Indian stock market prediction using artificial neural networks on tick data

Dharmaraja Selvamuthu (), Vineet Kumar () and Abhishek Mishra ()
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Dharmaraja Selvamuthu: Indian Institute of Technology Delhi
Vineet Kumar: Indian Institute of Technology Delhi
Abhishek Mishra: Indian Institute of Technology Delhi

Financial Innovation, 2019, vol. 5, issue 1, 1-12

Abstract: Abstract Introduction Nowadays, 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 description Support 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 evaluation Most 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. Conclusion All 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.

Keywords: Neural Networks; Indian Stock Market Prediction; Levenberg-Marquardt; Scale Conjugate Gradient; Bayesian Regularization; Tick by tick data (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (18)

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DOI: 10.1186/s40854-019-0131-7

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