New Methods and Portfolios for the Cross Section
Sunday, Jan. 3, 2021 10:00 AM - 12:00 PM (EST)
- Chair: Michael Weber, University of Chicago
New and Old Sorts: Implications for Asset Pricing
AbstractWe study the returns to characteristic-sorted portfolios up to five years after portfolio formation. Among a set of 56 characteristics, we find large pricing errors between the contemporaneous returns of new and old sorts, where new sorts use the most recent observations of firm characteristics. These relative pricing errors are not captured by existing asset pricing models and have been overlooked by standard tests using only returns to new sorts. Thus, pricing errors across horizons provide new and powerful information to test asset pricing models. Further, we show that these pricing errors are strongly related to a characteristic's market beta and connected to the difference in return between new and old stocks in the characteristic-sorted portfolios. We argue that investors can improve the performance of characteristic-based strategies by considering past observations of firm characteristics.
Forest through the Trees: Building Cross-Sections of Stock Returns
AbstractWe show how to build a cross-section of asset returns, that is, a small set of basis or test assets that capture complex information contained in a given set of stock characteristics and span the Stochastic Discount Factor (SDF). We use decision trees to generalize the concept of conventional sorting and introduce a new approach to the robust recovery of the SDF, which endogenously yields optimal portfolio splits. These low-dimensional value-weighted long-only investment strategies are well diversified, easily interpretable, and reflect many characteristics at the same time. Empirically, we show that traditionally used cross-sections of portfolios and their combinations, especially deciles and long-short anomaly factors, present too low a hurdle for model evaluation and serve as the wrong building blocks for the SDF. Constructed from the same pricing signals as conventional double or triple sorts, our cross-sections have significantly higher (up to a factor of three) out-of-sample Sharpe ratios and pricing errors relative to the leading reduced-form asset pricing models.
Macroeconomic Content of Characteristics-Based Asset Pricing Models: A Machine Learning Analysis
AbstractWe consider seven characteristics-based asset pricing models and explore whether the non-market components of their stochastic discount factors (SDFs) are associated with macroeconomic shocks. Our analysis involves a comprehensive set of 120 macroeconomic variables and uses machine learning techniques to mitigate the overfitting problem caused by a large number of explanatory variables. We find that macroeconomic shocks are totally unrelated to the non-market SDF components. This conclusion extends to several theory-motivated macroeconomic shocks. Our results suggest that the empirical success of characteristics-based asset pricing models is produced by their ability to identify behavioral factors in stock returns rather than macroeconomic risks.
- G1 - Asset Markets and Pricing