Predictive Modeling for Stock Market Volatility

Abstract

Stock market volatility is a key factor influencing investment decisions, portfolio optimization, risk management, derivative pricing, and financial planning. Traditional statistical models often struggle to capture the nonlinear, dynamic, and high-dimensional nature of modern financial markets. Recent advances in Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) provide more accurate and adaptive approaches for volatility prediction. This paper proposes a comprehensive predictive modeling framework that integrates financial data preprocessing, feature engineering, machine learning algorithms, and deep learning models for intelligent stock market volatility forecasting. The framework incorporates historical stock prices, technical indicators, trading volume, economic variables, financial news, and market sentiment to improve predictive performance. Advanced algorithms such as Random Forest, Support Vector Machine, Gradient Boosting, Extreme Gradient Boosting, Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU) are employed to capture complex market patterns and temporal dependencies. Model performance is evaluated using metrics including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), R², precision, recall, F1-score, and prediction accuracy. The proposed framework emphasizes scalability, computational efficiency, and adaptability to rapidly changing market conditions, supporting intelligent portfolio management and financial risk assessment. Results indicate that AI-driven predictive models outperform traditional statistical approaches in stock market volatility forecasting, providing a reliable foundation for data-driven investment strategies and next-generation intelligent financial analytics systems.