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Dark Pools, Programme Trades and the Impact of Hedge Fund Trading on Markets: Anthony Clake  (23 Sep)
 

Dark Pools, Programme Trades and the Impact of Hedge Fund Trading on Markets: Anthony Clake (23 Sep)

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A presentation delivered at a joint event with CFA Anthony Clake of Marshall Wace was joined by Colin McLean of SVM Asset Management and Professor Jens Hagendorff for a panel discussion on this topic. ...

A presentation delivered at a joint event with CFA Anthony Clake of Marshall Wace was joined by Colin McLean of SVM Asset Management and Professor Jens Hagendorff for a panel discussion on this topic.

Anthony Clake has been responsible for the evolution of the MW TOPS strategies since their inception in 2001. As the global product manager for MW TOPS, he has overseen the geographic expansion of this investment process across Europe, Asia, North and South America as well as Emerging Markets. In recognition of his contribution to the development of Marshall Wace, Anthony was made a partner of the firm in 2004.

Anthony joined Marshall Wace in August 2001 directly from university following consultancy work with the firm during 1999 and 2000. Previously he studied Philosophy, Politics and Economics at Queen's College, Oxford. He was elected for the Gibbs scholarship for obtaining the highest marks awarded in preliminary and final examinations.

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    Dark Pools, Programme Trades and the Impact of Hedge Fund Trading on Markets: Anthony Clake  (23 Sep) Dark Pools, Programme Trades and the Impact of Hedge Fund Trading on Markets: Anthony Clake (23 Sep) Presentation Transcript

