Journal of Industrial Engineering and Management Studies

Abstract Sudden and severe stock price crashes pose a significant challenge to stock markets. The substantial losses incurred from such events underscore the need for more effective forecasting tools. This study aims to enhance the predictive power of models for stock price crashes in Tehran Stock Exchange and commenced with a comprehensive literature review to identify key financial factors influencing stock price volatility. Given the high dimensionality of the dataset and the extended time period, metaheuristic algorithms were employed for feature selection. 10 algorithms, namely Ant Colony Optimization, Hill Climbing, Las Vegas, Whale Optimization, Simulated Annealing, Genetic Algorithm, Tabu Search, Particle Swarm Optimization (PSO), Honey Bee (HBA) and Firefly were utilized to reduce dimensionality and enhance model performance. Subsequently, Support Vector Machines were implemented to develop predictive models. The models were trained and evaluated using historical data from Tehran Stock Exchange spanning from 2001 to 2020. The findings of this research demonstrate that combining metaheuristic algorithms for model reduction and optimization, along with advanced machine learning techniques, yields results that can significantly improve investment decision-making.