NBIM-1: Understanding the Long-run Drivers of Asset Prices
Long-Term Discount Rates do not Vary Across Firms
1Aalto University, Finland; 2University of Southern California
Long-term expected returns appear to vary little, if at all, in the cross section of stocks. We devise a bootstrapping procedure that injects small amounts of variation into expected returns and show that even negligible differences in expected returns, if they existed, would be easy to detect. Markers of such differences, however, are absent from actual stock returns. Our estimates are consistent with production-based asset pricing models such as Berk, Green, and Naik (1999) and Gomes, Kogan, and Zhang (2003) in which firms’ risks change over time. Our results imply that stock market anomalies have only a limited effect on firm valuations.
The Cross-Section of Risk-Taking and Asset Prices
1Paris School of Economics, France; 2London Business School, United Kingdom
The distribution of institutional investor risk-taking carries significant explanatory power for the cross-section of asset returns. We compute an investor-level Value-at-Risk (VaR) measure - our proxy for ex-ante riskiness - from a atructural model with stochastic volatility that we estimate with a particle filter. Our pricing factor - CrossRisk - is then constructed from shocks to the procyclical dispersion of the time-varying VaR distribution. CrossRisk is able to price equity, bond, CDS, options, currency, and commodity market portfolios comparably to numerous single and multi-factor benchmarks. We show that the mechanism behind our results is the extensive margin - dynamic entry and exit of investors into the risky market. A synthetic highminus-low CrossRisk beta pre-sorted equity portfolio built on the full universe of CRSP firms has an annualized returns spread of 5.8%.
On the Economic Value of Stock Market Return Predictors
1University of Arizona; 2University of Texas at Austin; 3University of Missouri
Kandel and Stambaugh (1996) demonstrate that forecasting variables with weak statistical support in predictive return regressions can exert considerable economic influence on portfolio decisions. Using a Bayesian vector autoregression framework with stochastic volatility in market returns and predictor variables, we assess the economic value of return predictability and reach a complementary conclusion. Statistically strong predictors can be economically unimportant if they tend to take extreme values in high-volatility periods, have low persistence, and/or follow distributions with fat tails. Several popular predictors exhibit these properties such that their impressive statistical results do not translate into large economic gains for investors.