Empirical Study on Stock Market Prediction Using Machine Learning

Abstract

traditional predictive regression models face significant challenges in out-of sample predictability tests due to model uncertainty
and parameter instability. Recent studies introduce particular strategies that overcome these problems. SVR (Support Vector Regression) is a relatively new learning algorithm that has the desirable characteristics of the control of the decision function, the use of the kernel method, and the sparsity of the solution. In this paper, we present a theoretical and empirical framework to apply the SVR (Support Vector Regression) strategy to predict the stock market. Firstly, four company- specific and six macroeconomic factors that may influence the stock trend are selected for further stock multivariate analysis. Secondly, Support Vector Machine is used in analyzing the relationship of these factors and predicting the stock performance. Our results suggest that SVR is a powerful predictive tool for stock predictions in the financial market.