STOCK PRICE TREND PREDICTION USING SUPPORT VECTOR MACHINE AND CORAL REEF OPTIMIZATION ALGORITHM
DOI:
https://doi.org/10.51406/jnset.v18i1.2033Keywords:
Stock Market, Stock Price Trend Prediction, Support Vector Machine, Coral Reef Optimization, Stock Price indexAbstract
Due to non-linearity and non-stationary characteristics of stock market time series data, prior approaches have not been adequate enough for predicting stock market prices. Support vector machines are classifier that have been reported in the literature as having good recognition accuracy and have been applied in the area of predicting financial stock market prices and was found efficient. It is however noted that the performance of the SVM is affected by the values of the hyper-parameters used by the SVM. There is the need to find a way for searching for the best hyper-parameters that optimizes the performance of an SVM model. Coral Reef Optimization (CRO) is one of many nature-inspired algorithms used extensively to solve optimization problems. It is very effective in solving optimization problems because it is able to achieve global optimization. This paper’s contribution is the development of Coral Reef search algorithms for the improvement of the hyper-parameters of the SVM used for stock price trend prediction. The Algorithm is validated using stock data of two banks. The results obtained out-performed un-optimized SVM, and have the same performance as that of SVM optimized with the FireFly optimization algorithm.
References
AraúJo, R.D.A. and Ferreira, T.A. 2013. A morphological-rank-linear evolutionary method for stock market prediction. Information Sciences, 237: 3-17.
Basher, S.A., Haug, A. A. and Sadorsky, P. 2012. Oil Prices, Exchange Rates nd Emerging Stock Markets. Energy Economics, 34(1): 227-240.
Boyacioglu, M.A. and Avci, D. 2010. An adaptive network-based fuzzy inference system (ANFIS) for the prediction of stock market return: the case of the Istanbul stock exchange. Expert Systems with Applications, 37(12): 7908-7912.
Burton, F.E.T. and Shah, S.N. 2017. Efficient Market Hypothesis. CMT Level I 2017: An Introduction to Technical Analysis.
Chakravarty, S. and Dash, P.K. 2012. A PSO based integrated functional link net and interval type-2 fuzzy logic system for predicting stock market indices. Applied Soft Computing, 12(2): 931-941.
Chang C-C and Lin C-J LIBSVM 2011. A library for Support Vector Machines. ACM Transactions Intelligent Systems and Technology, 2(27): 1-27.
Chen, W. and Shih, J. 2006. Comparison of support vector machines and back propagation neural networks in forecasting the six major Asian stock markets. Journals Electronics Finance, (1): 49-67.
Chauhan, B., Bidave, U., Gangathade, A. and Kale, S. 2014. Stock Market Prediction Using Artificial Neural Networks.
Chen, B., Sun, F. and Hu, J. 2010. Local linear multi-SVM method for gene function classification. Proc of the World Congress on Nature and Biologically Inspired Computing (NaBIC’10) (Kitakyushu), 183–188.
de Oliveira, F.A., Nobre, C.N. and Zarate, L.E. 2013. Applying Artificial Neural Networks to prediction of stock price and improvement of the directional prediction index–Case study of PETR4, Petrobras, Brazil. Expert Systems with Applications, 40(18): 7596-7606.
Dutta, A., Bandopadhyay, G. and Sengupta, S. 2015. Prediction of Stock Performance In Indian Stock Market Using Logistic Regression. International Journal of Business and Information, 7(1): 105-136.
Gharehchopogh, F.S., Bonab, T.H. and Khaze, S.R. 2013. A linear regression approach to prediction of stock market trading volume: a case study. International Journal of Managing Value and Supply Chains, 4(3): 25.
Grigoryan H. 2017. Stock market prediction using support vector machine and variable selection method. Advances in intelligent system research (AISR). International conference on Applied Mathematics Modelling and Statistics Application (AMMSA 2017), 141: 141-213.
Guo, X., Sun, L., Li, G. and Wang, S. 2008. A hybrid wavelet analysis and support vector machines in forecasting development of manufacturing. Expert Systems with Applications, 35(1–2): 415–422.
Herz, F.S., Ungar, L.H., Eisner, J.M., Labys, W.P. and Fred Herz Patents, LLC, 2012. Stock market prediction using natural language processing. U.S. Patent 8: 285- 619.
Kara, Y., Boyacioglu, M. and Baykan, O. 2011. Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange, Expert Systems with Applications, 38(11): 5311–5319.
