Hybrid Deep Learning based Adaptive Model for Medium-Term Stock Market Forecasting
DOI:
https://doi.org/10.15662/IJARCST.2025.0802010Keywords:
Stock Market Forecasting, Deep Learning, Reinforcement Learning, LSTM, Transformer, CNN, Sentiment AnalysisAbstract
An adaptive hybrid deep learning model for medium-term stock market forecasting is presented in this work. The suggested approach combines Convolutional Neural Networks (CNN), Transformer models, Long Short- Term Memory (LSTM), and Reinforcement Learning (RL) to increase the forecast accuracy for key stock indexes such as the Dow Jones, DAX, and NASDAQ-100 over a trading horizon of 70 days. Sentiment analysis and technical indicators are examples of advanced feature engineering techniques that improve forecast reliability. By constantly adjusting to market swings, the suggested approach maximizes investment plans. Comparing experimental results to current methods, accuracy has improved by 85–90%.
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