The Value of Crowdsourced Earnings Forecasts

Russell Jame

University of Kentucky

Rick Johnston*

Cass Business School, City University London

Stanimir Markov

Southern Methodist University

Michael Wolfe

Virginia Tech University


Crowdsourcing — when a task normally performed by employees is outsourced to a large network of people via an open call — is making inroads into the investment research industry. We shed light on this new phenomenon by examining the value of crowdsourced earnings forecasts. Our sample includes 51,012 forecasts provided by Estimize, an open platform that solicits and reports forecasts from over 3,000 contributors. We find substantial accuracy benefits from combining IBES and Estimize forecasts at all horizons. These benefits are robust to controlling for difference in forecast horizon and forecast bias. Also, the Estimize consensus is a better proxy for the market expectation than the IBES consensus. Finally, Estimize consensus revisions generate significant two-day size-adjusted returns. The combined evidence suggests that crowdsourced forecasts are a useful, supplementary source of information in capital markets.

The Value of Crowdsourced Earnings Forecasts – Introduction

Traditionally, the task of investment research and disseminating stock recommendations and earnings forecasts has been conducted by sell-side analysts. As recent advances in technology have lowered the cost of gathering information, entrepreneurs have adopted the crowdsourcing model in an attempt to supplement sell-side research as a source of information.1 This phenomenon of outsourcing investment research to an undefined large network of people via an open call has received attention and accolades in the financial press (Costa, 2010; Hogan, 2010 and 2013; Boudway, 2012), but little academic research exists because the phenomenon is recent and multi-faceted, and data are limited. In this study, we aim to fill this void by examining the usefulness of crowdsourced earnings forecasts.

Founded in 2011 and declared one of the hottest startups by Forbes in 2013, Estimize crowdsources earnings forecasts. Estimize contributors include analysts, portfolio managers, independent investors, as well as corporate finance professionals and students. Estimize forecasts are available on and Bloomberg terminals, and they are sold as a data feed. During 2012 and 2013, 3,255 individuals submitted 51,012 quarterly earnings forecasts for 1,874 firms.

Firms covered by Estimize contributors are generally in the IBES universe but are larger, more growth oriented, and more heavily traded than the average IBES firm. Unlike IBES forecasts, Estimize forecasts are concentrated close to the earnings announcement date.

Approximately half of the Estimize forecasts are issued in the 2 days prior to the announcement, and only 7% are issued in the 30 to 90 day period prior. The corresponding numbers for IBES are 2% and 70%, consistent with Estimize forecasts compensating for IBES analyst reluctance to update forecasts late in the quarter. Individual Estimize forecasts are equally (less) accurate at the short (long) horizon, and they are generally less biased and bolder (further from the consensus) than IBES forecasts.

To shed light on the usefulness of Estimize forecasts, we examine whether they (1) facilitate accurate earnings forecasting, (2) are a superior proxy for the market expectations, and (3) convey new information to the market. A brief summary of our findings follows.

At all horizons, we find significant accuracy benefits from combining IBES and Estimize forecasts. For instance, a consensus that pools IBES and Estimize forecasts 30 days prior to the earnings announcement is more accurate than the IBES consensus 60% of the time, and that measure increases to 64% on the day prior to the earnings announcement. There are three possible explanations for why Estimize forecasts enhance consensus accuracy: they incorporate more public information because they are issued closer to earnings announcements than IBES forecasts; they adjust for IBES forecasts’ well-known biases (e.g., short-term pessimism); and most interestingly, they convey new information.

To isolate the impact of new information, we limit the sample to contemporaneous IBES and Estimize forecasts and compare the r-squared of two regressions: actual EPS on the IBES consensus versus actual EPS on a consensus that combines IBES and Estimize forecasts.2 We find the combined consensus model r-squared is higher, lending support to the idea that Estimize forecasts are a source of new information. Finally, our tests show neither consensus subsumes the other in predicting future earnings.

crowdsourced earnings forecasts

crowdsourced earnings forecasts

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