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Forecasting Stock Returns: Do Commodity Prices Help?

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  • Angela J. Black
  • Olga Klinkowska
  • David G. McMillan
  • Fiona J. McMillan

Abstract

ABSTRACT This paper examines the relationship between stock prices and commodity prices and whether this can be used to forecast stock returns. As both prices are linked to expected future economic performance they should exhibit a long‐run relationship. Moreover, changes in sentiment towards commodity investing may affect the nature of the response to disequilibrium. Results support cointegration between stock and commodity prices, while Bai–Perron tests identify breaks in the forecast regression. Forecasts are computed using a standard fixed (static) in‐sample/out‐of‐sample approach and by both recursive and rolling regressions, which incorporate the effects of changing forecast parameter values. A range of model specifications and forecast metrics are used. The historical mean model outperforms the forecast models in both the static and recursive approaches. However, in the rolling forecasts, those models that incorporate information from the long‐run stock price/commodity price relationship outperform both the historical mean and other forecast models. Of note, the historical mean still performs relatively well compared to standard forecast models that include the dividend yield and short‐term interest rates but not the stock/commodity price ratio. Copyright © 2014 John Wiley & Sons, Ltd.

Suggested Citation

  • Angela J. Black & Olga Klinkowska & David G. McMillan & Fiona J. McMillan, 2014. "Forecasting Stock Returns: Do Commodity Prices Help?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(8), pages 627-639, December.
    • Handle: RePEc:wly:jforec:v:33:y:2014:i:8:p:627-639
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    Citations

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    Cited by:

    1. Afees A. Salisu & Kazeem Isah & Ibrahim D. Raheem, 2018. "Testing the predictability of commodity prices in stock returns: A new perspective," Working Papers 061, Centre for Econometric and Allied Research, University of Ibadan.
    2. McMillan, David G., 2021. "When and why do stock and bond markets predict US economic growth?," The Quarterly Review of Economics and Finance, Elsevier, vol. 80(C), pages 331-343.
    3. Huck, Nicolas, 2019. "Large data sets and machine learning: Applications to statistical arbitrage," European Journal of Operational Research, Elsevier, vol. 278(1), pages 330-342.
    4. Takuro Hidaka & Yuta Saito & Jun Sakamoto, 2021. "Historical Relationships and International Market Return Predictability: The Role of the UK in the Former British Colonies, Protectorates and Mandates," Discussion Papers in Economics and Business 21-08-Rev., Osaka University, Graduate School of Economics, revised Oct 2023.
    5. Leila Dagher & Ibrahim Jamali & Nasser Badra, 2020. "The Predictive Power of Oil and Commodity Prices for Equity Markets," World Scientific Book Chapters, in: Stéphane Goutte & Khaled Guesmi (ed.), Risk Factors and Contagion in Commodity Markets and Stocks Markets, chapter 3, pages 47-82, World Scientific Publishing Co. Pte. Ltd..
    6. Jordan, Steven J. & Vivian, Andrew & Wohar, Mark E., 2016. "Can commodity returns forecast Canadian sector stock returns?," International Review of Economics & Finance, Elsevier, vol. 41(C), pages 172-188.
    7. Basak, Suryoday & Kar, Saibal & Saha, Snehanshu & Khaidem, Luckyson & Dey, Sudeepa Roy, 2019. "Predicting the direction of stock market prices using tree-based classifiers," The North American Journal of Economics and Finance, Elsevier, vol. 47(C), pages 552-567.
    8. Gkillas, Konstantinos & Konstantatos, Christoforos & Papathanasiou, Spyros & Wohar, Mark, 2023. "Estimation of value at risk for copper," Journal of Commodity Markets, Elsevier, vol. 32(C).
    9. Roberto Louis Forestal & Shih-Ming Pi, 2021. "Using Artificial Neural networks and Optimal Scaling Model to Forecast Agriculture Commodity Price: An Ecological-economic Approach," Advances in Management and Applied Economics, SCIENPRESS Ltd, vol. 11(3), pages 1-3.
    10. McMillan, David G., 2019. "Cross-asset relations, correlations and economic implications," Global Finance Journal, Elsevier, vol. 41(C), pages 60-78.
    11. Iyke, Bernard Njindan & Ho, Sin-Yu, 2021. "Stock return predictability over four centuries: The role of commodity returns," Finance Research Letters, Elsevier, vol. 40(C).
    12. Salisu, Afees A. & Isah, Kazeem O. & Raheem, Ibrahim D., 2019. "Testing the predictability of commodity prices in stock returns of G7 countries: Evidence from a new approach," Resources Policy, Elsevier, vol. 64(C).
    13. Bajo-Rubio, Oscar & Berke, Burcu & McMillan, David, 2017. "The behaviour of asset return and volatility spillovers in Turkey: A tale of two crises," Research in International Business and Finance, Elsevier, vol. 41(C), pages 577-589.

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