A Hybrid Machine Learning System for Stock Market Forecasting
In this paper, we propose a hybrid machine learning
system based on Genetic Algorithm (GA) and Support Vector
Machines (SVM) for stock market prediction. A variety of indicators
from the technical analysis field of study are used as input features.
We also make use of the correlation between stock prices of different
companies to forecast the price of a stock, making use of technical
indicators of highly correlated stocks, not only the stock to be
predicted. The genetic algorithm is used to select the set of most
informative input features from among all the technical indicators.
The results show that the hybrid GA-SVM system outperforms the
stand alone SVM system.
[1] Chen, A.S., Leung, M.T., and Daouk, H. Application of Neural
Networks to an Emerging Financial Market: Forecasting and Trading the
Taiwan Stock Index. Computers and Operations Research 30, 2003, 901-
923.
[2] W. Kreesuradej, D. Wunsch, and M. Lane, Time-delay neural network
for small time series data sets, in World Cong. Neural Networks, San
Diego, CA, June 1994.
[3] H. Tan, D. Prokhorov, and D. Wunsch, Probabilistic and time-delay
neural-network techniques for conservative short-term stock trend
prediction, in Proc. World Congr. Neural Networks, Washington, D.C.,
July 1995.
[4] E. Saad, D. Prokhorov, and D. Wunsch, Advanced neural-network
training methods for low false alarm stock trend prediction, in Proc.
IEEE Int. Conf. Neural Networks, Washington, D.C., June 1996.
[5] R. K. Wolfe, Turning point identification and Bayesian forecasting of a
volatile time series, Computers and Industrial Engineering, 1988, pp
378-386.
[6] M. A. Kanoudan, Genetic programming prediction of stock prices.
Computational Economics, 16, 2000, pp 207-236.
[7] K. J. Kim. Genetic algorithms approach to feature discretization in
artificial neural networks for the prediction of stock price index. Expert
Systems with Applications, 19(2), 2000, pp 125-132.
[8] S. Schulenburg and P. Ross, Explorations in LCS models of stock
trading, Advances in Learning Classifier Systems, 2001, pages 151-180.
[9] O. Castillo and P. Melin, Simulation and forecasting complex financial
time series using neural networks and fuzzy logic, Proceedings of IEEE
Conference on Systems, Man, and Cybernetics, 2001, pages 2664-2669.
[10] H Kim and K Shin, A hybrid approach based on neural networks and
genetic algorithms for detecting temporal patterns in stock markets,
Applied Soft Computing, Volume 7, Issue 2, March 2007, Pages 569-
576.
[11] Tsaih, R., Hsu, Y. and Lai, C.C., Forecasting S&P 500 stock index
futures with a hybrid AI system. Decision Support Systems 23 2, 1998,
pp. 161-174.
[12] Kohara, K., Ishikawa, T., Fukuhara, Y. and Nakamura, Y., Stock price
prediction using prior knowledge and neural networks. International
Journal of Intelligent Systems in Accounting, Finance and Management
6 1, 1997, pp. 11-22.
[13] L.J. Cao and F.E.H. Tay, Financial forecasting using support vector
machines, Neural Computing Applications 10, 2001, pp. 184-192.
[14] F.E.H. Tay and L.J. Cao, Application of support vector machines in
financial time series forecasting. Omega 29, 2001, pp. 309-317.
[15] F.E.H. Tay and L.J. Cao, Improved financial time series forecasting by
combining support vector machines with self-organizing feature map.
Intelligent Data Analysis 5, 2001, pp. 339-354.
[16] K Kim, Financial time series forecasting using Support Vector
Machines, Neurocomputing 55, May 2003, Pages 307 - 319.
[17] Wun-Hua Chen and Jen-Ying Shih, Comparison of support-vector
machines and back propagation neural networks in forecasting the six
major Asian stock markets, Int. J. Electronic Finance, Vol. 1, No. 1,
2006.
[18] V.N. Vapnik, An overview of statistical learning theory. IEEE
Transactions of Neural Networks 10, 1999, pp. 988-999.
[19] H. J. Kim, Y. K. Lee, B. N. Kahng, and I. M. Kim, Weighted scale-free
network in financial correlation, Journal of the Physical Society of
Japan, 71(9), 2002, pp 2133-2136.
[20] Y. K. Kwon, S. S. Choi, B. R. Moon, Stock prediction based on financial
correlation, GECCO, 2005, pp 2061-2066.
[21] P. J. Kaufman, Trading Systems and Methods, John Wiley & Sons,
1998.
[22] http://svmlight.joachims.org/
[23] http://in.finance.yahoo.com/
[1] Chen, A.S., Leung, M.T., and Daouk, H. Application of Neural
Networks to an Emerging Financial Market: Forecasting and Trading the
Taiwan Stock Index. Computers and Operations Research 30, 2003, 901-
923.
