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Optimization of the Radial Basis Function Neural Networks Using Genetic Algorithm for Stock Index Prediction
Open Access   Article

Optimization of the Radial Basis Function Neural Networks Using Genetic Algorithm for Stock Index Prediction
P.S. Mishra1

Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-6 , Page no. 43-51, Jun-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i6.4351

Online published on Jun 30, 2018

Copyright © P.S. Mishra . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 
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IEEE Style Citation: P.S. Mishra, “Optimization of the Radial Basis Function Neural Networks Using Genetic Algorithm for Stock Index Prediction”, International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.43-51, 2018.

MLA Style Citation: P.S. Mishra "Optimization of the Radial Basis Function Neural Networks Using Genetic Algorithm for Stock Index Prediction." International Journal of Computer Sciences and Engineering 6.6 (2018): 43-51.

APA Style Citation: P.S. Mishra, (2018). Optimization of the Radial Basis Function Neural Networks Using Genetic Algorithm for Stock Index Prediction. International Journal of Computer Sciences and Engineering, 6(6), 43-51.
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Abstract :
Stock index prediction is one of the important tasks in the domain of computational finance. A number of tools have been developed by various groups of researchers and are being used by many analysts to identify the future price index. However, due to the high degree of non-linearity of the problem and surrounded by many optimal solutions, this paper proposes Radial Basis Function Neural Networks (RBFNNs) learning using Genetic Algorithm (GA) to predict the stock price index and at the same time the connection weights between the layers and thresholds are optimised using GA. Further, potential indicators are used to make the model robust in terms of its efficiency and accuracy. The accuracy is compared to MLP-BP and GA models. Finally, the experimental results show that the optimized RBFNNs model is the optimum model in comparison to other conventional models.
Key-Words / Index Term :
Stock Index Prediction, RBF Neural Network, Genetic Algorithm, Real coding
References :
[1] S. Chen, C.F.N. Cowan and P.M. Gant, “Orthogonal Least Squares Learning Algorithm for Radial Basis Function Networks”, J. IEEE Transactions on Neural Networks, Vol. 3, pp.302-308, 1991.
[2] Y. Chao-gang, Y. Yi-bin, W. Jian-ping, N. Jamal and Y. Jia, “Determining Heating Pipe Temperature in Greenhouse Using Proportional Integral Plus Feed-forward Control and Radial Basis Function Neural Networks”, Journal of Zhejiang University-SCIENCE A, Vol. 6, No.4, pp. 265-269, 2005.
[3] J.H. Holland, “Adaptation in Natural and Artificial Systems”, University of Michigan Press, Ann Arbor, 1975.
[4] P. S. Mishra and S. Dehuri, “Potential Indictors for Stock Index Prediction: A Perspective”, International Journal of Electronic Finance, Vol. 6, No. 2, pp. 157-183, 2012, doi:10.1504/IJEF.2012.048465.
[5] P. S. Mishra and S. Dehuri, “Potential Indicators based Neural Networks for Cash Forecasting of an ATM, International Journal of Information Systems and Social Change, Vol. 5, No.4, pp. 41-57, 2014, doi:10.4018/ijissc.2014100103.
[6] A. Brabazon, “Financial time series modeling using neural networks: An assessment of the utility of a stacking methodology”, in proceedings of AICS 2002, Lecture Notes in Artificial Intelligence(2464),(Eds,) O’Neill et al., Springer,pp.137-144, 2002b.
[7] K. Nygren, “Stock Prediction—A Neural Network Approach”, Master’s Thesis, Royal Institute of Technology, KTH, Sweden, 2004.
[8] E. Altman, “Financial ratios, discriminant analysis and the prediction of corporate bankruptcy”, The Journal of Finance, Vol. 23, pp.589-609, 1968.
[9] E. Altman, “Corporate financial distress-A complete guide to predicting, avoiding and dealing with bankruptcy”, New York: John Wiley, 1983.
[10] J. Ohlson, “Financial ratios and the probabilistic prediction of bankruptcy”, Journal of Accounting Research, Vol. 18, No.1, pp.109-131, 1980.
[11] M.E. Zmijewski,“Methodological Issues Related to the Estimated of Financial Distress Prediction Model”, Journal of Accounting Research, Vol.22, No.1, pp.59-82,1984.
[12] R. Barniv, A. Agarwal and R. Leach, “Predicting the outcome following bankruptcy filing: A three-state classification using neural networks”, Intelligent Systems in Accounting, Finance and management, Vol.6, pp. 177-194, 1997.
[13] J. Boritz and D. Kennedy, “Effectiveness of neural network types for prediction of business failure”, Expert Systems with Applications, Vol.9, pp.503-512, 1995.
[14] A. Brabazon and M. O’Neill, M, “Diagnosing Corporate Stability using Grammatical Evolution”, International Journal of Applied Mathematics and Computer Science, Vol. 14, No. 3 pp.363-374,2004.
[15] F. Varetto, “Genetic algorithms in the analysis of insolvency risk”, Journal of Banking and Finance, Vol. 22, No.10, pp.1421-1439, 1998.
[16] A.M. Colin, “Genetic algorithms for financial modeling”, In Deboeck, G.J. (Eds.), Trading on the Edge, New York: John Wiley, pp. 148-173, 1994.
[17] G.J. Deboeck, “Using GAs to optimize a trading system”, In Deboeck, G.J (Eds.), Trading on the Edge, New York: John Wiley, pp. 174-188, 1994.
[18] S. Mahfoud and G. Mani, "Financial forecasting using genetic algorithms", Applied Artificial Intelligence, Vol. 10, No.6, pp. 543-565, 1996.
[19] E. Rutan,, “Experiments with optimal stock screens”, Proceedings of the 3rdInternationalConference on Artificial Intelligence Applications on Wall Street, pp.262-273,1993.
[20] J. Kingdom and K. Feldman, “Genetic algorithms for bankruptcy prediction”, Search Space Research Report, No.01-95, Search Space Ltd., London, 1995.
[21] R. Walker, E. Haaasdijk and M. Gerrets, “Credit evaluation using a genetic algorithm”, In Coonatilake, S. and Treleaven, P. (Eds.), Intelligent Systems for Financial and Business, Chichester, John Wiley, pp.35-59, 1995.
[22] N. Packard, “A genetic learning algorithm for the analysis of complex data”, Complex Systems, Vol. 4, pp.543-572, 1990.
[23] M. Durairaj and T. Sathyavathi, “Applying Rough Set Theory for Medical Informatics Data Analysis”, International Journal of Scientific Research in Computer Science and Engineering, Vol.1, No.5,pp.1-8, 2013.
[24] L. Davis, “Handbook of Genetic Algorithms”, Van Nostrand Reinhold, New-York, 1991.
[25] D.E. Goldberg, “Genetic Algorithms in Search, Optimization and Machine Learning”, MA: Addison-Wesley, 1989.
[26] A. Brabazon and M. O’ Neill, “Biologically Inspired Algorithms for Financial Modelling”, Springer, Berlin, Germany, 2006.
[27] D.S. Broomhead and D. Lowe, “Multivariable functional interpolation and adaptive networks”, Complex System, Vol. 2, pp.321-355, 1988.
[28] J. Park and I. W. Sandberg, “Universal Approximation using Radial-Basis-Function Networks”, Neural Computation, Vol. 3, pp.246-257, 1991.