Forecasting Stock Returns: Do Commodity Prices Help?

Angela J. Black, Olga Klinkowska, David G. McMillan*, Fiona J. McMillan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)


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 (c) 2014 John Wiley & Sons, Ltd.

Original languageEnglish
Pages (from-to)627-639
Number of pages13
JournalJournal of forecasting
Issue number8
Early online date20 Oct 2014
Publication statusPublished - Dec 2014


  • stock prices
  • commodity prices
  • forecasting
  • rolling
  • expected returns
  • dividend yields
  • equity premium
  • cointegration
  • volatility
  • predictability
  • accuracy
  • vectors
  • models
  • breaks


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