ramsingh, Kathryn Supinski, and Richard Vagnoni made
key contributions to this report.

Glossary of Terms

Ask price (offer/sell price)	The lowest price at which someone is willing
to sell a security at a given time.

Basis point A basis point is equal to 1/100 of 1 percent.

Bear market A market in which stock prices decline over a sustained period
of time.

Best execution requirement 	The obligation of broker-dealers to seek to
obtain the best terms reasonably available under the circumstances for
customer orders.

Bid-ask spread 	The difference between the price at which a market maker
is willing to buy a security (bid) and the price at which the firm is
willing to sell it (ask). The spread narrows or widens according to the
supply and demand for the security being traded. The spread is what the
market maker retains as compensation (or income) for his/her effort and
risk.

Bid price (buy price) 	The highest price at which someone is willing to
buy a security at a given time.

Block trade 	Represents the purchase or sale of (1) a large quantity of
stock, generally 10,000 shares or more or (2) shares valued at $200,000 or
more in total market value.

Broker 	An individual or firm who acts as an intermediary (agent) between
a buyer and seller and who usually charges a commission.

Bull market A market in which stock prices rise over a sustained period of
time.

Call option	A contract granting the right to buy a fixed amount of a given
security at a specified price within a limited period of time.

Commission	A fee paid to a broker for executing a trade based on the
number of shares traded or the dollar amount of the trade.

Dealer	An individual or firm in the business of buying and selling
securities for his or her own account (principal) through a broker or
otherwise.

Decimalization/decimal pricing 	The quoting and trading of securities in
dollars and cents ($2.25) instead of fractions ($8 1/8).

Delay cost	A type of market impact cost that occurs as the result of
changes in the price of the stock being traded during the time
institutional investors' portfolio mangers direct their traders to buy and
sell stock and the moment these orders are released to brokers.

Effective spread	Measures the trading costs relative to the midpoint of
the quoted spread at the time the trade occurred. It is defined as twice
(to reflect the implied roundtrip cost) the difference between the trade
price and the midpoint of the most recent bid and ask quotes. It reflects
the price actually paid or received by customers. It is considered a
better measure of execution costs than quoted spreads because orders do
not always execute exactly at the bid or offer price.

Electronic Communication An electronic trading system that automatically
matches buy and sell

Network (ECN) 	orders at specified prices. It is a type of alternative
trading system-an automated market in which orders are centralized,
displayed, matched, and otherwise executed.

Exchange 	An organized marketplace (stock exchange) in which members of
the exchange, acting both as brokers and dealers, trade securities.
Through exchanges, brokers and dealers meet to execute orders from
individual and institutional investors and to buy and sell securities.

Floor-based (or auction) market 	Is a stock exchange (like the American
Stock Exchange and the New York Stock Exchange) where buyers and sellers
meet through an intermediary- called a specialist. A specialist operates
in a centralized location or "floor" and primarily matches incoming orders
to buy and sell each stock. There is only one specialist designated for a
firm or several firms who is assigned to oversee the market for those
stocks.

Floor broker	A member of an exchange who is an employee of a member firm
and executes orders, as agent, on the floor of the exchange for their
clients.

Inside spread (inside quote)	The highest bid and lowest offer being quoted
among all the market makers competing in a security.

Intermarket linkage system	An electronic trading linkage between the major
exchanges (stock and option) and other trading centers. The system allows
brokers to seek best execution in any market within the system.

Institutional investor	An organization whose primary purpose is to invest
its own assets or those held in trust by it for others and typically buys
and sells large volumes of securities. Examples of such organizations
include mutual funds, pension funds, insurance companies, and charitable
organizations.

Limit order	An order to buy or sell a specified number of shares of a
security at or better than a customer-specified price. Limit orders supply
additional liquidity to the marketplace. A limit order book is a
specialist's record of unexecuted limit orders.

Liquidity	The ease with which the market can accommodate large volumes of
securities trading without significant price changes.

Listed stock The stock of a company that is listed on a securities
exchange.

Market depth	The numbers of shares available for trading around the best
bid and ask prices.

Market impact The degree to which an order affects the price of a
security.

Market maker	A dealer that maintains a market in a given security by
buying or selling securities at quoted prices.

Market order	An order to buy or sell a stated amount of a security at the
best price available when the order reaches the marketplace.

NASDAQ Stock Market A market for securities traded "over-the-counter"
through a network of

(NASDAQ)	computers and telephones, rather than on a stock exchange floor.
NASDAQ is an electronic communications system in which certain NASD member
broker-dealers act as market makers by quoting prices at which they are
willing to buy or sell securities for their own accounts or for their
customers. NASDAQ traditionally has been a "dealer" market in which prices
are set by the interaction of dealer quotes.

National best bid and offer Defined as the highest bid and lowest ask
across all U.S. markets providing (NBBO) quotes for an individual stock.

Order Handling Rules	SEC rules that require (1) the display of customer
limit orders that improve certain over-the-counter (OTC) market makers'
and specialists' quotes or add to the size associated with such quotes
(Rule 11Ac1-4 (Display Rule));

(2) OTC market makers and specialists who place priced orders with ECNs to
reflect those orders in their published quotes (Quote Rule); and (3) OTC
market makers and specialists that account for more than 1 percent of the
volume in any listed security to publish their quotations for that
security (Mandatory Quote Rule).

