1. Challenges for algorithmic execution Gordon Baker 2007-06-26 CARISMA conference1

2. Agenda Context Research To do 2007-06-26 CARISMA conference2

3. 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 – Model based trading 2007-06-26 CARISMA conference3

4. A brief history 1980s Technique VWAP, TWAP and participation strategies Empirical approaches to market impact Significance Market structure (electronic order books) determines spread of automated strategies 2007-06-26 CARISMA conference4

5. A brief history 1990s Technique Bertsimas & Lo 1998 Further gaming countermeasures Significance ITG is first algorithmic brokerage 1997-2000 day trading: everyone became a model driven trader 2007-06-26 CARISMA conference5

6. A brief history 2000s Techniques Landmark research – Almgren & Chriss 2000 – Almgren 2001 – Kissell, Glantz & Malamut 2003 – Kissell & Malamut 2005 Arrival price strategies Portfolio strategies 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 2007-06-26 CARISMA conference6

7. Algorithmic execution strategies A broad classification Completing Quantities – VWAP – TWAP Participation rates – Arrival price or shortfall Non-completing Participation Relative value 2007-06-26 CARISMA conference7

8. Where we are now “Algorithms are only good for liquid stocks” Broker-dealers Widespread use of algorithmic execution, either directly or when unwinding Brand explosion of strategies Investors Broker-dealers algorithms used for 15-30% of orders Increasing addition of constraints to orders Consequence: completing strategies become non- completing 2007-06-26 CARISMA conference8

9. Execution schedules Research has provided the benchmarks 2007-06-26 CARISMA conference9

10. Execution models Strengths Mathematically sound approaches to optimising variance Common ground for investors and broker- dealers Weaknesses Assume perfect markets Do not address tactics or order placement decisions 2007-06-26 CARISMA conference10

11. Other academic contributions Econophysics – Models for non-gaussian returns – Minority games Behavioural finance 2007-06-26 CARISMA conference11

12. VOLUME Challenge #1 2007-06-26 CARISMA conference12

13. The extremes for algorithmic execution Volume and trade frequency within the FT-SE 100 2007-06-26 CARISMA conference13

14. The well behaved … High trading frequency – low volume variance 2007-06-26 CARISMA conference14

15. … and the not so well behaved Low trading frequency – high volume variance 2007-06-26 CARISMA conference15

16. Even the more liquid … BP.L until mid November 2006 2007-06-26 CARISMA conference16

17. … can surprise BP.L traded twice its mean daily volume on November 17 2007-06-26 CARISMA conference17

18. Coping Towards a common understanding of behaviour Median curves? Volatile daily volume has no effect for completing strategies constrained by time Prediction of relative weighting? 2007-06-26 CARISMA conference18

19. EXECUTION MICRO-ECONOMICS Challenge #2 2007-06-26 CARISMA conference19

20. Order submission The fundamental state model is simple 2007-06-26 CARISMA conference20 LIM WAIT FAK

21. Order submission Some of the microeconomics 2007-06-26 CARISMA conference21

22. SUMMARY 2007-06-26 CARISMA conference22

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 2007-06-26 CARISMA conference23

24. Algorithmic execution Data provided by Analysis tools Further information 2007-06-26 CARISMA conference24 http://www.gbkr.com