The Value of Crowdsourced Earnings Forecasts

• Author: STANIMIR MARKOV,MICHAEL C. WOLFE,RUSSELL JAME,RICK JOHNSTON
• Published date: 01 September 2016
• Date: 01 September 2016
• DOI: http://doi.org/10.1111/1475-679X.12121

DOI: 10.1111/1475-679X.12121

Journal of Accounting Research

Vol. 54 No. 4 September 2016

Printed in U.S.A.

The Value of Crowdsourced

Earnings Forecasts

RUSSELL JAME,

∗RICK JOHNSTON,

†STANIMIR MARKOV,

‡

AND MICHAEL C. WOLFE

§

Received 6 January 2014; accepted 12 March 2016

ABSTRACT

Crowdsourcing—when a task normally performed by employees is out-

sourced 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 re-

ports forecasts from over 3,000 contributors. We find that Estimize forecasts

are incrementally useful in forecasting earnings and measuring the market’s

expectations of earnings. Our results are stronger when the number of Es-

timize contributors is larger, consistent with the benefits of crowdsourcing

increasing with the size of the crowd. Finally, Estimize consensus revisions

∗University of Kentucky; †University of Alabama at Birmingham; ‡Southern Methodist Uni-

versity; §Virginia Tech.

Accepted by Christian Leuz. Johnston thanks Leigh Drogen from Estimize for provid-

ing the Estimize data and answering questions about the business. This paper is a revision

of an earlier working paper titled “Crowdsourcing Forecasts: Competition for Sell-Side An-

alysts?” by Johnston and Wolfe. Johnston conceived of and initiated this project while at

Purdue University. Johnston thanks Constantin Cosereanu, V. Shah, and B. Markovich from

Bloomberg for answering related questions about Bloomberg. We also thank an anonymous

referee, Shail Pandit, Phil Stocken, Stephen Taylor, Tony Kang, and workshop participants

at Cass Business School City University London, Hong Kong Poly University, London Busi-

ness School Conference, Loyola Marymount University, McGill University, McMaster Univer-

sity, Rice University, Temple University Conference, UAB, University of Illinois at Chicago,

University of Kansas, University of San Francisco, University of Technology Sydney Confer-

ence, Wake Forest University, and York University for their helpful comments and sugges-

tions. An online appendix to this paper can be downloaded at http://research.chicagobooth.

edu/arc/journal-of-accounting-research/online-supplements.

1077

Copyright C, University of Chicago on behalf of the Accounting Research Center,2016

1078 R.JAME,R.JOHNSTON,S.MARKOV,AND M.C.WOLFE

generate significant two-day size-adjusted returns. The combined evidence

suggests that crowdsourced forecasts are a useful supplementary source of

information in capital markets.

JEL codes: G28; G29; M41; M43

Keywords: analyst; forecast; earnings response coefficients; crowdsourcing

Bolstered by the low cost of online publishing and the rising popularity

of blogs, discussion forums and commenting, a growing number of niche

web sites are creating opportunities for new forms of investment analysis

to emerge—and for buy-side professionals, even those at rival firms, to col-

laborate and learn directly from one another. These social media web sites

are supplementing, and in some cases supplanting, the traditional Wall

Street information ecosystem that transmits sell-side investment research

and stock calls to the buy side. (Costa [2010, p. 54])

1. Introduction

In the last two decades, technology has significantly lowered informa-

tion and communication costs and bolstered the creation of new informa-

tion sources (e.g., blogs, message boards, Facebook, and Twitter), thereby

changing the process by which investors acquire information. According to

a recent survey, nearly one in three individuals in the United States relies

on investment advice transmitted via social media outlets.1Recognizing the

increased importance of this new source of information in the capital mar-

kets, the Securities and Exchange Commission (SEC) now allows firms to

disclose news through social media.

Technology is also altering interactions between organizations and out-

siders in other ways. Increasingly, businesses are using technology to

capture the collective intelligence of online participants. This blend of

bottom-up, open, creative process to meet organizational goals is called

crowdsourcing (Brabham [2013]). Various entities, such as Seeking Alpha

and Estimize, seek to supplement or disrupt sell-side research with crowd-

sourcing. Seeking Alpha crowdsources investment research and publishes

it on its website. Estimize seeks to create an alternative to the sell-side earn-

ings consensus by crowdsourcing forecasts from analysts, investors, corpo-

rate finance professionals, students, and others. Prior to Estimize, whisper

forecasts were an alternative source of earnings forecasts. Whisper fore-

casts emerged in the 1990s, as concerns with sell-side bias and strategic

nonupdating in the period prior to earnings announcements increased.

Subsequently websites dedicated to publishing whisper forecasts were

established.

1http://www.experiencetheblog.com/2013/04/four-recent-studies-on-rapid-adoption.

html

THE VALUE OF CROWDSOURCED EARNINGS FORECASTS 1079

This paper offers a first look at the value of crowdsourced earnings fore-

casts from Estimize. These forecasts warrant research attention because

they have unique attributes relative to other sources of alternative earnings

information (e.g., Whisper sites and Seeking Alpha).2Specifically, a whis-

per site distributes a single forecast that aggregates information from vari-

ous sources using a proprietary approach. Thus, the role of crowdsourcing

is both limited and unidentified, and prior evidence on the value of whis-

per forecasts may not extrapolate to the crowdsourced forecast setting.3

Social media finance sites (e.g., The Motley Fool, StockTwits, and Seeking

Alpha) have crowdsourcing features, but offer unstructured data (i.e., com-

mentaries), limiting their usefulness as a source of earnings information.

Therefore, a crowdsourcing site able to attract and retain a large number

of capable earnings forecasters may become integral to the sourcing and

dissemination of earnings forecasts.

We assess the value of Estimize forecasts by investigating whether they

are incrementally useful in forecasting earnings and measuring the mar-

ket’s expectation of earnings, and whether they convey new information.

Our analyses are guided by two non–mutually exclusive hypotheses. The

first hypothesis is that crowdsourced forecasts are incrementally useful only

because they are less biased and incorporate more public information. The

second hypothesis implies a greater role for crowdsourced forecasts in capi-

tal markets: by capturing the collective wisdom of a large and diverse group

of individuals, they impart new information to the markets.

Our sample consists of 51,012 quarterly earnings forecasts for 1,874 firms

submitted to Estimize by 3,255 individuals in 2012 and 2013. 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.

Relative to IBES forecasts, individual Estimize forecasts tend to be less accu-

rate at long horizons, but equally accurate at shorter horizons; they are less

biased and bolder (further from the combined IBES–Estimize consensus).

Approximately half of Estimize forecasts are issued in the two days prior to

the earnings announcement date, while less than 2% of IBES forecasts are

issued in the same period. The stark difference in forecast timing suggests a

complementary relation between IBES analysts and Estimize contributors.

First, we explore whether Estimize forecasts are incrementally useful

in predicting earnings by quantifying the accuracy benefits from com-

bining Estimize forecasts with the IBES consensus or a statistical forecast

based on firm characteristics (So [2013]). Using either benchmark, we find

that incorporating Estimize forecasts yields significant improvements in

2Section 2 offers a more in-depth comparison of Estimize to other sources of crowdsourced

research, as well as whisper forecasts.

3Prior evidence on whether whisper numbers convey information to the market is mixed.

Analyzing a sample of 262 forecasts, Bagnoli, Beneish, and Watts [1999] find affirmative evi-

dence, but their findings have not been replicated in more recent and larger samples (Bhat-

tacharya, Sheikh, and Thiagarajan [2006], Brown and Fernando [2011]).