IDEAS home Printed from https://ideas.repec.org/a/wsi/qjfxxx/v11y2021i04ns2010139221500191.html
   My bibliography  Save this article

Time-Invariance Coefficients Tests with the Adaptive Multi-Factor Model

Author

Listed:
  • Liao Zhu

    (Department of Statistics and Data Science, Cornell University, Ithaca, New York 14853, USA)

  • Robert A. Jarrow

    (Ronald P. and Susan E. Lynch Professor of Investment Management, Samuel Curtis Johnson Graduate School of Management, Cornell University, Ithaca, New York 14853, USA3Kamakura Corporation, Honolulu, Hawaii p96815, USA)

  • Martin T. Wells

    (Charles A. Alexander Professor of Statistical Sciences, Department of Statistics and Data Science, Cornell University, Ithaca, New York 14853, USA)

Abstract

This paper tests a multi-factor asset pricing model that does not assume that the return’s beta coefficients are constants. This is done by estimating the generalized arbitrage pricing theory (GAPT) using price differences. An implication of the GAPT is that when using price differences instead of returns, the beta coefficients are constant. We employ the adaptive multi-factor (AMF) model to test the GAPT utilizing a Groupwise Interpretable Basis Selection (GIBS) algorithm to identify the relevant factors from among all traded exchange-traded funds. We compare the performance of the AMF model with the Fama–French 5-factor (FF5) model. For nearly all time periods less than six years, the beta coefficients are time-invariant for the AMF model, but not for the FF5 model. This implies that the AMF model with a rolling window (such as five years) is more consistent with realized asset returns than is the FF5 model.

Suggested Citation

  • Liao Zhu & Robert A. Jarrow & Martin T. Wells, 2021. "Time-Invariance Coefficients Tests with the Adaptive Multi-Factor Model," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., vol. 11(04), pages 1-30, December.
  • Handle: RePEc:wsi:qjfxxx:v:11:y:2021:i:04:n:s2010139221500191
    DOI: 10.1142/S2010139221500191
    as

    Download full text from publisher

    File URL: http://www.worldscientific.com/doi/abs/10.1142/S2010139221500191
    Download Restriction: Access to full text is restricted to subscribers

    File URL: https://libkey.io/10.1142/S2010139221500191?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Adrian, Tobias & Crump, Richard K. & Moench, Emanuel, 2015. "Regression-based estimation of dynamic asset pricing models," Journal of Financial Economics, Elsevier, vol. 118(2), pages 211-244.
    2. Stephen A. Ross, 2013. "The Arbitrage Theory of Capital Asset Pricing," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 1, pages 11-30, World Scientific Publishing Co. Pte. Ltd..
    3. Cooper, Ilan & Maio, Paulo, 2019. "New Evidence on Conditional Factor Models," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 54(5), pages 1975-2016, October.
    4. Ravi Jagannathan & Ernst Schaumburg & Guofu Zhou, 2010. "Cross-Sectional Asset Pricing Tests," Annual Review of Financial Economics, Annual Reviews, vol. 2(1), pages 49-74, December.
    5. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    6. Simon, Noah & Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2011. "Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 39(i05).
    7. Bien, Jacob & Tibshirani, Robert, 2011. "Hierarchical Clustering With Prototypes via Minimax Linkage," Journal of the American Statistical Association, American Statistical Association, vol. 106(495), pages 1075-1084.
    8. Stephen Reid & Jonathan Taylor & Robert Tibshirani, 2018. "A General Framework for Estimation and Inference From Clusters of Features," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(521), pages 280-293, January.
    9. Doron Avramov & Tarun Chordia, 2006. "Asset Pricing Models and Financial Market Anomalies," The Review of Financial Studies, Society for Financial Studies, vol. 19(3), pages 1001-1040.
    10. Merton, Robert C, 1973. "An Intertemporal Capital Asset Pricing Model," Econometrica, Econometric Society, vol. 41(5), pages 867-887, September.
    11. John Y. Campbell & Samuel B. Thompson, 2008. "Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1509-1531, July.
    12. Robert Tibshirani & Jacob Bien & Jerome Friedman & Trevor Hastie & Noah Simon & Jonathan Taylor & Ryan J. Tibshirani, 2012. "Strong rules for discarding predictors in lasso‐type problems," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 74(2), pages 245-266, March.
    13. Shumway, Tyler, 1997. "The Delisting Bias in CRSP Data," Journal of Finance, American Finance Association, vol. 52(1), pages 327-340, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Aysenur Tarakcioglu Altinay & Mesut Dogan & Bilge Leyli Demirel Ergun & Sevdie Alshiqi, 2023. "The Fama-French Five-Factor Asset Pricing Model: A Research on Borsa Istanbul," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 4, pages 3-21.
    2. Liao Zhu, 2021. "The Adaptive Multi-Factor Model and the Financial Market," Papers 2107.14410, arXiv.org, revised Aug 2021.
    3. Liao Zhu & Haoxuan Wu & Martin T. Wells, 2021. "A News-based Machine Learning Model for Adaptive Asset Pricing," Papers 2106.07103, arXiv.org.
    4. Liao Zhu & Ningning Sun & Martin T. Wells, 2022. "Clustering Structure of Microstructure Measures," Applied Economics and Finance, Redfame publishing, vol. 9(1), pages 85-95, December.

