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Evolutionary Decision Trees for Stock Index Options and Futures Arbitrage

How Not to Miss Opportunities
Chapter

Abstract

EDDIE-ARB (EDDIE stands for Evolutionary Dynamic Data Investment Evaluator) is a genetic program (GP) that implements a cross market arbitrage strategy in a manner that is suitable for online trading. Our benchmark for EDDIE-ARB is the Tucker (1991) put-call-futures (P-C-F) parity condition for detecting arbitrage profits in the index options and futures markets. The latter presents two main problems, (i) The windows for profitable arbitrage opportunities exist for short periods of one to ten minutes, (ii) Prom a large domain of search, annually, fewer than 3% of these were found to be in the lucrative range of £500-£800 profits per arbitrage. Standard ex ante analysis of arbitrage suffers from the drawback that the trader awaits a contemporaneous signal for a profitable price misalignment to implement an arbitrage in the same direction. Execution delays imply that this naive strategy may fail. A methodology of random sampling is used to train EDDIE-ARB to pick up the fundamental arbitrage patterns. The further novel aspect of EDDIE-ARB is a constraint satisfaction feature supplementing the fitness function that enables the user to train the GP how not to miss opportunities by learning to satisfy a minimum and maximum set on the number of arbitrage opportunities being sought. Good GP rules generated by EDDIE-ARB are found to make a 3-fold improvement in profitability over the naive ex ante rule.

Keywords

Genetic Programming Machine Learning Genetic Decision Trees Arbitrage Options Futures Constraint Satisfaction 

