Mosaics of Predictability
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.
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Copy CitationLin William Cong, Guanhao Feng, Jingyu He, and Yuanzhi Wang, "Mosaics of Predictability," NBER Working Paper 35158 (2026), https://doi.org/10.3386/w35158.Download Citation
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