Risks versus Mispricing: Decomposing Asset pricing Anomalies via Classification
74 Pages Posted: 17 Jun 2020 Last revised: 13 Jan 2021
Date Written: January 20, 2020
I use classification-based machine-learning methods to decompose 32 anomaly payoffs
into risk exposures and mispricing. The component driven by risk earns statistically insignificant
returns, despite its efficacy in explaining the time-series variation in anomaly payoffs.
The mispricing component is driven by biased expectations and earns significant returns that
also subsume anomaly payoffs. These findings indicate that the unconditional averages of
anomaly returns can be fully explained by biased expectations, whereas risk exposures play
an important role in explaining the time-series variation in anomaly returns.
Keywords: Analysts’ Expectation Bias, Investment Strategies, Classifications, Out-of-sample Forecasts,Classifications, Permutation Feature Importance, Consumption, Intermediary, Analysts’ Forecast Errors, Disagreement, Macroeconomic Variables, Supported Vector Machine, Decision Tree, Random Forest
JEL Classification: G02;G14
Suggested Citation: Suggested Citation