STANFORD GRADUATE SCHOOL OF BUSINESS — Like oracles in the stock market, securities analysts come up with earnings estimates that are supposed to signal the worth of a company’s stock. But what happens when a company’s actual performance proves an analyst’s quarterly forecast is wrong?
Instead of fully incorporating new information into their forecasts, many of those analysts stubbornly stick to their erroneous views on the company, a tendency that might contribute to market bubbles and busts, according to research from the Stanford Graduate School of Business.
Analysts who make “extreme” quarterly forecasts — above or below the consensus or median estimate among all analysts following a given stock — tend to dig in their heels after being proved wrong, says John Beshears, an assistant professor of finance and coauthor of research exploring the phenomenon of stubbornness among stock analysts. Once a company reports quarterly earnings showing the analyst was too optimistic or too pessimistic, the extreme incorrect analyst will revise his or her full-year forecast, but will move less aggressively in the direction of the earnings surprise than other analysts. This stubbornness hurts an analyst’s overall forecasting accuracy, say the researchers.
So what’s behind the stubborn streak?
“People become overcommitted to a previous course of action,” says Beshears. “Psychological factors like this play an important role in how people form expectations about the future.”
Beshears and Katherine L. Milkman of the Wharton School at the University of Pennsylvania looked at how so-called “escalation bias” affects analysts’ forecasts. Their paper, “Do Sell-Side Stock Analysts Exhibit Escalation of Commitment?” was published in March by the Journal of Economic Behavior and Organization.
The research has broader implications for understanding financial markets. Persistent, widespread over-optimism or over-pessimism by market participants could lead to mispricing of assets, says Beshears. He suggests, for example, that stubbornness contributed to the financial crisis of 2007-2008. Despite initial signs of a weakening real estate market, many analysts, investors, and lenders maintained or stepped up their commitment to housing-related assets, such as mortgage-backed securities. The result: The housing bubble continued longer than it should have, delaying and exacerbating the subsequent bust. Stubbornness, Beshears says, “is one mechanism that might allow market bubbles and crashes.”
The study underscored the importance of psychological biases in shaping market participants’ behavior. Previous research showed that when people make decisions that turn out to be wrong, they try to justify them. They may have invested time, money, and effort into a decision, so they want to recoup their “sunk costs.” Hence, they are reluctant to back down. “They want to recover their costs” and “hopefully vindicate themselves,” says Beshears, who researches how individuals and companies make financial decisions.
Securities analysts go out on a limb when they make extreme profit estimates. So when their quarterly forecasts are proven incorrect, these analysts adjust their full-year estimates less to coincide with fact than analysts who started at the quarterly consensus, according to the research.
Consider an analyst who forecasts quarterly earnings of $1.10 a share and a second analyst estimating $1.00 a share, which matches the consensus or median estimate. Suppose the company reports earnings of only 90 cents a share. The first analyst is wrong by 20 cents and the second analyst is wrong by 10 cents a share. Both would then revise their full-year forecasts downward, in the direction of the earnings surprise. The researchers found that the analyst who was more extreme in the wrong direction revised his or her full-year estimate by less than the second type of analyst.
Beshears and Milkman studied the Institutional Brokers’ Estimate System I/B/E/S database with more than 6,200 analysts’ quarterly forecasts on about 3,500 companies over more than 18 years, from 1990 to 2008. They created variables such as how far off a quarterly estimate was from reported earnings, how far off an estimate was from the consensus forecast, how much a full-year estimate was revised, and how accurate a full-year adjusted estimate turned out to be. Using statistical techniques, they looked at how the variables moved together or separately, discerning patterns in the analysts’ forecasting and updating behavior.
Among their specific findings:
- As analysts got more and more extreme, or “out-of-consensus,” they became less and less responsive to the new earnings information when revising their full-year forecasts.
- Stubbornness hurts forecasting accuracy. Revised full-year forecasts from extreme incorrect analysts were further off the mark from actual earnings than they would have been had the analysts’ updating behavior been like the behavior of analysts who started at the consensus point.
- Analysts are punished for stubbornness. The more extreme, incorrect, and stubborn an analyst was, the less likely that he or she would rank among Institutional Investor magazine’s “All-American” list of top analysts.
The study emerged from interviews Beshears and Milkman had with Wall Street analysts. Many of them said a common mistake in their profession was analysts getting “wedded to their calls” on a company’s prospects. “They saw this behavior in their colleagues, but didn’t do it themselves, of course,” quipped Beshears.
He believes research on escalation bias can be extended from equities to the debt markets to shed light on how credit analysts, loan officers, or risk management experts, for instance, view the performance of financial assets. Another future direction would be creating an “overarching model” of the psychological biases at play when people form expectations about the future or make financial decisions, he adds.
— Maria Shao
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