IDEAS home Printed from https://ideas.repec.org/p/rim/rimwps/18-05.html
   My bibliography  Save this paper

The Fama 3 and Fama 5 factor models under a machine learning framework

Author

Listed:
  • Periklis Gogas

    () (Department of Economics, Democritus University of Thrace, Greece; Rimini Centre for Economic Analysis)

  • Theofilos Papadimitriou

    (Department of Economics, Democritus University of Thrace, Greece)

  • Dimitrios Karagkiozis

    (Department of Economics, Democritus University of Thrace, Greece)

Abstract

We examine four empirical models which are popular in money and stock markets world. These models are Fama – French 3 & 5 factors model, the Capital Asset Pricing Model (CAPM) and the Arbitrage Pricing Theory (APT) model. These tools are intensively used by investors and market professionals as an important part of the investment decision process and for the evaluation of the applied investment strategies. The last years, several surveys and studies have done, and various methodologies were implemented to evaluate the effectiveness of these four models. The methodological approach of the current thesis focuses on the Support Vector Regression (SVR). This method is running in comparison with the Ordinary Least Squares linear regression.

Suggested Citation

  • Periklis Gogas & Theofilos Papadimitriou & Dimitrios Karagkiozis, 2018. "The Fama 3 and Fama 5 factor models under a machine learning framework," Working Paper series 18-05, Rimini Centre for Economic Analysis.
  • Handle: RePEc:rim:rimwps:18-05
    as