Opportunity cost	The cost from delaying execution to lessen market impact,
or not be able to make the execution at all, or abandoning part of it
because the market has turned against the strategy.

Price improvement Occurs when an order is executed at better than the
quoted price.

Put option	A contract granting the right to sell a fixed amount of a given
stock at a specified price within a limited period of time.

Quote The highest bid to buy and the lowest offer to sell any stock at a
given time.

Quote flickering	Where a given price quote is only visible for a brief
moment on the display screen.

Quoted spread	Measures the cost of executing a simultaneous buy and sell
order at the quoted prices. It is the simplest measure of trade execution
cost (or trading cost).

Retail investor	One who trades securities for himself/herself or who gives
money to any institution, such as a mutual fund, to invest for
himself/herself.

Securities and Exchange The federal regulatory agency created by the
Securities Exchange Act of

Commission	1934 that is responsible for ensuring investor protection and
market integrity in the U.S. securities markets.

Specialists	Members of an exchange who handle transactions on the trading
floor for the stocks for which they are registered and who have the
responsibility to maintain an orderly market in these stocks. They do this
by buying or selling a stock on their own accounts when there is a
temporary disparity between supply and demand for the stock.

Stepping ahead/penny jumping	The practice of improving the best price by a
penny or less in an attempt to gain execution priority.

Stock A financial instrument that signifies an ownership position in a
company.

Tick size (or minimum price The smallest price difference by which a stock
price can change (up or increment) down).

Trade-through	The execution of a customer order in a market at a price
that is inferior to a price displayed (or available) in another market.

Trading cost	The cost for executing the trade (brokerage commission, fees,
market impact).

Transparency	The degree to which trade and quotation information (price
and volume) is available to the public on a current basis.

Volatility A measure of the fluctuation in the market price of a security.

Volume	The number of shares traded in a security or an entire market
during a given period-generally on a daily basis. It is a measure of
liquidity in a market.

Volume weighted average price A trading benchmark used to evaluate the
performance of institutional

(VWAP)	traders. It is the average price at which a given day's trading in
a given security took place. VWAP is calculated by adding up the dollars
traded for every transaction (price times shares traded) and then dividing
by the total shares traded for the day. The theory is that if the price of
a buy trade is lower than the VWAP, then it is a good trade. The opposite
is true if the price is higher than the VWAP.

Related GAO Products

Securities Markets: Preliminary Observations on the Use of Subpenny
Pricing. GAO-04-968T. Washington, D.C.: July 22, 2004.

Securities Pricing: Trading Volumes and NASD System Limitations Led to
Decimal-Trading Delay. GAO/GGD/AIMD-00-319. Washington, D.C.: September
20, 2000.

Securities Pricing: Progress and Challenges in Converting to Decimals.

GAO/T-GGD-00-96. Washington, D.C.: March 1, 2000.

Securities Pricing: Actions Needed for Conversion to Decimals.
GAO/T-GGD-98-121. Washington, D.C.: May 8, 1998.

GAO's Mission	The Government Accountability Office, the audit, evaluation
and investigative arm of Congress, exists to support Congress in meeting
its constitutional responsibilities and to help improve the performance
and accountability of the federal government for the American people. GAO
examines the use of public funds; evaluates federal programs and policies;
and provides analyses, recommendations, and other assistance to help
Congress make informed oversight, policy, and funding decisions. GAO's
commitment to good government is reflected in its core values of
accountability, integrity, and reliability.

Obtaining Copies of The fastest and easiest way to obtain copies of GAO
documents at no cost

is through GAO's Web site (www.gao.gov). Each weekday, GAO postsGAO
Reports and newly released reports, testimony, and correspondence on its
Web site. To Testimony have GAO e-mail you a list of newly posted products
every afternoon, go to

www.gao.gov and select "Subscribe to Updates."

Order by Mail or Phone	The first copy of each printed report is free.
Additional copies are $2 each. A check or money order should be made out
to the Superintendent of Documents. GAO also accepts VISA and Mastercard.
Orders for 100 or more copies mailed to a single address are discounted 25
percent. Orders should be sent to:

U.S. Government Accountability Office 441 G Street NW, Room LM Washington,
D.C. 20548

To order by Phone:	Voice: (202) 512-6000 TDD: (202) 512-2537 Fax: (202)
512-6061

  To Report Fraud, Contact:
  Waste, and Abuse in Web site: www.gao.gov/fraudnet/fraudnet.htm

E-mail: [email protected] Programs Automated answering system: (800)
424-5454 or (202) 512-7470

Congressional	Gloria Jarmon, Managing Director, [email protected] (202)
512-4400 U.S. Government Accountability Office, 441 G Street NW, Room 7125

Relations Washington, D.C. 20548

Public Affairs	Paul Anderson, Managing Director, [email protected] (202)
512-4800 U.S. Government Accountability Office, 441 G Street NW, Room 7149
Washington, D.C. 20548
*** End of document. ***