Jack Treynor Prize Winners

2016

Credit-Implied Volatility
Bryan Kelly, University of Chicago
Gerardo Manzo, University of Chicago
Diogo Palhares, AQR

The pricing of corporate credit can be succinctly understood via the credit-implied volatility (CIV) surface. Using a method analogous to the estimation of implied volatility from options contracts, the authors compute credit-implied volatility each month from the firm-by-maturity panel of CDS spreads using the Merton model. The process transforms CDS spreads into units of asset volatility. The CIV surface facilitates direct comparison of credit spreads at different “moneyness” (firm leverage) and time to maturity. The authors use this framework to characterize the behavior of corporate credit markets. They examine moneyness (leverage); they show that of credit spreads dynamics can be parsimoniously described with three clearly interpretable factors; and they examine the cross section of CDS risk premia.

Extrapolation Bias and the Predictability of Stock Returns by Price-Scaled Variables
Stefano Cassella, Krannert School of Management, Purdue University
Huseyin Gulen, Krannert School of Management, Purdue University

Using survey data on expectations of future stock returns, the authors estimate the degree of extrapolation bias (DOX)—the belief that what has happened recently will continue to happen—in investor expectations. Considerable time-series variation exists in the DOX, evidence shows that it can predicted to a meaningful extent.  The authors show that the ability of the dividend-price ratio to predict the equity premium is contingent on the DOX. There is Predictability is when the DOX is high and weak otherwise.  These results help answer a critical question: when will an overvalued asset, or even a bubble, experience a correction?.

Lazy Prices
Lauren Cohen, Harvard Business School
Christopher Malloy, Harvard Business School
Quoc Nguyen, University of Illinois at Chicago

When making required regulatory financial reports, firms very often repeat the same MD&A texts that they most recently used. Changes in these texts can be quite informative. Using the complete history of regular quarterly and annual filings by U.S. corporations from 1995-2014, the authors show that changes to the language and construction of financial reports have strong implications for firms’ future returns: a portfolio that shorts “changers” and buys “non-changers” earns up to 188 basis points per month (over 22% per year) in abnormal returns in the future. Changes in language referring to the executive (CEO and CFO) team, or regarding litigation, are especially informative for future returns.


 

2015

The Credit Spread Puzzle in the Merton Model—Myth or Reality?
Peter Feldhutter, London Business School
Stephen Schaefer, London Business School

The Merton model links bond values to stock values through option pricing theory. Past tests of the model have been unsatisfactory because they depend on default rates which are hard to estimate. This paper shows that when default rates are measured over long periods, the model explains the average level of investment grade spreads and captures the time series variation of the BBB-AAA spread well. The paper further shows that using data on individual firms—rather than a representative firm—is important for matching the slope of the term structure of credit spread.

Low Risk Anomalies?
Paul Schneider, University of Lugano and Swiss Finance Institute
Christian Wagner, Copenhagen Business School
Josef Zechner, CEPR and ECGI, WU Vienna

The stocks of low risk firms have performed surprisingly well when compared to the predictions of standard asset pricing models. This study shows that their performance can be explained by return skewness—the tendency for large negative returns to be more common than large positive returns. Such returns generally are associated with financial distress and the risk of default. With increasing downside risk, the standard capital asset pricing model (CAPM) increasingly overestimates expected equity returns relative to firms’ true (skew-adjusted) market risk.

A Protocol for Factor Identification
Kuntara Pukthuanthong, University of Missouri
Richard Roll, California Institute of Technology

Asset pricing models generally examine various factors for their ability to predict average returns. Several hundred factors have been suggested in the literature. This study proposes a protocol for determining which factors are related to risks and which are related to mean returns. The results will allow quantitative investors to better construct portfolios and to understand the risk and expected returns associated with their portfolios.


 

2014

Betting Against Beta or Demand for Lottery
Turan G. Bali, Georgetown University
Stephen J. Brown, New York University and University of Melbourne
Scott Murray, University of Nebraska-Lincoln
Yi Tang, Fordham University

Recent academic research has shown that stocks with high betas have worse performance than low beta stocks, which violates conventional finance theory. Beta is the tendency for a stock to rise or fall with the broader market. The evidence in this paper suggests that high beta stocks also or fall with the broader market. The evidence in this paper suggests that high beta stocks also tend to provide lottery-like returns and investors seeking these lottery-like payoffs push up the prices of high beta stocks, causing them to subsequently under perform.

The Shorting Premium and Asset Pricing Anomalies
Itamar Drechsler, New York University and NBER
Qingyi (Freda) Drechsler, Wharton Research Data Service

Academics and practitioners have identified several asset-pricing anomalies that can be associated with seemingly profitable trading strategies. This paper shows that many of these anomalies occur in stocks for which there are a limited supply of shares that can be shorted, or where the costs of shorting are high. Thus, some of the apparent profits available to shorting these stocks don’t exist because either they cannot be shorted or it is too expensive to do so.

X-CAPM: An Extrapolative Capital Asset Pricing Model
Nicholas Barberis, Yale School of Management
Robin Greenwood, Harvard Business School
Lawrence Jin, Yale School of Management
Andrei Shleifer, Harvard University

The Capital Asset Pricing Model (CAPM) is the most important model practitioners and academics use to understand the relation between expected returns and risk. However, empirical studies suggest that it does not work well in practice. This paper argues that asset pricing is the result of a tension between rational investors whose behavior is consistent with the CAPM and other rational investors who extrapolate future investment returns from recent investment returns.