    • The Evolution of Stock Markets Anthony Clake September 23, 2013 1
    • Contents 2 1. Background 2. History of Exchanges 3. Ecosystem: trading costs 4. Algorithms 5. Issues of concern to long-term investors 6. Possible regulatory responses 7. Measuring market quality 8. Fragmenting liquidity 9. Dark Pools 10.Regulation / Evolution 11.MW Trading 12.Questions?
    • 1. Background 3 Investment strategy pyramid - Schematic
    • 1. Background 4 HFT activity by strategy type
    • 2. History of Exchanges 5  Specialist Model • Example: NYSE • People meet at a central point and trade blocks through a vocal negotiation process. • The specialist holds orders for brokers on his/her book and will step in to provide liquidity to dampen liquidity and trade for his/her own account. • Conflicts of interest?  Quote driven Model • Examples: NASDAQ (pre-SOES), LSE AIM • Brokers post electronic indicative prices and sizes in a central place. • Customers contact the brokers to “firm-up” their size and price. • Customers often complain about “quote-fade”  Order driven Model • Examples: LSE SETS, NYSE DOT, Chi-X Europe • Firm orders are sent electronically to a book in which people can trade against, provided the order has not been cancelled. • Most exchanges now trade this way
    • 2. History of Exchanges 6 Timeline of milestones leading up to the rise of CBT
    • 3. Ecosystem: trading costs 7 Life cycle of a trade – A simplified view
    • 3. Ecosystem: trading costs 8  Commission / exchange fees / taxes • We estimate about 10-20% of total trading cost for an institutional manager  Impact costs • We estimate about 80-90% of total trading cost for an institutional manager • Traders are trying to control impact costs:  Perceived impact: f(ADV%, volatility, aggression / participation rate)  Perceived alpha / toxicity  Perceived risk / time to completion o Need to forecast volume  Perceived information leakage  Time of day  Trade sizes are generally made to reflect perceived alpha – perceived cost = net alpha
    • 3. Ecosystem: trading costs 9 Equity trading ecosystem (schematic)
    • 4. Algorithms 10  People use algorithms to save money: • Break orders into smaller pieces, executed electronically  Anonymity (less information leakage)  Aggression control / more spread capture (don’t need instant risk price on entire piece) • React automatically to changing market dynamics better than hiring humans  Types: • Benchmark: Percentage of Volume, Vwap, Implementation Shortfall • Liquidity Seeking: Dark Only, Aggressive inside a price (“I would”) The voice trade The algorithmic trade
    • 4. Algorithms 11 Similarities and differences between HFT and AT
    • 4. Algorithms 12 Examples of predatory HFT strategies
    • 5. Issues of concern to long-term investors 13  Challenges in trading “HFT stocks”  “Phantom” vs. “real” liquidity  Order anticipation  “Mandatory fee” paid to market making HFTs  New order types  Less volume and heterogeneous trading
    • 6. Possible regulatory responses 14  Manage trade-off between cost and level of liquidity provisioning: Tick size policy  Increase order book execution predictability: Minimum execution ratios and minimum resting times  Reduce systemic and endogenous risk: Circuit breakers and notification of algorithms  Promote liquidity: Market-making obligations and regulation of internalisation  Reduce latency advantage of HFTs: Periodic call auctions  Reduce HFT role: Limit maker-taker prici9ng and introduction of financial transaction tax
    • 7. Measuring market quality 15
    • 8. Fragmenting Liquidity 16  Why use other exchanges? • Speed, ability to control orders (new and cancels) relative to aggression objectives and fair value models • Cost: rebates, fees, tick sizes / spreads • “Jump the queue” – compete for better queue position at price level on different exchange • “Hide in the weeds” – break up slices to anonymise repeated buying  Primary exchange • Opening auction, Continuous trading, Closing auction  MTF / ECN / PTS • Maker / taker pricing (inverted in some cases!)  Dark pools  OTC
    • Primary - Auction 4% Primary - Continuous 32% Other Venues* 18% Dark Pools 41% High Touch 5% Primary - Auction 1% Primary - Continuous 13% Other Venues* 7% Dark Pools 76% High Touch 3% Primary - Auction 9% Primary - Continuous 59% Other Venues* 9% Dark Pools 14% High Touch 9% – Direct Market Access connection to major exchanges • Lower cost of execution, ca. <1bp per trade • Provides access to alternative liquidity sources • Approx. 15% reduction in slippage across EU from 2009-2012 – Reduction in high touch trading has reduced execution costs – Development of proprietary algorithms to leverage extensive venue connectivity and quantitative research 8. Fragmenting liquidity EU Asia US 17 Note: Data relates to notional value traded on a typical day in January 2013, for illustrative purposes only. *Multilateral Trading Facility (EU), Electronic Communication Network (US), Proprietary Trading System (Asia). Source: Marshall Wace LLP. Confidential to recipient; not for reproduction or redistribution. Please refer to final pages for Important Disclosures.
    • 9. Dark Pools 18  Orders are conditional to trade on the other side being present in the pool – no one sees your order otherwise • Anonymity / less information leakage if no trade than a lit venue  Reference lit prices to determine crossing points • Most commonly cross at mid-point, or near / far peg • Can also cross at even tick increments if not mid-point • Market data speed important for reference calculation  Broker Pools (BCS) • Can segregate which flow to cross with (toxicity). • Brokers can save from paying exchange fees • Crossing clients can save costs by preventing competition with intermediaries • Examples: MS Pool, CS Crossfinder  MTFs / ATS • Everyone can access, no segregation • Examples: Chi-X Dark, Bats Dark, UBS MTF
    • 10. Regulation / Evolution 19  Flash Crash • All regulators around the world focused on preventing again  Canada, Australia • Must trade at a better price than the lit exchanges in dark pools (mid-point or better full tick size)  Dodd Frank Impact • Less trading activity from investment banking proprietary trading  Less active fund management by investors • More use of futures and ETFs for benchmark tracking  Resistance of fixed income markets to exchanges  Italian transaction tax • Less statistical arbitrage flow, market making still effectively exempt through net settlement tax  Germany on HFT
    • 11. MW Trading 20  Typical stages of investment process • Portfolio manager: Fundamental: “stock is very cheap: we should buy and hold for a while” • Trader: Statistical arbitrage / scheduling: “the stock is underperforming its pairs over the past few hours, we should be more aggressive buying now before it reverts” • Algorithm / broker: High Frequency / market making: “we can collect a little more edge by buying a bit more than usual right now on the bid”  Integrating statistical arbitrage / high frequency stages into portfolio construction • Constraints hamper the ability to trade more aggressively or even generate a trade at all • Effectively accessing liquidity and feeding back to portfolio construction (aggression vs. collecting edge). • Portfolio construction dynamically drives aggression through its utility desires (alpha, risk).  More creative liquidity consumption process o Sit in dark pools to help collect additional edge o Delay trading for spread collection o Delay trading for cheaper time of day (target aggression for certain times of day) o Trading security synthetically  Aggression is constantly changing via urgency desires
    • 12. Questions? 21
    • Appendix 22
    • Appendix
    • Appendix
    • References • Norges Bank Investment Management Discussion Note #1-2013: “High Frequency Trading – An Asset Manager’s Perspective” August, 2013 • Will Psomadelis, Schroders: “Know Your Counterparty: The New Paradigm of Equity Market Microstructure and The Impact to Institutional Investors.” August, 2013. 25