Stock Market Prediction Using LSTM Networks: Leveraging Machine Learning for Accurate Forecasting of Stock Price Trends

Stock market prediction is a complex task due to market volatility, global crises, and political factors. Traditional methods, such as technical and fundamental analysis, often fail to capture non-linear patterns. With advancements in machine learning, Long Short-Term Memory (LSTM) networks have become effective for time series forecasting, learning long-term dependencies in sequential data. This research employs LSTM networks to predict stock price trends using five years of structured historical stock data from Yahoo Finance, including features like closing price, trading volume, and moving averages. Preprocessing techniques such as data normalization, missing value handling, and outlier removal were applied to enhance data quality. The model’s performance, evaluated using MSE, RMSE, and MAE, was compared against traditional methods like ARIMA and simple RNNs, demonstrating LSTM’s superior accuracy. Experiments with different architectures, such as stacked and bidirectional LSTM, were conducted to optimize performance. Results highlight LSTM’s ability to model complex price movements, providing valuable insights for investors. This study underscores the potential of deep learning in financial forecasting, contributing to the advancement of predictive analytics in financial markets.