Wisdom of Crowds in Operations: Forecasting Using Prediction Markets

31 Pages Posted: 27 Oct 2015 Last revised: 26 Jan 2019

Date Written: October 25, 2015


Prediction is an important activity in various business processes, but it becomes difficult when historical information is not available, such as forecasting demand of a new product. One approach that can be applied in such situations is to crowdsource opinions from employees and the public. Our paper studies the application of crowd forecasting in operations management. In particular, we study how efficient crowds are in estimating parameters important for operational decisions that companies make, including sales forecasts, price commodity forecasts, and predictions of popular product features. We focus on a widely adopted class of crowd-based forecasting tools, referred to as prediction markets. These are virtual markets created to aggregate crowds' opinions and operate in a way similar to stock markets. We partnered with Cultivate Labs, a leading company that provides a prediction market engine, to test the forecast accuracy of prediction markets using the firm's data from its public markets and several corporate prediction markets, including a chemical company, a retail company and an automotive company. Using information extracted from employees and public crowds, we show that prediction markets produce well-calibrated forecasting results. In addition, we run a field experiment to study the conditions under which groups work well. Specifically, we explore how group size plays a role in the accuracy of the forecast and find that large groups (e.g., 18 participants) perform substantially better than smaller groups (e.g., 8 participants), highlighting the importance of group size and quantifying the right sizes needed to produce a good forecast using such mechanisms.

Keywords: Wisdom of crowds, demand forecasting, price forecasting

Suggested Citation

Bassamboo, Achal and Cui, Ruomeng and Moreno, Antonio, Wisdom of Crowds in Operations: Forecasting Using Prediction Markets (October 25, 2015). Available at SSRN: https://ssrn.com/abstract=2679663 or http://dx.doi.org/10.2139/ssrn.2679663

Achal Bassamboo

Northwestern University - Department of Managerial Economics and Decision Sciences (MEDS) ( email )

2001 Sheridan Road
Evanston, IL 60208
United States

Ruomeng Cui (Contact Author)

Emory University - Goizueta Business School ( email )

1300 Clifton Road
Atlanta, GA 30322
United States

HOME PAGE: http://www.ruomengcui.com

Antonio Moreno

Harvard University - Technology & Operations Management Unit ( email )

Boston, MA 02163
United States

HOME PAGE: http://www.hbs.edu/faculty/Pages/profile.aspx?facId=1029325

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