A Full Insider Trading Ban Could Harm Prediction Markets

Prediction markets are once again at the center of the debate over insider trading. Researcher Balbinder Singh Gill believes that a full and overly strict ban on such operations could have the opposite effect: instead of protecting the market, it could reduce the amount of useful information reflected in prices.

His argument is not about defending insiders. It is about a more complex balance. In prediction markets, the price often acts as a signal of collective expectations about an event. If any trading based on private or more deeply researched information is prohibited, the market may lose part of the very signals that make it more accurate.

Why insider trading has a different logic in prediction markets

In traditional financial markets, insider trading is usually seen as a threat to fairness. If someone trades stocks based on closed corporate information, other participants are placed in a weaker position.

In prediction markets, the situation is less straightforward. Here, participants trade not only assets, but also expectations about specific events: political decisions, economic indicators, election results or corporate scenarios. The price of such a contract can be useful precisely because it absorbs information from people who understand the topic better.

The problem appears when private information starts to scare away other participants. If traders believe that the market is fully controlled by people with inaccessible data, they may simply stop participating.

In that case, the price becomes less dynamic, liquidity falls, and the market gradually loses its ability to properly reflect expectations.

What Gill proposes for regulating prediction markets

Gill believes that regulation should not be maximal, but calibrated. In other words, the rules should distinguish between types of information and the level of risk for the market.

The softest approach, according to his logic, can apply to information that a trader obtained through their own analysis. If a person spent time researching, found a pattern or assessed a scenario better, an overly harsh punishment could simply kill the incentive to do that work.

Another situation is information obtained through a leak, professional access or a breach of trust. Here, the risk for the market is much higher, so control should also be stricter.

The strictest approach is needed where the trader can personally influence the outcome of the event. For example, if a politician bets on their own campaign or an official trades a contract whose result depends on their decisions. In that case, it is no longer only about an informational advantage, but about potential manipulation.

• own research can add useful information to the market;
• leaks and professional data create serious risks for trust;
• the participation of people who can influence the outcome of an event requires the strictest control.

Why a full ban can harm prediction markets

A maximal ban looks like a simple answer, but for prediction markets it may be too blunt an instrument. If all participants with any informational advantage are removed from the market, prices may become less accurate.

This is the main dilemma. Insider trading can improve price accuracy today, but at the same time reduce the participation of other traders tomorrow.

If the market starts to be perceived as unequal, people leave it. If the regulator restricts informed participants too strongly, the market loses part of the knowledge that previously helped form the price.

For prediction platforms, this is a painful issue. They need to show regulators that they can control manipulation and suspicious activity. But if control becomes too broad, platforms may lose the very informational advantage that makes such markets interesting in the first place.

What this means for the prediction market industry

The debate around insider trading shows that prediction markets are entering a more complex stage of development. They are no longer seen as a niche entertainment product, but as a tool that can influence expectations, media, politics and financial decisions.

That is why regulation becomes inevitable. But a simple rule in the format of banning everything may not solve the problem. For such markets, it is important to separate useful information, unfair access and a direct conflict of interest.

What Gill’s main conclusion is

Gill’s main conclusion is that the market needs balance. Weak regulation opens the door to manipulation. Excessive regulation can make prices less informative. For prediction markets, both extremes are dangerous, so future rules should be more precise than a simple full ban.