    Download full text from publisher

    File URL: http://rcea.org/RePEc/pdf/wp18-05.pdf
    Download Restriction: no

    References listed on IDEAS

    as
    1. Fortin, Ines & Hlouskova, Jaroslava, 2011. "Optimal asset allocation under linear loss aversion," Journal of Banking & Finance, Elsevier, vol. 35(11), pages 2974-2990, November.
    2. Berkelaar, Arjan & Kouwenberg, Roy, 2009. "From boom 'til bust: How loss aversion affects asset prices," Journal of Banking & Finance, Elsevier, vol. 33(6), pages 1005-1013, June.
    3. Göran Therborn & K.C. Ho, 2009. "Introduction," City, Taylor & Francis Journals, vol. 13(1), pages 53-62, March.
    4. Kazuhiko Kakamu & Wolfgang Polasek & Hajime Wago, 2012. "Production technology and agglomeration for Japanese prefectures during 1991–2000," Papers in Regional Science, Wiley Blackwell, vol. 91(1), pages 29-41, March.
    5. Kelejian, Harry H & Prucha, Ingmar R, 1998. "A Generalized Spatial Two-Stage Least Squares Procedure for Estimating a Spatial Autoregressive Model with Autoregressive Disturbances," The Journal of Real Estate Finance and Economics, Springer, vol. 17(1), pages 99-121, July.
    6. Epstein, Larry G. & Zin, Stanley E., 1990. "'First-order' risk aversion and the equity premium puzzle," Journal of Monetary Economics, Elsevier, vol. 26(3), pages 387-407, December.
    7. Ulrich Schmidt & Chris Starmer & Robert Sugden, 2008. "Third-generation prospect theory," Journal of Risk and Uncertainty, Springer, vol. 36(3), pages 203-223, June.
    8. Siegmann, Arjen, 2007. "Optimal investment policies for defined benefit pension funds," Journal of Pension Economics and Finance, Cambridge University Press, pages 1-20.
    9. Tversky, Amos & Kahneman, Daniel, 1992. "Advances in Prospect Theory: Cumulative Representation of Uncertainty," Journal of Risk and Uncertainty, Springer, vol. 5(4), pages 297-323, October.
    10. Kelejian, Harry H. & Prucha, Ingmar R., 2007. "HAC estimation in a spatial framework," Journal of Econometrics, Elsevier, vol. 140(1), pages 131-154, September.
    11. James Paul Lesage & Wolfgang Polasek, 2008. "Incorporating Transportation Network Structure in Spatial Econometric Models of Commodity Flows," Spatial Economic Analysis, Taylor & Francis Journals, vol. 3(2), pages 225-245.
    12. Arjen Siegmann & André Lucas, 2005. "Discrete-Time Financial Planning Models Under Loss-Averse Preferences," Operations Research, INFORMS, vol. 53(3), pages 403-414, June.
    13. Nicholas Barberis & Ming Huang, 2001. "Mental Accounting, Loss Aversion, and Individual Stock Returns," NBER Working Papers 8190, National Bureau of Economic Research, Inc.
    14. Harry H. Kelejian & Ingmar R. Prucha, 1997. "Estimation of Spatial Regression Models with Autoregressive Errors by Two-Stage Least Squares Procedures: A Serious Problem," International Regional Science Review, , vol. 20(1-2), pages 103-111, April.
    15. Neudecker, Heinz & Polasek, Wolfgang & Liu, Shuangzhe, 1995. "The heteroskedastic linear regression model and the Hadamard product a note," Journal of Econometrics, Elsevier, vol. 68(2), pages 361-366, August.
    16. Liu, Shuangzhe & Polasek, Wolfgang & Sellner, Richard, 2011. "Sensitivity Analysis of SAR Estimators," Economics Series 262, Institute for Advanced Studies.
    17. Jaroslava Hlouskova & Panagiotis Tsigaris, 2012. "Capital income taxation and risk taking under prospect theory," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 19(4), pages 554-573, August.
    18. Hwang, Soosung & Satchell, Steve E., 2010. "How loss averse are investors in financial markets?," Journal of Banking & Finance, Elsevier, vol. 34(10), pages 2425-2438, October.
    19. Jan R. Magnus & Andrey L. Vasnev, 2007. "Local sensitivity and diagnostic tests," Econometrics Journal, Royal Economic Society, vol. 10(1), pages 166-192, March.
    20. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    21. repec:umd:umdeco:kelepruc is not listed on IDEAS
    22. Xue Dong He & Xun Yu Zhou, 2011. "Portfolio Choice Under Cumulative Prospect Theory: An Analytical Treatment," Management Science, INFORMS, vol. 57(2), pages 315-331, February.
    23. Kahneman, Daniel & Tversky, Amos, 1979. "Prospect Theory: An Analysis of Decision under Risk," Econometrica, Econometric Society, vol. 47(2), pages 263-291, March.
    24. André Lucas & Arjen Siegmann, 2008. "The Effect of Shortfall as a Risk Measure for Portfolios with Hedge Funds," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 35(1-2), pages 200-226.
    25. Raymond J. G. M. Florax & Arno J. Van der Vlist, 2003. "Spatial Econometric Data Analysis: Moving Beyond Traditional Models," International Regional Science Review, , vol. 26(3), pages 223-243, July.
    26. Lung-Fei Lee, 2004. "Asymptotic Distributions of Quasi-Maximum Likelihood Estimators for Spatial Autoregressive Models," Econometrica, Econometric Society, vol. 72(6), pages 1899-1925, November.
    27. Nicholas Barberis, 2001. "Mental Accounting, Loss Aversion, and Individual Stock Returns," Journal of Finance, American Finance Association, vol. 56(4), pages 1247-1292, August.
    28. Francisco J. Gomes, 2005. "Portfolio Choice and Trading Volume with Loss-Averse Investors," The Journal of Business, University of Chicago Press, vol. 78(2), pages 675-706, March.
    29. Luc Anselin, 2010. "Thirty years of spatial econometrics," Papers in Regional Science, Wiley Blackwell, vol. 89(1), pages 3-25, March.
    30. Kapoor, Mudit & Kelejian, Harry H. & Prucha, Ingmar R., 2007. "Panel data models with spatially correlated error components," Journal of Econometrics, Elsevier, vol. 140(1), pages 97-130, September.
    31. Liu, Shuangzhe & Neudecker, Heinz, 2009. "On pseudo maximum likelihood estimation for multivariate time series models with conditional heteroskedasticity," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(8), pages 2556-2565.
    32. repec:wop:ubisop:0081 is not listed on IDEAS
    33. Enrico Giorgi & Thorsten Hens, 2006. "Making prospect theory fit for finance," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 20(3), pages 339-360, September.
    34. J. Barkley Rosser, 2009. "Introduction," Chapters,in: Handbook of Research on Complexity, chapter 1 Edward Elgar Publishing.
    35. Kakamu, Kazuhiko & Polasek, Wolfgang & Wago, Hajime, 2008. "Spatial interaction of crime incidents in Japan," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 78(2), pages 276-282.
    36. Lung-fei Lee, 2003. "Best Spatial Two-Stage Least Squares Estimators for a Spatial Autoregressive Model with Autoregressive Disturbances," Econometric Reviews, Taylor & Francis Journals, vol. 22(4), pages 307-335.
    37. Lee, Lung-fei & Yu, Jihai, 2010. "Some recent developments in spatial panel data models," Regional Science and Urban Economics, Elsevier, vol. 40(5), pages 255-271, September.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    stock markets; stock returns; machine learning; support vector regression;

    JEL classification:

    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • F37 - International Economics - - International Finance - - - International Finance Forecasting and Simulation: Models and Applications
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling

    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:rim:rimwps:18-05. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Marco Savioli). General contact details of provider: http://edirc.repec.org/data/rcfeait.html .

    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.

    We have no references for this item. You can help adding them by using 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.