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New Methods in Asset Pricing

Paper Session

Friday, Jan. 3, 2020 10:15 AM - 12:15 PM (PDT)

Manchester Grand Hyatt, Seaport G
Hosted By: American Finance Association
  • Chair: Svetlana Bryzgalova, London Business School

Correcting Misspecified Stochastic Discount Factors

Raman Uppal
,
EDHEC Business School
Paolo Zaffaroni
,
Imperial College London
Irina Zviadadze
,
Stockholm School of Economics

Abstract

We show how, given a misspecified stochastic discount factor (SDF), one can construct an admissible SDF, namely an SDF that prices assets correctly. We first extend the traditional Arbitrage Pricing Theory (APT) to capture misspecification from both pervasive (systematic) pricing errors and idiosyncratic pricing errors. The constructed admissible SDF, which uses the extended APT as its foundation, satisfies the Hansen and Jagannathan (1991) bound exactly. If the number of assets N is large, the admissible SDF recovers the contribution of the missing pervasive factors completely without requiring one to identify the missing factors. Indeed, projecting the correction term of the SDF on the space spanned by the candidate missing factors, achieves an R-square that converges to one as N increases. Our approach applies also to nonlinear SDFs that typically characterize equilibrium asset-pricing models, where our correction fully accounts for the nonlinear components. Simulations demonstrate that the theory we develop is remarkably effective in correcting various sources of misspecification.

New Factors Wanted: Evidence from a Simple Specification Test

Ai He
,
Emory University
Dashan Huang
,
Singapore Management University
Guofu Zhou
,
Washington University-St. Louis

Abstract

How many factors do we need to explain the cross section of expected returns on US stocks? Well-known factor models have no more than five factors, which include the Fama-French three-factor model, the Carhart four-factor model, the Fama-French five-factor model, the Hou-Xue-Zhang Q-factor model, and the Stambaugh-Yuan mispricing-factor model. We examine the pricing errors (PEs) of these models, and models with up to 50 factors extracted from 70 factor proxies or from a large set of basis assets, with and without asset pricing restrictions. We find a systematic PE reversal pattern: a trading strategy that buys low PE decile portfolio and sells high PE decile portfolio earns significant abnormal returns across all the models. Our results show that the number of factors is much greater than previously thought in the literature. Of the economic forces, the reversal is partially driven but cannot be fully explained by limits-to-arbitrage, lottery demand, and expectation extrapolation.

Nowcasting Net Asset Values: The Case of Private Equity

Gregory Brown
,
University of North Carolina-Chapel Hill
Eric Ghysels
,
University of North Carolina-Chapel Hill
Oleg Gredil
,
Tulane University

Abstract

We apply advances in analysis of mix frequency and sparse data to estimate ``unsmoothed'' private equity (PE) Net Asset Values (NAVs) at the weekly frequency for individual funds. Using simulations and a large sample of buyout and venture funds, we show that our method yields superior estimates of fund asset values than a simple approach based on comparable public asset and as-reported NAVs. Our method easily accommodates additional data on PE fund portfolios, such as individual holdings, relevant mergers and acquisitions, secondary trades with fund stakes. The method is easily extended to other illiquid portfolios that are subject to appraisal bias while generating irregular and infrequent cash flows. We find significant variation in systematic and idiosyncratic risk exposures across PE funds and through time. In particular, the risk-return profile based on the samples from the 1990s is not representative of currently operating funds.

Distance-Based Metrics: A Bayesian Solution for Asset-Pricing Tests

Amit Goyal
,
University of Lausanne
Zhongzhi (Lawrence) He
,
Brock University
Sahn-Wook Huh
,
State University of New York-Buffalo

Abstract

We propose a unified set of distance-based performance metrics that address the power problems inherent in traditional measures for asset-pricing tests. From a Bayesian perspective, the distance metrics coherently incorporate both pricing errors and their standard errors. Measured in units of return, the metrics have an economic interpretation as the minimum cost of holding a dogmatic belief in a model. Our metrics identify the six-factor model of Fama and French (2018), the q^5 model of Hou, Mo, Xue, and Zhang (2018), and the Stambaugh and Yuan (2017) model as the top performers whose performance is economically indistinguishable. By contrast, the GRS and average-alpha-based statistics often lead to counter-intuitive rankings.
Discussant(s)
Cesare Robotti
,
University of Warwick
Mikhail Chernov
,
University of California-Los Angeles
Sophie Shive
,
University of Notre Dame
Alexander Chinco
,
University of Illinois
JEL Classifications
  • G1 - General Financial Markets