An Optimization Model for Stock Market Direction Prediction

Abstract

Stock market direction prediction becomes an essential task in the business sector. The inherent volatile behavior of stock markets worldwide makes the prediction process difficult. The improvement in the prediction accuracy of the stock market direction prediction helps to avoid the risks involved in the investment process. In this aspect, this study designs a swallow swarm optimization (SSO) with a fuzzy support vector machine (FSVM) model for stock market direction prediction. The proposed SSO-FSVM model encompasses preprocessing, feature extraction, FSVM, and SSO based parameter tuning. The usage of the SSO algorithm to fine-tune the parameters involved in the FSVM model helps to significantly improve the overall predictive performance. To validate the improved performance of the SSO-FSVM model, a wide range of experiments were carried out using two benchmark datasets. The experimental outcomes reported the betterment of the SSO-FSVM model over the recent approaches in terms of several evaluation metrics.