Financial prediction using back propagation neural networks with opposition based learning

作     者：K Kalaiselvi K Velusamy C Gomathi 

作者机构：Department of Computer Science and Applications The Oxford College of Science Bangalore Karnataka India Department of Computer Science Sri Vasavi College Erode Tamilnadu India Department of Computer Applications Vidyaa Vikas College of Engineering & Technology Tiruchengodu Tamilnadu India 

出 版 物：《Journal of Physics: Conference Series》 

年 卷 期：2018年第1142卷第1期

学科分类：07[理学] 0702[理学-物理学] 

摘      要：Stock price prediction has recently brought together significant attention among the researchers. In the past decades, the stock price prediction has contributed a primary role in the stock market. The shareholder and investigators should give concentration a reliable method to predict stock price/index. An accurate prediction is an important to enlarge an effective market trading strategies. A back propagation neural network is widely used well known multi-layer supervised feed forward neural network algorithm since its simplicity and high problem-solving ability. In the traditional back propagation neural network, weight updating done by gradient decent based learning algorithm which is falling into local minima and learning rate is slow. Hence, keep away from above mentioned drawbacks; the opposition based learning (OBL) algorithm is used for weight adjustment in a back propagation neural network. The empirical result shows that the proposed prediction model demonstrates a superior performance in financial time series forecasting. For evaluating the performance of the proposed model, the empirical research is applied to well known stock market data sets such as S & P BSE Sensex and Nifty 50.