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Hybrid Data Decomposition-Based Deep Learning for Bitcoin Prediction and Algorithm Trading

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Document pages: 33 pages

Abstract: Although Bitcoin has attracted significant attention from investors and policy makers, the empirical works in the Bitcoin forecasting and trading support systems are still at an early stage. This study proposes a novel data decomposition based hybrid bidirectional deep learning model, namely VMD-LMH-BiGRU, in forecasting the daily price change in the Bitcoin market and conducting algorithmic trading on the market. Two main steps are involved in our methodology framework, i.e., data-decomposition for inner factors extraction, and bidirectional deep learning for forecasting the Bitcoin price. The results demonstrate that the proposed model outperforms four other benchmark models, including econometric models, machine learning models and deep learning models. Furthermore, the proposed model achieved higher investment returns than all benchmark models and the buy-and-hold strategy in the trading simulation. The robustness of the model is verified through multiple forecasting periods and testing intervals.

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