HLCE: Framework for Enhanced Stock Price Forecasting

Accurate stock price forecasting is a key element of risk management and investment decision-making. A key element of this study is the introduction of a Hybrid LSTM-Conventional Ensemble (HLCE) model, which addresses the limitations of traditional models in capturing nonlinear financial patterns. Utilizing the advantages of both deep learning and conventional forecasting techniques, the HLCE framework combines Long Short-Term Memory (LSTM) networks with traditional statistical models and machine learning methods, including Random Forest, XGBoost, and Support Vector Regression (SVR). The model is assessed using important performance metrics, such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and R-squared (R²), in a case study using Apple Inc. (AAPL) stock data, where MinMaxScaler is utilized for data preprocessing. With an RMSE of 0.16, MAE of 0.16, MAPE of 0.12%, and R² of 0.95, the HLCE model performs better than individual models, according to experimental results, demonstrating its greater capacity to identify intricate financial patterns. By contrast, isolated models exhibit far lower predictive efficiency and much higher error rates. These results highlight the promise of ensemble and hybrid approaches in financial forecasting, offering a more reliable and accurate framework for predicting stock prices. The work adds to the expanding body of research supporting the combination of deep learning and conventional techniques to enhance risk assessment and financial market analysis.