Friday, October 6, 2017

Have Corporate Prediction Markets Had Their Heyday?

Summary and critique by Kevin Muvunyi
Thomas Wolfram in his article “Have Corporate Prediction Markets Had Their Heyday”, examines the reasons behind the relatively low adoption of prediction markets by large business entities as a forecasting tool, and then proceeds to provide possible avenues to revive this important prediction technique. To achieve the purpose of his research, the author reviewed existing literature on the subject and conducted interviews with 32 key business executives.

 In his attempt to understand the contradiction between the strong academic support for prediction markets and its slow uptake by businesses, Wolframm first examines the rationale behind the popularity of this methodology in the world of academia. According to the author, multiple research studies have proven that prediction markets are more efficient in decision making and forecasting, because they eliminate the problematic caused by bias and group pressure in traditional decision making settings by allowing the participants to make their decision anonymously. Furthermore, the technique incentivizes individuals through the promise of possible remuneration to bring forth new information, which can then be aggregated to predict the outcome of a future event, thus, making them more efficient in comparison to long-standing forecasting instruments like questionnaires, surveys, and polls according to the author. 

After examining the merits of the methodology, Wolframm then moves to provide possible explanations to the current withering status of prediction markets in the business world, primarily basing his assertions on interviews conducted with key corporate decision makers. The author summarizes the explanations into three key points as follows:
  • Finding appropriate and knowledgeable experts (traders) is complicated; it does not help if participants are diverse but ignorant of the issue as it undermines their predictions
  • Lack of trust from the top echelons of management: management trusts consulting groups more than own employees, and also believes that prediction markets disturb the concept of hierarchy in an organization due to the fact that their ideas have a chance of being rejected based on this model, thus, undermining their authority.
  •  Businesses “going digital” in a different direction from prediction markets; as businesses turn digital prediction markets become obsolete, because everything becomes data driven, therefore, big data analysis and other similar techniques are more insightful and appropriate.
In the light of apparent insurmountable obstacles to the revival of prediction markets in corporate circles, Wolframm advocates that they are possible ways to remediate this issue. For example, the author suggests that to ensure knowledgeable traders, a convenient graphical user interface and communication exercises accompanying prediction market implementations would be more appropriate. Furthermore, Wolframm brings forth the notion of idea markets as an innovative approach to the forecasting tool. In contrast to traditional prediction market whereby participants are allowed to trade on the outcome of uncertain future events, idea markets could provide a platform for the generation and assessment of ideas through the trading of virtual stocks representing products and concepts.


The author does a great job at analyzing the root cause of disaffection of prediction markets as a forecasting technique in the corporate world. He also equally provides sound alternatives to revive the technique. Nonetheless, the only problem with his article is that it fails to demonstrate in a tangible manner, the superiority of improved prediction markets in comparison to big data analytical tools in the new data driven world that we live in today.



  1. At the very least, prediction markets can help organizations in reducing uncertainties. Management can benefit from the information that can be harnessed from prediction markets. Prediction markets can also give management insights into the diversity of employees' knowledge base.

  2. Kevin, it's interesting to hear your point as a data science guy that no one has compared the prediction market model to more sophisticated metadata tools. I'd be curious to see what that finds.

  3. Oddi, I absolutely agree with the point that you are making. Prediction markets definitely provide management with accurate and unbiased information about a firms ecosystem. Ultimately, studies need to be conducted to examine whether prediction markets are still relevant in this new era of big data.

  4. Sam, personally I think that the future lies in the use of big data tools, especially that everything is becoming increasingly digitalized

  5. I agree with Kevin that compared to big data tools prediction markets are inferior. Big Data tools can be backed up with advanced statistics, where as prediction markets are the culmination of multiple decisions by analysts and experts. However, a prediction market can be great for decision makers to see big moving pieces and see where their analysts/experts are in an easy to understand way.

  6. The success of prediction markets in organizations requires buy-in from the organizations as a whole. Harnessing the wisdom of the crowd can have great benefits in decision-making especially when participants get the satisfaction of being right when things go as planned. Management need to get to a point where they can rely more on the benefits on prediction markets which will help fine-tune their decision-making process.