Enhancing Stock Price Predictions: The Impact of Sentiment Analysis on Forecasting Accuracy Across Models and Time Horizons

Abstract

This study tests the performance of various models of forecasting stock prices at short-term (oneyear) and long-term (five-year) horizons, with sentiment analysis to determine its contribution to predictability. Tested models are SARIMAX, Random Forest, SVM, LSTM, GRU, and Regression, on various industry sectors and volatility classes. Our findings indicate that models perform better in one-year forecasts than five-year forecasts, with Regression and Random Forest performing with the least Root Mean Square Percentage Error (RMSPE) across the board. Sentiment analysis was of greatest benefit to certain models, particularly SARIMAX, whose topperforming setups tended to be those in which it was utilized. Sentiment analysis benefited most for stocks and industries that had high volatility and high-speed movement in the market, such as the energy and technology sectors. In addition, the study examined optimal lag values for prediction and established a trend towards decreased lags in fast-changing industries and increased lags in stable industries. The study emphasizes tailoring prediction models to the specific nature of stocks, industry movements, and volatility, with the caveat that a complex strategy is necessary to enhance the accuracy and validity of stock price predictions. This research contributes to the body of knowledge on how different determinants, including sentiment and industry forces, lead to stock price predictability and is of interest to investors and financial analysts