InvariantStock: Learning Invariant Features for Mastering the Shifting Market
Abstract: Accurately predicting stock returns is crucial for effective portfolio management. However, existing methods often overlook a fundamental issue in the market, namely, distribution shifts, making them less practical for predicting future markets or newly listed stocks. This study introduces a novel approach to address this challenge by focusing on the acquisition of invariant features across various environments, thereby enhancing robustness against distribution shifts. Specifically, we present InvariantStock, a designed learning framework comprising two key modules: an environment-aware prediction module and an environment-agnostic module. Through the designed learning of these two modules, the proposed method can learn invariant features across different environments in a straightforward manner, significantly improving its ability to handle distribution shifts in diverse market settings. Our results demonstrate that the proposed InvariantStock not only delivers robust and accurate predictions but also outperforms existing baseline methods in both prediction tasks and backtesting within the dynamically changing markets of China and the United States.
Submission Length: Regular submission (no more than 12 pages of main content)
Changes Since Last Submission: In response to the reviewers' comments, we have made the following revisions:
1. We have added a new subsection, 4.4 Inference Progress, in Section 4 to clarify the computational aspects.
2. In Section 5.2, we have provided a clear description of the evaluation metrics.
3. In Section 5.9, we have included a comparison of results using different feature selection techniques.
4. In Section 7, we have summarized the limitations of our study and outlined potential directions for future research.
Code: https://github.com/Haiyao-Nero/InvariantStock
Assigned Action Editor: ~Elliot_Meyerson1
Submission Number: 2545
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