Mitigating Distribution Shift in Stock Price Data via Return-Volatility Normalization for Accurate Prediction

ArXiv ID: 2508.20108 “View on arXiv”

Authors: Hyunwoo Lee, Jihyeong Jeon, Jaemin Hong, U Kang

Abstract

How can we address distribution shifts in stock price data to improve stock price prediction accuracy? Stock price prediction has attracted attention from both academia and industry, driven by its potential to uncover complex market patterns and enhance decisionmaking. However, existing methods often fail to handle distribution shifts effectively, focusing on scaling or representation adaptation without fully addressing distributional discrepancies and shape misalignments between training and test data. We propose ReVol (Return-Volatility Normalization for Mitigating Distribution Shift in Stock Price Data), a robust method for stock price prediction that explicitly addresses the distribution shift problem. ReVol leverages three key strategies to mitigate these shifts: (1) normalizing price features to remove sample-specific characteristics, including return, volatility, and price scale, (2) employing an attention-based module to estimate these characteristics accurately, thereby reducing the influence of market anomalies, and (3) reintegrating the sample characteristics into the predictive process, restoring the traits lost during normalization. Additionally, ReVol combines geometric Brownian motion for long-term trend modeling with neural networks for short-term pattern recognition, unifying their complementary strengths. Extensive experiments on real-world datasets demonstrate that ReVol enhances the performance of the state-of-the-art backbone models in most cases, achieving an average improvement of more than 0.03 in IC and over 0.7 in SR across various settings.

Keywords: Distribution shift mitigation, Attention mechanisms, Stock price prediction, Normalization techniques, Geometric Brownian motion, Equity (Stocks)

Complexity vs Empirical Score

• Math Complexity: 7.0/10
• Empirical Rigor: 8.5/10
• Quadrant: Holy Grail
• Why: The paper includes advanced mathematical concepts like Geometric Brownian Motion and stochastic processes, alongside rigorous empirical validation with real-world datasets, code availability, and specific performance metrics (IC, SR).

  flowchart TD
    A["Research Goal: Mitigate distribution shifts<br>in stock price data to improve prediction"] --> B["Data Processing: Raw Stock Price Data"]
    B --> C["ReVol Methodology"]
    
    subgraph C ["ReVol Framework"]
        C1["Step 1: Normalization<br>Remove return, volatility, price scale"] --> C2["Step 2: Attention Module<br>Estimate characteristics to reduce anomalies"] --> C3["Step 3: Reintegration<br>Restore traits lost during normalization"]
    end
    
    C3 --> D["Hybrid Modeling"]
    
    subgraph D ["Computational Processes"]
        D1["Long-term: Geometric Brownian Motion"]
        D2["Short-term: Neural Networks"]
    end
    
    D1 & D2 --> E["Final Prediction"]
    
    E --> F["Key Findings"]
    
    subgraph F ["Experimental Outcomes"]
        F1["Avg IC improvement > 0.03"]
        F2["Avg SR improvement > 0.7"]
        F3["Enhances SOTA backbones"]
    end