Market Structure, Fragmentation, and Market Quality Paul Bennett Li Wei New York Stock Exchange December, 2005

2 Optimal Market Design and Structure Goal : Liquidity Provision and Price Efficiency Market Structure –US: Fragmentation with both competition among market centers and competition among order flows –Asia, Europe, and others: Centralization with only competition among order flows Market Design –Auction: best price is enforced –OTC: broker/dealer fiduciary responsibility Optimality

3 NYSE vs NASDAQ Difference Fundamental difference between the NYSE and NASDAQ Today Pre – NASDAQ Reform Pre – Decimal Post – Decimal –Decimalization Has Driven the Transaction Cost Down –New Technology of Order Routing Increases Competition NYSE vs NASDAQ Difference –Difference in spread is narrowing –Fragmentation

4 Market Fragmentation Executions of 11Ac1-5-eligible orders < 10,000 shares, Source: 11Ac1-5 disclosures and Market Systems, Inc. Other ECNs & broker/ dealers SuperMontage July 2003

5 Market Fragmentation Executions of 11Ac1-5-eligible orders < 10,000 shares, Source: February Ac1-5 disclosures and Market Systems, Inc. Note: Percents may not add up to 100% due to rounding. February 2005 Super Montage 17.6% BRUT 19.9% INET 34.0%

6 Quantify Fragmentation NASDAQ NYSE

7 Findings 39 stocks switching from Nasdaq to NYSE during 2002 & Q1‘03 Lower price volatility & tighter quotes More efficient pricing (less noise, fewer price reversals) Lower execution costs Degree of fragmentation affects market quality

8 Literature Switching Samples Matched Samples Christie and Huang (1994), Barclay (1997), Bessimbinder (1999), Heidle and Huang (1999), Jones and Lipson (1999), Weston (2000), Sapp and Yan (2003) Huang and Stoll (1996), Keim and Madhavan (1996), LaPlante and Muscarella (1997), Bessimbinder and Kaufman (1997), SEC (2001), Weaver (2002), Boehmer (2005) Fragmentation Cohen, Maier, Schwartz, and Whitcomb (1982), Cohen, Conroy and Maier (1985), Mendelson (1987), Madhavan (1985), Amihud, Lauterbach, and Mendelson (2002)

9 Sample and Data Sample 39 companies switching from Nasdaq to the NYSE from Jan ‘02 through Mar ‘03 Data Trades and Quotes SEC 11Ac1-5 Reports Window 60 trading days before and after for trades and quotes. 3 months before and after for the Dash-5 data

10 Sample Statistics Table 1 Volatility is the S.D. of daily returns. The above statistics are examined during the 60-day window before the switch.

11 Voluntary Order Flow Migration Figure 1

12 Market Quality Volatility Price Efficiency Quoted Spread: pre-trade Effective Spread: post-trade Realized Spread: competition of supplying liquidity Execution Speed

13 Change of Volatility and Price Efficiency Table 3

14 Variance Decomposition Hasbrouck (1993) S is measured at a trade-by-trade basis. P is computed as natural log. P t = m t + s t, where m t is the efficient price

15 Change of Volatility Figure 1

16 Intraday Volatility Figure 2 Nasdaq NYSE

17 Variance Ratio Test Variance Ratio = VAR(R 10 ) / [ 2 * VAR(R 5 )] Note: 1. Return is measured by quote midpoint. 2. R 10 and R 5 indicates 10/5-minute return based on quote midpoint Sample NasdaqNYSE NYSE - Nasdaq NYSE NYSE - Nasdaq Mean p-value Median p-value by Interval Open Quoteby Interval Close Quote

18 Effective Spreads Table 2 SEC 11Ac1 – 5 Reports Difference between transaction price to quote midpoint at order arrival time (x2) Caveats –Accuracy (idiosyncratic errors) –Order types –Cancellation rates –Time measurements for effective spreads

19 Change of Spreads Table 4

20 Quotes Figure 3

21 Intraday Quote Figure 4

22 Effective Spreads Figure Effective Spread ($0.01) Months Relative to Switch Share-weighted Monthly Effective Spread NasdaqNYSE

23 Expected Transaction Cost Hasbrouck (1993) Expected Transaction Cost: E|S| = [SQRT (2/  )]  (s) Based on Hasbrouck (1993), the expected transaction costs due to price noise are: Nasdaq = 14.1 bps NYSE = 4.8 bps  (s) is the S.D. of noise or pricing error

24 Selection Bias Simple Comparison –39 transferring stock –660 Nasdaq NYSE-eligible stocks Matching Sample –Select 39 Nasdaq “sister” stocks –Mcap, volume, volatility, price Heckman (1997) 2-Stage Probit Model –660 firms in the first stage probit regression

25 Simple Comparison Variables –Market Cap + Daily Trading Volume + Price –Daily High-Low Price Range & Daily Return Volatility –Share Outstanding, –Daily Close-to-Close Return, –Registered Market Maker Count –Distance –SIC Exchange Industry Concentration Index by mcap Compare medians

26 Matching Sample 39 non-switching Nasdaq sisters –Match: MCAP, Trading Volume, Return Volatility, Price –2001 matching period Replicate analysis on sisters Sisters: No significant changes between the 60- day pre-switch and post-switch periods.

27 2-Stage Probit Model Explanatory Variables in Probit Model –MCAP + Volume + Price + Volatility + Return –Number of Market Maker Count –Bid-Ask Spread of the Daily Closing Spread –Geographical Distance from New York City –Exchange Industry Concentration Index by Mcap –Number of Public Companies in the SIC Major Group Sample: –660: Switching and non-switching Combine with Fragmentation Analysis

28 Fragmentation Analysis Herfindahl – Hirschman Index (HHI) –Market Share of Covered Orders in the SEC 11Ac1-5 –Nasdaq median = –NYSE median = –Alternative measure = number of market centers

29 Fragmentation on Volatility Table 5: Panel A

30 Fragmentation on Quoted Spread Table 5: Panel B

31 Fragmentation on Effective Spread Table 5: Panel C

32 Conclusions NYSE provides better liquidity provision and price efficiency. Order Flow Fragmentation Affects Market Quality Competition among market centers does not dominate the benefits of order flow consolidation Market fragmentation is particular detrimental for less liquid securities.

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34 Market Capitalization Regions Financial Co. 5/3/02 $8 Billion Concord EFS Co. 11/7/02 $7.3 Billion II

35 Daily and 5-Minute Volatility

36 Volatility — Price High-Low Range

37 Quotes