Stock Price Prediction Using Machine Learning and LSTM Techniques

Accurate prediction of stock prices remains a challenging problem due to the stochastic, non-stationary, and highly volatile nature of financial time-series data. This study presents a hybrid approach for stock price forecasting by integrating traditional Machine Learning methods with advanced Deep Learning techniques, specifically Long Short-Term Memory (LSTM) networks. Historical stock market data comprising Open, High, Low, Close, and Volume (OHLCV) attributes is utilized for model development. The proposed framework involves comprehensive data preprocessing, including normalization using Min-Max scaling, feature engineering, and sequence generation for time-series modeling. A Linear Regression model is employed as a baseline to establish performance benchmarks, while the LSTM model is designed with multiple hidden layers and dropout regularization to capture temporal dependencies and mitigate overfitting. The models are trained and evaluated using standard performance metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R²). Experimental results indicate that the LSTM-based model demonstrates superior predictive performance compared to conventional approaches by effectively learning long-term patterns in sequential data. The findings emphasize the applicability of deep learning architectures in financial forecasting tasks, while also acknowledging inherent limitations due to market uncertainty. Future enhancements may include the incorporation of exogenous variables such as news sentiment and macroeconomic indicators to further improve prediction accuracy.