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Testing the new Fama and French factors with illiquidity: A panel data investigation

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  • François-Éric Racicot
  • William F. Rentz
  • Raymond Théoret

Abstract

We investigate the new Fama-French (FF, 2015, 2016) five factors augmented with a well-known illiquidity measure (Pástor and Stambaugh, 2003), using an innovative GMM robust instrumental variables estimator casted in a panel data framework. When using OLS, the augmented FF model seems to have explanatory power regarding the FF 12-sector returns. However, our panel data framework suggests that the only consistently significant factor is the market risk factor. Nevertheless, depending on the technique we use, we find that measurement errors may be the cause of this result, thus providing some empirical evidence in support of the new FF five-factor approach. As robustness checks, we also experiment with other liquidity measures ? like the Amihud (2002) ratio and the term-spread ? and bond-oriented factors. Across our 12 portfolios, the results are largely unchanged. We also apply our extended model to managed portfolios ? i.e., hedge fund portfolios. The returns of hedge fund strategies seem more responsive to the augmented FF five-factor model that includes illiquidity measures, especially when accounting for the subprime crisis. There is also evidence that the new FF factors embed illiquidity.

Suggested Citation

  • François-Éric Racicot & William F. Rentz & Raymond Théoret, 2018. "Testing the new Fama and French factors with illiquidity: A panel data investigation," Finance, Presses universitaires de Grenoble, vol. 39(3), pages 45-102.
    • Handle: RePEc:cai:finpug:fina_393_0045
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    Citations

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    Cited by:

    1. 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.
    2. Racicot, François-Éric & Théoret, Raymond & Gregoriou, Greg N., 2021. "The response of hedge fund higher moment risk to macroeconomic and illiquidity shocks," International Review of Economics & Finance, Elsevier, vol. 72(C), pages 289-318.
    3. Killins, Robert N., 2020. "Firm-specific, industry-specific and macroeconomic factors of life insurers’ profitability: Evidence from Canada," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    4. Han, Yufeng & Hu, Ou & Huang, Zhaodan, 2023. "A tale of idiosyncratic volatility and illiquidity shocks: Their correlation and effects on stock returns," International Review of Financial Analysis, Elsevier, vol. 86(C).
    5. François-Éric Racicot & Raymond Théoret, 2022. "Tracking market and non-traditional sources of risks in procyclical and countercyclical hedge fund strategies under extreme scenarios: a nonlinear VAR approach," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-56, December.
    6. López-García, M.N. & Trinidad-Segovia, J.E. & Sánchez-Granero, M.A. & Pouchkarev, I., 2021. "Extending the Fama and French model with a long term memory factor," European Journal of Operational Research, Elsevier, vol. 291(2), pages 421-426.
    7. Javier Rojo-Suárez & Ana Belén Alonso-Conde, 2020. "Impact of consumer confidence on the expected returns of the Tokyo Stock Exchange: A comparative analysis of consumption and production-based asset pricing models," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-31, November.

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