Predictive Models for Stock Market Volatility Using Deep Learning

Abstract

This research investigates the prediction of stock market volatility using deep learning methods, particularly Generative Adversarial Networks (GANs). In order to overcome the problem of sparse historical data and increase the training dataset, GANs were used to create synthetic financial data. For increased accuracy, the model makes use of sophisticated data augmentation approaches by combining GANs with Python-based modules. Principal Component Analysis (PCA), which optimizes feature selection and lowers computing complexity, further improves the method by reducing dimensionality. With notable decreases in Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE), the suggested model outperforms more conventional techniques like GARCH. The model is especially useful for risk management and financial decision-making since it also exhibits greater stability during times of high volatility. The findings demonstrate how GANs and Python-based frameworks may be used to provide reliable and effective stock market volatility prediction solutions.