Stock Market Price Prediction Using LSTM

Abstract

Predicting stock prices is a challenging task because of the volatility and indeterminacy of financial markets. Machine learning algorithms are capable of efficiently processing historical data, extracting patterns, and predicting future stock prices. This paper presents the implementation of the Long Short-Term Memory (LSTM) model for forecasting stock prices, after a detailed comparison with Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN). The research demonstrated that LSTM outperformed RNN and CNN with minimal loss and maximum accuracy. It is the capability of LSTM to grasp long-term dependencies and sequential patterns in time series data, which facilitated the improved results. The dataset is taken from Yahoo Finance. The study would be valuable for investors and analysts.