IDEAS home Printed from https://ideas.repec.org/p/ces/ceswps/_8377.html
   My bibliography  Save this paper

Are Characteristics Covariances? A Comment on Instrumented Principal Component Analysis

Author

Listed:
  • Christian Fieberg
  • Lars Hornuf
  • Gerrit Liedtke
  • Thorsten Poddig

Abstract

We present analytical and simulation-based evidence that instrumented principal component analysis (IPCA) cannot reliably distinguish between whether covariances or characteristics explain asset returns because the question has to be answered jointly with the question of how many factors have to be modeled. IPCA finds a covariance-based explanation when estimating too many factors (“alpha-eating”) and a characteristic-based explanation when estimating too few factors (“beta-eating”). Our results therefore call into question the empirical evidence recently obtained that stocks (Kelly et al., 2019), options (Büchner and Kelly, 2022), and bonds (Kelly et al., 2021) are explained by covariances.

Suggested Citation

  • Christian Fieberg & Lars Hornuf & Gerrit Liedtke & Thorsten Poddig, 2020. "Are Characteristics Covariances? A Comment on Instrumented Principal Component Analysis," CESifo Working Paper Series 8377, CESifo.
    • Handle: RePEc:ces:ceswps:_8377
    as

    Download full text from publisher

    File URL: https://www.cesifo.org/DocDL/cesifo1_wp8377_1.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Lewellen, Jonathan, 2015. "The Cross-section of Expected Stock Returns," Critical Finance Review, now publishers, vol. 4(1), pages 1-44, June.
    3. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," Review of Finance, European Finance Association, vol. 33(5), pages 2223-2273.
    4. R. David Mclean & Jeffrey Pontiff, 2016. "Does Academic Research Destroy Stock Return Predictability?," Journal of Finance, American Finance Association, vol. 71(1), pages 5-32, February.
    5. Thomas J. George & Chuan-Yang Hwang, 2004. "The 52-Week High and Momentum Investing," Journal of Finance, American Finance Association, vol. 59(5), pages 2145-2176, October.
    6. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
    7. Chamberlain, Gary & Rothschild, Michael, 1983. "Arbitrage, Factor Structure, and Mean-Variance Analysis on Large Asset Markets," Econometrica, Econometric Society, vol. 51(5), pages 1281-1304, September.
    8. Joachim Freyberger & Andreas Neuhierl & Michael Weber, 2020. "Dissecting Characteristics Nonparametrically," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2326-2377.
    9. Evgeny Lyandres & Le Sun & Lu Zhang, 2008. "The New Issues Puzzle: Testing the Investment-Based Explanation," The Review of Financial Studies, Society for Financial Studies, vol. 21(6), pages 2825-2855, November.
    10. Yuriy Gorodnichenko & Michael Weber, 2016. "Are Sticky Prices Costly? Evidence from the Stock Market," American Economic Review, American Economic Association, vol. 106(1), pages 165-199, January.
    11. Hirshleifer, David & Kewei Hou & Teoh, Siew Hong & Yinglei Zhang, 2004. "Do investors overvalue firms with bloated balance sheets?," Journal of Accounting and Economics, Elsevier, vol. 38(1), pages 297-331, December.
    12. Chung, Kee H. & Zhang, Hao, 2014. "A simple approximation of intraday spreads using daily data," Journal of Financial Markets, Elsevier, vol. 17(C), pages 94-120.
    13. Jon A. Garfinkel, 2009. "Measuring Investors' Opinion Divergence," Journal of Accounting Research, Wiley Blackwell, vol. 47(5), pages 1317-1348, December.
    14. Haigang Zhou & John Zhu, 2011. "Jump risk and cross section of stock returns: evidence from China’s stock market," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 35(3), pages 309-331, July.
    15. Palazzo, Berardino, 2012. "Cash holdings, risk, and expected returns," Journal of Financial Economics, Elsevier, vol. 104(1), pages 162-185.
    16. Patton, Andrew J. & Timmermann, Allan, 2010. "Monotonicity in asset returns: New tests with applications to the term structure, the CAPM, and portfolio sorts," Journal of Financial Economics, Elsevier, vol. 98(3), pages 605-625, December.
