2015 Jack Treynor Prize Winners

The Institute for Quantitative Research in Finance (The Q Group) is pleased to announce the 2015 winners of its annual Jack Treynor Prizes. The Prize was instituted last year to recognize superior academic working papers with potential applications in the fields of investment management and financial markets.

The three 2015 winners
(in alphabetical order by paper title) are:

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. Read full paper.

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. Read full paper.

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. Read full paper.