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Lifting the veil: An analysis of pre-trade transparency at the NYSE

Lifting the veil: An analysis of pre-trade transparency at the NYSE. Ekkehart Boehmer, Texas A&M Gideon Saar, NYU Lei Yu, NYU. Motivation. The ongoing proliferation of trading platforms raises important market design issues We look at market transparency

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Lifting the veil: An analysis of pre-trade transparency at the NYSE

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  1. Lifting the veil:An analysis of pre-trade transparency at the NYSE Ekkehart Boehmer, Texas A&M Gideon Saar, NYU Lei Yu, NYU

  2. Motivation • The ongoing proliferation of trading platforms raises important market design issues • We look at market transparency • Great regulatory interest: SEC’s Market 2000 study recommends greater pre-trade transparency (display of customer limit orders) • Limited empirical and theoretical evidence on the effects of transparency in auction markets • This paper: analyze economic consequences of an increase in pre-trade transparency

  3. OpenBook • Implemented by NYSE on January 24, 2002 • Before OpenBook, only specialist could see LOB • Reveals limit order volume at all price points for all NYSE stocks, refreshed every 10 seconds, 7:30 am – 4:30 pm • Exogenous to issuers • Revenue generator for NYSE: subscribers increased from 2,700 to 6,000 between Jan and May 2002 • Represents significant supply of liquidity • 99% of all orders and 75% of all volume is submitted electronically • About 2/3 of these are limit orders

  4. Our goals • Investigate how a change in pre-trade transparency affects • Trading strategies of investors • Behavior of specialists • Informational efficiency • Liquidity

  5. Predictions for increase in pre-trade transparency: Trading strategies • Limit order traders face two risks that are amplified with more transparency (Harris 1996) • Information leakage • Front-running • Remedies • Break up orders • Cancel and resubmit more often • Use floor brokers to selectively disclose trading interests

  6. Predictions for increase in pre-trade transparency: Market quality • Greater informational efficiency and greater liquidity • Baruch 2002, Glosten 1999 • Less liquidity and wider spreads • Madhavan, Porter, Weaver 2000 • Supported by empirical results from TSE change in 1990

  7. Predictions for increase in pre-trade transparency: Specialists • May trade less because they lose part of their informational advantage • May trade more as limit-order traders withdraw

  8. Research design to identify permanent effects of the change in transparency • Compare pre- and post-OpenBook periods • Choose the two most recent full trading weeks before Jan 24, 2002 • Learning about changes in (others’) trading strategies takes time: expect gradual adjustment to new equilibrium • Use four 2-week post-event periods: February, March, April, and May

  9. Sample construction • All common stocks of domestic issuers that are continuously traded Jan to May 2002 • Exclude trusts, funds, firms with multiple share classes • Results in 1332 NYSE-listed securities • Choose 400 of these, stratified by median dollar trading volume during 2001Q4 • Standard data from TAQ, Factset, using typical filters • Proprietary data from SOD, CAUD, Lofopen Descriptive statistics over time Descriptive statistics over volume quartiles

  10. Changes in order cancellation rates Columns represent median pairwise changes from pre-event to four different post-event periods Orange indicates significance at 5% or better (Wilcoxon Test) – grey indicates no significance January median: 61%

  11. Traders cancel orders faster and more often (consistent with Harris) Cancellation rate (Jan: 61%) Time to cancel (Jan: 290 sec) Univariate Cox - Cancellation rate Weibull - Time to cancel • Duration models to control for • Censoring • Distance from • quote

  12. Orders become smaller (consistent with Harris) … Limit order size (January median: 543 shares) decreases … but less intermediated (inconsistent with Harris) Floor-to-limit ratio decreases (based on shares traded) Visibility effect?