    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. Luyang Chen & Markus Pelger & Jason Zhu, 2019. "Deep Learning in Asset Pricing," Papers 1904.00745, arXiv.org, revised Aug 2021.
    2. Svetlana Bryzgalova & Jiantao Huang & Christian Julliard, 2023. "Bayesian Solutions for the Factor Zoo: We Just Ran Two Quadrillion Models," Journal of Finance, American Finance Association, vol. 78(1), pages 487-557, February.
    3. Hanauer, Matthias X. & Kalsbach, Tobias, 2023. "Machine learning and the cross-section of emerging market stock returns," Emerging Markets Review, Elsevier, vol. 55(C).
    4. Tobek, Ondrej & Hronec, Martin, 2021. "Does it pay to follow anomalies research? Machine learning approach with international evidence," Journal of Financial Markets, Elsevier, vol. 56(C).
    5. Joachim Freyberger & Andreas Neuhierl & Michael Weber, 2020. "Dissecting Characteristics Nonparametrically," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2326-2377.
    6. Geertsema, Paul & Lu, Helen, 2020. "The correlation structure of anomaly strategies," Journal of Banking & Finance, Elsevier, vol. 119(C).
    7. Clarke, Charles, 2022. "The level, slope, and curve factor model for stocks," Journal of Financial Economics, Elsevier, vol. 143(1), pages 159-187.
    8. Andrew Y. Chen & Tom Zimmermann, 2022. "Open Source Cross-Sectional Asset Pricing," Critical Finance Review, now publishers, vol. 11(2), pages 207-264, May.
    9. Ma, Tian & Leong, Wen Jun & Jiang, Fuwei, 2023. "A latent factor model for the Chinese stock market," International Review of Financial Analysis, Elsevier, vol. 87(C).
    10. Dichtl, Hubert & Drobetz, Wolfgang & Otto, Tizian, 2023. "Forecasting Stock Market Crashes via Machine Learning," Journal of Financial Stability, Elsevier, vol. 65(C).
    11. 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.
    12. 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.
    13. Cederburg, Scott & O’Doherty, Michael S. & Wang, Feifei & Yan, Xuemin (Sterling), 2020. "On the performance of volatility-managed portfolios," Journal of Financial Economics, Elsevier, vol. 138(1), pages 95-117.
    14. Christian Fieberg & Daniel Metko & Thorsten Poddig & Thomas Loy, 2023. "Machine learning techniques for cross-sectional equity returns’ prediction," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 45(1), pages 289-323, March.
    15. Andrew Y. Chen & Jack McCoy, 2022. "Missing Values Handling for Machine Learning Portfolios," Papers 2207.13071, arXiv.org, revised Jan 2024.
    16. Doron Avramov & Si Cheng & Lior Metzker, 2023. "Machine Learning vs. Economic Restrictions: Evidence from Stock Return Predictability," Management Science, INFORMS, vol. 69(5), pages 2587-2619, May.
    17. Lioui, Abraham & Tarelli, Andrea, 2022. "Chasing the ESG factor," Journal of Banking & Finance, Elsevier, vol. 139(C).
    18. Langlois, Hugues, 2023. "What matters in a characteristic?," Journal of Financial Economics, Elsevier, vol. 149(1), pages 52-72.
    19. Rubesam, Alexandre, 2022. "Machine learning portfolios with equal risk contributions: Evidence from the Brazilian market," Emerging Markets Review, Elsevier, vol. 51(PB).
    20. Cakici, Nusret & Fieberg, Christian & Metko, Daniel & Zaremba, Adam, 2023. "Machine learning goes global: Cross-sectional return predictability in international stock markets," Journal of Economic Dynamics and Control, Elsevier, vol. 155(C).

    More about this item

    Keywords

    IPCA; covariances; characteristics; cross section of asset returns;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

    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:ces:ceswps:_8377. 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: Klaus Wohlrabe (email available below). General contact details of provider: https://edirc.repec.org/data/cesifde.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.