New Methods in Asset Pricing
Friday, Jan. 3, 2020 10:15 AM - 12:15 PM (PDT)
- Chair: Svetlana Bryzgalova, London Business School
New Factors Wanted: Evidence from a Simple Specification Test
AbstractHow 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
AbstractWe 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
AbstractWe 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.
- G1 - General Financial Markets