Predicting the Direction of Stock Market Index Movement Using an Optimized Artificial Neural Network Model

In the business sector, it has always been a difficult task to predict the exact daily price of the stock market index; hence, there is a great deal of research being conducted regarding the prediction of the direction of stock price index movement. Many factors such as political events, general economic conditions, and traders’ expectations may have an influence on the stock market index. There are numerous research studies that use similar indicators to forecast the direction of the stock market index. In this study, we compare two basic types of input variables to predict the direction of the daily stock market index. The main contribution of this study is the ability to predict the direction of the next day’s price of the Japanese stock market index by using an optimized artificial neural network (ANN) model. To improve the prediction accuracy of the trend of the stock market index in the future, we optimize the ANN model using genetic algorithms (GA). We demonstrate and verify the predictability of stock price direction by using the hybrid GA-ANN model and then compare the performance with prior studies. Empirical results show that the Type 2 input variables can generate a higher forecast accuracy and that it is possible to enhance the performance of the optimized ANN model by selecting input variables appropriately.

[1]  Myung Won Kim,et al.  The effect of initial weights on premature saturation in back-propagation learning , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.

[2]  S. Kokubo,et al.  Universal scaling for the dilemma strength in evolutionary games. , 2015, Physics of life reviews.

[3]  Yves Chauvin,et al.  Backpropagation: theory, architectures, and applications , 1995 .

[4]  Cheng-Lung Huang,et al.  A hybrid SOFM-SVR with a filter-based feature selection for stock market forecasting , 2009, Expert Syst. Appl..

[5]  Mahesh Panchal,et al.  Optimizing Weights of Artificial Neural Networks using Genetic Algorithms , 2012 .

[6]  Tugrul U. Daim,et al.  Using artificial neural network models in stock market index prediction , 2011, Expert Syst. Appl..

[7]  Md. Ashraful Islam Khan Financial Volatility Forecasting by Nonlinear Support Vector Machine Heterogeneous Autoregressive Model: Evidence from Nikkei 225 Stock Index , 2011 .

[8]  Chi-Jie Lu,et al.  Integrating independent component analysis-based denoising scheme with neural network for stock price prediction , 2010, Expert Syst. Appl..

[9]  Wu Meng,et al.  Application of Support Vector Machines in Financial Time Series Forecasting , 2007 .

[10]  Massimiliano Versace,et al.  Predicting the exchange traded fund DIA with a combination of genetic algorithms and neural networks , 2004, Expert Syst. Appl..

[11]  Kyoung-jae Kim,et al.  Financial time series forecasting using support vector machines , 2003, Neurocomputing.

[12]  Mohamed M. Mostafa,et al.  Forecasting stock exchange movements using neural networks: Empirical evidence from Kuwait , 2010, Expert Syst. Appl..

[13]  Binoy B. Nair,et al.  A GA-artificial neural network hybrid system for financial time series forecasting , 2011 .

[14]  Guido Deboeck,et al.  Trading on the Edge: Neural, Genetic, and Fuzzy Systems for Chaotic Financial Markets , 1994 .

[15]  Donald F. Specht,et al.  A general regression neural network , 1991, IEEE Trans. Neural Networks.

[16]  Lin Wang,et al.  Coupled disease–behavior dynamics on complex networks: A review , 2015, Physics of Life Reviews.

[17]  Ken-ichi Funahashi,et al.  On the approximate realization of continuous mappings by neural networks , 1989, Neural Networks.

[18]  Meltem Ozturan,et al.  Stock Price Direction Prediction Using Artificial Neural Network Approach: The Case of Turkey , 2008 .

[19]  Chih-Chou Chiu,et al.  Neural network forecasting of an opening cash price index , 2002, Int. J. Syst. Sci..

[20]  S. Sosvilla‐Rivero,et al.  On the profitability of technical trading rules based on artificial neural networks:: Evidence from the Madrid stock market , 2000 .

[21]  Paul J. Werbos,et al.  The Roots of Backpropagation: From Ordered Derivatives to Neural Networks and Political Forecasting , 1994 .

[22]  Ingoo Han,et al.  Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index , 2000 .

[23]  Michael Y. Hu,et al.  Forecasting with artificial neural networks: The state of the art , 1997 .

[25]  Michele Marchesi,et al.  A hybrid genetic-neural architecture for stock indexes forecasting , 2005, Inf. Sci..

[26]  E. Mine Cinar,et al.  Neural Networks: A New Tool for Predicting Thrift Failures , 1992 .

[27]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[28]  Taeho Jo,et al.  VTG schemes for using back propagation for multivariate time series prediction , 2013, Appl. Soft Comput..

[29]  Dawei Zhao,et al.  Immunity of multiplex networks via acquaintance vaccination , 2015 .

[30]  Mohammad Bagher Menhaj,et al.  Investigating the performance of technical indicators in electrical industry in Tehran's Stock Exchange using hybrid methods of SRA, PCA and Neural Networks , 2014, 2014 5th Conference on Thermal Power Plants (CTPP).

[31]  Jie Zhang,et al.  Stock Data Analysis Based on BP Neural Network , 2009, 2010 Second International Conference on Communication Software and Networks.

[32]  Alfredo Vellido,et al.  Neural networks in business: a survey of applications (1992–1998) , 1999 .

[33]  Yin Zhong,et al.  Extended s-wave pairing symmetry on the triangular lattice heavy fermion system , 2015 .

[34]  Lin Wang,et al.  Evolutionary games on multilayer networks: a colloquium , 2015, The European Physical Journal B.

[35]  Shouyang Wang,et al.  Forecasting stock market movement direction with support vector machine , 2005, Comput. Oper. Res..

[36]  Lawrence Davis,et al.  Training Feedforward Neural Networks Using Genetic Algorithms , 1989, IJCAI.

[37]  Ömer Kaan Baykan,et al.  Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange , 2011, Expert Syst. Appl..

[38]  Yi-Hsien Wang,et al.  Nonlinear neural network forecasting model for stock index option price: Hybrid GJR-GARCH approach , 2009, Expert Syst. Appl..

[39]  Jatinder N. D. Gupta,et al.  Comparative evaluation of genetic algorithm and backpropagation for training neural networks , 2000, Inf. Sci..