What Earnings Explain, and What They Don’t: Insights from 150 Years of Market Data

Stock prices and corporate earnings move closely together over long horizons, a relationship confirmed by more than a century of data compiled by Robert Shiller. This analysis examines the strength of that long-term linkage and tests whether changes in the earnings–price correlation offer insight into future stock market returns.

The results show that while earnings help explain market behavior over time, fluctuations in the correlation itself do not provide a useful basis for forecasting returns. The sections that follow document empirical patterns across multiple rolling periods and assess the limits of using correlation measures as market-timing tools. The findings may also help financial advisors frame long-term market behavior for clients in a grounded and intuitive way.

What This Analysis Aims to Clarify

I examine the long-term relationship between stock prices and corporate earnings for two main reasons.

First, the findings offer a straightforward way to explain stock market behavior over long investment horizons. I define a long horizon as more than 10 years, which is a useful minimum timeframe for retirement planning and for making asset allocation decisions.

Second, after calculating the correlations between prices and earnings, I tested whether changes in the correlation over time might serve as a leading indicator of future returns. Specifically, I asked whether periods of unusually low historical correlation were followed by stronger or weaker subsequent stock market performance.

Correlation Results

The analysis uses monthly averages of the S&P Composite earnings-per-share and the S&P Composite price. The reported monthly earnings, stock price, and returns data for the S&P Composite companies are based on Shiller’s data from 1871 through December 2024.

Across multiple time periods, the correlations between earnings and prices were consistently high.

I chose common time periods to examine the data and note the following:

• One starting point is the 1940 Investors Act, used to test whether results differed after investor protections and more uniform accounting standards were introduced. The difference appears negligible.
• The past 10- and 20-year periods were included to reflect what is often considered a typical retirement-planning horizon.

Correlation Changes Over Time

The correlation between earnings and stock prices does fluctuate over time, particularly across shorter horizons such as the five-, 10-, and 20-year windows. The rolling 50-year correlations also vary, though within a much narrower range.

Source: Robert J. Shiller S&P data; Archer Bay Capital LLC

The lowest rolling 50-year correlation occurred during the first half of the 20th century, when the data series reached 0.6. Given the backdrop of two world wars, the Great Depression, and limited market regulation prior to 1940, it is notable that the correlation did not fall further.

Variability increased as the time horizon shortened. In the rolling 20-year series, correlations fell below 0.50 for a full decade between February 1918 and December 1928, and again briefly in December 1948.

Source: Robert J. Shiller S&P data; Archer Bay Capital LLC

The rolling 10-year correlations fell below zero during three periods: at the end of World War I and World War II, and during the high inflation era of the late 1970s and early 1980s. 

Source: Robert J. Shiller S&P data; Archer Bay Capital LLC

Rolling five-year correlations naturally showed the most volatility, with deeper drops and more frequent swings, including multiple periods of negative correlation. Both the average and median rolling five-year correlations were lower than those observed over longer horizons.

Source: Robert J. Shiller S&P data; Archer Bay Capital LLC

Does the Variability in Correlations Correspond with Returns?

To test whether variation in the earnings–price correlation has any predictive value for stock returns, we ran regressions of correlation levels against subsequent annualized returns.

The R² between S&P Composite earnings and price from 1871 through 2024 is very high at 0.95. Given the strength of this long-term relationship—and the relative rarity of low-correlation periods—it is reasonable to ask whether those periods might function as buy or sell signals. In other words, does variation in the earnings–price correlation help predict future returns?

I evaluated this question across multiple rolling time horizons. The resulting R² values — linking correlation levels to subsequent annualized returns — were far lower than the R² between earnings and price themselves. For the rolling 10-year and five-year windows, the R² fell close to zero, indicating virtually no predictive relationship.

The rolling 50-year period showed the strongest relationship with a R² of 0.53.

Source: Robert J. Shiller S&P data; Archer Bay Capital LLC

For the rolling 20-year windows, the R² was 0.24, reflecting considerably more variability.

Source: Robert J. Shiller S&P data; Archer Bay Capital LLC

Variability increased further in the rolling 10-year series, where the R² fell to 0.06.

Source: Robert J. Shiller S&P data; Archer Bay Capital LLC

The rolling five-year periods show no consistent pattern. R² is nearly 0.0 (actual: 1.27E-07).

Source: Robert J. Shiller S&P data; Archer Bay Capital LLC

Overall, I found no evidence that changes in the earnings–price correlation predict future annualized returns. The data show that the two measures do not move together in any meaningful way for horizons shorter than 50 years.

Predictive Power of Correlation

The strong long-term relationship between earnings and prices offers a clear explanation for the rise and fall of stock markets over extended periods. It provides a simple and intuitive framework for understanding long-run equity trends.

However, the second goal – determining whether changes in the correlation could serve as a predictive measure for annualized returns – was not achieved. The evidence suggests that other factors beyond the earnings–price relationship drive the rate of change in annualized returns, even though the two series move closely together over long horizons.

Key Takeaways

• Earnings and stock prices move closely together over long horizons. More than 150 years of Shiller data show a consistently strong relationship between the two series.
• Shorter windows introduce substantial noise. Correlations fluctuate meaningfully over five-, 10-, and 20-year periods, reflecting wars, inflation shocks, and structural changes.
• Correlation strength does not imply predictive power. Shifts in the earnings–price correlation have little ability to forecast subsequent returns at horizons relevant to most investors.
• Only the longest windows show limited explanatory power. Even the 50-year regressions, with an R² of 0.53, offer only modest insight, while shorter horizons fall close to zero.

Earnings help explain long-term market behavior, but they do not help time the market.

The author is a Registered Investment Advisor representative of Archer Bay Capital LLC/Integrated Advisors Network – a SEC Registered Investment Adviser. The information contained herein represents Campbell’s independent view or research and does not represent solicitation, advertising, or research from Integrated Advisors Network or Archer Bay Capital LLC. It has been obtained from or is based upon sources believed to be reliable, but its accuracy and completeness are not guaranteed. This is not intended to be an offer to buy, sell, or hold any securities.