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.
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References
Allen, F. and R. Karjalainen (1999). “Using Genetic Algorithms to Find Technical Trading Rules,” Journal of Financial Economics, 51(2), 245–271.
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.
Backus, J. W. (1959). “The Syntax and Semantics of the Proposed International Algebraic Language of Zurich,” ACM-GAMM Conference, ICIP.
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.
Bauer, R. J. Jr. (1994). Genetic Algorithms and Investment Strategies, New York: John Wiley & Sons, Inc.
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.
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.
Cornell, B. and K. French (1988). “Taxes and the Pricing of Stock Index Futures,” Journal of Finance, 38, 675–694.
Evnine, J. and A. Rudd (1985). “Index Options: The Early Evidence.” Journal of Finance, 40, 743–756
Fama, E. F. and M. E. Blume (1966). “Filter Rules and Stock-Market Trading,” Journal of Business, 39(1), 226–241.
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.
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.
Gemmill, G. (1993). Options Pricing, Maidenhead, UK: Mc Graw-Hill.
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.
Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley.
Holland, J. H. (1975). Adaptation in Natural and Artificial System. University of Michigan Press.
Koza, J. R. (1992). Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press.
Koza, J. R. (1994). Genetic Programming II: Automatic Discovery of Reusable Programs. MIT Press.
Koza, J., D. Goldberg, D. Fogel, and R. Riolo (1996). Proceedings of the First Annual Conference on Genetic programming. MIT Press.
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.
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.
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.
Mahfoud, S. and Mani, G. (1997). “Financial Forecasting Using Genetic Algorithms,” Journal of Applied Artificial Intelligence, 10(6), 543–565.
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.
Mitchell, M. (1996). An Introduction to Genetic Algorithms. MIT Press.
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.
Stall, H. R. (1969). “The Relationship between Put and Call Option Prices,” Journal of Finance, 25, 801–824.
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.
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.
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.
Sweeney, R. J. (1988). “Some New Filter Rule Tests: Methods and Results,” Journal of Financial and Quantitative Analysis, 23, 285–300.
Tucker, A. L. (1991). Financial Futures, Options and Swaps, St. Paul, MN: West Publishing Company.
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.
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/>.
Yadav, P. K. and P. Pope (1990). “Stock Index Futures Arbitrage: International Evidence,” Journal of Futures Markets, 10, 573–603.
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Markose, S., Tsang, E., Er, H. (2002). Evolutionary Decision Trees for Stock Index Options and Futures Arbitrage. In: Chen, SH. (eds) Genetic Algorithms and Genetic Programming in Computational Finance. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-0835-9_14
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DOI: https://doi.org/10.1007/978-1-4615-0835-9_14
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