Stock Price Analysis Using Long Short-Term Memory Based on Recurrent Neural Networks

Abstract

Stock price analysis plays a crucial role in financial decision-making and predicting market trends. With the growing complexity and volatility of financial markets, there is an increasing need for accurate and efficient forecasting models. This abstract presents a study on stock price analysis utilizing Long Short-Term Memory (LSTM)-based Recurrent Neural Networks (RNN). The LSTM-based RNN architecture is particularly suitable for capturing long-term dependencies in time series data, making it well-suited for stock price analysis. This research focuses on the development and evaluation of an LSTM-based RNN model for predicting future stock prices. To evaluate the model's performance, a comprehensive set of performance metrics is employed, including Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). The model's predictions are compared against actual stock prices, enabling an assessment of its accuracy and effectiveness. The experimental results demonstrate the effectiveness of the LSTM-based RNN model in stock price prediction. The model's ability to capture temporal dependencies and learn intricate patterns in the data contributes to its superior performance compared to traditional forecasting techniques. The research findings suggest that LSTM-based RNNs can be valuable tools for investors, traders, and financial analysts in making informed decisions in the stock market.