Op Risk High Frequency Trading June 14 Final

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Presentation on High Frequency Trading risks delivered during OpRisk conference in London in June 2012. Content includes an overview of key risks affecting high frequency trading.
1. Failure to meet regulatory and exchange requirements.
2. Removal of human decision making once the algorithms are finished.
3. Extreme market behaviour: Flash Crash (2010).
4. Theft or loss of Intellectual Property.
5. Errors or problems suffered by clients using Direct Market Access and Algo/HFT.
6. Business impact of latency (system errors may increase delays).
7. Limited security controls at the infrastructure level.
8. Failure of hedges. 9. Incorrect/untested strategies.
David Ramirez
IT Audit Director

Op Risk High Frequency Trading June 14 Final

  1. 1. High Frequency TradingOperational Risk Issues and Mitigation MeasuresDavid Ramirez – Director, IT Audit14 June 2012 – London 11.10-11.50 am
  2. 2. 2Agenda 1 • Introduction and Key Concepts 2 • Details of Algorithmic Trading and HFT 3 • Key Risks 4 • Mitigating Mechanisms
  3. 3. 3Taxonomy of Algorithmic Trading “The use of computer algorithms to Algorithmi automatically make certain trading c Trading decisions, submit orders, and manage those orders after submission”. (Hendershott and Riordan, 2009). High Frequency “Employs extremely fast automated Trading programs for generating, routing, cancelling , and executing orders in electronic markets.” (Cvitani and Kirilenko, 2010) Trading Strategies “Market Making, Electronic Liquidity Provision, Statistical Arbitrage, Liquidity Detection, Latency Arbitrage, etc” (Gomber and Arndt, 2011)
  4. 4. 4Agenda 1 • Introduction and Key Concepts 2 • Details of Algorithmic Trading and HFT 3 • Key Risks 4 • Mitigating Mechanisms
  5. 5. 5 Latency vs. Position Timeline High Traditional Long-Term InvestmentLatency Algorithmic Trading HF Low T Short Long How Long Position Held
  6. 6. 6Latency? - Key Concepts Trading Risk BookMarket Data Trade Order Logic Management Processing There is some The Algorithm (algo) Risk Management The order needs to latency within the Data from exchange, would need to take checks on the orders: arrive from the exchange, tends to news, other decisions based on size, frequency, fat system hosting the be minimal at participants. high volumes of fingers, VAR, short algo, to the selling, etc. around 0.5 data. exchange. milliseconds.
  7. 7. 7Arbitrage:•The practice of taking advantage of a pricedifference between two or more markets: striking acombination of matching deals that capitalize uponthe imbalance, the profit being the differencebetween the market prices.Collocation:•Servers are hosted by the exchange (NYSE, LSE,NASDAQ) in large data centres; access granteddirectly to the exchange infrastructure.
  8. 8. 8HFT Trading Strategies•Market Making: Earn the •Market Neutral Arbitrage:spread between bid and ask. Long and short; gain the difference.•Rebate Driven Strategies:Leverage rebates offered by •Cross Asset/Market andExchange. Exchange Traded Fund (ETF) arbitrage: Leverage•Statistical Arbitrage: Predict price inefficiencies acrossdiscrepancies in the market. asset/markets. •Latency Arbitrage: Predicting the ‘National Best Bid and Offer’ value.
  9. 9. 9Agenda 1 • Introduction and Key Concepts 2 • Details of Algorithmic Trading and HFT 3 • Key Risks 4 • Mitigating Mechanisms
  10. 10. 10Key Risks Related to HFT Environments1. Failure to meet regulatory and exchange requirements.2. Removal of human decision making once the algorithms are finished.3. Extreme market behaviour: Flash Crash (2010).4. Theft or loss of Intellectual Property.5. Errors or problems suffered by clients using Direct Market Access and Algo/HFT.
  11. 11. 11Key Risks Related to HFT Environments - cont6. Business impact of latency (system errors may increase delays).7. Limited security controls at the infrastructure level.8. Failure of hedges. Incorrect/untested strategies.
  12. 12. 1. Failure to Meet Regulatory and Exchange 12Requirements•Regulators and exchanges define message structures that must beadhered to (regulatory and contractual); this includes specific flags onthe packets (short selling, max order size, frequency on same name,dealing on restricted names/securities).•September 2011, the SEC announced that it would start collectingcopies of algorithms for analysis. There is also a plan to collect livelogs from all exchanges.•Time compliance: Have you closed a trade on time? How is it beingmeasured? (GPS and the IEEE1588v2 Precision Time Protocol (PTP);Financial and stock exchange data centers are increasingly deployingGPS receivers on the roof of the data center and then distributing GPStiming throughout the data center.)
