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With apologies to author Dan Brown, Wall Street’s earnings estimates contain a Da Vinci Code of their own. Investors rightly pay attention to the size of the numbers; the higher the expected profit, the more valuable the shares, all else held equal. But other clues are hidden in the shape and movement of the numbers.

Clue 1: A recently raised estimate. When analysts raise their earnings forecasts for a company, its shares tend to jump right away. But they also tend to gradually outperform for months afterward, an effect documented 20 years ago in a study by Scott Stickel of the University of Pennsylvania, and in numerous studies since. (It’s similar to a phenomenon called post-earnings announcement drift, first explained in a 1968 study by professors Ray Ball and Philip Brown. Shares of companies that beat earnings forecasts gradually outperform, too.)

Clue 2: Tightly clustered estimates. What we call a company’s earnings estimate is usually an average of many estimates. Investors call that a consensus, as if to suggest agreement, but the numbers sometimes show anything but agreement. A consensus for earnings of $1.50 a share might contain individual estimates that are tightly clustered between $1.40 and $1.60, or ones that are wildly scattered from 95 cents to $2.05. Anna Scherbina of the University of California at Davis showed in a 2002 study that tight earnings consensuses tend to predict better stock returns than scattered ones. It’s not entirely clear why, but one theory holds that companies with good news tend to communicate more with investors and analysts, leading to greater agreement about their future profits.

Clue 3: A breakaway fan. Analysts, being humans, are more comfortable in groups than outside of them. Since analysts are paid in part according to the accuracy of their forecasts compared with other analysts, ones who stray away from the herd take risks, and had better have good reasons. Often, they do. Professors Christi Gleason and Charles Lee published a study of the matter in 2003. They compared estimate revisions that move toward the consensus with ones that move away from it, regardless of the size of the change. For example, within a $1.50 consensus, an estimate change from $1.40 to $1.49 merely falls in line with the consensus, but a change from $1.51 to $1.53, while smaller, strays from the herd. Straying estimates are more powerful predictors of stock performance, the two professors found. Stocks with straying estimate increases went on to outperform ones with straying decreases by a whopping 15 percentage points a year. A follow-up study two years later helped explain why: Straying estimate revisions, it found, tend to be more accurate than herding ones about future earnings.

Put all the clues together and investors should perhaps take an interest in stocks with rising, tightly clustered consensuses that contain at least one estimate breaking away from the consensus to the upside. The first two attributes are easy to search for using stock screening programs. (The degree of clustering, in the language of these programs, is called standard deviation of estimates.) The third takes some digging through individual estimates that comprise consensuses, which are only available by paid subscription to research services from companies like Thomson Reuters. I recently searched current-quarter consensuses for S&P 500 companies and found two dozen examples of tightly clustered consensuses that increased this month, but only three that contained estimates that strayed from the herd to the upside. They’re listed below.

General Mills (GIS), which makes packaged foods like breakfast cereals and soups, is growing its sales through the current recession, as consumers eat more meals at home. Constellation Brands (STZ), whose booze business depends largely on restaurant and bar traffic, is seeing sales slip, but is still managing to increase profit. Waters (WAT) makes scientific equipment for use in drug development, food and water testing and more, and has beaten earnings estimates in each of its past four quarters. 

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