InvariantStock: Learning Invariant Features for Mastering the Shifting Market
Preprint
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.Keywords
All Related Versions
This publication has 8 references indexed in Scilit:
- Stock Movement Prediction Based on Bi-Typed Hybrid-Relational Market Knowledge Graph Via Dual Attention NetworksIEEE Transactions on Knowledge and Data Engineering, 2022
- Pearson Correlation Coefficient-Based Performance Enhancement of Broad Learning System for Stock Price PredictionIEEE Transactions on Circuits and Systems II: Express Briefs, 2022
- Hierarchical Adaptive Temporal-Relational Modeling for Stock Trend Prediction2021
- Enhancing Stock Movement Prediction with Adversarial Training2019
- Stock Market Trend Prediction Using High-Order Information of Time SeriesIEEE Access, 2019
- Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation2014
- Combining multiple feature selection methods for stock prediction: Union, intersection, and multi-intersection approachesDecision Support Systems, 2010
- Gradient-based learning applied to document recognitionProceedings of the IEEE, 1998