Prediction of the Bombay Stock Exchange (BSE) Market Returns Using Artificial Neural Network and Genetic Algorithm


Stock Market is the market for security where organized issuance and trading of Stocks take place either through exchange or over the counter in electronic or physical form. It plays an important role in canalizing capital from the investors to the business houses, which consequently leads to the availability of funds for business expansion. In this paper, we investigate to predict the daily excess returns of Bombay Stock Exchange (BSE) indices over the respective Treasury bill rate returns. Initially, we prove that the excess return time series do not fluctuate randomly. We are applying the prediction models of Autoregressive feed forward Artificial Neural Networks (ANN) to predict the excess return time series using lagged value. For the Artificial Neural Networks model using a Genetic Algorithm is constructed to choose the optimal topology. This paper examines the feasibility of the prediction task and provides evidence that the markets are not fluctuating randomly and finally, to apply the most suitable prediction model and measure their efficiency.

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Y. Perwej and A. Perwej, "Prediction of the Bombay Stock Exchange (BSE) Market Returns Using Artificial Neural Network and Genetic Algorithm," Journal of Intelligent Learning Systems and Applications, Vol. 4 No. 2, 2012, pp. 108-119. doi: 10.4236/jilsa.2012.42010.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] O. B. Antwerpen, “6de Eeuwse Traditionele Baken Zandsteenarchitectuur,” 2010.
[2] B. G. Malkei, “A Random Walk Down Wall Street,” 7th Edition, W. W. Norton & Company, New York, 1999.
[3] R. Gupta., “Emerging Markets Diversification: Are Correlations Changing over Time?” International Academy of Business and Public Administration Disciplines (IABPAD) Conference, Orlando, 3-6 January 2006, pp. 331-351.
[4] T. Hellstrom and K. Holmstrom, “Predictable Pattern in the Stock Return,” 1998.
[5] P. Dennis, “Stock Splits and Liquidity: The Case of the Nasdaq-100 Index Tracking Stock,” Financial Review, Vol. 38, No. 3, 2003, pp. 415-433.
[6] G. S. Maddala, “Introduction to Econometrics,” 1st Edition, Macmillan Publishing Company, New York, 1992.
[7] H. M. Pesaran and A. Timmermann, “Forecasting Stock Returns: An Examination of Stock Market Trading in the Presence of Transaction Costs,” Journal of Forecasting, Vol. 13, No. 4, 1994, pp. 335-367. doi:10.1002/for.3980130402
[8] R. S. Michalski and G. Tecuci, “Machine Learning: A Multistrategy Approach,” Morgan Kaufmann, Waitham, 1994.
[9] E. Alpayd?n, “Introduction to Machine Learning (Adaptive Computation and Machine Learning),” MIT Press, Cambridge, 2004.
[10] Reserve Bank of India, “Handbook of Statistics on Indian Economy,” 2001.
[11] M. T. Mitchell, “Machine Learning,” 1st Edition, The McGraw-Hill Companies, New York, 1997.
[12] M. C. Bishop, “Neural Networks for Pattern Recognition,” Oxford University Press, New York, 1996.
[13] E. D. Goldberg, “Genetic Algorithm in Search, Optimization, and Machine Learning,” Addison-Wesley, New York, 1989.
[14] M. T. Mitchell, “An Introduction to Genetic Algorithms,” MIT Press, Cambridge, 1997.
[15] R. J. Koza, “Genetic Programming on the Programming of Computers by Means of Natural Selection,” MIT Press, Cambridge, 1992.
[16] P. Healy, J. Ledyard, S. Linardi and R. J. Lowery, “Prediction Markets: Alternative Mechanisms for Complex Environments with Few Traders,” Management Science, Vol. 56, No. 11, 2010, pp. 1977-1996. doi:10.1287/mnsc.1100.1226
[17] H. White, “Economic Prediction Using Neural Networks: The Case of IBM Daily Stock Returns,” Proceedings of the 2nd Annual IEEE Conference on Neural Networks, San Diego, 24-27 July 1988, pp. 451-458. doi:10.1109/ICNN.1988.23959
[18] H. Demuth and M. Beale, “Neural Network Toolbox: For Use with Matlab,” 4th Edition, The MathWorks Inc., Natick, 1997.
[19] O. Castillo and P. Melin, “Simulation and Forecasting Complex Financial Time Series Using Neural Networks and Fuzzy Logic,” IEEE International Conference on Systems, Man, and Cybernetics, Tucson, 7-10 October 2001, pp. 2664-2669.
[20] G. B. Antonio, O. U. Claudio, M. S. Manuel and O. M. Nelson, “Stock Market Indices in Santiago de Chile: Forecasting Using Neural Networks,” IEEE International Conference on Neural Networks, Washington DC, 3-6 June 1996, pp. 2172-2175.

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