Hybridzing chemical reaction optimization and Artificial Neural Network for stock future index forecasting

Stock index forecasting has been a cornerstone and challenging task in computational statistics and financial mathematics since last few decades. Several machine learning methods have been proposed in order to forecast the future value of stocks effectively as well as efficiently. In this paper we considered an Artificial Neural Network (ANN) combined with a Chemical Reaction Optimization (CRO) algorithm forming a hybridized model (ANN-CRO) to forecast the Bombay Stock Exchange (BSE) future indices. Uniform population method (UP) has been used as initial population for CRO. The preprocessed data which includes the daily closing prices of BS E have been used for training and testing purpose. The predictability performance of the model is evaluated in terms of Average Percentage of Errors (APE), and compared with the result obtained by using a multilayer perceptron (MLP) model. It may be concluded that the ANN-CRO model can be a promising tool for the purpose of stock index prediction.