abstract
- In financial analysis, the prediction of stock market volatility is a critical task that has an impact on investment strategies, risk management, and policy formulation. This study is about using Artificial Intelligence (AI) models to predict stock market volatility by taking advantage of the ability of AI models to analyze complex and non-linear relationships in financial data. We employ a combination of machine learning algorithms such as Long Short-Term Memory (LSTM), Support Vector Machines (SVM), and Gradient Boosting Methods on historical and real-time market data. A combination of feature engineering techniques and financial indicators have been employed to improve the predictive power of our method. Also, the model performance was compared with MAE, RMSE, and directional accuracy methods that are often used in time series analyses. The evaluation results illustrated that AI models are significantly better than traditional statistical time series models in discovering the underlying behavior of the market and providing similar insights. This study illustrates that AI-driven volatility prediction could support authorities in achieving both improvement of market stability and better-informed decision-making in volatile financial environments.