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References

  1. Allen, F. and R. Karjalainen (1999). “Using Genetic Algorithms to Find Technical Trading Rules,” Journal of Financial Economics, 51(2), 245–271.CrossRefGoogle Scholar
  2. Alexander, S. S. (1964). “Price Movement in Speculative Markets: Trend or Random Walks,” in Cootner, P. (ed.), The Random Character of Stock Market Prices, No. 2, 338-372. Cambridge, MA: MIT Press.Google Scholar
  3. Backus, J. W. (1959). “The Syntax and Semantics of the Proposed International Algebraic Language of Zurich,” ACM-GAMM Conference, ICIP.Google Scholar
  4. Bae, K. H., Chan, K., and Cheung, Y. L. (1998). “The Profitability of Index Futures Arbitrage: Evidence from Bid-Ask Quotes,” Journal of Futures Markets, 18, 743–763.CrossRefGoogle Scholar
  5. Bauer, R. J. Jr. (1994). Genetic Algorithms and Investment Strategies, New York: John Wiley & Sons, Inc.Google Scholar
  6. Brock, W., J. Lakonishok, and B. LeBaron (1992). “Simple Technical Trading Rules and the Stochastic Properties of Stock Returns,” Journal of Finance, 47, 1731–1764.CrossRefGoogle Scholar
  7. Chen, S.-H. and C.-H. Yeh (1997). “Toward a Computable Approach to the Efficient Market Hypothesis: An Application of genetic programming,” Journal of Economic Dynamics and Control, 21, 1043–1063.CrossRefGoogle Scholar
  8. Cornell, B. and K. French (1988). “Taxes and the Pricing of Stock Index Futures,” Journal of Finance, 38, 675–694.CrossRefGoogle Scholar
  9. Evnine, J. and A. Rudd (1985). “Index Options: The Early Evidence.” Journal of Finance, 40, 743–756CrossRefGoogle Scholar
  10. Fama, E. F. and M. E. Blume (1966). “Filter Rules and Stock-Market Trading,” Journal of Business, 39(1), 226–241.CrossRefGoogle Scholar
  11. Fung, J., W. Chan, and C. Kam (1994). “On the Arbitrage Free Relationship Between Index Futures and Index Options: A Note,” Journal of Futures Markets, 14, 957–962.CrossRefGoogle Scholar
  12. Fung, J., L. Cheng, T. W. Chan, and C. Kam (1997). “The Intraday Pricing Efficiency of Hong Kong Hang Seng Index Option and Futures Markets,” Journal of Futures Markets, 17, 797–815.CrossRefGoogle Scholar
  13. Gemmill, G. (1993). Options Pricing, Maidenhead, UK: Mc Graw-Hill.Google Scholar
  14. Gwilm, O. P. and Buckle M. (1999). “Volatility Forecasting in the Framework of the Option Expiry Cycle,” The European Journal of Finance, 5, 73–94.CrossRefGoogle Scholar
  15. Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley.Google Scholar
  16. Holland, J. H. (1975). Adaptation in Natural and Artificial System. University of Michigan Press.Google Scholar
  17. Koza, J. R. (1992). Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press.Google Scholar
  18. Koza, J. R. (1994). Genetic Programming II: Automatic Discovery of Reusable Programs. MIT Press.Google Scholar
  19. Koza, J., D. Goldberg, D. Fogel, and R. Riolo (1996). Proceedings of the First Annual Conference on Genetic programming. MIT Press.Google Scholar
  20. Lee, J. H., and N. Nayar (1993). “A Transactions Data Analysis of Arbitrage between Index Options and Index Futures,” Journal of Futures Markets, 13, 889–902.CrossRefGoogle Scholar
  21. Li, J. and E. P. K. Tsang (1999a). “Improving Technical Analysis Predictions: An Application of Genetic Programming,” Proceedings of The 12th International Florida AI Research Society Conference, 108–112.Google Scholar
  22. Li, J. and E. P. K. Tsang (1999b). “Investment Decision Making Using FGP: A Case Study,” Proceedings of Congress on Evolutionary Computation (CEC′99), 1253–1259.Google Scholar
  23. Mahfoud, S. and Mani, G. (1997). “Financial Forecasting Using Genetic Algorithms,” Journal of Applied Artificial Intelligence, 10(6), 543–565.CrossRefGoogle Scholar
  24. Markose, S. and H. Er (2000). “The Black (1976) Effect and Cross Market Arbitrage in FTSE-100 Index Futures and Options,” Working Paper, No. 522, Economics Department, University of Essex.Google Scholar
  25. Mitchell, M. (1996). An Introduction to Genetic Algorithms. MIT Press.Google Scholar
  26. Modest, D. and M. Sunderesan (1983). “The Relationship between Spot and Futures Prices in Stock Index Futures Markets: Some Preliminary Evidence,” Journal of Futures Markets, 3, 15–41.CrossRefGoogle Scholar
  27. Stall, H. R. (1969). “The Relationship between Put and Call Option Prices,” Journal of Finance, 25, 801–824.CrossRefGoogle Scholar
  28. Neely, C., P. Weiler, and R. Ditmar (1997). “Is Technical Analysis in the Foreign Exchange Market Profitable? A Genetic Programming Approach,” Journal of Financial and Quantitative Analysis, 32, 405–426.CrossRefGoogle Scholar
  29. Oussaidene, M., B. Chopard, O. Pictet, and M. Tomassini (1997). “Practical Aspects and Experiences — Parallel Genetic Programming and Its Application to Trading Model Induction,” Journal of Parallel Computing, 23(8), 1183–1198.CrossRefGoogle Scholar
  30. Saad, E., D. Prokhorov, and D. Wunsch (1998). “Comparative Study of Stock Trend Prediction Using Time Delay, Recurrent and Probabilistic Neural Networks,” IEEE Transactions on Neural Networks, 9, 1456–1470.CrossRefGoogle Scholar
  31. Sweeney, R. J. (1988). “Some New Filter Rule Tests: Methods and Results,” Journal of Financial and Quantitative Analysis, 23, 285–300.CrossRefGoogle Scholar
  32. Tucker, A. L. (1991). Financial Futures, Options and Swaps, St. Paul, MN: West Publishing Company.Google Scholar
  33. Tsang, E. P. K., J. Li, and J. M. Butler (1998). “EDDIE Beats the Bookies,” International Journal of Software, Practice and Experience, 28(10), 1033–1043.CrossRefGoogle Scholar
  34. Tsang, E. P. K., J. Li, S. Markose, E. Hakan, A. Salhi, and G. Iori (2000). “EDDIE in Financial Decision Making,” Journal of Management Economics, 4(4). <http://www.econ.uba.ar/www/servicos/publicaciones/journal3/>.
  35. Yadav, P. K. and P. Pope (1990). “Stock Index Futures Arbitrage: International Evidence,” Journal of Futures Markets, 10, 573–603.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2002

Authors and Affiliations

  1. 1.Economics Department and Institute of Studies in Finance University of EssexUK
  2. 2.Computer Science Department University of EssexUK
  3. 3.Economics Department University of EssexUK

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