Uncertainty-Adjusted Sorting for Asset Pricing with Machine Learning

ArXiv ID: 2601.00593 “View on arXiv”

Authors: Yan Liu, Ye Luo, Zigan Wang, Xiaowei Zhang

Abstract

Machine learning is central to empirical asset pricing, but portfolio construction still relies on point predictions and largely ignores asset-specific estimation uncertainty. We propose a simple change: sort assets using uncertainty-adjusted prediction bounds instead of point predictions alone. Across a broad set of ML models and a U.S. equity panel, this approach improves portfolio performance relative to point-prediction sorting. These gains persist even when bounds are built from partial or misspecified uncertainty information. They arise mainly from reduced volatility and are strongest for flexible machine learning models. Identification and robustness exercises show that these improvements are driven by asset-level rather than time or aggregate predictive uncertainty.

Keywords: Uncertainty-Adjusted Prediction, Asset Pricing, Portfolio Sorting, Machine Learning Models, Estimation Uncertainty, Equities

Complexity vs Empirical Score

  • Math Complexity: 7.0/10
  • Empirical Rigor: 9.0/10
  • Quadrant: Holy Grail
  • Why: The paper employs advanced statistical learning concepts like prediction intervals, quantile estimation, and cross-sectional uncertainty attribution, which requires a high level of mathematical sophistication. However, it is grounded in extensive empirical implementation, testing across a broad set of ML models on a large U.S. equity panel with rigorous robustness checks, transaction cost analysis, and detailed performance metrics.
  flowchart TD
    A["Research Goal:<br>Can uncertainty-adjusted sorting<br>improve ML portfolio performance?"]
    
    B["Data & Inputs<br>US Equity Panel<br>ML Model Predictions"]
    
    C["Methodology:<br>Construct Uncertainty-Adjusted<br>Prediction Bounds"]
    
    D["Compute Portfolios<br>Sort assets via:<br>1. Point Predictions<br>2. Uncertainty-Adjusted Bounds"]
    
    E["Key Findings<br>1. Improved Risk-Adjusted Returns<br>2. Driven by Asset-Level Uncertainty<br>3. Strongest with Flexible ML Models"]

    A --> B
    B --> C
    C --> D
    D --> E