Recent Papers

This paper explores the role of Long Short-Term Memory (LSTM) networks and Recurrent Neural Networks (RNN) in predicting trends and forecasting the S&P 500 index. Financial markets are characterized by their temporal dependencies and volatility, making accurate predictions challenging. Traditional statistical methods often fall short in capturing the complex patterns inherent in financial time series data. This study implements LSTM and RNN models, which are particularly suited for sequential data due to their ability to remember past information and learn long-term dependencies.The research utilizes historical price data of the S&P 500 index, incorporating various technical indicators as input features. The performance of LSTM and RNN models is compared against traditional forecasting methods, such as ARIMA and moving averages. Evaluation metrics including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and accuracy percentages are employed to assess predictive performance.Results indicate that LSTM models significantly outperform RNN and traditional methods, demonstrating higher accuracy in trend prediction and improved robustness against noise. The findings suggest that the unique architecture of LSTM, which mitigates issues like vanishing gradients, enhances its predictive capability in financial contexts. This study highlights the potential of deep learning techniques in financial forecasting and provides a framework for future research in the domain of automated trading and investment strategies.

Copyright © 2024 Nishkarsh Mittal. This is an open access article distributed under the Creative Commons Attribution License.