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Session Overview
Session
APE-4: Factor Models
Time:
Saturday, 26/Aug/2017:
9:00am - 10:30am

Session Chair: Andrea Gamba, University of Warwick
Location: SN169

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Presentations

One-Factor Asset Pricing

Stefanos Delikouras1, Alexandros Kostakis2

1University of Miami; 2University of Manchester

Discussant(s): Adam Farago (University of Gothenburg)

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.

EFA2017-723-APE-4-Delikouras-One-Factor Asset Pricing.pdf

A Diagnostic Criterion for Approximate Factor Structure

Elisa Ossola1, Patrick Gagliardini2, Olivier Scaillet3

1European Commission Joint Research Centre; 2Università della Svizzera italiana, Swiss Finance Institute; 3University of Geneve, Swiss Finance Institute

Discussant(s): Cesare Robotti (University of Georgia)

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.

EFA2017-1263-APE-4-Ossola-A Diagnostic Criterion for Approximate Factor Structure.pdf

A Portfolio Perspective on the Multitude of Firm Characteristics

Victor DeMiguel1, Albert Martin-Utrera2, Francisco J. Nogales3, Raman Uppal4

1London Business School; 2Lancaster University; 3Universidad Carlos III de Madrid; 4EDHEC

Discussant(s): Georgios Skoulakis (University of British Columbia)

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.

EFA2017-716-APE-4-DeMiguel-A Portfolio Perspective on the Multitude of Firm Characteristics.pdf


 
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