SentiVol-GA: A Volatility-Scaled Genetic Fusion of Predictive Models and Financial Sentiment for Adaptive Stock Forecasting

SentiVol-GA is a robust and adaptive hybrid framework for stock price prediction that integrates statistical forecasting, deep learning, sentiment analysis, and volatility-awareness through Genetic Algorithm-based optimization. The system combines five predictive models—Linear Regression, LSTM, GRU, Bi-LSTM, and ARIMA—with sentiment insights extracted from FinBERT, VADER, and the Loughran–McDonald dictionary. A key innovation of the framework lies in its volatility-scaling mechanism, which adaptively modulates the influence of sentiment based on market turbulence, enhancing responsiveness to real-world fluctuations. Genetic Algorithm (GA) based optimization dynamically adjusts both model and sentiment weights to maintain predictive robustness over time. Experimental validation on eight Indian IT-sector stocks—spanning large-cap, mid-cap, and small-cap categories—demonstrates that SentiVol-GA consistently outperforms all baseline models across RMSE, MAE, R², and tolerance-based accuracy. It achieved up to 12% higher R², 30–60% lower RMSE, and 20–35% greater tolerance-based accuracy compared to individual models. Statistical significance was confirmed using the Friedman and Wilcoxon tests. Additionally, real-time deployment feasibility was verified, with daily inference times under one second and full GA optimization completing in under one minute per stock. These results position SentiVol-GA as a practical, scalable, and interpretable solution for intelligent stock forecasting in dynamic financial markets.