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Asset pricing models with measurement error problems: A new framework with Compact Genetic Algorithms

Author

Listed:
  • Erkin Diyarbakirlioglu

    (IRG - Institut de Recherche en Gestion - UPEC UP12 - Université Paris-Est Créteil Val-de-Marne - Paris 12 - Université Gustave Eiffel)

  • Marc Desban

    (IRG - Institut de Recherche en Gestion - UPEC UP12 - Université Paris-Est Créteil Val-de-Marne - Paris 12 - Université Gustave Eiffel)

  • Souad Lajili Jarjir

    (IRG - Institut de Recherche en Gestion - UPEC UP12 - Université Paris-Est Créteil Val-de-Marne - Paris 12 - Université Gustave Eiffel)

Abstract

We implement a new framework to mitigate the errors-in-variables (EIV) problem in the estimation of asset pricing models. Considering an international data of portfolio stock returns from 1990 to 2021 widely used in empirical studies, we highlight the importance of the estimation method in time-series regressions. We compare the traditional Ordinary-Least Squares (OLS) method to an alternative estimator based on a Compact Genetic Algorithm (CGA) in the case of the CAPM, three-, and five-factor models. Based on intercepts, betas, adjusted R2 , and the Gibbons, Ross and Shanken (1989) test, we find that the CGA-based method outperforms overall the OLS for the three asset pricing models. In particular, we obtain less statistically significant intercepts, smoother R2 across different portfolios and lower GRS test statistics. Specifically, in line with Roll's critique (1977) on the unobservability of the market portfolio, we reduce the attenuation bias in market risk premium estimates. Moreover, our results are robust to alternative methods such as Instrumental Variables estimated with Generalized-Method of Moments (GMM). Our findings have several empirical and managerial implications related to the estimation of asset pricing models as well as their interpretation as a popular tool in terms of corporate financial decision-making.

Suggested Citation

  • Erkin Diyarbakirlioglu & Marc Desban & Souad Lajili Jarjir, 2022. "Asset pricing models with measurement error problems: A new framework with Compact Genetic Algorithms," Post-Print hal-03643083, HAL.
    • Handle: RePEc:hal:journl:hal-03643083
    DOI: 10.3917/fina.432.0001
    Note: View the original document on HAL open archive server: https://hal.science/hal-03643083
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    References listed on IDEAS

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