Forecasting Stock Price: A Deep Learning Approach with LSTM And Hyperparameter Optimization

Abstract

This research embarks on the development and implementation of a comprehensive exploration of stock price forecasting, harnessing the potential of Long Short-Term Memory (LSTM) neural networks—a renowned tool in time series analysis and deep learning. Its overarching aim is to construct a robust model for precise stock price predictions, leveraging the intrinsic capabilities of LSTM and deep learning techniques. In the intricate landscape of financial markets, the ability to forecast stock prices accurately holds paramount significance, facilitating well-informed decision-making and effective risk management. This comprehensive study encompasses the essential phases of the research process, including meticulous data collection, preprocessing, architectural configuration centered around LSTM, and rigorous model evaluation. The automated optimization of hyperparameters is executed to fine-tune model performance, ensuring that it attains optimal precision. The outcomes of this study underscore the remarkable efficacy of the LSTM-based deep learning model in capturing intricate patterns inherent in stock market data. These findings not only offer promising insights for the application of LSTM and deep learning in finance but also advance the field by elevating the accuracy and reliability of stock price predictions.