Applying Market News Sentiment Analysis To Stock Market Prediction

The ability to accurately predict market movements has become increasingly important in light of the ongoing problems that have unquestionably had an impact on the financial sector. Due to the low success rate of portfolio risk management models, there is a need to investigate how risk managers can more accurately foresee the evolution of portfolios while also taking into account the systemic risks that are caused by a systemic crisis. The analysis of sentiment in natural language sentences has the potential to improve the accuracy of market predictions. This is because the sentiments of investors have an impact on the financial markets. There are a great number of investors who rely their choices on the information they obtain from newspapers or on their own instincts. The purpose of this study is to demonstrate how sentiment analysis can improve the accuracy of regression models when it comes to anticipating the opening price movements of selected equities. Our goal is to accomplish this by comparing the results and accuracy of two different market prediction scenarios that make use of regression models. One of these scenarios includes market news sentiment analysis, while the other does not include it. Through the utilization of sentiment analysis as an exogenous variable, the nonlinear autoregression model exhibits a higher degree of goodness of fit. When compared to linear models, the data suggest that polynomial autoregressions provide a better fit than linear models. It may be concluded that the utilization of sentiment scores in market modeling displays significant improvements in the efficiency of linear autoregressions.