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Prediction markets have been gaining popularity in recent years as a way for businesses to forecast future events and make strategic decisions. These markets, also known as information markets or idea futures, allow individuals to buy and sell shares in the outcome of a particular event. The price of these shares reflects the collective wisdom of the market participants, providing a valuable tool for businesses to predict future trends and opportunities. Internal markets generally use virtual currency as a basis for pricing events, and a separate prize pool is set up to incentivize and reward participants and best performers.
Academic research has shown that prediction markets are an effective forecasting mechanism. Studies have found that these markets are often more accurate than traditional methods such as surveys or expert opinions. For example, a study published in the Journal of Political Economy in 2008 found that prediction markets were more accurate than polls in predicting the outcomes of U.S. presidential elections. Another study, published in the Journal of Forecasting in 2011, found that prediction markets were more accurate than individual expert forecasts in predicting the sales of new products. These findings, among others, show that prediction markets are a valuable tool for businesses looking to make accurate forecasts and strategic decisions.
How businesses can leverage prediction markets
Businesses can use prediction markets to forecast a variety of goals, including product demand, market trends, and even employee performance. By allowing employees and stakeholders to participate in these markets, businesses can tap into a wealth of collective knowledge and insight.
One potential use of prediction markets for businesses is in product development. By creating a market around the potential success of a new product, businesses can gather valuable insights into consumer demand and identify potential issues early on. This can help businesses make more informed decisions about which products to invest in and how to improve existing products.
Another way businesses can use prediction markets is to forecast market trends. By creating a market around a particular industry or sector, businesses can gain a better understanding of where the market is headed and identify potential opportunities for growth. This can help businesses make more informed decisions about where to invest resources and how to position themselves for success in the future.
In addition to forecasting market trends and product demand, businesses can also use prediction markets to forecast employee performance. By creating a market around specific employee metrics, such as sales numbers or customer satisfaction, businesses can gain a better understanding of which employees are likely to be successful in the future. This can help businesses make more informed decisions about promotions, bonuses, and other incentives.
These ideas are not theoretical; several corporations, such as Google, EA (Electronic Arts), Ford, HP, and Microsoft have run or are running corporate prediction markets. In “Corporate Prediction Markets: Evidence from Google, Ford and Firm X*” (Cowgill and Zitzewitz, 2013), we learn that Ford’s experiments with prediction markets outperform expert forecasts on two important topics: predicting weekly sales volumes and predicting which car features would be popular with customers. Not only did employees help avoid expensive market research into dead-end features (like a bike carrier and in-car vacuum), but it also found qualitative comments made in the market software to be of independent value as boosting employee engagement and education.
Google has experimented several times with prediction markets, and one important takeaway was learning about how information flows across the organization. In “Using Prediction Markets to Track Information Flows: Evidence from Google” (Cowgill, Wolfers, and Zitzewitz, 2009), we learn that Googlers that were collocated and/or shared interest groups within the company (hobbies, mailing lists, etc.), tended to forecast events in the same direction, and be correct more often in aggregate. The same study showed that Googlers have an optimism bias, which is beneficial in many ways to the success of the company but not necessarily in making the best predictions about its performance, as performance forecasts tended to exceed the actual results. That said, over time, more experienced traders trade against this bias and earn higher returns, helping to calibrate the collective forecasts.
Corporate prediction markets have been used for a variety of purposes. Microsoft has used prediction markets to ask questions internally like, “will the project be completed according to schedule?” and “how many bugs will be in the software?”. EA used internal markets to judge the quality of the games they developed. Interestingly, in their case, the experiments were well-received by both executives at the top and lower level employees, with middle management proving to be the primary source of resistance to their uptake. HP found that internal markets were more accurate than other corporate forecasting tools 75% of the time. Their research revealed that an internal market has access to — and can absorb in real time, anonymously — other types of information from a wide pool of employees.
In conclusion, prediction markets are a powerful tool for businesses to forecast future events and make strategic decisions. By allowing employees and stakeholders to participate in these markets, businesses can tap into a wealth of collective knowledge and insight. Businesses can use prediction markets to forecast a variety of goals, including product demand, market trends, and employee performance, as has already been demonstrated by world-leading companies.
Reach out to the Polkamarkets Labs team if you’re interested in setting up a Prediction Markets competition related to your business or industry, and gaining strategic insights that help you make better decisions.
Polkamarkets is an Autonomous Prediction Market Protocol built for multi-chain information exchange and trading where users take positions on outcomes of real-world events–in decentralized and interoperable EVMs.