A Comparative Hybrid Machine Learning Model for Stock Market Prediction Using Sentiment and Performance Data - Santosh Raghuwanshi; Satish Agnihotri (Revista Electronica de Veterinaria, 2024)
Stock market prediction remains one of the most challenging problems in financial analytics because price fluctuations are influenced by both structured financial indicators and unstructured qualitative signals such as news sentiment, investor reactions, and company disclosures. This research paper develops a hybrid machine learning framework integrating Support Vector Machine (SVM), Random Forest (RF), and Long Short-Term Memory (LSTM) to predict stock price movements using sentiment and company performance data. The study examines stock price percentage variations across 25 major publicly traded companies using three event categories: positive news events (Q1), negative news events (Q2), and yearly company performance reports (Q3). Data collected from 2021–2025 included 12,500 financial news articles, 35,000 social media posts, and 125 company performance reports. Results reveal that negative news has a stronger influence on stock prices than positive news, with an average decline of −4.6% compared to a +3.8% increase under positive events. The hybrid model achieved the highest classification accuracy (86.7%), lowest RMSE (0.21), and macro F1-score of 0.84, outperforming individual SVM, RF, and LSTM models. Statistical validation through ANOVA, regression, and independent t-tests confirmed that sentiment and event type significantly affect stock price variations. The findings establish sentiment as a leading indicator for short-term stock prediction while yearly reports contribute long-term stability. The study highlights the superiority of hybrid machine learning approaches for financial forecasting and decision support.