[2] W. Kreesuradej, D. Wunsch, and M. Lane, Time-delay neural network
for small time series data sets, in World Cong. Neural Networks, San
Diego, CA, June 1994.
[3] H. Tan, D. Prokhorov, and D. Wunsch, Probabilistic and time-delay
neural-network techniques for conservative short-term stock trend
prediction, in Proc. World Congr. Neural Networks, Washington, D.C.,
July 1995.
[4] E. Saad, D. Prokhorov, and D. Wunsch, Advanced neural-network
training methods for low false alarm stock trend prediction, in Proc.
IEEE Int. Conf. Neural Networks, Washington, D.C., June 1996.
[5] R. K. Wolfe, Turning point identification and Bayesian forecasting of a
volatile time series, Computers and Industrial Engineering, 1988, pp
378-386.
[6] M. A. Kanoudan, Genetic programming prediction of stock prices.
Computational Economics, 16, 2000, pp 207-236.
[7] K. J. Kim. Genetic algorithms approach to feature discretization in
artificial neural networks for the prediction of stock price index. Expert
Systems with Applications, 19(2), 2000, pp 125-132.
[8] S. Schulenburg and P. Ross, Explorations in LCS models of stock
trading, Advances in Learning Classifier Systems, 2001, pages 151-180.
[9] O. Castillo and P. Melin, Simulation and forecasting complex financial
time series using neural networks and fuzzy logic, Proceedings of IEEE
Conference on Systems, Man, and Cybernetics, 2001, pages 2664-2669.
[10] H Kim and K Shin, A hybrid approach based on neural networks and
genetic algorithms for detecting temporal patterns in stock markets,
Applied Soft Computing, Volume 7, Issue 2, March 2007, Pages 569-
576.
[11] Tsaih, R., Hsu, Y. and Lai, C.C., Forecasting S&P 500 stock index
futures with a hybrid AI system. Decision Support Systems 23 2, 1998,
pp. 161-174.
[12] Kohara, K., Ishikawa, T., Fukuhara, Y. and Nakamura, Y., Stock price
prediction using prior knowledge and neural networks. International
Journal of Intelligent Systems in Accounting, Finance and Management
6 1, 1997, pp. 11-22.
[13] L.J. Cao and F.E.H. Tay, Financial forecasting using support vector
machines, Neural Computing Applications 10, 2001, pp. 184-192.
[14] F.E.H. Tay and L.J. Cao, Application of support vector machines in
financial time series forecasting. Omega 29, 2001, pp. 309-317.
[15] F.E.H. Tay and L.J. Cao, Improved financial time series forecasting by
combining support vector machines with self-organizing feature map.
Intelligent Data Analysis 5, 2001, pp. 339-354.
[16] K Kim, Financial time series forecasting using Support Vector
Machines, Neurocomputing 55, May 2003, Pages 307 - 319.
[17] Wun-Hua Chen and Jen-Ying Shih, Comparison of support-vector
machines and back propagation neural networks in forecasting the six
major Asian stock markets, Int. J. Electronic Finance, Vol. 1, No. 1,
2006.
[18] V.N. Vapnik, An overview of statistical learning theory. IEEE
Transactions of Neural Networks 10, 1999, pp. 988-999.
[19] H. J. Kim, Y. K. Lee, B. N. Kahng, and I. M. Kim, Weighted scale-free
network in financial correlation, Journal of the Physical Society of
Japan, 71(9), 2002, pp 2133-2136.
[20] Y. K. Kwon, S. S. Choi, B. R. Moon, Stock prediction based on financial
correlation, GECCO, 2005, pp 2061-2066.
[21] P. J. Kaufman, Trading Systems and Methods, John Wiley & Sons,
1998.
[22] http://svmlight.joachims.org/
[23] http://in.finance.yahoo.com/
@article{"International Journal of Information, Control and Computer Sciences:58024", author = "Rohit Choudhry and Kumkum Garg", title = "A Hybrid Machine Learning System for Stock Market Forecasting", abstract = "In this paper, we propose a hybrid machine learning
system based on Genetic Algorithm (GA) and Support Vector
Machines (SVM) for stock market prediction. A variety of indicators
from the technical analysis field of study are used as input features.
We also make use of the correlation between stock prices of different
companies to forecast the price of a stock, making use of technical
indicators of highly correlated stocks, not only the stock to be
predicted. The genetic algorithm is used to select the set of most
informative input features from among all the technical indicators.
The results show that the hybrid GA-SVM system outperforms the
stand alone SVM system.", keywords = "Genetic Algorithms, Support Vector Machines,
Stock Market Forecasting.", volume = "2", number = "3", pages = "824-4", }
