Time Series Forecasting of Stock Market Volatility Using LSTM Networks

Forecasting stock market volatility is a pivotal concern for investors and financial institutions alike. This research paper employs Long Short-Term Memory (LSTM) networks, a potent class of recurrent neural networks, to predict stock market volatility. LSTM networks have proven adept at capturing intricate temporal dependencies, rendering them a fitting choice for time series data analysis. We commence by elucidating the notion of stock market volatility and its profound significance in financial decision-making. Traditional methodologies, such as GARCH models, exhibit shortcomings in deciphering the convoluted dynamics inherent in financial time series data. LSTM networks, with their capacity to model extended temporal relationships, present an encouraging alternative. In this study, we assemble historical stock price and trading volume data for a diverse array of assets, diligently preprocessing it to ensure its aptness for LSTM modeling. We systematically explore various network architectures, hyperparameter configurations, and input features to optimize the efficacy of our models. Our empirical investigations decisively underscore the supremacy of LSTM networks in capturing the subtleties of stock market volatility compared to conventional techniques. As the study progresses, we delve deeper into the complexities of LSTM network training, leveraging advanced techniques such as batch normalization and dropout to fortify model resilience. Moreover, we delve into the interpretability of LSTM models within the context of stock market forecasting.