CLAM: A Synergistic Deep Learning Model for Multi-Step Stock Trend Forecasting

This paper introduces CLAM, a hybrid deep learning framework that integrates CNNs, LSTMs, and Attention Mechanism (AM) for straightforward multi-step stock trend forecasting. By leveraging CNNs for spatial feature extraction, LSTMs for capturing temporal dependencies, and AM for dynamically focusing on relevant data, CLAM significantly outperforms traditional models in predictive accuracy. Evaluated on diverse stock datasets from different industries, CLAM demonstrates an average reduction of over 80% in MAE and RMSE compared to standalone CNN, LSTM, and fused CNN-LSTM. The model’s ability to capture both short-term and long-term trends is particularly advantageous for real-time financial trading, resulting in 75% trend prediction accuracy, with most cases witnessing consecutive accurate forecasts of flash crashes or uptrends, which aids in strategic investment decisions and risk management. Code and data are available at: https://anonymous.4open.science/r/CNN-LSTM-AM-AB13/src/CLAM.ipynb .