The growing use of crowdsourced earnings forecasts threatens to eclipse the use of analyst consensus estimates. And with much more earnings information available via the crowd, calmer investors will provide more liquidity to public companies.

So says Russell Jame, an assistant professor of finance at the University of Kentucky’s Gatton College of Business and Economics. Jame has been researching the effect of the thousands of earnings estimates being pumped out by Estimize, an “open platform” claiming  47,561 contributors and consensus estimates of more than 2,000 stocks each quarter.

Russell Jame Russell Jame, Crowdsourcing

Currently, Jame is embarked on a project “showing that stocks covered by Estimize seem to experience improvement in liquidity.” The notion is that crowdsourced coverage boosts the visibility of a company and broadens retail investors’ exposure to information about it.

And the more information available about a company, the more confident potential investors are about putting money behind it, the theory goes. Previously, providers of consensus earnings estimates have been largely limited to those of sell-side analysts by means of the Institutional Brokers’ Estimate Systems. IBES has been mainly a vendor to institutional investors, with individual investors having much less access to such predictive information.

But with more forecasts becoming available to retail investors via crowdsourcing, there will be more trading of company equity and a drop in corporations’ cost of equity, according to Jame.

By means of crowdsourcing, for instance, a company that had five sell-side analysts covering it could then add 20  crowdsourced earnings forecasts. And as investors get more information about a stock’s future earnings, “there is more information about earnings [incorporated] into the price,” the professor says.

The implication is that, spurred by increased investor demand, a company’s share price could rise. “If you think about this from an investor relations standpoint,  a CFO can basically shape the liquidity of their firm by encouraging more crowdsourcing participation,” he adds.

In the case of Estimize, the company can sponsor a “league” — similar to a fantasy sports league — in which participants can estimate the company’s future earnings. Another way is for a company to  include crowdsourced consensus estimates side by side with IBES estimates in the press releases it issues to announce its earnings forecasts.

To be sure, in a paper co-authored by Jame and published last year, the authors fall short of saying that crowdsourced data providers will actually replace consensus analyst estimates. Rather, they find that Estimize is a good complement to IBES.

The platform, which provides its consensus estimates free of charge to anyone who registers on the site, provides more accurate and less biased forecasts than IBES when the two are combined, according to the authors.

In their research, the authors suggest that Estimize earnings forecasts “are incrementally useful in forecasting earnings,” and more representative of the market’s earnings expectations, especially as the size of the crowd of forecasters increases, according to a March University of Kentucky press release on the study.

When compared with IBES forecasts made 30 days before an earnings announcement, combined IBES and Estimize forecasts produce a more accurate consensus 60% of the time, the university reported. That measure increases to 64% on the day before an earnings announcement.

While the two sources of forecasts are about equal when the predictions are short term, when the time horizon of a consensus forecasts goes beyond 30 days, IBES forecasts are more accurate than Estimize’s predictions, according to the study. “Crowdsourced forecasts are available … generally at much shorter horizons than sell-side forecasts,” they note.

Nevertheless, Jame does suggest that crowdsourced estimates are the wave of the future. “I think we’re moving away from the expert model, where only sell-side analysts … provide information, and more toward [the] crowd model,” says Jame.

One big reason for the shift may be the inherent bias that analysts bring to the table, he suggests. In a paper released in January, Jame and another batch of co-authors assert that “the sell-side research industry is fraught with conflicts of interest. Dependent on managers for information and subsidized by investment banking revenues, analysts have incentives to bias their research to please managers and facilitate investment banking activities.”

The authors especially like what they see as the potential gadfly role of crowdsourcing in relation to traditional consensus estimates. “Our hypothesis is that crowdsourced research, which is informative, prone to fewer conflicts of interest, and readily available, can make it easier for investors to unravel sell-side biases, and therefore exert a disciplining effect on the sell-side.”

They are especially bullish on Estimize itself, which they say is “freely providing investors with a clear benchmark forecast[.]” Although Jame has established an ongoing relationship with Estimize, he says that the only money that’s changed hands is the standard fee that the platform charges academics for more substantial data usage than its non-paying members get.

Not unexpectedly, Estimize founder Leigh Drogen, a former hedge fund manager at Surfview Capital who started up the platform in 2012, says that the best way for CFOs to get a clear grasp of their companies’ earnings prospects is to buy one of his products, such as a real-time data feed.

But the crowdsourcing maven also has more objective advice for finance chiefs. “They should be setting up internal systems within their companies to crowdsource expectations about their own firm’s performance from their own people,” he says.

“Let’s find the person within that organization who is really good at forecasting how their business is going to go, and let’s listen to him [rather than] forecasting departments that are wildly wrong every quarter of the year,” he adds.