Mosaics of Predictability

Existing studies on asset return predictability focus on aggregate performance. We examine the often-overlooked heterogeneity in return predictability across different assets and macroeconomic regimes. A novel tree-based asset clustering methodology is introduced to partition the panel of asset-return observations according to return predictability, using high-dimensional asset characteristics and aggregate time-series predictors. When implemented on U.S. equities over the past five decades, we find that some characteristics-managed (unexpected earnings, earnings-to-price, and cashflow-to-price) and/or macro-based (term spread and net equity issuance) clusters are more predictable, resulting in a heterogeneous predictive model with outperformance. Finally, it is revealed that less predictable clusters exhibit lower risk-adjusted investment performance, highlighting the empirical link between return predictability and trading profitability.