IDEAS home Printed from https://ideas.repec.org/p/ucm/doicae/1902.html
   My bibliography  Save this paper

Drawbacks in the 3-factor approach of Fama and French

Author

Listed:
  • David E. Allen

    (School of Mathematics and Statistics, University of Sydney, Department of Finance, Asia University, Taiwan, and School of Business and Law, Edith Cowan University, Australia.)

  • Michael McAleer

    ( Department of Quantitative Finance National Tsing Hua University, Taiwan and Econometric Institute Erasmus School of Economics Erasmus University Rotterdam, The Netherlands and Department of Quantitative Economics Complutense University of Madrid, Spain And Institute of Advanced Sciences Yokohama National University, Japan.)

Abstract

This paper features a statistical analysis of the monthly three factor Fama/French return series. We apply rolling OLS regressions to explore the relationship between the 3 factors, using monthly and weekly data from July 1926 to June 2018, that are freely available on French's website. The results suggest there are significant and time-varying relationships between the factors. This is confirmed by non-parametric tests. We then switch to a sub-sample from July 1990 to July 2018, also taken from French's website. The three series and their interrelationships are analysed using two stage least squares and the Hausman test to check for issues related to endogeneity, the Sargan overidentification test and the Cragg-Donald weak instrument test. The relationship between factors is also examined using OLS, incorporating Ramsey's RESET tests of functional form misspecification, plus Naradaya-Watson kernel regression techniques. The empirical results suggest that the factors, when combined in OLS regression analysis, as suggested by Fama and French (2018), are likely to suffer from endogeneity. OLS regression analysis and the application of Ramsey's RESET tests suggest a non-linear relationship exists between the three series, in which cubed terms are significant. This non-linearity is also confirmed by the kernel regression analysis. We use two instruments to estimate the market betas, and then use the factor estimates in a second set of panel data tests using a small sample of monthly returns for US firms that are drawn from the online data source “tingo”. These issues are analysed using methods suggested by Petersen (2009) to permit clustering in the panels by date and firm. The empirical results suggest that using an instrument to capture endogeneity reducesthe standard error of market beta in subsequent crosssectional tests, but thatclustering effects, as suggested by Petersen (2009), will also impact on the estimated standard errors. The empirical results suggest that using these factorsin linear regression analysis, such as suggested by Fama and French (2018), as a method of screening factor relevance, is problematic in that the estimated standard errors are highly sensitive to the correct model specification.

Suggested Citation

  • David E. Allen & Michael McAleer, 2019. "Drawbacks in the 3-factor approach of Fama and French," Documentos de Trabajo del ICAE 2019-02, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    • Handle: RePEc:ucm:doicae:1902
    as

