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What drives gold returns? A decision tree analysis

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  • Malliaris, A.G.
  • Malliaris, Mary

Abstract

The behavior of gold as an investment asset has been researched extensively. For the very long run, that is several decades, gold does not outperform equities. However, for shorter periods, gold responds to fears of inflation, stock market corrections, currency crises and financial instabilities very vigorously. In this paper we follow a decision tree methodology to investigate the behavior of gold prices using both traditional financial variables such as equity returns, equity volatility, oil prices, and the euro. We also use the new Cleveland Financial Stress Index to investigate its effectiveness in explaining changes in gold prices. We find that gold returns depend on different determinants across various regimes.

Suggested Citation

  • Malliaris, A.G. & Malliaris, Mary, 2015. "What drives gold returns? A decision tree analysis," Finance Research Letters, Elsevier, vol. 13(C), pages 45-53.
    • Handle: RePEc:eee:finlet:v:13:y:2015:i:c:p:45-53
    DOI: 10.1016/j.frl.2015.03.004
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    Cited by:

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    4. Harris, Richard D.F. & Shen, Jian, 2017. "The intrinsic value of gold: An exchange rate-free price index," Journal of International Money and Finance, Elsevier, vol. 79(C), pages 203-217.
    5. Rangan Gupta & Sayar Karmakar & Christian Pierdzioch, 2022. "Safe Havens, Machine Learning, and the Sources of Geopolitical Risk: A Forecasting Analysis Using Over a Century of Data," Working Papers 202201, University of Pretoria, Department of Economics.
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    8. Abdelbari El Khamlichi & Thi Hong Van Hoang & Wing‐keung Wong, 2016. "Is Gold Different for Islamic and Conventional Portfolios? A Sectorial Analysis," Post-Print hal-02964594, HAL.
    9. Plakandaras, Vasilios & Ji, Qiang, 2022. "Intrinsic decompositions in gold forecasting," Journal of Commodity Markets, Elsevier, vol. 28(C).
    10. Döpke, Jörg & Fritsche, Ulrich & Pierdzioch, Christian, 2017. "Predicting recessions with boosted regression trees," International Journal of Forecasting, Elsevier, vol. 33(4), pages 745-759.
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    12. Huang, Xiaoyong & Jia, Fei & Xu, Xiangyun & Yu shi,, 2019. "The threshold effect of market sentiment and inflation expectations on gold price," Resources Policy, Elsevier, vol. 62(C), pages 77-83.
    13. Charteris, Ailie & Kallinterakis, Vasileios, 2021. "Feedback trading in retail-dominated assets: Evidence from the gold bullion coin market," International Review of Financial Analysis, Elsevier, vol. 75(C).
    14. Joscha Beckmann & Theo Berger & Robert Czudaj & Thi-Hong-Van Hoang, 2019. "Tail dependence between gold and sectorial stocks in China: perspectives for portfolio diversification," Empirical Economics, Springer, vol. 56(3), pages 1117-1144, March.
    15. Dichtl, Hubert, 2020. "Forecasting excess returns of the gold market: Can we learn from stock market predictions?," Journal of Commodity Markets, Elsevier, vol. 19(C).
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    17. Luo, Xingguo & Qin, Shihua & Ye, Zinan, 2016. "The information content of implied volatility and jumps in forecasting volatility: Evidence from the Shanghai gold futures market," Finance Research Letters, Elsevier, vol. 19(C), pages 105-111.
    18. Rigamonti, Alessandro Paolo & Greco, Giulio & Capocchi, Alessandro, 2024. "Futures, provisional sales, and earnings management in the global gold mining industry," Finance Research Letters, Elsevier, vol. 59(C).
    19. Naeem, Muhammad Abubakr & Qureshi, Fiza & Arif, Muhammad & Balli, Faruk, 2021. "Asymmetric relationship between gold and Islamic stocks in bearish, normal and bullish market conditions," Resources Policy, Elsevier, vol. 72(C).
    20. Jörg Döpke & Ulrich Fritsche & Christian Pierdzioch, 2015. "Predicting Recessions in Germany With Boosted Regression Trees," Macroeconomics and Finance Series 201505, University of Hamburg, Department of Socioeconomics.
    21. Vasilios Plakandaras & Periklis Gogas & Theophilos Papadimitriou, 2021. "Gold Against the Machine," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 5-28, January.
    22. Tiwari, Aviral Kumar & Abakah, Emmanuel Joel Aikins & Karikari, Nana Kwasi & Hammoudeh, Shawkat, 2022. "Time-varying dependence dynamics between international commodity prices and Australian industry stock returns: a Perspective for portfolio diversification," Energy Economics, Elsevier, vol. 108(C).
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    24. Christian Pierdzioch & Marian Risse, 2020. "Forecasting precious metal returns with multivariate random forests," Empirical Economics, Springer, vol. 58(3), pages 1167-1184, March.

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    More about this item

    Keywords

    Gold prices; Uncertainty; Decision tree analysis; Financial Stress Index;
    All these keywords.

    JEL classification:

    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G1 - Financial Economics - - General Financial Markets

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