    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. Liao Zhu, 2021. "The Adaptive Multi-Factor Model and the Financial Market," Papers 2107.14410, arXiv.org, revised Aug 2021.
    2. Liao Zhu & Sumanta Basu & Robert A. Jarrow & Martin T. Wells, 2020. "High-Dimensional Estimation, Basis Assets, and the Adaptive Multi-Factor Model," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., vol. 10(04), pages 1-52, December.
    3. Robert A. Jarrow & Rinald Murataj & Martin T. Wells & Liao Zhu, 2023. "The Low-Volatility Anomaly And The Adaptive Multi-Factor Model," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 26(04n05), pages 1-33, August.
    4. Fernando M. Duarte & Carlo Rosa, 2015. "The equity risk premium: a review of models," Economic Policy Review, Federal Reserve Bank of New York, issue 2, pages 39-57.
    5. Hollstein, Fabian & Prokopczuk, Marcel, 2022. "Testing Factor Models in the Cross-Section," Journal of Banking & Finance, Elsevier, vol. 145(C).
    6. Amit Goyal, 2012. "Empirical cross-sectional asset pricing: a survey," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 26(1), pages 3-38, March.
    7. Hansen, Erwin, 2022. "Economic evaluation of asset pricing models under predictability," Journal of Empirical Finance, Elsevier, vol. 68(C), pages 50-66.
    8. Jie Xiong & Zhitong Bing & Yanlin Su & Defeng Deng & Xiaoning Peng, 2014. "An Integrated mRNA and microRNA Expression Signature for Glioblastoma Multiforme Prognosis," PLOS ONE, Public Library of Science, vol. 9(5), pages 1-8, May.
    9. Huynh, Nhan, 2023. "Unemployment beta and the cross-section of stock returns: Evidence from Australia," International Review of Financial Analysis, Elsevier, vol. 86(C).
    10. Robert Jarrow, 2016. "Bubbles And Multiple-Factor Asset Pricing Models," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 19(01), pages 1-19, February.
    11. Shailesh Rana & William H. Bommer & G. Michael Phillips, 2020. "Predicting Returns for Growth and Value Stocks: A Forecast Assessment Approach Using Global Asset Pricing Models," International Journal of Economics and Financial Issues, Econjournals, vol. 10(4), pages 88-106.
    12. Avanidhar Subrahmanyam, 2010. "The Cross†Section of Expected Stock Returns: What Have We Learnt from the Past Twenty†Five Years of Research?," European Financial Management, European Financial Management Association, vol. 16(1), pages 27-42, January.
    13. Qi Lin, 2020. "Idiosyncratic momentum and the cross‐section of stock returns: Further evidence," European Financial Management, European Financial Management Association, vol. 26(3), pages 579-627, June.
    14. Peter Van Tassel & Erik Vogt, 2016. "Global variance term premia and intermediary risk appetite," Staff Reports 789, Federal Reserve Bank of New York.
    15. J. Davies & Jonathan Fletcher & Andrew Marshall, 2015. "Testing index-based models in U.K. stock returns," Review of Quantitative Finance and Accounting, Springer, vol. 45(2), pages 337-362, August.
    16. Silva, Thiago Christiano & Wilhelm, Paulo Victor Berri & Tabak, Benjamin Miranda, 2023. "The effect of interconnectivity on stock returns during the Global Financial Crisis," The North American Journal of Economics and Finance, Elsevier, vol. 67(C).
    17. Wolfgang Drobetz & Tizian Otto, 2021. "Empirical asset pricing via machine learning: evidence from the European stock market," Journal of Asset Management, Palgrave Macmillan, vol. 22(7), pages 507-538, December.
    18. Liao Zhu & Haoxuan Wu & Martin T. Wells, 2021. "A News-based Machine Learning Model for Adaptive Asset Pricing," Papers 2106.07103, arXiv.org.
    19. Christian Fieberg & Armin Varmaz & Thorsten Poddig, 2016. "Covariances vs. characteristics: what does explain the cross section of the German stock market returns?," Business Research, Springer;German Academic Association for Business Research, vol. 9(1), pages 27-50, April.
    20. Chou, Pin-Huang & Ko, Kuan-Cheng & Lin, Shinn-Juh, 2010. "Do relative leverage and relative distress really explain size and book-to-market anomalies?," Journal of Financial Markets, Elsevier, vol. 13(1), pages 77-100, February.

    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:wsi:qjfxxx:v:11:y:2021:i:04:n:s2010139221500191. 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: Tai Tone Lim (email available below). General contact details of provider: http://www.worldscinet.com/qjf/qjf.shtml .

    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.