Google: An Example of a Corporate Prediction Market

I have recently come across a very interesting example of a corporate prediction market. Did you know, that Google has started one of the largest corporate prediction markets in 2005? Prediction markets are used in a similar way as stock markets. However, they do not trade in stock but in securities of certain outcomes of projects or events that will happen in the future (O’Leary, 2013). Participants of the market can trade with these securities. Depending on how participants estimate the likeliness of an event occurring, the price of the security increases (if the likelihood increases) or decreases (if the likeliness decreases).

Google has used the Iowa Prediction Market as a baseline to set up their own prediction market (McAfee, Lakhani & Coles, 2007). Google uses prediction markets in order to aggregate the information that each employee participating in the market possesses about the company performance on certain projects or events (Cowgill, Wolfers, & Zitzewitz, 2009). Furthermore, they are used as an addition to more conventional forecasting methods on the performance of certain company projects and development activities. In the Google prediction market, a Google currency is used, so no real money is involved. With the normal forecasting methods, it is much more difficult to incorporate the expertise of a huge amount of employees, even if they have valuable information to contribute, and thus the information incorporated into a regular forecast can often be incomplete. Furthermore, it would take more time and effort to for example talk to employees in person or to evaluate questionnaires. Prediction markets can thus make use of the wisdom of the crowds in an efficient way (Cowgill, 2005). Google has been able to get relatively accurate information about project outcomes and projections from its internal prediction market.

A company deciding to implement an internal prediction market needs to make sure to create the right incentives to get people participating. If the critical level of participants is not reached, the information created by the market will most likely not be very accurate (O’Leary, 2015). In addition, one of the problems at Google was that newly hired employees tended to overvalue optimistic predictions compared to the long-term employees (Cowgill, Wolfers, & Zitzewitz, 2009). Another bias can also be that if trading in the market does not involve real money, participants tend to behave more extreme than in the real world (O’Leary, 2013). Companies need to make sure they think about the design of their prediction market beforehand in order to get some valuable results from it.

What do you think of corporate prediction markets? Do you have other examples of companies implementing prediction markets?


Ball, P. (2014). ‘Wisdom of the crowd’: The myths and realities. Retrieved 8 October 2015, from

Cowgill, B. (2005). Putting crowd wisdom to work. Official Google Blog. Retrieved 8 October 2015, from

Cowgill, B., Wolfers, J., & Zitzewitz, E. (2009). Using Prediction Markets to Track Information Flows: Evidence from Google. In AMMA (p. 3). Retrieved October 8, 2015 from

Financial Times,. (2013). Prediction markets: value among the crowd – Retrieved 8 October 2015, from

Leinweber, D. (2013). Google Trends Market Prediction: Right Again. Correct On 80% Of The Calls. Retrieved 8 October 2015, from

McAfee, A., Lakhani, K., & Coles, P. (2007). Prediction Markets at Google (1st ed.). Harvard Business School. Retrieved from

O’Leary, D. (2013). Internal corporate prediction markets: “From each according to his bet”. International Journal Of Accounting Information Systems, 14(2), 89-103.

Wall Street Journal,. (2008). A Look at Google’s Prediction Market. Retrieved 8 October 2015, from


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