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Measurement of factor strength: Theory and practice

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  • Natalia Bailey
  • George Kapetanios
  • M. Hashem Pesaran

Abstract

This paper proposes an estimator of factor strength and establishes its consistency and asymptotic distribution. The estimator is based on the number of statistically significant factor loadings, taking multiple testing into account. Both cases of observed and unobserved factors are considered. The small sample properties of the proposed estimator are investigated using Monte Carlo experiments. It is shown that the proposed estimation and inference procedures perform well and have excellent power properties, especially when the factor strength is sufficiently high. Empirical applications to factor models for asset returns show that out of 146 factors recently considered in the finance literature, only the market factor is truly strong, while all other factors are at best semi‐strong, with their strength varying considerably over time. Similarly, we only find evidence of semi‐strong factors using a large number of US macroeconomic indicators.

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  • Natalia Bailey & George Kapetanios & M. Hashem Pesaran, 2021. "Measurement of factor strength: Theory and practice," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(5), pages 587-613, August.
    • Handle: RePEc:wly:japmet:v:36:y:2021:i:5:p:587-613
    DOI: 10.1002/jae.2830
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    Cited by:

    1. M. Hashem Pesaran & Ron P. Smith, 2023. "The Role of Pricing Errors in Linear Asset Pricing Models with Strong, Semi-Strong, and Latent Factors," CESifo Working Paper Series 10282, CESifo.
    2. Ge, S., 2020. "Text-Based Linkages and Local Risk Spillovers in the Equity Market," Cambridge Working Papers in Economics 20115, Faculty of Economics, University of Cambridge.
    3. Jie Wei & Yonghui Zhang, 2023. "Does Principal Component Analysis Preserve the Sparsity in Sparse Weak Factor Models?," Papers 2305.05934, arXiv.org, revised Nov 2024.
    4. Jianqing Fan & Yuling Yan & Yuheng Zheng, 2024. "When can weak latent factors be statistically inferred?," Papers 2407.03616, arXiv.org, revised Sep 2024.
    5. M Hashem Pesaran & Takashi Yamagata, 2024. "Testing for Alpha in Linear Factor Pricing Models with a Large Number of Securities," Journal of Financial Econometrics, Oxford University Press, vol. 22(2), pages 407-460.
    6. Choi, In & Lin, Rui & Shin, Yongcheol, 2023. "Canonical correlation-based model selection for the multilevel factors," Journal of Econometrics, Elsevier, vol. 233(1), pages 22-44.
    7. Freyaldenhoven, Simon, 2022. "Factor models with local factors — Determining the number of relevant factors," Journal of Econometrics, Elsevier, vol. 229(1), pages 80-102.
    8. Pesaran, M. Hashem & Smith, Ron P., 2023. "Arbitrage pricing theory, the stochastic discount factor and estimation of risk premia from portfolios," Econometrics and Statistics, Elsevier, vol. 26(C), pages 17-30.
    9. Ge, Shuyi & Li, Shaoran & Linton, Oliver, 2023. "News-implied linkages and local dependency in the equity market," Journal of Econometrics, Elsevier, vol. 235(2), pages 779-815.
    10. M. Hashem Pesaran & Run Smith, 2021. "Arbitrage pricing theory, the stochastic discount factor and estimation of risk premia in portfolios," BCAM Working Papers 2108, Birkbeck Centre for Applied Macroeconomics.

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    More about this item

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • E20 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - General (includes Measurement and Data)
    • G20 - Financial Economics - - Financial Institutions and Services - - - General

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