Abstract
This paper employs genetic programming to develop trading rules, then uses these rules to test the efficient markets hypothesis. Unlike most similar research, the study both incorporates margin trading and returns trading rules that are more than simple buy-sell signals. Consistent with the standard portfolio model, a trading rule is defined here as the proportion of an investor’s total wealth that is held in the form of stocks; because margin trading is allowed, the proportion can be greater than 1. The results show that the 24 individual stock markets studied were adaptively efficient between 1985 and 2005.
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References
Allen, F., & Karjalainen, R. (1999). Using genetic algorithms to find technical trading rules. Journal of Financial Economics, 51, 245–271.
Álvarez-Díaz, M., & Álvarez, A. (2005). Genetic multi-model composite forecast for non-linear prediction of exchange rates. Empirical Economics, 30, 643–663.
Ayres, I., & Nalebuff, B. (2008). Buying stock on margin can reduce retirement risk. Working paper.
Bessembinder, H., & Chan, K. (1998). Market efficiency and the returns to technical analysis. Financial Management, 27, 5–17.
Chen, S. H., & Yeh, C. H. (1997). Toward a computable approach to the efficient market hypothesis: an application of genetic programming. Journal of Economic Dynamics and Control, 21, 1043–1063.
Chen, S. H., & Yeh, C. H. (2002). On the emergent properties of artificial stock markets: the efficient market hypothesis and the rational expectations hypothesis. Journal of Economic Behavior and Organization, 49, 217–239.
Dacorogna, M. M., Gençay, R., Müller, U. A., & Pictet, O. V. (2001). Effective return, risk aversion and drawdowns. Physica A, 289, 229–248.
Daniel, K., & Titman, S. (1999). Market efficiency in an irrational world. Financial Analysts Journal, 55, 28–40.
Dempster, M. A. H., & Jones, C. (2001). A real-time adaptive trading system using genetic programming. Quantitative Finance, 1, 397–413.
Fortune, P. (2000). Margin requirements, margin loans, and margin rates: practice and principles. New England Economic Review, 19–44.
Fyfe, C., Marney, J. P., & Tarbert, H. F. E. (1999). Technical analysis versus market efficiency: a genetic programming approach. Applied Financial Economics, 9, 183–191.
Fyfe, C., Marney, J. P., & Tarbert, H. F. E. (2005). Risk adjusted returns from technical trading: a genetic programming approach. Applied Financial Economics, 15, 1073–1077.
Gençay, R. (1998). The predictability of security returns with simple technical trading rules. Applied Financial Economics, 5, 347–359.
Gençay, R. (1999). Linear, non-linear and essential foreign exchange rate prediction with simple technical trading rules. Journal of Empirical Finance, 47, 91–107.
Gollier, C. (1997). On the inefficiency of bang–bang and stop-loss portfolio strategies. Journal of Risk and Uncertainty, 14, 143–154.
Kaboudan, M. A. (2000). Genetic programming prediction of stock market prices. Computational Economics, 16, 207–236.
Koza, J. R. (1992). Genetic programming: on the programming of computers by means of natural selection. Cambridge: MIT Press.
Kubler, F., & Willen, P. (2006). Collateralized borrowing and life-cycle portfolio choice. NBER working paper.
Lensberg, T. (1999). Investment behavior under Knightian uncertainty: an evolutionary approach. Journal of Economic Dynamics and Control, 23, 1587–1604.
Lensberg, T., Eilifsen, A., & McKee, T. E. (2006). Bankruptcy theory development and classification via genetic programming. European Journal of Operational Research, 169, 677–697.
Lunde, A., & Timmermann, A. (2004). Duration dependence in stock prices: an analysis of bull and bear markets. Journal of Business and Economic Statistics, 22, 253–273.
McKee, T. E., & Lensberg, T. (2002). Genetic programming and rough sets: a hybrid approach to bankruptcy classification. European Journal of Operational Research, 138, 436–451.
Neely, C. J. (2003). Risk-adjusted, ex ante, optimal technical trading rules in equity markets. International Review of Economics and Finance, 12, 69–87.
Neely, C. J., & Weller, P. A. (1999). Technical trading rules in the European monetary system. International Review of Economics and Finance, 18, 429–458.
Neely, C. J., & Weller, P. A. (2003). Intraday technical trading in the foreign exchange market. Journal of International Money and Finance, 22, 223–237.
Neely, C. J., Weller, P., & Ditmar, R. (1997). Is technical analysis in the foreign exchange market profitable? A genetic programming approach. Journal of Financial and Quantitative Analysis, 32, 405–426.
Potvin, J. Y., Soriano, P., & Vallee, M. (2004). Generating trading rules on the stock markets with genetic programming. Computers and Operations Research, 31, 1033–1047.
Ready, M. J. (2002). Profits from technical trading rules. Financial Management, 31, 43–62.
Samuelson, P. A. (1997). Proof by certainty equivalents that diversification-across-time does worse risk corrected, than diversification-throughout-time. Journal of Risk and Uncertainty, 14, 129–142.
Sharpe, W. F. (1966). Mutual fund performance. Journal of Business, 39, 119–138.
Skouras, S. (2001). Financial returns and efficiency as seen by an artificial technical analyst. Journal of Economic Dynamics and Control, 25, 213–244.
Sullivan, R., Timmermann, A., & White, H. (1999). Data snooping, technical trading rule performance, and the bootstrap. The Journal of Finance, 54, 1647–1691.
Sullivan, R., Timmermann, A., & White, H. (2001). Dangers of data-mining: the case of calendar effects in stock returns. The Journal of Finance, 105, 249–286.
Sullivan, R., Timmermann, A., & White, H. (2003). Forecast evaluation with shared data sets. International Journal of Forecasting, 19, 217–227.
Sweeney, R. J. (1988). Some new filter rule tests: methods and results. Journal of Financial and Quantitative Analysis, 23, 285–300.
Sweeney, R. J., & Lee, E. J. Q. (1990). International dimensions of securities and currency markets. In R. Aggarwal & C. F. Lee (Eds.), Advances in financial planning and forecasting series (Vol. 4, pp. 55–79). Greenwich: JAI Press.
Wang, J. (2000). Trading and hedging in S&P 500 spot and futures markets using genetic programming. The Journal of Futures Markets, 20, 911–942.
Acknowledgements
We gratefully acknowledge financial support from the Canadian Social Sciences and Humanities Research Council. We thank Ian Davis for assistance with the computer coding. We thank, for their comments, seminar participants in the 14th International Conference on Computing in Economics and Finance, the Canadian Economics Association 2008 Annual Meeting, and the Fifth World Congress of the Bachelier Finance Society.
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Miles, S., Smith, B. (2011). Can Investors Benefit from Using Trading Rules Evolved by Genetic Programming? A Test of the Adaptive Efficiency of U.S. Stock Markets with Margin Trading Allowed. In: Dawid, H., Semmler, W. (eds) Computational Methods in Economic Dynamics. Dynamic Modeling and Econometrics in Economics and Finance, vol 13. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16943-4_5
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DOI: https://doi.org/10.1007/978-3-642-16943-4_5
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