Enhancing Stock Price Forecasting and Trading Strategy through Bidirectional LSTM Integration
Published in 2024 5th International Conference on Machine Learning and Human-Computer Interaction (MLHMI), 2024
This paper proposes a Bidirectional LSTM (Bi-LSTM) architecture for time-series stock price prediction, demonstrating superior accuracy over standard LSTM and a trading strategy that outperforms a buy-and-hold baseline.
Recommended citation: Jiayi Liu, Yufeng Yang, Teng Lin, Chuanhui Peng, and Yancong Deng. (2024). "Enhancing Stock Price Forecasting and Trading Strategy through Bidirectional LSTM Integration." 2024 5th International Conference on Machine Learning and Human-Computer Interaction (MLHMI), pp. 22–25. DOI: 10.1109/MLHMI63000.2024.00013
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