    Download full text from publisher

    File URL: https://eprints.ucm.es/id/eprint/54661/1/1902.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wu, De-Min, 1973. "Alternative Tests of Independence Between Stochastic Regressors and Disturbances," Econometrica, Econometric Society, vol. 41(4), pages 733-750, July.
    2. Maasoumi, Esfandiar & Racine, Jeff, 2002. "Entropy and predictability of stock market returns," Journal of Econometrics, Elsevier, vol. 107(1-2), pages 291-312, March.
    3. Fama, Eugene F. & French, Kenneth R., 1993. "Common risk factors in the returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 33(1), pages 3-56, February.
    4. John H. Cochrane, 2011. "Presidential Address: Discount Rates," Journal of Finance, American Finance Association, vol. 66(4), pages 1047-1108, August.
    5. Gibbons, Michael R & Ross, Stephen A & Shanken, Jay, 1989. "A Test of the Efficiency of a Given Portfolio," Econometrica, Econometric Society, vol. 57(5), pages 1121-1152, September.
    6. Nakamura, Alice & Nakamura, Masao, 1981. "On the Relationships among Several Specification Error Tests Presented by Durbin, Wu, and Hausman," Econometrica, Econometric Society, vol. 49(6), pages 1583-1588, November.
    7. Cragg, John G. & Donald, Stephen G., 1993. "Testing Identifiability and Specification in Instrumental Variable Models," Econometric Theory, Cambridge University Press, vol. 9(2), pages 222-240, April.
    8. Francisco Barillas & Jay Shanken, 2018. "Comparing Asset Pricing Models," Journal of Finance, American Finance Association, vol. 73(2), pages 715-754, April.
    9. Hayfield, Tristen & Racine, Jeffrey S., 2008. "Nonparametric Econometrics: The np Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i05).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. David E. Allen & Michael McAleer, 2023. "Drawbacks in the 3-Factor Approach of Fama and French (2018)," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 18(01), pages 1-26, March.
    2. Allen, David, 2022. "Asset Pricing Tests, Endogeneity issues and Fama-French factors," MPRA Paper 113610, University Library of Munich, Germany.
    3. Maysam Khodayari Gharanchaei & Prabhu Prasad Panda & Xilin Chen, 2024. "Quantitative Investment Diversification Strategies via Various Risk Models," Papers 2407.01550, arXiv.org.
    4. Hanauer, Matthias X. & Jansen, Maarten & Swinkels, Laurens & Zhou, Weili, 2024. "Factor models for Chinese A-shares," International Review of Financial Analysis, Elsevier, vol. 91(C).
    5. Doron Avramov & Si Cheng & Lior Metzker & Stefan Voigt, 2023. "Integrating Factor Models," Journal of Finance, American Finance Association, vol. 78(3), pages 1593-1646, June.
    6. Kelly, Bryan T. & Pruitt, Seth & Su, Yinan, 2019. "Characteristics are covariances: A unified model of risk and return," Journal of Financial Economics, Elsevier, vol. 134(3), pages 501-524.
    7. Loïc Maréchal, 2023. "A tale of two premiums revisited," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(5), pages 580-614, May.
    8. Guanhao Feng & Stefano Giglio & Dacheng Xiu, 2020. "Taming the Factor Zoo: A Test of New Factors," Journal of Finance, American Finance Association, vol. 75(3), pages 1327-1370, June.
    9. Virk, Nader Shahzad & Butt, Hilal Anwar, 2022. "Asset pricing anomalies: Liquidity risk hedgers or liquidity risk spreaders?," International Review of Financial Analysis, Elsevier, vol. 81(C).
    10. Shaun Bond & Chen Xue, 2017. "The Cross Section of Expected Real Estate Returns: Insights from Investment-Based Asset Pricing," The Journal of Real Estate Finance and Economics, Springer, vol. 54(3), pages 403-428, April.
    11. Gagliardini, Patrick & Ossola, Elisa & Scaillet, Olivier, 2019. "A diagnostic criterion for approximate factor structure," Journal of Econometrics, Elsevier, vol. 212(2), pages 503-521.
    12. Kozak, Serhiy & Nagel, Stefan & Santosh, Shrihari, 2020. "Shrinking the cross-section," Journal of Financial Economics, Elsevier, vol. 135(2), pages 271-292.
    13. John H. Cochrane, 2017. "Macro-Finance," Review of Finance, European Finance Association, vol. 21(3), pages 945-985.
    14. Clarke, Charles, 2022. "The level, slope, and curve factor model for stocks," Journal of Financial Economics, Elsevier, vol. 143(1), pages 159-187.
    15. Lu Zhang, 2017. "The Investment CAPM," European Financial Management, European Financial Management Association, vol. 23(4), pages 545-603, September.
    16. Shafiqur Rahman & Matthew J. Schneider, 2019. "Tests of Alternative Asset Pricing Models Using Individual Security Returns and a New Multivariate F-Test," Review of Pacific Basin Financial Markets and Policies (RPBFMP), World Scientific Publishing Co. Pte. Ltd., vol. 22(01), pages 1-34, March.
    17. Cong, Lin William & George, Nathan Darden & Wang, Guojun, 2023. "RIM-based value premium and factor pricing using value-price divergence," Journal of Banking & Finance, Elsevier, vol. 149(C).
    18. Kan, Raymond & Wang, Xiaolu & Zheng, Xinghua, 2024. "In-sample and out-of-sample Sharpe ratios of multi-factor asset pricing models," Journal of Financial Economics, Elsevier, vol. 155(C).
    19. Hoechle, Daniel & Schmid, Markus & Zimmermann, Heinz, 2017. "Does Unobservable Heterogeneity Matter for Portfolio-Based Asset Pricing Tests?," Working Papers on Finance 1717, University of St. Gallen, School of Finance, revised Mar 2020.
    20. Massa, Massimo & O'Donovan, James & Zhang, Hong, 2021. "International Asset Pricing with Strategic Business Groups," CEPR Discussion Papers 15746, C.E.P.R. Discussion Papers.

    More about this item

    Keywords

    Fama-French Factors; Correct specification; Ramsey's RESET; Hausman tests; Endogeneity; Consistent standard errors.;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ucm:doicae:1902. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Águeda González Abad (email available below). General contact details of provider: https://edirc.repec.org/data/feucmes.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.