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Genetic Programming Prediction of Stock Prices

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Abstract

Based on predictions of stock-pricesusing genetic programming (or GP), a possiblyprofitable trading strategy is proposed. A metricquantifying the probability that a specific timeseries is GP-predictable is presented first. It isused to show that stock prices are predictable. GPthen evolves regression models that produce reasonableone-day-ahead forecasts only. This limited ability ledto the development of a single day-trading strategy(SDTS) in which trading decisions are based onGP-forecasts of daily highest and lowest stock prices.SDTS executed for fifty consecutive trading days ofsix stocks yielded relatively high returns oninvestment.

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References

  • Andrew, M. and Prager, R. (1994). Genetic programming for the acquisition of double auction market strategies. In K. Kinnear, Jr. (ed.), Advances in Genetic Programming, 355-368. The MIT Press, Cambridge, MA.

    Google Scholar 

  • Bhattacharyya, S., Pietet, O. and Zumbach, G. (1998). Representational semantics for genetic programming based learning in high-frequency financial data. In J. Koza, W. Banzhaf, K. Chellapilla, K. Deb, M. Dorigo, D. Fogel, M. Garzon, D. Goldberg, H. Iba and R. Riolo (eds.), Genetic Programming 1998: Proceedings of the Third Annual Conference, 11-16. Morgan Kaufmann, San Francisco, CA.

    Google Scholar 

  • Brock, W., Lakonishok, J. and LeBaron, B. (1992). Simple technical trading rules and the stochastic properties of stock returns. Journal of Finance, 47, 1731-1764.

    Google Scholar 

  • Chen, S.-H. (1996). Genetic programming, predictability, and stock market efficiency. In L. Vlacic, T. Nguyen and D. Cecez-Kecmanovic (eds.), Modelling and Control of National and Regional Economies, 283-288. Pergamon Press, Oxford, Great Britain.

    Google Scholar 

  • Chen, S.-H. (1998). Hedging derivative securities with genetic programming. In Application of Machine Learning and Data Mining in Finance: Workshop at ECML-98. ECML-98 Workshop 6, Dorint-Parkhotel, Chemnitz, Germany, 24 April 1998.

    Google Scholar 

  • Chen, S.-H. and Ni, C.-C. (1997). Evolutionary artificial neural networks and genetic programming: a comparative study based on financial data. In G.D. Smith (ed.), Artificial Neural Networks and Genetic Algorithms, forthcoming. Springer-Verlag, Vienna.

    Google Scholar 

  • Chen, S.-H. and Yeh, C.-H. (1995). Predicting stock returns with genetic programming: do the short-term nonlinear regularities exist? In D. Fisher (ed.), Proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, 95-101. Ft. Lauderdale, Florida.

    Google Scholar 

  • Chen, S.-H. and Yeh, C.-H. (1996). Genetic programming and the efficient market hypothesis. In John Koza, David Goldberg, David Fogel and Rick Riolo (eds.), Genetic Programming 1996: Proceedings of the First Annual Conference, 45-53. The MIT Press, Cambridge, MA.

    Google Scholar 

  • Chen, S.-H. and Yeh, C.-H. (1997a). Using genetic programming to model volatility in financial time series. In J. Koza, K. Deb, M. Dorigo, D. Fogel, M. Garzon, H. Iba and R. Riolo (eds.), Genetic Programming 1997: Proceedings of the Second Annual Conference, 58-63. Morgan Kaufmann, Stanford University, CA.

    Google Scholar 

  • Chen, S.-H. and Yeh, C.-H. (1997b). Toward a computable approach to the efficient market hypothesis: an application of genetic programming, Journal of Economic Dynamics and Control, 21, 1043-1063.

    Google Scholar 

  • Chen, S., Yeh, C. and Lee, W. (1998). Option pricing with genetic programming. In J. Koza, W. Banzhaf, K. Chellapilla, K. Deb, M. Dorigo, D. Fogel, M. Garzon, D. Goldberg, H. Iba and R. Riolo (eds.), Genetic Programming 1998: Proceedings of the Third Annual Conference, 32-37. Morgan Kaufmann, San Francisco, CA.

    Google Scholar 

  • Chidambaran, N., Lee, C. and Trigueros, J. (1998). An adaptive evolutionary approach to option pricing via genetic programming. In J. Koza, W. Banzhaf, K. Chellapilla, K. Deb, M. Dorigo, D. Fogel, M. Garzon, D. Goldberg, H. Iba and R. Riolo (eds.), Genetic Programming 1998: Proceedings of the Third Annual Conference, 187-192. Morgan Kaufmann, San Francisco, CA.

