Swedroe: Measuring Value In Forecasts

May 20, 2019

The financial media tends to focus much of its attention on market forecasts by so-called gurus. They do so because they know that is what gets the investment public’s attention. Investors must believe they have value or they wouldn’t tune in. Nor would they subscribe to investment newsletters and other financial publications such as Barron’s.

Unfortunately, there’s a large body of evidence demonstrating that market forecasts have no value in terms of adding alpha (though they provide plenty of fodder for my blog)—the accuracy of forecasts is no better than one would randomly expect.

For investors who haven’t learned that forecasts should only be considered as entertainment, or what Jane Bryant Quinn called “investment porn,” they actually have negative value, because forecasts can cause them to stray from well-developed plans. This is especially true when forecasts confirm their own views, subjecting them to confirmation bias.

Despite the evidence, many investors rely on market experts and forecasters when making investment decisions, such as when to buy or sell securities. To help you, I’ll review the findings of two studies on the accuracy of guru forecasts.

Key Forecast Research

One of the first major studies providing evidence on forecasting accuracy was done by CXO Advisory Group. CXO set out to determine if stock market experts, whether self-proclaimed or endorsed by others (e.g., in publications), reliably provide stock market timing guidance.

To find the answer, from 2005 through 2012, they collected and investigated 6,584 forecasts for the U.S. stock market offered publicly by 68 experts, bulls and bears employing technical, fundamental and sentiment indicators. Their collection included forecasts, all of which were publicly available on the Internet, from as far back as the end of 1998. They selected experts, based on web searches for public archives, with enough forecasts spanning enough market conditions to gauge accuracy.

Their methodology was to compare forecasts for the U.S. stock market to the return of the S&P 500 Index over the future interval(s) most relevant to the forecast horizon. They excluded forecasts that were too vague, and forecasts that included conditions requiring consideration of data other than stock market returns. They matched the frequency of a guru’s commentaries (such as weekly or monthly) to the forecast horizon, unless the forecast specified some other timing.

And importantly, they considered the long-run empirical behavior of the S&P 500 Index. For example, if a guru said investors should be bullish on U.S. stocks over the year, and the S&P 500 Index was up by just a few percent, they judged the call incorrect (because the long-term average annual return had been much higher). Finally, they graded complex forecasts with elements proving both correct and incorrect as both right and wrong (not half right and half wrong).

Following is a summary of their findings:

  • Across all forecasts, accuracy was worse than the proverbial flip of a coin—just under 47%.
  • The average guru also had a forecasting accuracy of about 47%.
  • The distribution of forecasting accuracy by the gurus looked very much like the bell curve—what you would expect from random outcomes. That makes it very difficult to tell if there is any skill present.

  • The highest accuracy score was 68% and the lowest was 22%.

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