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Challenges for algorithmic execution

Challenges for algorithmic execution. Gordon Baker. Agenda. Context Research To do. Some definitions Execution vs. trading. Execution No choice of asset, direction or quantity Usually intra-day or good for day orders Trading … everything else Market making Arbitrage

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Challenges for algorithmic execution

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  1. Challenges for algorithmic execution Gordon Baker

  2. Agenda • Context • Research • To do

  3. Some definitionsExecution vs. trading • Execution • No choice of asset, direction or quantity • Usually intra-day or good for day orders • Trading … everything else • Market making • Arbitrage • Model based trading

  4. A brief history1980s Technique Significance Market structure (electronic order books) determines spread of automated strategies • VWAP, TWAP and participation strategies • Empirical approaches to market impact

  5. A brief history1990s Technique Significance ITG is first algorithmic brokerage 1997-2000 day trading: everyone became a model driven trader • Bertsimas & Lo 1998 • Further gaming countermeasures

  6. A brief history2000s Techniques Significance Credit Suisse launches AES in 2000 DMA and algorithmic execution account for 50% of NYSE turnover Some broker-dealers have 80% of order flow executed by algorithmic strategies • Landmark research • Almgren & Chriss 2000 • Almgren 2001 • Kissell, Glantz & Malamut 2003 • Kissell & Malamut 2005 • Arrival price strategies • Portfolio strategies

  7. Algorithmic execution strategiesA broad classification Completing Non-completing Participation Relative value • Quantities • VWAP • TWAP • Participation rates • Arrival price or shortfall

  8. Where we are now“Algorithms are only good for liquid stocks” Broker-dealers Investors Broker-dealers algorithms used for 15-30% of orders Increasing addition of constraints to orders Consequence: completing strategies become non-completing • Widespread use of algorithmic execution, either directly or when unwinding • Brand explosion of strategies

  9. Execution schedulesResearch has provided the benchmarks

  10. Execution models Strengths Weaknesses Assume perfect markets Do not address tactics or order placement decisions • Mathematically sound approaches to optimising variance • Common ground for investors and broker-dealers

  11. Other academic contributions • Econophysics • Models for non-gaussian returns • Minority games • Behavioural finance

  12. Challenge #1 volume

  13. The extremes for algorithmic executionVolume and trade frequency within the FT-SE 100

  14. The well behaved …High trading frequency – low volume variance

  15. … and the not so well behavedLow trading frequency – high volume variance

  16. Even the more liquid …BP.L until mid November 2006

  17. … can surpriseBP.L traded twice its mean daily volume on November 17

  18. CopingTowards a common understanding of behaviour • Median curves? • Volatile daily volume has no effect for completing strategies constrained by time • Prediction of relative weighting?

  19. Challenge #2 Execution Micro-economics

  20. Order submissionThe fundamental state model is simple LIM WAIT FAK

  21. Order submissionSome of the microeconomics

  22. summary

  23. Algorithmic execution • One of the major execution methods • Contribution from research is clear • More is needed to keep the mutual expectations of buy- and sell-side in line • Two challenges today • Turning attention on the volume dimension • Formalising the different levels of execution model

  24. Algorithmic execution Data provided by Analysis tools Further information http://www.gbkr.com

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