    Google Scholar 

  • Efron, B. (1982). The Jackknife, the Bootstrap, and Other Resampling Plans. Society for Industrial and Applied Mathematics, Philadelphia.

    Google Scholar 

  • Eglit, J.T. (1994). Trend prediction in financial time series. In J. Koza (ed.), Genetic Algorithms at Stanford. Stanford Bookstore, Stanford, CA, pp. 31-40.

    Google Scholar 

  • Fama, E. and French, K.R. (1988). Dividend yields and expected stock returns. Journal of Financial Economics, 22, 3-25.

    Google Scholar 

  • Fogel, D. and Fogel, L. (1996). Preliminary experiments on discriminating between chaotic signals and noise using evolutionary programming. In John Koza, David Goldberg, David Fogel and Rick Riolo (eds.), Genetic Programming 1996: Proceedings of the First Annual Conference, 512-520. The MIT Press, Cambridge, MA.

    Google Scholar 

  • Granger, C. and Andersen, A. (1978). An Introduction to Bilinear Time Series Models. Vandenhoek and Ruprecht, Gottingen.

    Google Scholar 

  • Greene, W. (1997). Econometric Analysis. Prentice Hall, Upper Saddle River, NJ, 3rd Edition.

    Google Scholar 

  • Henon, M. (1976). A two-dimensional mapping with a strange attractor. Comm. Math. Phys., 50, 69.

    Google Scholar 

  • Hsieh, D. (1989). Testing for nonlinear dependence in daily foreign exchange rates. Journal of Business, 62, 339-368.

    Google Scholar 

  • Hsieh, D. (1991). Chaos and nonlinear dynamics: application to financial markets. The Journal of Finance, XLVI, 1839-1877.

    Google Scholar 

  • Ibbotson, R. and Sinquefield, R. (1998). Stocks, Bonds, Bills and Inflation: 1998 Yearbook.TM Ibbotson Associates, Chicago.

    Google Scholar 

  • Jensen, M. (1978). Some anomalous evidence regarding market efficiency, Journal of Financial Economics, 6, 95-101.

    Google Scholar 

  • Jegadeesh, N. (1990). Evidence of predictable behavior in security returns. The Journal of Finance, XLV, 881-898.

    Google Scholar 

  • Kaboudan, M. (1998a). Forecasting stock returns using genetic programming in C++. In D. Cook (ed.), FLAIRS Proceedings of the Eleventh International Florida Artificial Intelligence Research Symposium Conference, 73-77. AAAI Press, Menlo Park, CA.

    Google Scholar 

  • Kaboudan, M. (1998b). Statistical properties of time-series-complexity measure applied to stock returns, Computational Economics, 11, 167-187.

    Google Scholar 

  • Kaboudan, M. (1998c). A GP approach to distinguish chaotic from noisy signals. In J. Koza, W. Banzhaf, K. Chellapilla, K. Deb, M. Dorigo, D. Fogel, M. Garzon, D. Goldberg, H. Iba and R. Riolo (eds.), Genetic Programming 1998: Proceedings of the Third Annual Conference, 187-192. Morgan Kaufmann, San Francisco, CA.

    Google Scholar 

  • Kendall, M.G. (1953). The analysis of economic time-series, Part I. Prices. Journal of the Royal Statistical Society, 96, 11-25.

    Google Scholar 

  • Koza, J. (1992). Genetic Programming. The MIT Press, Cambridge, MA.

    Google Scholar 

  • Lehman, B. (1990). Fads, martingales, and market efficiency. Quarterly Journal of Economics, CV, 1-28.

    Google Scholar 

  • Lo, A. and MacKinlay, C. (1988). Stock prices do not follow random walks: evidence from a simple specification test. Review of Financial Studies, 1, 41-66.

    Google Scholar 

  • Lo, A. and MacKinlay, C. (1999). A Non-Random Walk Down Wall Street. Princeton University Press, Princeton, NJ.

    Google Scholar 

  • Mackey, M. and Glass, L. (1977). Oscillation and chaos in physiological control systems. Science, 197, 287.

    Google Scholar 

  • Mason, R., Lind, D. and Marchal, W. (1999). Statistical Techniques in Business and Economics. Irwin-McGraw-Hill, Boston, MA, 10th Edition.

    Google Scholar 

  • May, R. (1976). Simple mathematical models with very complicated dynamics, Nature 261, 459-467.

