DJIA-Gold Ratio as a Stock Market Indicator

August 13, 2015 • Posted in Gold, Technical Trading

A reader requested a test of the following hypothesis from the article “Gold’s Bluff – Is a 30 Percent Drop Next?”: “Ironically, gold is more than just a hedge against market turmoil. Gold is actually one of the most accurate indicators of the stock market’s long-term direction. The Dow Jones measured in gold is a forward looking indicator.” To test this assertion, we examine relationships between the spot price of gold and the level of the Dow Jones Industrial Average (DJIA). Using monthly data for the spot price of gold in dollars per ounce and DJIA over the period January 1971 through July 2015 (535 months), we find that:

The following chart plots the ratio of the level of DJIA to the price of gold in dollars per ounce (DJIA/gold) over the entire sample period. The ratio ranges from 1.3 at the end of January 1980 to 42.2 at the end of August 1999, with an average value of 12.9 over the entire sample period. Its value at the end of July 2015 is 16.1.

Also shown is the inception-to-date (ITD) average of DJIA/Gold, which ranges from 6.1 to 23.3. The wide variation in ITD average suggests that the sample period is too short to determine a reliable average. In other words, this sample indicates no obvious “normal” value for the ratio.

To test predictive power, we first relate monthly level of DJIA/gold to next-month change in DJIA.


The following scatter plot relates next-month return on DJIA to monthly value of DJIA/gold over the entire sample period. The Pearson correlation for the two series is -0.06 and the R-squared statistic 0.003, indicating that the monthly level of DJIA/gold explains less than 1% of the variation in DJIA the following month. In other words, the ratio of DJIA level to spot gold price appears not to be a useful indicator at a one-month horizon.

The Pearson correlation between next-monthly change in gold price and monthly value of DJIA/gold is 0.00 and the R-squared statistic 0.000, indicating no relationship.

To test for an exploitable non-linearity, we calculate average future DJIA returns by ranked fifth (quintile) of DJIA/gold.


The next two charts summarize average next-month returns for DJIA (upper chart) and gold (lower chart) by quintile of DJIA/gold over the entire sample period and two equal subperiods (break point at end of March 1993). There are 107 observations per quintile for the total sample.

While variation in average future DJIA monthly returns across quintiles is not completely systematic, returns for the highest fifths of DJIA/gold observations are the lowest for the entire sample period and the subperiods. However, subperiod results differ noticeably from each other and from the results for the entire sample period.

The DJIA/gold ratio quintiles show no ability to predict average future monthly returns for gold.

Note that these analyses are in-sample, meaning that an investor would need foresight through the end of the sample period to trade on this information during the sample period. A realistic investment scenario would limit an investor to past data only, and the sample is scant from that out-of-sample perspective. For example, as shown in the first chart, an investor operating in the 1980s would have calculated a much lower average for DJIA/gold than an investor operating in the 2000s.

Might DJIA/gold work more convincingly at an annual forecast horizon?



The next scatter plot relates next-year DJIA and gold returns to end-of-year DJIA/gold over the entire sample period (43 annual returns).

The Pearson correlation for future DJIA returns is -0.20 and the R-squared statistic 0.04, indicating that end-of-year DJIA/gold explains about 4% of DJIA return the following year. In other words, a low (high) DJIA/gold is a little bit predictive of a strong (weak) stock market at a forecast horizon of one year.

The Pearson correlation for future gold returns is 0.02 and the R-squared statistic 0.00, indicating that end-of-year DJIA/gold explains none of gold return the following year. In other words, stocks and not gold drive any reversion in DJIA/gold at an annual horizon.

The average annual return for DJIA for the 21 years with the lowest (20 years with the highest) values of DJIA/gold is 9.3% (7.7%). However, excluding either the best (1975) or the worst (2008) year for DJIA during the sample period makes returns for the highest and lowest values of DJIA/gold about the same. In other words, a single extreme year alters the conclusion, demonstrating the unreliability of any predictive power.

Might the predictive horizon of DJIA/gold be longer than a year?


The final chart summarizes correlations between annual DJIA return or annual gold return and annual DJIA/gold for lead-lag relationship ranging from returns lead DJIA/gold by four years (-4) to DJIA/gold leads returns by four years (4). Results suggest that:

Strong DJIA and weak gold returns over the past four years suggest (as would be expected) high DJIA/gold values this year. Conversely, weak DJIA and strong gold returns over the past four years suggest low DJIA/gold values this year. 

A high DJIA/gold this year suggests strong gold returns two to four years hence and weak DJIA returns one to four years hence. Conversely, a low DJIA/gold this year suggests weak gold returns two to four years hence and strong DJIA returns one to four years hence. These findings may persist beyond four years.

However, the sample is very short for this kind of analysis. The large peak in DJIA/gold in the late 1990s may dominate findings. Also, the analysis is in-sample, so an investor operating in real time may have draw different conclusions at different times.


In summary, evidence from the past 44.5 years offers little support for belief that the ratio of DJIA to gold price usefully predicts stock market returns and no support for belief that the ratio predicts gold returns.

Use of gold prices before 1971 would have little incremental value since: “On March 17, 1968, …the price of gold on the private market was allowed to fluctuate…[, and] in 1975…the price of gold was left to find its free-market level.”

Cautions regarding findings include:

  • As noted, the available sample of free market gold prices may be too short to expose a reliable DJIA-gold relationship (which, if it exists, may be too long to be useful to most investors).
  • Also as noted, the above analyses are in-sample. An investor operating in real time during the sample period would have: (1) had even less historical data to use for estimating future returns; and, (2) drawn different conclusions at different points in time.
  • A rolling historical window analysis (rather than ITD) may produce different results, but the dynamics of DJIA/gold indicate a very slow process, suggesting that only a very long window is reasonable.
  • Because of weak/inconsistent evidence and the short sample, the above analyses test no trading strategies based on DJIA/gold.
Why not subscribe to our premium content?
It costs less than a single trading commission. Learn more here.
Email Subscribe
Current Momentum Winners

ETF Momentum Signal
for June 2016 (Final)

Winner ETF

Second Place ETF

Third Place ETF

Gross Compound Annual Growth Rates
(Since August 2006)
Top 1 ETF Top 2 ETFs
10.5% 11.1%
Top 3 ETFs SPY
12.2% 7.3%
Strategy Overview
Current Value Allocations

ETF Value Signal
for June 2016 (Final)





The asset with the highest allocation is the holding of the Best Value strategy.
Gross Compound Annual Growth Rates
(Since September 2002)
Best Value Weighted 60-40
12.7% 9.8% 7.8%
Strategy Overview
Recent Research
Popular Posts
Popular Subscriber-Only Posts