Dark Pools and Block Trading: Discussion

1. Dark Pools and Block Trading: Discussion Bob Schwartz Zicklin School of Business Baruch College, CUNY

2. The Plain Vanilla Order Drive Market We have considered • The plain vanilla, order driven market • A simple limit order book • Continuous trading and call auction facilities

3. Is Trading Really This Simple?

4. Electronic Continuous Order Book Systems Work Well For • Retail order flow • Liquid stocks • Non-stressful conditions But A Plain VanillaElectronic Trading SystemCannot do it All

5. More Structure is Needed!

6. Two-Sided Markets

7. Definition of “Two-Sided” In the same, brief interval of time, some participants are actively looking to buy shares while others are actively looking to sell shares In short time intervals, the arrivals of buyer-initiated and seller-initiated trades are positively correlated

8. Some Good News Markets are typically two-sided When buyers come to market, most likely sellers will also be there When sellers come to market, most likely buyers will also be there This is likely to be the case for both retail and institutional customers The TraderEx market is generally two-sided

9. Some Interesting News Equity trades tend to cluster in half-hour periods for NYSE and Nasdaq stocks News and non-news days Different times of the day Opening ½ hour Middle of the day Closing ½ hour A spectrum of trade sizes

10. “Market Sidedness: Insights into Motives for Trade Initiation,” Asani Sarkar & Bob Schwartz, Journal of Finance, 2008, forthcoming Sample: TAQ data, January 2003 to May 2003. 41 NYSE & 41 Nasdaq stocks (a matched sample, using Dec 31, 2002 closing prices and market caps) Correlation Measures: • Count the number of buyer-triggered and seller-triggered trades in 5 minute trading windows for • Days with no news events (control sample) • Days before and after news events • Correlate the no. of buyer-triggered & seller-triggered trades

11. NYSE Nasdaq No News 0.36 0.49 Before, All 0.27 0.32 Before, Large Dispersion 0.47 0.35 Before, Small Dispersion 0.15 0.26 After, All 0.51 0.62 After, Large Surprise 0.60 0.81 After, Small Surprise 0.51 0.64 Correlations:Earnings Report Events Correlation generally greater for • Before, large than small dispersion • After, large than small surprise

12. NYSENasdaq No News 0.36 0.49 Before, All 0.32 0.50 Before, Large Dispersion 0.40 0.58 Before, Small Dispersion 0.29 0.35 After, All 0.33 0.53 After, Large Surprise 0.36 0.51 After, Small Surprise 0.32 0.43 Correlations: 8:30 am Macro Announcement Events Correlation greater for • Before, large than small dispersion • After, large than small surprise

13. NYSE Nasdaq No News 0.36 0.49 Before 0.39 0.39 After 0.65 0.55 Correlations: Corporate Restructuring News Events Correlation greater after than before

14. Why Are Markets Generally Two-Sided? The Diversity of Motives for Trading Liquidity traders Technical traders Information traders Divergent Expectations! These motives are represented in TraderEx

15. Why Do Trades Cluster in Time? Buyside trading strategies Do they cluster in TraderEx? Presumably, but we haven’t analyze the data yet

16. Block Trading

17. The Challenge How do you handle an order to buy half a million shares of a stock that, on average, trades 300,000 shares a day? • Dealer capital • Shop the order • Slice and dice the order and submit the tranches to an electronic platform • Call auction • Block trading facility

18. Slicing and Dicing • Average Trade Size at NYSE • 1988 2,303 shares • July 2007 297 shares • Block Trading Volume at the NYSE • 1988 52 percent • July 2007 15 percent

19. Costs • Bid-ask spread • Market impact • Opportunity cost • Implementation short fall • Losses due to bad market timing

20. Performance Measure Difficult to measure performance • Need a good benchmark • Do not make assessments on a trade-by-trade basis • TraderEx: do not make assessments on the basis of a single simulation run • TX point score

21. Best Execution Obligation to execute customer orders at best possible price with minimum market maker intervention What does this mean? Insights gained from TraderEx • Demonstrating that you have met a best execution obligation is not simple – even in a simplified environment • Alternatives exist for handling order in TX. Which is best? • Strategic decisions are made in the face of uncertainty • Performance must be averaged over a number of simulation runs • Good, implementable benchmarks are hard to define

22. Electronic Intermediaries Dark pools • Crossing network (e.g., Posit, Matchpoint) • Negotiation venue (e.g., Liquidnet) • Order matching system (e.g., Pipeline)

23. Buyers and Sellers Can Meet in a Block Trading Facility Pipeline and Liquidnet customers trade in size Minimum order size in Pipeline for a liquid stock: 100,000 shares Liquidity motivated? Noise trading? I don’t think so • Further evidence of divergent expectations

24. Dark Pools Free Riding On Price Discovery While Offering Quantity Discovery • Institutions keep their orders hidden to control their transaction costs • How do they find each other and trade? • The problem is called Quantity Discovery

25. Dark PoolsAnything New Under the Sun? “Dark orders as a tool have been around for as long as people have existed. What is happening now is that it’s done in cyberspace” Timothy Mahoney, Bids Trading, CEO Securities Industry News April 7, 2008, page 1 • The NYSE trading floor • Upstairs dealer desks

26. Shortcomings of Dark Pools • Lack transparency • Low crossing rates • Exclusivity • Sheer numbers

27. Back To TraderEx To think about while using a block trading facility When did you enter your orders? How did you size your orders? How did you price your orders? Did you feel at risk if a contra did not show up?