    Google Scholar 

  • McDonnell, J. and Waagen, D. (1994). Evolving recurrent perceptrons for time-series modeling. IEEE Transactions on Neural Networks, 5, 24-38.

    Google Scholar 

  • Mills, T. (1993). The Econometric Modelling of Financial Time Series. Cambridge University Press, Cambridge.

    Google Scholar 

  • Mulloy, B., Riolo, R. and Savit, R. (1996). Dynamics of genetic programming and chaotic time series prediction. In John Koza, David Goldberg, David Fogel and Rick Riolo (eds.), Genetic Programming 1996: Proceedings of the First Annual Conference, 166-174. The MIT Press, Cambridge, MA.

    Google Scholar 

  • Neely, C., Weller, P. and Dittmar, R. (1996). Is technical analysis in the foreign exchange market profitable? A genetic programming approach. Research Division Working Papers, The Federal Reserve Bank of St. Louis, 96-006B.

  • Numata, M., Sugawara, K., Yoshihara, I. and Abe, K. (1998). Time series prediction by genetic programming. In Genetic Programming 1998: Late-Breaking Papers, 176-179.

  • Oakley H. (1994). Two scientific applications of genetic programming: stack filters and non-linear equation fitting to chaotic data, In K.E. Kinnear Jr. (ed.), Advances in Genetic Programming, 369-389. MIT Press.

  • Oakley, H. (1995). Genetic programming as a means of assessing and reflecting chaos. In Genetic Programming: AAAI-95 Fall Symposium Series, 68-72. AAAI Press.

  • Oakley, H. (1996). Genetic programming, the reflection of chaos, and the bootstrap: toward a useful test for chaos. In John Koza, David Goldberg, David Fogel and Rick Riolo (eds.), Genetic Programming 1996: Proceedings of the First Annual Conference, 175-181. The MIT Press, Cambridge, MA.

    Google Scholar 

  • Oussaidene, M., Chopard, B., Pictet, O. and Tomassini, M. (1996). Parallel genetic programming: an application to trading models evolution. In John Koza, David Goldberg, David Fogel and Rick Riolo (eds.), Genetic Programming 1996: Proceedings of the First Annual Conference, 357-362. The MIT Press, Cambridge, MA.

    Google Scholar 

  • Ozaki, T. (1985). Non-linear time series models and dynamical systems. In E. Hannan, P. Krishnaiah and M. Rao (eds.), Handbook of Statistics, 5, North-Holland, Amsterdam.

    Google Scholar 

  • Pindyck, R. and Rubinfeld, D. (1998). Econometric Models and Economic Forecasts. Irwin-McGraw-Hill, Boston, MA, 4th Edition.

    Google Scholar 

  • Richardson, M. and Smith, T. (1993). A test of multivariate normality of stock returns. Journal of Business, 66, 295-321.

    Google Scholar 

  • Sathyanarayan, S. and Chellapilla, K. (1996). Evolving reduced parameter bilinear models for time series prediction using fast evolutionary programming. In John Koza, David Goldberg, David Fogel and Rick Riolo (eds.), Genetic Programming 1996: Proceedings of the First Annual Conference, 528-535. The MIT Press, Cambridge, MA.

    Google Scholar 

  • Scheinkman, J. and LeBaron, B. (1989). Nonlinear dynamics and stock returns. Journal of Finance, 62, 311-337.

    Google Scholar 

  • Singleton, A. (1995). Genetic programming with C++, public domain genetic programming package. Creation Mechanics, Dublin, NH. http://www.cen.uiuc.edu/~klipp/research/ssprob.cxx.

    Google Scholar 

  • Tong, H. (1990). Non-linear Time Series: A Dynamical System Approach. Oxford University Press, Oxford.

    Google Scholar 

  • Warren, M.A. (1994). Stock price prediction using genetic programming. In J. Koza (ed.), Genetic Algorithms at Stanford 1994, 180-184. Stanford Bookstore, Stanford, CA.

    Google Scholar 

  • Wolfe, A. and Vastomous, J. (1986). Intermediate length scale effects in Lyapunov exponent estimation. In G. Mayer-Kress (ed.), Dimensions and Entropies in Chaotic System. Springer-Verlag, Berlin, Germany.

    Google Scholar 

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Kaboudan, M.A. Genetic Programming Prediction of Stock Prices. Computational Economics 16, 207–236 (2000). https://doi.org/10.1023/A:1008768404046

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