A Hybrid Framework for Stock-Market Prediction Using COVID-19 Tweets through Nature-Inspired Algorithms and Machine Learning - Muhammad Nauman Khan; Saman Iftikhar; Daniah Al-Madani; Umer Zaheer; Danish khan; Kiran Fatima; Ammar Saeed (Decision Making: Applications in Management and Engineering, 2025)
The COVID-19 outbreak generated severe disturbances across international financial systems, intensifying volatility within equity markets. Stock market behaviour during this period exhibited pronounced instability, shaped by broader economic conditions as well as collective investor sentiment. This study investigates the effect of COVID-19 related discourse on Twitter on market behaviour and proposes a hybrid modelling framework aimed at enhancing the accuracy of stock price movement forecasts. The proposed framework incorporates nature inspired optimisation techniques, including genetic algorithms, Harris hawk’s optimisation, particle swarm optimisation, and differential evolution. The methodological focus lies in embedding social media sentiment into predictive models to uncover latent relationships that are typically overlooked by conventional statistical approaches. Empirical results demonstrate that all hybrid configurations consistently surpassed traditional forecasting methods. Among them, the support vector regression model optimised using Harris hawk’s optimisation achieved superior performance, recording an MSE of 0.00014, an RMSE of 0.00214, and an MAE of 0.00170. These findings underscore the substantial influence of public sentiment on financial market dynamics during periods of global disruption and highlight the effectiveness of hybrid predictive approaches. Such models offer valuable support for stock market forecasting and provide actionable insights for decision makers operating under heightened economic uncertainty.