Enhancing Stock Market Prediction: A Hybrid RNN-LSTM Framework with Sentiment Analysis

Abstract

Objectives: Predicting stock prices with accuracy is a difficult but crucial endeavor for market participants. To increase the precision of stock market forecasts, this study suggests a novel method that blends sophisticated neural network algorithms with sentiment assessment. Methods: News data is pre-processed. Each text document received a sentiment score reflecting overall sentiment. These scores were integrated into the feature set, combining textual sentiment information with historical stock price data from BSE Sensex. The proposed model of hybrid RNN-LSTM is applied and compared with the Random Forest Regressor (RFR), and Support Vector Regressor (SVR). The LSTM model is also applied and tested on data without sentiment analysis scores. Findings: The proposed model yields promising results in stock market prediction accuracy. It significantly gives a low value for mean absolute error (0.036), mean squared error (0.021), and root mean square error (0.046) when compared with the SVR and RFR models. The R2 value is also compared with literature methods, and it shows a 0.40% to 5.5% enhancement in the scores. The results prove that the incorporation of sentiment analysis enriches the predictive capabilities of the model. Novelty: Sentiment analysis combined with the hybrid RNN-LSTM framework provides a new technique to increase the accuracy of stock market forecasts. Using sophisticated knowledge of market dynamics and sentiments, the proposed approach gives important results to market participants, investors, and analysts of financial markets. Keywords: Stock Market, Sentiment Analysis, Evaluation Metric, Prediction