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Despite the popularity of trend-following strategies in financial markets, they often lack adaptability to the emerging varied markets. Recently, deep learning (DL) methods demonstrate the effectiveness in stock-market analysis. Thus, the application of DL methods to enhance trend-following strategies has received substantial attention. However, there are two key challenges to be solved before the adoption of DL methods in enhancing trend-following strategies: (1) how to design an effective data selector to include more related data? (2) how to design a profit-based model to enhance strategies? To address these two challenges, this paper contributes to a new framework, namely profit-based deep architecture with the integration of reinforced data selector (PDA-RDS) to improve the effectiveness of DL methods. In particular, profit-based deep architecture (PDA) integrates a dynamic profit weight and a focal loss function to obtain high profits. In addition, reinforced data selector (RDS) is constructed to select high-quality training samples and a training-aware immediate reward is designated to improve the effectiveness of RDS. Extensive experiments on both U.S. and China stock-market datasets demonstrate that PDA-RDS outperforms the state-of-the-art baseline methods in terms of higher cumulative percentage rate and average percentage rate, both of which are crucial to investment strategies.
|Journal||World Wide Web|
|Publication status||E-pub ahead of print - 14 Oct 2022|
Bibliographical noteFunding Information:
The research is supported by the Key-Area Research and Development Program of Guangdong Province (2020B010165003), the National Natural Science Foundation of China under project (62032025), and the Technology Program of Guangzhou, China (202103050004), Faculty Research Grants (DB22A5 and DB22B7) of Lingnan University, Hong Kong.
© 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
- Data selection
- Deep learning
- Reinforcement learning
- Transfer learning
- Trend-following strategy
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Modeling Bitcoin Transaction Network via Structural Identity Representation
XIE, H. & DAI, H. H.
1/07/22 → 30/06/23
Project: Grant Research
Parallel NMTF for Text Data Co-clustering
1/12/21 → 30/11/22
Project: Grant Research