Kavousi-Fard, A., Samet, H. and Marzbani, F. 2014. A new hybrid modified firefly algorithm and support vector regression model for accurate short term load forecasting. Expert systems with applications, 41(13): 6047-6056.
Kazeem, A., Sharifi, E., Hussain, F.K., Saberi, M. and Hussain, O.K. 2013. Support vector regression with chaos-based firefly algorithm for stock market price forecasting. Applied soft computing, 13(2): 947-958.
Kim, H., and Han, S. T. 2016. The Enhanced Classification for the Stock Index Prediction. Procedia Computer Science, 91: 284-286.
Kuo, R.J. and Li, P.S. 2016. Taiwanese export trade forecasting using firefly algorithm based K-means algorithm and SVR with wavelet transform. Computers & Industrial Engineering, 99: 153-161.
Li, B., Wang Q. and Hu, J. 2011. Feature subset selection: A correlation based SVM filter approach. IEEJ Trans. on Electrical and Electronic Engineering, 6(2): 173–179.
Li, F. and Liu, C. 2009. Application Study of BP Neural Network on Stock Market Prediction. Ninth International Conference on Hybrid Intelligent Systems, 174-178.
Lin, Y., Guo, H. and Hu, J. 2013. August. An SVM-based approach for stock market trend prediction. Neural Networks (IJCNN), The 2013 International Joint Conference, 1-7. IEEE.
Liu, F. and Wang, J. 2012. Fluctuation prediction of stock market index by Legendre neural network with random time strength function. Neurocomputing, 83: 12-21.
Mani, S., Kothandaraman, P., Kashyap, R. and Ashnai, B. 2017. Stock Market and Network Influence on Alliance Formation: Evidence from the Biopharmaceutical Industry. Journal of Marketing Theory and Practice, 25(1): 69-85.
Patil. S.S., Patidar K. and Jain, M. 2016. Stock market prediction using support vector machine. International journal of current trends in Engineering and Technology 2: 18-25.
Ratsch, G., Onoda, T. and Muller, K. 2001. Soft margins for AdaBoost. Machine Learning, 42(3): 287–320.
Salcedo-Sanz, S., Del Ser, J., Landa-Torres, I., Gil-López, S. and Portilla-Figueras, J.A. 2014. The coral reefs optimization algorithm: a novel metaheuristic for efficiently solving optimization problems. The Scientific World Journal, 2014.
Shaikh, A., Pritam, T., Ankita, P., Shital, W. and Pooja, T. 2014. Stock Exchange Market Prediction. International Journal of Advance in Computer Science and Technology, India, 3(5): 349-351.
Utthammajai K. and Leesutthipornchai P. 2015. Association Mining on Stock Index Indicators. International Journal of Computer and Communication Engineering, 4(1): 46-49.
Vohandu L. and Krusberg H. 1977. A direct factor analysis method. The proceedings of TTU, 426: 11-21.
Xu, X., Zhou, C. and Wang, Z. 2009. Credit scoring algorithm based on link analysis ranking with support vector machine. Expert Systems with Applications, 36: 2625-2632.
Basher, S.A., Haug, A. A. and Sadorsky, P. 2012. Oil Prices, Exchange Rates nd Emerging Stock Markets. Energy Economics, 34(1): 227-240.
Boyacioglu, M.A. and Avci, D. 2010. An adaptive network-based fuzzy inference system (ANFIS) for the prediction of stock market return: the case of the Istanbul stock exchange. Expert Systems with Applications, 37(12): 7908-7912.
Burton, F.E.T. and Shah, S.N. 2017. Efficient Market Hypothesis. CMT Level I 2017: An Introduction to Technical Analysis.
Chakravarty, S. and Dash, P.K. 2012. A PSO based integrated functional link net and interval type-2 fuzzy logic system for predicting stock market indices. Applied Soft Computing, 12(2): 931-941.
Chang C-C and Lin C-J LIBSVM 2011. A library for Support Vector Machines. ACM Transactions Intelligent Systems and Technology, 2(27): 1-27.
Chen, W. and Shih, J. 2006. Comparison of support vector machines and back propagation neural networks in forecasting the six major Asian stock markets. Journals Electronics Finance, (1): 49-67.
Chauhan, B., Bidave, U., Gangathade, A. and Kale, S. 2014. Stock Market Prediction Using Artificial Neural Networks.
Chen, B., Sun, F. and Hu, J. 2010. Local linear multi-SVM method for gene function classification. Proc of the World Congress on Nature and Biologically Inspired Computing (NaBIC’10) (Kitakyushu), 183–188.
de Oliveira, F.A., Nobre, C.N. and Zarate, L.E. 2013. Applying Artificial Neural Networks to prediction of stock price and improvement of the directional prediction index–Case study of PETR4, Petrobras, Brazil. Expert Systems with Applications, 40(18): 7596-7606.