  13. Specialists become less active • Increase in risk of proprietary trading? • “Crowding out” effect? • Shift from floor to book • Note: reduced activity does not necessarily imply lower profits Specialist participation rate in share volume (Jan median: 18%) Quoted depth ($) added to the book by specialists and floor (January: $60,000)

  14. How did OpenBook affect informational efficiency of prices? • Hasbrouck’s (1993) measure • Decompose variance of (log) transaction prices into an efficient-price and a transitory component • Compute ratio of transitoryto total price variance • Deviations from a random walk • Compute 30 and 60 minute autocorrelation of quote-midpoint returns • Larger (absolute) autocorrelation indicates less resemblance to a random walk • A decline in either measure would suggest improvement in info efficiency

  15. Informational efficiency improves Deviation from efficient prices • Rather weak results • But direction of changes is consistent with Baruch 2002, Glosten 1999 30-minute autocorrelation 60-minute autocorrelation

  16. Changes in liquidity • How does cumulative depth displayed in the book change? • Construct 5-minute snapshots of the book (10am-4pm) • Compute depth at different intervals from the relevant quote • Average across snapshots • Do traders pay more for execution? • Compute effective spreads • Control for changes in volume, volatility, and price

  17. Conditional book depth increases Cross-sectional regression (N=400) Pooled TS-CS regression (N=8000) • Different specifications yield virtually identical results • Results inconsistent with Madhavan, Porter, and Weaver

  18. Conditional effective spreads decline Cross-sectional regression (N=400) Pooled TS-CS regression (N=8000) • Similar results using orders (as opposed to trades) • Different specifications yield virtually identical results • Results inconsistent with Madhavan, Porter, and Weaver

  19. Do the results just reflect a trend? • Markets generally became more liquid and order size declined during recent years • Are we picking up this trend? • Not likely • We measure changes in our variables from pre 9/11 to January 2002 (OpenBook) • For all variables, changes are very small relative to OpenBook effects and often in the opposite direction

  20. Conclusions • Regulatory interest in greater pre-trade disclosure faces academic debate without consensus • We find, contrary to evidence from the TSE, that opening the book • Improves market quality • Encourages active order management • Changes the role of specialists and floor brokers • Our analysis reemphasizes the importance of market design for innovation and as an instrument for regulatory changes

  21. Why do results differ between NYSE and TSE? • Network technology (TSE change preceded internet): traders may not have been able to use the book effectively for technological reasons • TSE displayed only a few ticks beyond the inside • Simultaneous other changes on TSE (e.g., reserve order display requirements) • TSE had two-tiered system, one was electronic (CATS)

  22. Cumulative shares on the book – weak increase Categories: 0.16%, 0.83%, 3.3%, 16.7% from quote, entire book Based on five-minute snapshots

  23. Effective spreads decline Group 1 – most active stocks Group 2 Group 3 Group 4 – least active stocks

  24. Effects on share valuation – partial analysis of welfare consequences CARs and sensitivity to liquidity changes

  25. Welfare consequences • Focus on share price responses to change in market structure • Liquidity affects required returns (Amihud and Mendelson, 1986) • Gradual change in trader strategies and liquidity makes it difficult to isolate valuation effects • Underlying assumption: liquidity effects are not anticipated • Estimate market model of daily returns during 2001 • Compute CAR from Jan 24 to the end of each post-event period • One-factor model using value-weighted NNM/AMEX index • Three-factor model adding JPM commodity futures and US T-Bond indexes • Estimate relation between CAR and changes in liquidity

  26. OpenBook is followed by a period of positive abnormal returns Standard event-study test: Wilcoxon test on median CAR Alternative test: Pooled regression, Wilcoxon test on median daily post-event dummy • Estimate pooled regression of returns on three factors and daily dummies, one for each day of the post-event period • Uses only time-series variation in coefficients to construct test • Alleviates correlation problem due to calendar time clustering

  27. CARs increase with declining relative effective spreads • Regress CARs cross-sectionally (from beginning of pre-event to the end of each post period) on corresponding changes in relative effective spreads • Coefficients measure responsiveness of CAR to changes in spreads, and medians are all negative and significant Each month shows the median coefficient on changes in relative effective spreads for each of the four volume groups (ordered from most active to least active)

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