The stock market turned out to be a poor predictor of this year’s US presidential election, and it’s important to explore why.
In several recent columns, I reported on a simple model that correlated the incumbent political party’s chances of retaining the White House with the full-year DJIA return of the Dow Jones Industrial Average. Just before the elections, that model gave US Vice President Kamala Harris, the Democratic candidate, a high chance of 70% to defeat former US President Donald Trump.
When a model fails, investors should take the opportunity to explore what can be learned. Was this just one of those moments where a model is proven wrong – something that inevitably happens sooner or later because no model works all the time? Or have there been more fundamental changes in the US economy and financial markets that make the model less useful?
Now that the election is over, I feel like the collapse of the model has to do with the growing divide between Wall Street and the broader economy – what many call the Wall Street-Main Street divide. The reason my simple model has worked so well over the decades is that voters tend to vote with their wallets and the Dow Jones has been a decent barometer of overall economic health. This may no longer be true, or at least as true as it once was.
To demonstrate this, I analyzed the quarterly changes in US GDP with the quarterly changes in the S&P 500’s SPX earnings per share (EPS) going back to 1947. More specifically, I calculated the rolling 20-year correlation coefficient of these quarterly changes. (That coefficient ranges from a theoretical maximum of 100%, which would indicate that GDP and earnings per share are moving in perfect line with each other, to a theoretical minimum of minus 100%, which would indicate that the two are moving in opposite directions . of zero would mean that the two have no discernible relationship to each other.)
The graph above shows what I found. Apart from a brief moment in the aftermath of the 2008-2009 global financial crisis, when both GDP and earnings per share fell in lockstep, the correlation between the economy and the stock market has been steadily declining for decades and is currently only marginally higher than that of the economy. zero.
For example, when Democratic strategist James Carville coined the phrase “It’s the economy, dumbass” in 1992, the correlation over twenty years was almost 40%. The last value is only 15%.
This decline helps us understand why a strong stock market this year did not translate into a better performance for Kamala Harris, the incumbent political party’s candidate. Even as the stock market has soared, many Americans are struggling with both personal and family finances.
The implications for the future are profound; economic forecasts may not be as useful in the future. Financial analysts may need to focus more on the idiosyncrasies of particular companies rather than macroeconomic cycles. This point was recently made by Vincent Deluard, director of global macro strategy at investment firm StoneX. He argued that “investors spend far too much time worrying about the next recession. Economic growth is only a small driver of stock prices. Margins and multiples are much more important for stock prices.”
To rephrase Deluard’s point, the dollar value of a company’s revenue is less important than the percentage of revenue that comes from operating income and the price-to-earnings ratio that investors are willing to put on that profit. This is not to say that predicting profit margins or price/earnings ratios is easier than predicting the economy. But once you realize how small a role economic growth plays, you can focus on these more consequential factors.
Mark Hulbert is a regular contributor to MarketWatch. His Hulbert Ratings tracks investment newsletters that pay a flat fee to be reviewed. He can be reached at
More: What the stock market can expect if Republicans control the White House, Senate and House of Representatives
Also read: Stock Market Bulls Rely on 2016 ‘Trump Trade’ Playbook – But 2024 Is Very Different
