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New Evidence on Conditional Factor Models

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  • Cooper, Ilan
  • Maio, Paulo

Abstract

We estimate conditional multifactor models over a large cross section of stock returns matching 25 CAPM anomalies. Using conditioning information associated with different instruments improves the performance of the Hou, Xue, and Zhang (HXZ) (2015) and Fama and French (FF) (2015), (2016) models. The largest increase in performance holds for momentum, investment, and intangibles-based anomalies. Yet, there are significant differences in the performance of scaled models: HXZ clearly dominates FF in explaining momentum and profitability anomalies, while the converse holds for value–growth anomalies. Thus, the asset pricing implications of alternative investment and profitability factors (in a conditional setting) differ in a nontrivial way.

Suggested Citation

  • 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.
    • Handle: RePEc:cup:jfinqa:v:54:y:2019:i:05:p:1975-2016_00
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    Citations

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    Cited by:

    1. Atanasov, Victoria, 2021. "Unemployment and aggregate stock returns," Journal of Banking & Finance, Elsevier, vol. 129(C).
    2. Vigo Pereira, Caio, 2021. "Portfolio efficiency with high-dimensional data as conditioning information," International Review of Financial Analysis, Elsevier, vol. 77(C).
    3. Ilan Cooper & Liang Ma & Paulo Maio, 2022. "What Does the Cross‐Section Tell About Itself? Explaining Equity Risk Premia with Stock Return Moments," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 54(1), pages 73-118, February.
    4. Kostakis, Alexandros & Magdalinos, Tassos & Stamatogiannis, Michalis P., 2023. "Taking stock of long-horizon predictability tests: Are factor returns predictable?," Journal of Econometrics, Elsevier, vol. 237(2).
    5. Naresh Bansal & Robert A. Connolly & Chris Stivers, 2022. "Beta and size equity premia following a high‐VIX threshold," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(8), pages 1491-1517, August.
    6. 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.
    7. Pedro M. Mirete-Ferrer & Alberto Garcia-Garcia & Juan Samuel Baixauli-Soler & Maria A. Prats, 2022. "A Review on Machine Learning for Asset Management," Risks, MDPI, vol. 10(4), pages 1-46, April.
    8. Huynh, Nhan, 2023. "Unemployment beta and the cross-section of stock returns: Evidence from Australia," International Review of Financial Analysis, Elsevier, vol. 86(C).
    9. Artikis, Panagiotis G. & Diamantopoulou, Lydia & Papanastasopoulos, Georgios A. & Sorros, John N., 2022. "Asset growth and stock returns in european equity markets: Implications of investment and accounting distortions," Journal of Corporate Finance, Elsevier, vol. 73(C).
    10. Maio, Paulo & Silva, André C., 2020. "Asset pricing implications of money: New evidence," Journal of Banking & Finance, Elsevier, vol. 120(C).
    11. 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).
    12. Gao, Yang & Leung, Henry & Satchell, Stephen, 2022. "Partial moment momentum," Journal of Banking & Finance, Elsevier, vol. 135(C).
    13. Dong, Liang & Yu, Bo & Qin, Zhenjiang & Lam, Keith S.K., 2024. "Liquidity risk and expected returns in China’s stock market: A multidimensional liquidity approach," Research in International Business and Finance, Elsevier, vol. 69(C).
    14. Liao Zhu, 2021. "The Adaptive Multi-Factor Model and the Financial Market," Papers 2107.14410, arXiv.org, revised Aug 2021.
    15. Fabian Hollstein & Marcel Prokopczuk, 2023. "Managing the Market Portfolio," Management Science, INFORMS, vol. 69(6), pages 3675-3696, June.

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