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Can the relative price ratio of gold to platinum predict the Chinese stock market?

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  • Han, Xing
  • Ruan, Xinfeng
  • Tan, Yongxian

Abstract

In this paper, we examine whether the relative price ratio of gold to platinum (GP ratio) can predict the aggregate stock market return in the US and China. We confirm that the GP ratio is a strong predictor of US market excess return; however, it is not a reliable predictor for excess return in the Chinese stock market. The evidence highlights the limitation of relying on the GP ratio as a non-parametric, real-time return predictor, and indicates the diversification benefits of investing in the Chinese stock market.

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  • Han, Xing & Ruan, Xinfeng & Tan, Yongxian, 2020. "Can the relative price ratio of gold to platinum predict the Chinese stock market?," Pacific-Basin Finance Journal, Elsevier, vol. 62(C).
    • Handle: RePEc:eee:pacfin:v:62:y:2020:i:c:s0927538x20301104
    DOI: 10.1016/j.pacfin.2020.101379
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    1. Ivo Welch & Amit Goyal, 2008. "A Comprehensive Look at The Empirical Performance of Equity Premium Prediction," Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1455-1508, July.
    2. 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.
    3. Han, Xing & Li, Youwei, 2017. "Can investor sentiment be a momentum time-series predictor? Evidence from China," Journal of Empirical Finance, Elsevier, vol. 42(C), pages 212-239.
    4. Hodrick, Robert J, 1992. "Dividend Yields and Expected Stock Returns: Alternative Procedures for Inference and Measurement," Review of Financial Studies, Society for Financial Studies, vol. 5(3), pages 357-386.
    5. Liu, Jianan & Stambaugh, Robert F. & Yuan, Yu, 2019. "Size and value in China," Journal of Financial Economics, Elsevier, vol. 134(1), pages 48-69.
    6. 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.
    7. Huang, Darien & Kilic, Mete, 2019. "Gold, platinum, and expected stock returns," Journal of Financial Economics, Elsevier, vol. 132(3), pages 50-75.
    8. Li, Yan & Ng, David T. & Swaminathan, Bhaskaran, 2013. "Predicting market returns using aggregate implied cost of capital," Journal of Financial Economics, Elsevier, vol. 110(2), pages 419-436.
    9. Goh, Jeremy C. & Jiang, Fuwei & Tu, Jun & Wang, Yuchen, 2013. "Can US economic variables predict the Chinese stock market?," Pacific-Basin Finance Journal, Elsevier, vol. 22(C), pages 69-87.
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    Cited by:

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    2. Lee, Chien-Chiang & Lee, Hsiang-Tai, 2023. "Optimal portfolio diversification with a multi-chain regime-switching spillover GARCH model," Global Finance Journal, Elsevier, vol. 55(C).

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