A COMPARATIVE STUDY ON MACHINE LEARNING BASED STOCK PREDICTION BY INCORPORATING SENTIMENT ANALYSIS USING FINBERT

Abstract

Swift evolution of ML techniques, the financial forecasting scenario have been transformed tremendously. The domain of stock market prediction is one of the prominent applications of ML considering its inherent complexity and economic implication. Time series and statistical model was conventional cornerstone of market analysis, but they have limited potential for capturing intricate pattern. NLP sentiment analysis played a game changing role for more efficient stock prediction. NLP taps into huge pool of textual data of different sources, comprising of news and social media outlets. The collective mood and opinion of market participants can now be harness using power of NLP sentiment analysis which is to be fed to predictive model for better forecasting. Fusion of sentiment derived insights with ML algorithms presents a substantial leap which not only surges the predictive power of existing models but also provide nuanced understanding of the psychology of market movements driving factors. Consequently, financial industry witnessing a paradigm shift for the anticipation of stock prices fluctuations, with the support of AI driven sentiment analysis This paper presents a machine learning-based stock prediction model that integrates sentiment analysis using FinBERT, it is a specialized model for financial sentiment analysis that uses BERT. This study focuses on enhancing financial stock forecasting by adding investors sentiment data with conventional stock price data. This study takes into consideration traditional time series model like SARIMA for stock price prediction and three FinBERT infused ML models namely SVR, RFR, GBR. Eventually all predictive models are compared through regression evaluation metrics like MAE, MSE, RMSE, R2.