Indian stock market prediction using artificial neural networks on tick data

IntroductionNowadays, the most significant challenges in the stock market is to predict the stock prices. The stock price data represents a financial time series data which becomes more difficult to predict due to its characteristics and dynamic nature.Case descriptionSupport Vector Machines (SVM) and Artificial Neural Networks (ANN) are widely used for prediction of stock prices and its movements. Every algorithm has its way of learning patterns and then predicting. Artificial Neural Network (ANN) is a popular method which also incorporate technical analysis for making predictions in financial markets.Discussion and evaluationMost common techniques used in the forecasting of financial time series are Support Vector Machine (SVM), Support Vector Regression (SVR) and Back Propagation Neural Network (BPNN). In this article, we use neural networks based on three different learning algorithms, i.e., Levenberg-Marquardt, Scaled Conjugate Gradient and Bayesian Regularization for stock market prediction based on tick data as well as 15-min data of an Indian company and their results compared.ConclusionAll three algorithms provide an accuracy of 99.9% using tick data. The accuracy over 15-min dataset drops to 96.2%, 97.0% and 98.9% for LM, SCG and Bayesian Regularization respectively which is significantly poor in comparison with that of results obtained using tick data.

[1]  Efraim Turban,et al.  Neural Networks in Finance and Investing: Using Artificial Intelligence to Improve Real-World Performance , 1992 .

[2]  Chung-Ming Kuan,et al.  Forecasting exchange rates using feedforward and recurrent neural networks , 1992 .

[3]  Martin Fodslette Møller,et al.  A scaled conjugate gradient algorithm for fast supervised learning , 1993, Neural Networks.

[4]  Laurene V. Fausett,et al.  Fundamentals Of Neural Networks , 1993 .

[5]  Mohammad Bagher Menhaj,et al.  Training feedforward networks with the Marquardt algorithm , 1994, IEEE Trans. Neural Networks.

[6]  Martin T. Hagan,et al.  Neural network design , 1995 .

[7]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

[8]  Steven Walczak,et al.  An Empirical Analysis of Data Requirements for Financial Forecasting with Neural Networks , 2001, J. Manag. Inf. Syst..

[9]  Miguel Angel Pérez Chavarría,et al.  Predicción de series de tiempo aplicando redes neuronales artificiales , 2002 .

[10]  Jimmy Shadbolt,et al.  Neural Networks and the Financial Markets: "Predicting, Combining And Portfolio Optimisation" , 2002 .

[11]  Hikmet Kerem Cigizoglu,et al.  Estimation, forecasting and extrapolation of river flows by artificial neural networks , 2003 .

[12]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[13]  Guoqiang Peter Zhang,et al.  Trend Time–Series Modeling and Forecasting With Neural Networks , 2008, IEEE Transactions on Neural Networks.

[14]  Time series prediction using artificial neural networks , 2009 .

[15]  Reza Gharoie Ahangar,et al.  The Comparison of Methods Artificial Neural Network with Linear Regression Using Specific Variables for Prediction Stock Price in Tehran Stock Exchange , 2010, ArXiv.

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

[17]  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..

[18]  Raducanu Razvan,et al.  MATHEMATICAL MODELS and METHODS in APPLIED SCIENCES , 2012 .

[19]  Marion O. Adebiyi,et al.  Stock Price Prediction using Neural Network with Hybridized Market Indicators , 2012 .

[20]  Jonathan L. Ticknor.  A Bayesian regularized artificial neural network for stock market forecasting , 2013, Expert Syst. Appl..

[21]  M. Khashei,et al.  Performance evaluation of series and parallel strategies for financial time series forecasting , 2017, Financial Innovation.