An Empirical Study on Stock Market Prediction by Machine Learning Algorithms

This study delves into the utilization of machine learning (ML) algorithms for predicting stock prices, aiming to aid investors in making informed financial decisions. We explore the effectiveness of Long Short-Term Memory (LSTM) networks, a specialized form of recurrent neural networks, in addressing sequence prediction problems inherent in stock price forecasting. The historical pricing data of stocks serve as a pivotal component in developing our predictive model. While the precise direction of stock prices remains uncertain, our model strives to assess the likelihood of price movement trends, providing valuable insights for both intraday and long-term investment strategies. This paper demonstrates the nuanced approach of combining mathematical models with ML techniques to enhance the predictive accuracy of stock market trends.