Dutta, A., Bandopadhyay, G. and Sengupta, S. 2015. Prediction of Stock Performance In Indian Stock Market Using Logistic Regression. International Journal of Business and Information, 7(1): 105-136.
Gharehchopogh, F.S., Bonab, T.H. and Khaze, S.R. 2013. A linear regression approach to prediction of stock market trading volume: a case study. International Journal of Managing Value and Supply Chains, 4(3): 25.
Grigoryan H. 2017. Stock market prediction using support vector machine and variable selection method. Advances in intelligent system research (AISR). International conference on Applied Mathematics Modelling and Statistics Application (AMMSA 2017), 141: 141-213.
Guo, X., Sun, L., Li, G. and Wang, S. 2008. A hybrid wavelet analysis and support vector machines in forecasting development of manufacturing. Expert Systems with Applications, 35(1–2): 415–422.
Herz, F.S., Ungar, L.H., Eisner, J.M., Labys, W.P. and Fred Herz Patents, LLC, 2012. Stock market prediction using natural language processing. U.S. Patent 8: 285- 619.
Kara, Y., Boyacioglu, M. and Baykan, O. 2011. Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange, Expert Systems with Applications, 38(11): 5311–5319.
Kavousi-Fard, A., Samet, H. and Marzbani, F. 2014. A new hybrid modified firefly algorithm and support vector regression model for accurate short term load forecasting. Expert systems with applications, 41(13): 6047-6056.
Kazeem, A., Sharifi, E., Hussain, F.K., Saberi, M. and Hussain, O.K. 2013. Support vector regression with chaos-based firefly algorithm for stock market price forecasting. Applied soft computing, 13(2): 947-958.
Kim, H., and Han, S. T. 2016. The Enhanced Classification for the Stock Index Prediction. Procedia Computer Science, 91: 284-286.
Kuo, R.J. and Li, P.S. 2016. Taiwanese export trade forecasting using firefly algorithm based K-means algorithm and SVR with wavelet transform. Computers & Industrial Engineering, 99: 153-161.
Li, B., Wang Q. and Hu, J. 2011. Feature subset selection: A correlation based SVM filter approach. IEEJ Trans. on Electrical and Electronic Engineering, 6(2): 173–179.
Li, F. and Liu, C. 2009. Application Study of BP Neural Network on Stock Market Prediction. Ninth International Conference on Hybrid Intelligent Systems, 174-178.
Lin, Y., Guo, H. and Hu, J. 2013. August. An SVM-based approach for stock market trend prediction. Neural Networks (IJCNN), The 2013 International Joint Conference, 1-7. IEEE.
Liu, F. and Wang, J. 2012. Fluctuation prediction of stock market index by Legendre neural network with random time strength function. Neurocomputing, 83: 12-21.
Mani, S., Kothandaraman, P., Kashyap, R. and Ashnai, B. 2017. Stock Market and Network Influence on Alliance Formation: Evidence from the Biopharmaceutical Industry. Journal of Marketing Theory and Practice, 25(1): 69-85.
Patil. S.S., Patidar K. and Jain, M. 2016. Stock market prediction using support vector machine. International journal of current trends in Engineering and Technology 2: 18-25.
Ratsch, G., Onoda, T. and Muller, K. 2001. Soft margins for AdaBoost. Machine Learning, 42(3): 287–320.
Salcedo-Sanz, S., Del Ser, J., Landa-Torres, I., Gil-López, S. and Portilla-Figueras, J.A. 2014. The coral reefs optimization algorithm: a novel metaheuristic for efficiently solving optimization problems. The Scientific World Journal, 2014.
Shaikh, A., Pritam, T., Ankita, P., Shital, W. and Pooja, T. 2014. Stock Exchange Market Prediction. International Journal of Advance in Computer Science and Technology, India, 3(5): 349-351.
Utthammajai K. and Leesutthipornchai P. 2015. Association Mining on Stock Index Indicators. International Journal of Computer and Communication Engineering, 4(1): 46-49.
Vohandu L. and Krusberg H. 1977. A direct factor analysis method. The proceedings of TTU, 426: 11-21.
Xu, X., Zhou, C. and Wang, Z. 2009. Credit scoring algorithm based on link analysis ranking with support vector machine. Expert Systems with Applications, 36: 2625-2632.
