Tips to navigate the program:
- Overview of all papers on a specific day: click on the day (e.g. Date: Thursday, 24/Aug/2017). To download papers, you will need to access the session by clicking on its title first.
- Program index: click on the Authors tab below.
- Location name: to display all sessions taking place in that room
- Search box: to search for authors, papers and sessions.
Please notes that changes in the program might occur.
If your name is not displayed in the program, please register in our conference system.
If your paper information is not up to date, please send us an email at firstname.lastname@example.org.
APE-4: Factor Models
One-Factor Asset Pricing
1University of Miami; 2University of Manchester
We propose a single-factor asset pricing model based on an indicator function of consumption growth being less than its endogenous certainty equivalent. This certainty equivalent is derived from generalized disappointment aversion preferences, and it is located approximately one standard deviation below the conditional mean of consumption growth. Our single-factor model can explain the cross-section of expected returns for size, value, reversal, profitability, and investment portfolios at least as well as the Fama-French multi-factor models. Our results show strong empirical support for asymmetric preferences over gains and losses, and question the effectiveness of the smooth utility framework, which is traditionally used in consumption-based asset pricing.
A Diagnostic Criterion for Approximate Factor Structure
1European Commission Joint Research Centre; 2Università della Svizzera italiana, Swiss Finance Institute; 3University of Geneve, Swiss Finance Institute
We build a simple diagnostic criterion for approximate factor structure in large cross-sectional equity datasets. Given a model for asset returns with observable factors, the criterion checks whether the error terms are weakly cross-sectionally correlated or share at least one unobservable common factor. It only requires computing the largest eigenvalue of the empirical cross-sectional covariance matrix of the residuals of a large unbalanced panel. A general version of this criterion allows us to determine the number of omitted common factors. The panel data model accommodates both time-invariant and time-varying factor structures. The theory applies to random coefficient panel models with interactive fixed effects under large cross-section and time-series dimensions. The empirical analysis runs on monthly and quarterly returns for about ten thousand US stocks from January 1968 to December 2011 for several time-invariant and time-varying specifications. For monthly returns, we can choose either among time-invariant specifications with at least four financial factors, or a scaled three-factor specification. For quarterly returns, we cannot select macroeconomic models without the market factor.
A Portfolio Perspective on the Multitude of Firm Characteristics
1London Business School; 2Lancaster University; 3Universidad Carlos III de Madrid; 4EDHEC
A multitude of variables have been proposed to predict the cross section of expected stock returns. Our objective is to study which are jointly significant from a portfolio perspective; that is, for an investor who cares not only about expected returns, but also about portfolio risk, transaction costs, and out-of-sample performance. Based on a dataset with more than 50 firm-specific characteristics, we highlight three findings. First, without transaction costs, only a small number of characteristics - about six - are significant. Second, with transaction costs, the number of jointly significant characteristics increases from six to about 15. This is because the trades in the underlying stocks required to rebalance different characteristics often cancel each other out and thus, transaction costs decrease with the number of characteristics exploited. Third, investors can identify ex-ante combinations of characteristics that result in abnormal out-of-sample returns net of transaction costs that are not explained by the Fama and French (2015) and Hou, Xue, and Zhang (2014) factors. Finally, we demonstrate how our portfolio approach relates to standard time-series and cross-sectional regression.
Contact and Legal Notice · Contact Address:
Conference: EFA 2017
|Conference Software -
ConfTool Pro 2.6.113+TC
© 2001 - 2017 by H. Weinreich, Hamburg, Germany