A Dual Output Temporal Convolutional Network With Attention Architecture for Stock Price Prediction and Risk Assessment - Arindam Kishor Biswas; Md Shariful Alam Bhuiyan; Md Nazmul Hossain Mir; Ashifur Rahman; M. F. Mridha; Md Rashedul Islam; Yutaka Watanobe (IEEE Access, 2025)
In this paper, we propose a novel deep learning model that integrates a Temporal Convolutional Network (TCN) with an Attention mechanism to predict stock prices and assess risk for MasterCard (MA) and Visa (V). The model is designed with a dual output to forecast future stock prices (Open, Close, High, Low) while simultaneously predicting risk metrics, including volatility and the Sharpe Ratio. By utilizing dilated convolutions, the TCN captures both short-term and long-term dependencies in the stock price data, and the attention layer focuses on critical time steps for enhanced predictive accuracy. On a dataset covering over 15 years (2008-2024), the TCN with attention model achieved a mean absolute error (MAE) of 1.23 for MasterCard and 1.45 for Visa, outperforming LSTM and ARIMA baselines. Additionally, the model demonstrated strong performance in predicting risk metrics, with an MAE of 0.012 for volatility and 0.065 for the Sharpe Ratio. Backtesting on unseen data further validated the model’s robustness, with a backtest MAE of 1.25 for MasterCard and 1.50 for Visa. This dual-output architecture provides an accurate and interpretable solution for both stock price forecasting and risk assessment, offering a valuable tool for investors and risk managers.