A Causal Perspective of Stock Prediction Models

ArXiv ID: 2503.20987 “View on arXiv”

Authors: Unknown

Abstract

In the realm of stock prediction, machine learning models encounter considerable obstacles due to the inherent low signal-to-noise ratio and the nonstationary nature of financial markets. These challenges often result in spurious correlations and unstable predictive relationships, leading to poor performance of models when applied to out-of-sample (OOS) domains. To address these issues, we investigate \textit{“Domain Generalization”} techniques, with a particular focus on causal representation learning to improve a prediction model’s generalizability to OOS domains. By leveraging multi-factor models from econometrics, we introduce a novel error bound that explicitly incorporates causal relationships. In addition, we present the connection between the proposed error bound and market nonstationarity. We also develop a \textit{“Causal Discovery”} technique to discover invariant feature representations, which effectively mitigates the proposed error bound, and the influence of spurious correlations on causal discovery is rigorously examined. Our theoretical findings are substantiated by numerical results, showcasing the effectiveness of our approach in enhancing the generalizability of stock prediction models.

Keywords: domain generalization, causal representation learning, invariant feature representations, causal discovery, out-of-sample (OOS) generalizability, Equities

Complexity vs Empirical Score

• Math Complexity: 8.0/10
• Empirical Rigor: 6.5/10
• Quadrant: Holy Grail
• Why: The paper involves advanced mathematics with formal error bounds, Wasserstein distances, and causal derivation (High Math). It validates theory with numerical experiments and discusses implementation details like domain definition, though it lacks full backtest results or code (Moderate-High Rigor).

  flowchart TD
    A["Research Goal: Improve Out-of-Sample Generalizability of Stock Prediction Models"] --> B{"Methodology"}
    B --> C["Domain Generalization & Causal Representation Learning"]
    B --> D["Causal Discovery for Invariant Feature Representations"]
    C --> E["Theoretical Framework"]
    D --> E
    E --> F["Novel Error Bound<br>incorporating causal relationships &amp; market nonstationarity"]
    F --> G["Experiments: Mitigating Spurious Correlations"]
    G --> H["Outcome: Enhanced Generalizability &amp; Stable Predictions OOS"]
    E --> H

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    class C,D,E methodology;
    
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    class H outcome;

    subgraph Data_Inputs["Data &amp; Inputs"]
        direction LR
        I1["Financial Market Data"]
        I2["Multi-Factor Models"]
    end

    Data_Inputs --> B
    B --> Data_Inputs

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    class I1,I2 data;