Working Paper: NBER ID: w18554
Authors: Stefan Nagel
Abstract: I review recent research efforts in the area of empirical cross-sectional asset pricing. I start by summarizing the evidence on cross-sectional return predictability and the failure of standard (consumption) CAPM models and their conditional versions to explain these predictability patterns. One response in part of the recent literature is to focus on ad-hoc factor models, which summarize the cross-section of expected returns in parsimonious form, or on production-based approaches, which suggest links between firm characteristics and expected returns. Without imposing restrictions on investor preferences and beliefs, neither one of these two approaches can answer the question why investors price assets the way they do. Within the rational expectations paradigm, recent research that imposes such restrictions has focused on the ICAPM, long-run risks models, as well as frictions and liquidity risk. Approaches based on investor sentiment have focused on the development of empirical proxies for sentiment and for the limits to arbitrage that allow sentiment to affect prices. Empirical work that considers learning and adaptation of investors has worked with out-of-sample tests of cross-sectional predictability.
Keywords: No keywords provided
JEL Codes: G02; G12; G14
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| assets with different risk exposures (G11) | varying rates of return (G11) |
| stochastic discount factor (SDF) (D15) | expected returns (G17) |
| characteristics of the assets and the risk preferences of investors (G11) | expected returns (G17) |
| deviations from rational expectations (D84) | mispricing (D49) |
| learning (C91) | investor behavior (G41) |
| investor adaptation (G11) | asset pricing dynamics (G19) |
| sentiment (G41) | prices (P22) |
| rational models (D01) | understanding pricing (D49) |
| empirical evidence (C90) | complexities that challenge rational models (D80) |
| learning and sentiment (C92) | cross-sectional return predictability (G17) |