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Country-level energy-related uncertainties and stock market returns: Insights from the U.S. and China

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  • Zhang, Xincheng

Abstract

Scholars have widely acknowledged the influence of energy on stock market returns, yet the role of energy-related uncertainty in the stock market remains to be explored. This study employs scaled principal component analysis (sPCA), partial least squares (PLS), and principal component analysis (PCA) techniques to investigate the impact of country-level energy-related uncertainty indices (EUIs) on stock market returns. The results indicate that an EUI contains prediction information for U.S. and Chinese stock market returns. Furthermore, the diffusion index derived from all EUIs utilizing supervised dimensionality reduction methods (sPCA and PLS) facilitates predicting stock market returns better and more robustly than country-level EUIs and traditional macroeconomic predictors. The results of the economic value assessment also indicate that models considering the EUI diffusion index tend to yield greater economic value than models considering individual EUIs or traditional predictive factors. Our results emphasize the significance of the EUI diffusion index in predicting stock market returns, providing valuable guidance for portfolio management and decision-making for market stakeholders.

Suggested Citation

  • Zhang, Xincheng, 2024. "Country-level energy-related uncertainties and stock market returns: Insights from the U.S. and China," Technological Forecasting and Social Change, Elsevier, vol. 204(C).
  • Handle: RePEc:eee:tefoso:v:204:y:2024:i:c:s0040162524002336
    DOI: 10.1016/j.techfore.2024.123437
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    as
    1. Ivo Welch & Amit Goyal, 2008. "A Comprehensive Look at The Empirical Performance of Equity Premium Prediction," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1455-1508, July.
    2. repec:bla:jfinan:v:53:y:1998:i:5:p:1563-1587 is not listed on IDEAS
    3. Dang, Tam Hoang-Nhat & Nguyen, Canh Phuc & Lee, Gabriel S. & Nguyen, Binh Quang & Le, Thuy Thu, 2023. "Measuring the energy-related uncertainty index," Energy Economics, Elsevier, vol. 124(C).
    4. Liang, Chao & Luo, Qin & Li, Yan & Huynh, Luu Duc Toan, 2023. "Global financial stress index and long-term volatility forecast for international stock markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 88(C).
    5. Pettenuzzo, Davide & Timmermann, Allan & Valkanov, Rossen, 2014. "Forecasting stock returns under economic constraints," Journal of Financial Economics, Elsevier, vol. 114(3), pages 517-553.
    6. Yousfi, Mohamed & Ben Zaied, Younes & Ben Cheikh, Nidhaleddine & Ben Lahouel, Béchir & Bouzgarrou, Houssem, 2021. "Effects of the COVID-19 pandemic on the US stock market and uncertainty: A comparative assessment between the first and second waves," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    7. Liang, Chao & Huynh, Luu Duc Toan & Li, Yan, 2023. "Market momentum amplifies market volatility risk: Evidence from China’s equity market," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 88(C).
    8. Hui Guo, 2006. "On the Out-of-Sample Predictability of Stock Market Returns," The Journal of Business, University of Chicago Press, vol. 79(2), pages 645-670, March.
    9. Kim, Jae H. & Rahman, Md Lutfur & Shamsuddin, Abul, 2019. "Can energy prices predict stock returns? An extreme bounds analysis," Energy Economics, Elsevier, vol. 81(C), pages 822-834.
    10. Lin, Boqiang & Su, Tong, 2020. "The linkages between oil market uncertainty and Islamic stock markets: Evidence from quantile-on-quantile approach," Energy Economics, Elsevier, vol. 88(C).
    11. Ghosh, Indranil & Jana, Rabin K., 2024. "Clean energy stock price forecasting and response to macroeconomic variables: A novel framework using Facebook's Prophet, NeuralProphet and explainable AI," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    12. Khalfaoui, Rabeh & Mefteh-Wali, Salma & Viviani, Jean-Laurent & Ben Jabeur, Sami & Abedin, Mohammad Zoynul & Lucey, Brian M., 2022. "How do climate risk and clean energy spillovers, and uncertainty affect U.S. stock markets?," Technological Forecasting and Social Change, Elsevier, vol. 185(C).
