Gold Oil Price Ratio As a Predictor of Stock Market Returns

Analyzing intermarket relationships between assets can help identify trends and predict returns. Traditionally, analysts use commodity, currency, and interest-rate data to predict the direction of the stock market. In this regard, reference [1] brings a fresh new perspective. It utilized price ratios of gold over other assets in order to forecast stock market returns. Specifically, the authors constructed ten gold price ratios: the gold oil ratio, gold silver ratio, gold CPI ratio, gold corn ratio, gold copper ratio, gold Dow Jones Industrial Average ratio, gold yield dividend ratio, gold treasury bond yield ratio, and gold federal fund rate ratio. They then used univariate and bivariate predictive regressions to investigate the forecasting power of the constructed gold price ratios in the stock market. The authors pointed out,

We empirically investigate the predictive ability of ten gold price ratios for US excess stock returns. Gold price ratios are constructed as the natural logarithm of gold to other asset prices. We find that gold price ratios positively predict future stock returns and have higher predictive ability than traditional predictors studied in Welch and Goyal (2008) on average. Among these ratios, the gold oil ratio (GO) is the most powerful return predictor, and the information contained in GO does not overlap with that contained in traditional predictors and other gold price ratios. A one standard deviation increase in GO is associated with a 6.60% increase in the annual excess return for the next month in sample. GO also significantly outperforms the historical mean model out of sample and generates substantial economic gains for a mean variance investor. Therefore, the predictive ability of GO is both statistically and economically significant.

In short, among the constructed gold price ratios, the gold oil ratio is a good predictor of the stock market returns.

This article showed that we can use not only asset prices as independent variables in a predictive model but also combinations of them.

References

[1] T. Fang, Z. Su and L Yin, Gold price ratios and aggregate stock returns, 2021. Available at SSRN: https://ssrn.com/abstract=3950940

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