Data Envelopment Analysis and Multifactor Asset Pricing Models

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.

Neural networks for estimating Macro Asset Pricing model in football clubs

“…In conclusion, there are no previous studies that have estimated the price of assets of football clubs through the macro asset pricing model with non‐linear techniques. Solórzano‐Taborga et al (2020) state that further research is needed on the robustness of the efficiency of asset pricing models, by implementing sophisticated computational methodologies. Ozbayoglu et al (2020) conclude that the asset pricing model has usually been estimated with the OLS statistical methodology and Deep Learning has not been implemented in the football industry field.…”

“…Second, we improve the precision of asset pricing models concerning that obtained in previous studies with artificial neural networks, since Deep Learning can help solve many of the world's problems associated with unpredictable investor behaviour (Kim et al, 2020). In addition, Solórzano‐Taborga et al (2020) introduce an innovative and unexplored focus on creating new factors that can be exploited in asset pricing models. However, they state that further research is needed on the robustness of the efficiency factor to changes in inputs and outputs, by implementing sophisticated computational methodologies.…”

The Gibbons, Ross, and Shanken Test for Portfolio Efficiency: A Note Based on Its Trigonometric Properties

“…Suarez and Alonso-Conde [13] looked at an entropy-based decomposition that captures the divergence between the factor-mimicking portfolio and the minimum-variance pricing kernel as distinct from quadratic test statistics, such as the GRS-test (determined as a function of pricing errors). Solórzano-Taborga et al [14], utilize the GRS test for identifying restrictions (they termed 'efficiency factor') to test the null of asset pricing errors equaling zero. Barillas et al [15] utilized the GRS test to accommodate the comparison of nonnested models as in a squared Sharpe-ratio (Sharpe [16]).…”

Business Analytics for Managing Performance of Microfinance Institutions: A Flexible Management of the Implementation Process

“…Data Envelopment Analysis (DEA) provides managers with an analytics approach that allows them to assess and manage the performance of either different branches or units, implement a benchmarking process, and identify best practices [37], not always visible when using other more common management methodologies. This approach has been thought as very relevant for supporting the BA solution and providing guidance to managers [34].…”