The Value of Crowdsourced Earnings Forecasts

• 成果类型: Article
• 署名作者: Jame, Russell; Johnston, Rick; Markov, Stanimir; Wolfe, Michael C.
• 署名单位: University of Kentucky; University of Alabama System; University of Alabama Birmingham; Southern Methodist University; Virginia Polytechnic Institute & State University
• 刊物名称: JOURNAL OF ACCOUNTING RESEARCH
• ISSN/ISSBN: 0021-8456
• DOI: 10.1111/1475-679X.12121
• 发表日期: 2016
• 页码: 1077-1110
• 关键词: information-content; whisper forecasts; analysts; superiority; accuracy; talk

摘要

Crowdsourcingwhen a task normally performed by employees is outsourced to a large network of people via an open callis 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 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 Estimize contributors is larger, consistent with the benefits of crowdsourcing increasing with the size of the crowd. 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.