Dhaka stock market timing decisions by hybrid machine learning technique

Stock market prediction has been a challenging task due to the nature of the data which is very noisy and time varying. However, this theory has been faced by many empirical studies and a number of researchers have successfully applied machine learning approaches to predict stock market. The problem studied here is about stock prediction for the use of investors. It is true investors usually get loss because of unclear investment objective and blind investment. This paper proposes to investigate the rough set model, the artificial neural network model and the hybrid artificial neural network model and the rough set model for determining the optimal buy and sell of a share on a Dhaka stock exchange. Confusion matrix is used to evaluate the performance of the observed and predicted classes for selected models. Our experimental result shows that the proposed hybrid model has higher accuracy than the single rough set model and the artificial neural network model. We believe this paper will be useful to stock investors to determine the optimal buy and sell time on Dhaka Stock Exchange.