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Industrial Electricity Usage and Stock Returns

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  • Da, Zhi
  • Huang, Dayong
  • Yun, Hayong

Abstract

The growth rate of industrial electricity usage predicts future stock returns up to 1 year with an R 2 of 9%. High industrial electricity usage today predicts low stock returns in the future, consistent with a countercyclical risk premium. Industrial electricity usage tracks the output of the most cyclical sectors. Our findings bridge a gap between the asset pricing literature and the business cycle literature, which uses industrial electricity usage to gauge production and output in real time. Industrial electricity growth compares favorably with traditional financial variables, and it outperforms Cooper and Priestley’s output gap measure in real time.

Suggested Citation

  • Da, Zhi & Huang, Dayong & Yun, Hayong, 2017. "Industrial Electricity Usage and Stock Returns," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 52(1), pages 37-69, February.
    • Handle: RePEc:cup:jfinqa:v:52:y:2017:i:01:p:37-69_00
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    Citations

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    Cited by:

    1. , & Stein, Tobias, 2021. "Equity premium predictability over the business cycle," CEPR Discussion Papers 16357, C.E.P.R. Discussion Papers.
    2. Stig V. Møller & Jesper Rangvid, 2018. "Global Economic Growth and Expected Returns Around the World: The End-of-the-Year Effect," Management Science, INFORMS, vol. 64(2), pages 573-591, February.
    3. Pei, Duo & Vasarhelyi, Miklos A., 2020. "Big data and algorithmic trading against periodic and tangible asset reporting: The need for U-XBRL," International Journal of Accounting Information Systems, Elsevier, vol. 37(C).
    4. Chen Lin & Thomas Schmid & Michael S. Weisbach, 2019. "Climate Change, Operating Flexibility and Corporate Investment Decisions," NBER Working Papers 26441, National Bureau of Economic Research, Inc.
    5. Pham, Quynh Thi Thuy & Rudolf, Markus, 2021. "Gold, platinum, and industry stock returns," International Review of Economics & Finance, Elsevier, vol. 75(C), pages 252-266.
    6. Karmakar, Sayar & Demirer, Riza & Gupta, Rangan, 2021. "Bitcoin mining activity and volatility dynamics in the power market," Economics Letters, Elsevier, vol. 209(C).
    7. Ashley Lim & Yihui Lan & Sirimon Treepongkaruna, 2020. "Asset pricing and energy consumption risk," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 60(4), pages 3813-3850, December.
    8. Denholm, Paul & Mai, Trieu, 2019. "Timescales of energy storage needed for reducing renewable energy curtailment," Renewable Energy, Elsevier, vol. 130(C), pages 388-399.
    9. Atanasov, Victoria, 2018. "World output gap and global stock returns," Journal of Empirical Finance, Elsevier, vol. 48(C), pages 181-197.
    10. Talebian, Hoda & Herrera, Omar E. & Tran, Martino & Mérida, Walter, 2018. "Electrification of road freight transport: Policy implications in British Columbia," Energy Policy, Elsevier, vol. 115(C), pages 109-118.
    11. Fragaki, Aikaterini & Markvart, Tom & Laskos, Georgios, 2019. "All UK electricity supplied by wind and photovoltaics – The 30–30 rule," Energy, Elsevier, vol. 169(C), pages 228-237.
    12. Chen, Yong & Da, Zhi & Huang, Dayong, 2022. "Short selling efficiency," Journal of Financial Economics, Elsevier, vol. 145(2), pages 387-408.
    13. Deepak, K. & Varma, V.B. & Prasanna, G. & Ramanujan, R.V., 2019. "Hybrid thermomagnetic oscillator for cooling and direct waste heat conversion to electricity," Applied Energy, Elsevier, vol. 233, pages 312-320.
    14. Bourdeau-Brien, Michael & Kryzanowski, Lawrence, 2020. "Natural disasters and risk aversion," Journal of Economic Behavior & Organization, Elsevier, vol. 177(C), pages 818-835.
    15. Wang, Qiao & Balvers, Ronald, 2021. "Determinants and predictability of commodity producer returns," Journal of Banking & Finance, Elsevier, vol. 133(C).
    16. Deepak, K. & Pattanaik, M.S. & Ramanujan, R.V., 2019. "Figure of merit and improved performance of a hybrid thermomagnetic oscillator," Applied Energy, Elsevier, vol. 256(C).
    17. Ji, Haoran & Wang, Chengshan & Li, Peng & Zhao, Jinli & Song, Guanyu & Ding, Fei & Wu, Jianzhong, 2017. "An enhanced SOCP-based method for feeder load balancing using the multi-terminal soft open point in active distribution networks," Applied Energy, Elsevier, vol. 208(C), pages 986-995.
    18. Tu, Ran & Gai, Yijun (Jessie) & Farooq, Bilal & Posen, Daniel & Hatzopoulou, Marianne, 2020. "Electric vehicle charging optimization to minimize marginal greenhouse gas emissions from power generation," Applied Energy, Elsevier, vol. 277(C).

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