  13. 13. 1. Failure to Meet Regulatory and Exchange 13Requirements– contSecurities and Exchange Act 1934 and MAS•“For the purpose of creating a false or misleadingappearance of active trading in any security registeredon a national securities exchange, or a false ormisleading appearance with respect to the market forany such security,
  14. 14. 2. Removal of human decision making once thealgorithms are finished.•Algorithms will be executing instructions withoutany supervision, the potential for errors increasessignificantly.•Human intervention should be available at alltimes, as expected by exchanges.
  15. 15. 153. Extreme market behaviour: Flash Crash(2010).Flash Crash – May 6 2010 – Runaway Algos – Domino Effect? Wikipedia.org•The Flash Crash, was a United States stock market crashon May 6, 2010 in which the Dow Jones Industrial Averageplunged about 1000 points—or about nine percent—only torecover those losses within minutes. It was the secondlargest point swing, 1,010.14 points, and the biggest one-day point decline, 998.5 points, on an intraday basis inDow Jones Industrial Average history.•"HFTs began to quickly buy and then resell contracts toeach other—generating a hot-potato volume effect as thesame positions were passed rapidly back and forth."
  16. 16. 3. Extreme market behaviour: Flash Crash 16(2010). - contHigh volume days tend to be high execution days for HFT – based onnetwork capacity it can impact traditional trading technology and pipesassigned to that business.Volumes can be massive and add up quickly – e.g. a bug in the codeorder will become a very large order error and then lead to an errorwith the exchange or network or exchange connectivity.A coding error (which is big and means the Algo is wrong from thestart) can be (mis)understood to be a routing issue with an exchange(which is small and easier to fix).
  17. 17. 4. Theft or loss of Intellectual Property. 17 ‘Secret sauce’• There are examples in the industry of at least four legalcases in relation to algorithms being stolen.•These programs are key intellectual property, it is veryeasy for staff to leave the firm with the code underlying thetrading strategy.•Firms struggle with understanding when does an Algobecome an Algo.
  18. 18. 5. Errors or problems suffered by clients usingDirect Market Access and Algo/HFT .•Firms offer Direct Market access to prime clients,this creates a risk as the activities of clients canimpact the compliance with exchange rules andregulations.
  19. 19. 6. Business impact of latency (system errors 19 may increase delays).•Latency has direct impact on the P&L, an Ultra-HFTstrategy and some forms of arbitrage will fail if latency ishigher than expected.•Communications from the servers (collocated or not) tothe exchange must be done over low latency links. Trading ApplicationsPackaged Applications Proprietary Applications Network Network
  20. 20. 207. Limited controls at the infrastructure level.•Algorithmic Trading environments tend to have avery limited number of infrastructure controls, mostare between the local corporate network and theHFT equipment.•Operating systems are modified to gain speedadvantages; this has an impact on the securityconfiguration and layers of security available.•There is a significant demand increases on theunderlying infrastructure.
  21. 21. 8. Failure of hedges. Incorrect/untestedstrategies.•Poorly tested algorithms or interpretation errorscould disrupt the market or drive trading losses.The magnitude of these will be related to availableliquidity and market conditions.
  22. 22. 22Agenda 1 • Introduction and Key Concepts 2 • Details of Algorithmic Trading and HFT 3 • Key Risks 4 • Mitigating Mechanisms
  23. 23. 23Mitigating Measures•Increased oversight and •Measuring latencyvisibility over algorithms. across applications, operating systems and•Built-in and regulatory networks.algorithmic limits/checks(e.g., circuit breakers). •Security reviews overActive data leakage the environment.controls. • Robust change management controls and testing/validation over new algorithms.
  24. 24. 24Thank youQ&A
  25. 25. Evolution of Order Processing Time (1995-2011) Source: NYSE Technologies – Eric Bertrand 2011 1200 1000Latency (microseconds) 800 600 400 200 §¦ ¥¤ £ §¦ ¨¤ £ 0 ¢¢ ¡  1995 2000 2005 2006 2008 2009 1 second = 1,000 millisecond =1’000,000 microseconds.
  26. 26. How Many Transactions? (Approximate Numbers!) Number of HFT Transactions For Each Action Blink of an EyeBrain Recognises Human Expression Hard Disk Read Housefly Wing Flap 0 10,000 20,000 30,000 40,000 50,000 60,000 70,000 80,000 Brain Recognises Human Housefly Wing Flap Hard Disk Read Blink of an Eye Expression Series1 600 800 40,000 80,000

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