What influences prices, and how prediction markets anticipate crises

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Professor Spann, you conduct research on prices and platforms such as Polymarket, where people can bet on whether a war will break out or a politician will resign. From a research perspective, is it interesting to you how prices are formed here? Or are these simply bets on real crises?

Spann: It’s both at the same time. On the one hand, these are digital markets where events are traded. So you can bet or speculate. At the same time, participants feed their information into the market. This results in a price that can be interpreted as a forecast for the event. In this sense, it is both a bet and an application of digital markets. These forecasts can serve as a basis for decision-making—for companies, investors or political observers, for example.

You have been studying such markets for more than 20 years. What exactly do you measure there? Expectations? Probabilities? Or behaviors?

Spann: My work is rooted in microeconomic theory, which states that markets reflect the expectations of participants. There is also research on this in experimental economics. One early example is the University of Iowa, where a market for the US presidential election was created in the late 1980s. It turned out that these forecasts work relatively well. Starting in the late 1990s and early 2000s, I worked on developing and programming such markets—marketplaces where forecasts can be traded. Later, my focus shifted to applying this concept to new areas, such as forecasting product sales, the success of market launches or product ideas. At its core, the goal is to map expectations onto actual market behaviors.

When expectations become prices

Prediction markets have been around for a long time. Why are platforms like Kalshi and Polymarket gaining so much traction right now?

Spann: There are two key developments behind this phenomenon. The first is regulation: Kalshi has been approved by the relevant regulatory authority in the US and is therefore considered a regulated market. The second is technological development: Platforms like Polymarket use blockchain, which makes it easier to transfer money and participate—even internationally. This situation also leads to differences: Kalshi is regulated and is not allowed to offer certain topics, while Polymarket, operating outside the framework of traditional regulation, can also offer highly speculative or political events.

In this context, you often speak of “swarm intelligence.” Are such markets therefore more reliable than traditional polls? And is there even a need for them anymore?

Spann: The principle of the “wisdom of the crowd” comes from statistics. When lots of people make an assessment, individual errors cancel each other out. A classic example comes from the British researcher Francis Galton: At a fair, people were asked to guess the weight of an ox. The average of the many estimates was very close to the actual value. And with prediction markets, the economic perspective also comes into play: Participants trade based on their expectations and react to prices. This information feeds back into the market price, which then reflects a collective assessment. However, this only works if participants have access to information, such as in elections or at the Oscars. When there is hardly any information available and everyone relies solely on existing forecasts, the system reaches its limits. Prediction markets are therefore more of a supplement to polls than a replacement for them.

Who uses these marketplaces: private investors, political insiders or professional investors?

Spann: You find a mix. Such markets are of interest to researchers because they provide a good way to observe how information is processed. For private investors, they are an additional way to invest money—albeit a very risky one. And for decision-makers, they can be an additional source of information.

If people working in government agencies or in politics, for example, used internal knowledge to place bets on political events and thereby make money, would that be legally permissible? And would that also be conceivable in Germany?

Spann: I can’t imagine that such bets would be permitted in Germany. Essentially, only sports betting is allowed here. In the US, too, there are discussions about insider knowledge from political circles, and Congress is already considering stricter rules. I’m not a lawyer, but one thing is clear: The issue is viewed critically.

Critics warn of manipulation. How vulnerable are such markets really—to buying in a certain direction or to insider knowledge, for example?

Spann: Manipulation is a risk: If a small number of participants specifically bet on certain events, they can influence prices. There have been cases where smaller groups have tried to push predictions in a certain direction. A second issue is insider trading. From a forecasting perspective, it can be useful if first-hand information is incorporated. But from a fairness point of view, that is problematic. Lastly, there are also security concerns—such as if sensitive information about military events becomes visible on the market prematurely.

You have also studied prediction markets within companies, such as at Carl Zeiss. How does that work in practice? And why does such an internal market often make better decisions than a management team?

Spann: We’ve done this within companies, for example to evaluate product ideas. Employees could submit ideas, which were then traded as “idea shares” using play money. Others could then invest in these ideas to reach an assessment of how successful they might be. At Carl Zeiss, around 250 ideas were collected and evaluated this way. It worked well. But it takes up a lot of resources, so it has so far only been used to a limited extent.

You also conduct research on prices in everyday life. Supermarkets are testing digital price tags, where the prices can change at any time. What does this mean for customers: greater efficiency or more uncertainty?

Spann: From a consumer’s perspective, this might initially seem a bit stressful or even intimidating. But dynamic pricing works both ways, as prices can also go down. After all, companies want to sell their products. For customers, this initially means more work: You have to compare more carefully and think about when to buy. On the other hand, digital tools also help with comparison.

Could it be that I pay more for a product because I am using an expensive iPhone or come from a wealthy region? Or is this impression misleading?

Spann: Technically, that would be possible, but it’s not a particularly good indicator. Companies themselves don’t know exactly how meaningful such attributes are. Maybe the customer is using a second-hand device, or is just passing through. Often, the impression of rising prices is due to the fact that prices change over time—like with airline tickets as the departure date approaches. Price differentials are generally attributable to supply and demand rather than to deliberate individual targeting.

What about gas station prices? Germany’s federal government wants to limit price increases to just once a day. How will that play out?
Spann: I don’t believe that will automatically lead to lower prices. If providers know they can only raise prices to a limited extent, they’re more likely to set them higher. For consumers, it’s more important that they can compare prices—for example, via apps. A world with fixed prices would be simpler, but probably more expensive overall.

About the person:

Professor Martin Spann is Director of the Institute of Electronic Commerce and Digital Markets at LMU. His research lies at the intersection of business informatics, marketing and behavioral economics.

He earned his PhD with a dissertation on virtual stock exchanges as a tool for forecasting market and consumer behavior—a topic that remains a central focus of his research to this day.

For over 20 years, he has been investigating how digital technologies are transforming familiar markets through prediction markets, dynamic pricing and platform economies, for example.

His work is published regularly in leading international journals such as Management Science, Marketing Science and MIS Quarterly.