Implementation of Long Short-Term Memory (LSTM) Networks for Stock Price Prediction

Abstract

In this research, we explore the potential of Long Short-Term Memory (LSTM) networks for predicting stock prices. Due to the complexities of the financial markets and the inherent volatility of stock prices, accurate forecasting is now crucial for investors and financial specialists. It has been shown that LSTM, a type of recurrent neural network (RNN), can recognise temporal correlations and patterns in serial data. Training and assessing LSTM models in this work involves analysing stock price data, relevant financial measures, and market sentiment indicators. We looked into other ideas, hyper parameters, and preprocessing methods to see if we might boost the networks' performance. To further improve the model's generalizability, we utilise series normalisation and removal to reduce overfitting. The outcomes demonstrate that the LSTM network outperforms more standard series temporal prediction methods in capturing and anticipating shifts in action pricing. We also conduct extensive back testing and evaluation, using measures like mean squared error (MSE) and mean absolute error (MAE), to assess the model's accuracy and resilience. The results of this study shed light on how deep learning techniques, in particular LSTM networks, can be applied to the prediction of stock prices, potentially assisting traders, investors, and other financial decision-makers in navigating complex and volatile financial markets.