Data Envelopment Analysis and Multifactor Asset Pricing Models

Pablo Solórzano-Taborga,Ana Belén Alonso-Conde,Javier Rojo-Suárez

doi:10.3390/ijfs8020024

Pablo Solórzano-Taborga, Ana Belén Alonso-Conde + Show 1 more

Open Access

https://doi.org/10.3390/ijfs8020024

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Abstract

Recent literature shows that market anomalies have significantly diminished, while research on market factors has largely improved the performance of asset pricing models. In this paper we study the extent to which data envelopment analysis (DEA) techniques can help improve the performance of multifactor models. Specifically, we test the explanatory power of the Fama and French three-factor model, combined with an additional factor based on DEA, on a sample of 2101 European equity funds, for the period from 2001 to 2016. Accordingly, we first form the fund portfolios that constitute our test assets and create the efficiency factor. Secondly, we estimate the prices of risk tied to the four factors using ordinary least squares (OLS) on a two-stage cross-sectional regression. Finally, we use the R-squared statistic estimated by generalized least squares (GLS), as well as the Gibbons Ross and Shanken test and the J-test for overidentifying restrictions in order to study the performance of the model, including and omitting the efficiency factor. The results show that the efficiency factor improves the performance of the model and reduces the pricing errors of the assets under consideration, which allows us to conclude that the efficiency index may be used as a factor in asset pricing models.

Highlights

In recent decades, research on financial market anomalies has given rise to a large number of new factors strongly related to asset returns, which have contributed to substantially improving the performance of multifactor asset pricing models
We use the R-squared statistic estimated by generalized least squares (GLS), as well as the Gibbons Ross and Shanken test and the J-test for overidentifying restrictions in order to study the performance of the model, including and omitting the efficiency factor
The results show that the efficiency factor improves the performance of the model and reduces the pricing errors of the assets under consideration, which allows us to conclude that the efficiency index may be used as a factor in asset pricing models

Summary

Introduction

Research on financial market anomalies has given rise to a large number of new factors strongly related to asset returns, which have contributed to substantially improving the performance of multifactor asset pricing models. Previous research has rarely exploited the relationship that exists between DEA rankings and efficient portfolios to improve the performance of asset pricing models, but it has typically focused on using DEA to suggest specific methods for sorting mutual funds On this basis, in this paper we combine the Fama–French three-factor model (Fama and French 1993) with DEA tools, resulting in a new four-factor model. To the best of our knowledge, DEA methods have scarcely been used to design risk factors useful in asset pricing models, with the exception of Rubio et al (2018), who take a different approach than the one used in this paper to study the explanatory power of DEA scores for mutual fund returns in the US market.

Data and Efficiency Analysis

Findings

Multifactor Asset Pricing Models Results