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Understanding industry betas

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  • Baele, Lieven
  • Londono, Juan M.

Abstract

This paper models and explains the dynamics of market betas for 30 US industry portfolios between 1970 and 2009. We use DCC–MIDAS and kernel regression techniques as alternatives to the standard ex-post measures. We find betas to exhibit substantial persistence, time variation, ranking variability, and heterogeneity in their business cycle exposure. While we find only a limited amount of structural breaks in the betas of individual industries, we do identify a common structural break in March 1998. We propose two practical applications to understand the economic significance of these results. We find the cross-sectional dispersion in industry betas to be countercyclical and negatively related to future market returns. We also find DCC–MIDAS betas to outperform other beta measures in terms of limiting the downside risk and ex-post market exposure of a market-neutral minimum-variance strategy.

Suggested Citation

  • Baele, Lieven & Londono, Juan M., 2013. "Understanding industry betas," Journal of Empirical Finance, Elsevier, vol. 22(C), pages 30-51.
    • Handle: RePEc:eee:empfin:v:22:y:2013:i:c:p:30-51
    DOI: 10.1016/j.jempfin.2013.02.003
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    Cited by:

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    2. González, Mariano & Nave, Juan & Rubio, Gonzalo, 2018. "Macroeconomic determinants of stock market betas," Journal of Empirical Finance, Elsevier, vol. 45(C), pages 26-44.
    3. Fang, Libing & Yu, Honghai & Li, Lei, 2017. "The effect of economic policy uncertainty on the long-term correlation between U.S. stock and bond markets," Economic Modelling, Elsevier, vol. 66(C), pages 139-145.
    4. Rapheedah Musneh & Mohd. Rahimie Abdul Karim & Caroline Geetha A/P Arokiadasan Baburaw, 2021. "Liquidity risk and stock returns: empirical evidence from industrial products and services sector in Bursa Malaysia," Future Business Journal, Springer, vol. 7(1), pages 1-10, December.
    5. Anirut Pisedtasalasai, 2021. "Hedging Stocks in Crises and Market Downturns with Gold and Bonds: Industry Analysis," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 11(1), pages 1-16, January.
    6. Hossein Asgharian & Charlotte Christiansen & Ai Jun Hou & Weining Wang, 2017. "Long- and Short-Run Components of Factor Betas: Implications for Equity Pricing," CREATES Research Papers 2017-34, Department of Economics and Business Economics, Aarhus University.
    7. Pedro Antonio Martín-Cervantes & María del Carmen Valls Martínez, 2023. "Unraveling the relationship between betas and ESG scores through the Random Forests methodology," Risk Management, Palgrave Macmillan, vol. 25(3), pages 1-29, September.
    8. Naeem, Muhammad Abubakr & Balli, Faruk & Shahzad, Syed Jawad Hussain & de Bruin, Anne, 2020. "Energy commodity uncertainties and the systematic risk of US industries," Energy Economics, Elsevier, vol. 85(C).
    9. Yu, Honghai & Fang, Libing & Du, Donglei & Yan, Panpan, 2017. "How EPU drives long-term industry beta," Finance Research Letters, Elsevier, vol. 22(C), pages 249-258.
    10. Lioui, Abraham & Tarelli, Andrea, 2020. "Factor Investing for the Long Run," Journal of Economic Dynamics and Control, Elsevier, vol. 117(C).

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    More about this item

    Keywords

    Industry betas; Component models; DCC–MIDAS; Dispersion in betas; Stock return predictability; Minimum variance strategies;
    All these keywords.

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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