Research on Stock Price Prediction by LSTM

Abstract

The stock market’s influence on the contemporary economy is profound, with its fluctuations directing the investment strategies of traders and investors. The intricacies and volatility of the market have consistently drawn the interest of data scientists. Forecasting stock prices is a complex task, influenced by various factors, ranging from individual company financials to global economic indicators. The effectiveness of Long Short-Term Memory (LSTM) models in predicting stock prices has been demonstrated in multiple studies and practical applications. They can capture non-linear trends and seasonal patterns in stock price movements, providing valuable insights for investors and traders. For those interested in implementing LSTM models for stock price prediction, numerous resources are available, including tutorials, research papers, and platforms that offer pre-trained models or APIs for financial forecasting. This study aims to develop a deep learning framework using LSTM networks to identify underlying trends within the data. The objective is to predict future stock market price movements by leveraging LSTM’s ability to model time series data effectively. By tapping into the predictive power of LSTM, this model can seek to provide investors with a sophisticated tool for making well-informed investment decisions, enhancing the precision of stock price predictions.