    13. Campbell, John Y., 1987. "Stock returns and the term structure," Journal of Financial Economics, Elsevier, vol. 18(2), pages 373-399, June.
    14. Ren, Xiaohang & Zhong, Yan & Cheng, Xu & Yan, Cheng & Gozgor, Giray, 2023. "Does carbon price uncertainty affect stock price crash risk? Evidence from China," Energy Economics, Elsevier, vol. 122(C).
    15. Martin Lettau & Sydney Ludvigson, 2001. "Consumption, Aggregate Wealth, and Expected Stock Returns," Journal of Finance, American Finance Association, vol. 56(3), pages 815-849, June.
    16. Dashan Huang & Fuwei Jiang & Kunpeng Li & Guoshi Tong & Guofu Zhou, 2022. "Scaled PCA: A New Approach to Dimension Reduction," Management Science, INFORMS, vol. 68(3), pages 1678-1695, March.
    17. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    18. Chava, Sudheer & Gallmeyer, Michael & Park, Heungju, 2015. "Credit conditions and stock return predictability," Journal of Monetary Economics, Elsevier, vol. 74(C), pages 117-132.
    19. Fama, Eugene F. & French, Kenneth R., 1988. "Dividend yields and expected stock returns," Journal of Financial Economics, Elsevier, vol. 22(1), pages 3-25, October.
    20. Li, Yan & Liang, Chao & Huynh, Toan Luu Duc, 2022. "Forecasting US stock market returns by the aggressive stock-selection opportunity," Finance Research Letters, Elsevier, vol. 50(C).
    21. John Y. Campbell & Samuel B. Thompson, 2008. "Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1509-1531, July.
    22. Chen, Yan & Qiao, Gaoxiu & Zhang, Feipeng, 2022. "Oil price volatility forecasting: Threshold effect from stock market volatility," Technological Forecasting and Social Change, Elsevier, vol. 180(C).
    23. Ghosh, Indranil & Chaudhuri, Tamal Datta & Alfaro-Cortés, Esteban & Gámez, Matías & García, Noelia, 2022. "A hybrid approach to forecasting futures prices with simultaneous consideration of optimality in ensemble feature selection and advanced artificial intelligence," Technological Forecasting and Social Change, Elsevier, vol. 181(C).
    24. Salisu, Afees A. & Gupta, Rangan, 2021. "Oil shocks and stock market volatility of the BRICS: A GARCH-MIDAS approach," Global Finance Journal, Elsevier, vol. 48(C).
    25. Liang, Chao & Wang, Lu & Duong, Duy, 2024. "More attention and better volatility forecast accuracy: How does war attention affect stock volatility predictability?," Journal of Economic Behavior & Organization, Elsevier, vol. 218(C), pages 1-19.
    26. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    27. Yaman Omer Erzurumlu & Giray Gozgor, 2022. "Effects of economic policy uncertainty on energy demand: evidence from 72 countries," Journal of Chinese Economic and Business Studies, Taylor & Francis Journals, vol. 20(1), pages 23-38, January.
    28. Rapach, David & Zhou, Guofu, 2013. "Forecasting Stock Returns," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 328-383, Elsevier.
    29. Obaid, Khaled & Pukthuanthong, Kuntara, 2022. "A picture is worth a thousand words: Measuring investor sentiment by combining machine learning and photos from news," Journal of Financial Economics, Elsevier, vol. 144(1), pages 273-297.
    30. Lintner, John, 1975. "Inflation and Security Returns," Journal of Finance, American Finance Association, vol. 30(2), pages 259-280, May.
    31. Abakah, Emmanuel Joel Aikins & Tiwari, Aviral Kumar & Ghosh, Sudeshna & Doğan, Buhari, 2023. "Dynamic effect of Bitcoin, fintech and artificial intelligence stocks on eco-friendly assets, Islamic stocks and conventional financial markets: Another look using quantile-based approaches," Technological Forecasting and Social Change, Elsevier, vol. 192(C).
    32. Peng, Lijuan & Liang, Chao, 2023. "Sustainable development during the post-COVID-19 period: Role of crude oil," Resources Policy, Elsevier, vol. 85(PA).
    33. Ha, Le Thanh & Nham, Nguyen Thi Hong, 2022. "An application of a TVP-VAR extended joint connected approach to explore connectedness between WTI crude oil, gold, stock and cryptocurrencies during the COVID-19 health crisis," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
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    More about this item

    Keywords

    Stock market returns; Energy-related uncertainties; Supervised dimensionality reduction technique;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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