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Mosaics of Predictability

Author

Listed:
  • Lin William Cong
  • Guanhao Feng
  • Jingyu He
  • Yuanzhi Wang

Abstract

We argue that return predictability is a latent, asset-specific, and state-dependent characteristic. We develop an interpretable Panel Tree that endogenously partitions the U.S. equity panel into out-of-sample and persistent “mosaic” patterns, and estimate cluster-specific forecasting models. Predictability concentrates in stocks with large earnings surprises, high earnings–price ratios, and low trading volume. It is countercyclical, stronger when market dividend yields are high and liquidity is low. Accounting for predictability heterogeneity, which conventional models ignore, improves forecasts and yields portfolios with out-of-sample Sharpe ratios around 2. Across 50 years of data, the mosaic map shows where signals arise and where noise dominates.

Suggested Citation

  • Lin William Cong & Guanhao Feng & Jingyu He & Yuanzhi Wang, 2026. "Mosaics of Predictability," NBER Working Papers 35158, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:35158
    Note: AP
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    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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