The Terrible Track Record of Wall Street Forecasts

This quote, attributed to Yogi Berra, captures the essence of the problem with predictions: "Prediction is very difficult, especially about the future."

The financial media is full of stock market "experts" who confidently predict the market's direction, which stocks are likely to become "winners," and which actively managed mutual funds will outperform. Investors often rely on these predictions when buying and selling stocks and bonds.

Sometimes they are correct, but rarely more frequently than you would expect from random chance. When they get it right, they quickly anoint themselves as gurus with unique insight into the future. When they're wrong, they rarely accept accountability.

It's one reason we built our approach around evidence rather than forecasting. At Daner Wealth Management, our investment philosophy starts with a simple premise: no one consistently knows what the market will do next, and a sound financial plan shouldn't depend on anyone getting it right.

So what is the actual track record of Wall Street forecasts?

It's not pretty.

Terrible track record (what the data actually shows)

One study looked at the track record of stock market "experts" who predicted the market's direction.

Their findings were eye-popping.

Overall their accuracy rate was only 47%, less than you might expect from random chance.

Jim Cramer, a fixture on CNBC, had an accuracy rating of 46.8% based on 62 forecasts. Abby Joseph Cohen, formerly a partner and chief U.S. investment strategist at Goldman Sachs, fared even worse. Her accuracy rating was only 35%, well below the average of 47%.

Cramer's record inspired its own real-world test. In March 2023, Tuttle Capital Management launched the Inverse Cramer Tracker ETF (ticker: SJIM), which took short positions on stocks Cramer recommended. If his calls were reliably wrong, the inverse should have been reliably right. It wasn't. By the time the fund was liquidated in February 2024, SJIM was down roughly 15% since inception while the S&P 500 had gained 25% over the same period. Even betting against a forecaster whose individual calls are no better than chance does not produce a reliable signal, because there is no consistent signal to trade against in the first place.

A follow-up study by David Bailey, Jonathan Borwein, Amir Salehipour, and Marcos López de Prado re-examined the same underlying data with a weighting adjustment that accounted for forecast time horizon (longer-horizon calls were weighted more heavily, since short-term market moves contain more noise). Their conclusions tightened the picture: 66% of forecasters scored below 50%, and under the re-weighted methodology, Cramer's accuracy dropped to 37%. Cohen and Gary Shilling both scored 34%. Only 6% of forecasters scored above 70%, and the overall distribution resembled a bell curve, which is what you would expect from random outcomes.

Over the 25 years from 2000 to 2024, Paul Hickey of Bespoke Investment Group found the average yearly forecast missed the actual market return by 14.2 percentage points. Just as striking: the Wall Street consensus predicted gains every single year for 25 straight years, even though the S&P 500 finished negative in 7 of them. Not once in a quarter century did the consensus call for a down year.

The recent track record has been especially rough:

The 2022 miss was the largest single-year gap in Hickey's dataset: a 23.3 percentage point spread between the consensus call for a 3.9% gain and the actual 19.4% decline. In 2023 and 2024, strategists missed in the opposite direction, this time to the downside, underestimating back-to-back years of 20%-plus gains.

You can't predict the unpredictable.

The Efficient Markets Hypothesis states that new information about securities is immediately reflected in stock prices. Therefore, a price change results from new information, which is impossible to predict.

Information that can impact stock prices can be idiosyncratic to a particular stock (like good or bad earnings), a technology change that impacts an entire industry, or a geopolitical event that affects the whole stock market. There's no way to predict these events or the impact they will have on market prices.

The next time you hear or read a prediction about the direction of a stock or the stock market, ask yourself: "Does this person know what tomorrow's news will be?"

Then weigh the prediction accordingly.

Why Stock Analyst Predictions Are Unreliable

Stock market analysts are paid to focus on specific stocks and make predictions about the future performance of that stock.

Here's why you shouldn't rely on them.

Conflicts of interest

Analysts employed by investment banks may have meaningful conflicts of interest, especially if the bank is handling the underwriting of a public offering for a company that is the subject of a report by the analyst.

It is in the interest of the investment bank for the public offering to be successful. A negative report on earnings by the analyst could undermine that effort. It's difficult to see how the analyst could remain objective.

Even when there is no imminent public offering, an analyst may be pressured to issue a favorable report to curry favor with actual and potential clients of the investment bank.

Decision fatigue

Stock market analysts are required to cover a number of different companies. They also need to revise their forecasts when necessary.

According to an article by David Hirshleifer, published by the National Bureau of Economic Research, "forecast accuracy declines over the course of a day" as the number of forecasts increases.

This "decision fatigue" also increases the probability that the analyst will take a quick shortcut, like reissuing a previous forecast or agreeing with the consensus forecast.

First impression bias

Hirshleifer examined over 1.6 million firm-analyst observations from 1984-2017. He found that an analyst's first impression of a company (based, for example, on stellar earnings) positively impacted future forecasts.

Recency bias

Analysts also appear to be unduly influenced by recent events.

The same study found that analysts placed "greater weight on recent experiences" and their earliest experiences than on their intermediate experiences.

Three follow-up studies published between 2021 and 2024 have since confirmed and extended Hirshleifer's findings using newer data and different methodologies, suggesting these biases are structural rather than specific to one sample or time period.

Other issues

According to this article in CBS News, analysts often can't anticipate factors impacting earnings, like changing customer habits and competitive threats. This inability, coupled with pressure to issue favorable reports and an overall reluctance to acknowledge bad news, causes analysts "Like ostriches...to put their heads in the sand."

Daner Wealth Management takes a different approach, one built on evidence, not predictions. Instead of chasing forecasts, our Alpharetta wealth management team builds globally diversified portfolios informed by decades of academic research.

If you're not sure whether your current strategy is based on data or Wall Street guesswork, a quick conversation can help you find out.

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Disclosures

This article is for informational and educational purposes only and does not constitute investment, tax, or legal advice. References to specific studies, forecasters, investment products, or market data are illustrative and do not constitute recommendations to buy, sell, or hold any security. Past performance is not indicative of future results. Market indices, including the S&P 500, are unmanaged and cannot be invested in directly. The Inverse Cramer Tracker ETF (SJIM) is referenced solely as a historical example and not as a recommendation or critique of any individual security or strategy. Daner Wealth Management is a registered investment adviser. Additional information about our firm, including our investment philosophy and services, is available at danerwealth.com. Clients and prospective clients should consider their individual circumstances before implementing any investment strategy. Statistics and studies cited were accurate as of the dates referenced and may have been updated since publication.

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