Institutional Adoption of Prediction Markets Stuck at the Third Stage

Original Title:Prediction Markets: They Grow Up So Fast, Author: Alex Immerman (@aleximm)

Compiled by | Odaily (@OdailyChina); Translator | Asher (@Asher_ 0210)

Editor's Note: At the end of March this year, the prediction market, once considered a niche field, reached a critical juncture. Kalshi Research, the research arm of Kalshi, hosted its inaugural research conference in New York, bringing together academics, Wall Street executives, former politicians, and frontline traders. The composition of the attendees sent a clear signal—prediction markets are moving from the fringe to the mainstream.

The conference opened with a conversation between Kalshi co-founders Tarek Mansour and Luana Lopes Lara, moderated by Bloomberg journalist Katherine Doherty. This article excerpts and summarizes the key insights from the conference.

Prediction Markets Are More Than Just Elections and Sports

For a long time, prediction markets have been defined by certain "highlight moments"—U.S. elections, the Super Bowl, March Madness. These events dominate news cycles and naturally consume most of the trading volume, leading outsiders to mistakenly believe that the value of prediction markets ends there.

But this perception is being shattered. Just as the conference was held, weekly trading volume for sports predictions had just approached $3 billion, accounting for about 80% of Kalshi's total trading volume. While this seems dominant, it hides a more critical trend: the share of sports is actually at a historical low.

In other words, all other categories are growing faster. Entertainment, crypto, politics, and culture are driving stronger user growth and more stable retention. Sports function more like an entry product—intuitive, emotionally driven, with a clear rhythm, suitable for attracting mass participation. Meanwhile, the long-tail markets, which account for over 20% of total trading volume, are growing rapidly. These markets will play an important role in institutional hedging and information pricing in the future.

This point is also confirmed by the institutional side. Cyril Goddeeris, Global Co-Head of Equity Business at Goldman Sachs, stated that predictions related to macro events and CPI are currently the most watched category on Wall Street; Sally Shin, Head of Growth Platform at CNBC, mentioned that she already uses predictions related to the Fed Chair and non-farm payroll data as narrative tools; Troy Dixon, Global Co-Head of Markets at Tradeweb, envisioned a future where large investment banks will establish dedicated prediction market trading desks, with financial contracts as core products.

Prediction markets are shifting from "entertainment trading" to "information and risk tools."

Why Kalshi Attracts Wall Street's Attention

The efficient operation of traditional financial markets largely relies on widely accepted benchmarks for various assets—the S&P 500 represents the average performance of 500 stocks, and crude oil has the ICE benchmark price. However, for political and economic events (such as who will win an election, whether a certain tariff will pass, the outcome of a Supreme Court case), there was previously almost no widely recognized and dynamically updated "benchmark."

Prediction markets change this. Today, the future of almost any event can have a real-time, liquid price benchmark. When the market can provide credible pricing for "the probability of a 30% tariff passing," institutions can trade around this price or hedge other risks in their portfolios. This makes the event itself a directly tradable object.

As Tradeweb's Troy Dixon said: "If you go back to when Trump was first elected, many people were hedging in the stock market, for example by shorting the S&P, because they thought his election would cause the market to fall. But that was the wrong trade. The question is, how should these events be priced? Where is the benchmark?"

Tarek also mentioned that one motivation for founding Kalshi stemmed from his previous work at Goldman Sachs providing trading advice around the 2024 election and Brexit. In the absence of prediction markets, when institutions hedge political or macro events through related assets, they actually need to bear two layers of judgment simultaneously—they must judge both the outcome of the event itself and the relationship between that event and the asset being traded, with the latter carrying its own risk of failure.

When the event itself has a direct price benchmark, the originally separate dual risks are merged into a single judgment. As Tarek said, the market has already begun pricing various events.

Three Stages Towards Institutional Adoption

It is still too early to claim that Wall Street institutions are participating in Kalshi trading on a large scale. Currently, most institutions primarily use it for data reference rather than actual trading.

However, Luana pointed out that the path to institutional adoption is already clear and can be divided into three stages:

• The first stage is data access: Integrating prediction market prices into institutions' daily workflows, for example, enabling Goldman Sachs investment managers to view Kalshi odds just like they view the VIX index. This stage has already been achieved to some extent. Professor Jonathan Wright from Johns Hopkins University, a former Fed official, stated that Kalshi is almost the sole reference source for Fed decisions, unemployment rates, and GDP;
• The second stage is system integration: This includes compliance approval, legal confirmation, technical integration, and internal education—essentially incorporating prediction markets into the usable financial tool system;
• The third stage is actual trading: Institutions begin hedging risks on the platform, trading volume and liquidity gradually accumulate, forming a positive feedback loop. More hedgers attract more speculators, tighter spreads attract more hedgers, and the benchmark price continuously strengthens.

Currently, most institutions are still in the first stage, some have entered the second stage, and only a few are in the third stage.

A major obstacle preventing institutions from entering the third stage is that current prediction market trading requires full margin—a $100 position requires depositing $100. This is acceptable for retail investors but is a significant limitation for hedge funds or banks that rely on leverage and capital efficiency. As Tarek said, if you want to hedge $100, you must put in $100, which is too costly for institutions; firms like Citadel or Millennium would not operate this way. Kalshi has already obtained permission from the National Futures Association and is working with the Commodity Futures Trading Commission to introduce margin trading mechanisms.

What Happens Next?

Michael McDonough, Head of Market Innovation at Bloomberg, offered the most direct judgment: the sign of success is when these things become boring. He compared prediction markets to the options market in the 1970s, which also faced controversies over manipulation and regulatory uncertainty, but these issues were eventually digested, evolving into an almost taken-for-granted piece of infrastructure.

Toby Moskowitz, Partner at AQR, stated that he is willing to bet on the development of prediction markets. Within five years, or even sooner, it will become a viable tool at the institutional level.

Garrett Herren of Vote Hub described the final form: the question is no longer whether to use prediction markets, but how to use them. Once the discussion shifts to this level, it means they have become indispensable. In fact, although prediction markets are still relatively small in scale, the hedging market itself is extremely vast.

The normalization of prediction markets is already happening.

In discussions on political issues, former Congressman Mondaire Jones mentioned that senior figures from both parties, including Trump, House Minority Leader Jeffries, and Senate Minority Leader Schumer, have already begun publicly citing Kalshi odds. Scott Tranter of DDHQ also confirmed that prediction market data has now become an important input for intra-party decision-making. Meanwhile, Vote Hub announced that it has directly integrated Kalshi data into its midterm election forecasting model.

All of this hardly existed two years ago. Back then, the most successful traders on Kalshi were still seen as amateurs. But now, the situation has changed, and it's even difficult to define them with that term anymore.

During a roundtable, four traders shared their paths. One spent eleven years studying the Billboard charts, while another has been participating in prediction markets since 2006—when it was still a non-monetary, somewhat geeky hobby. They did not come from the finance industry but from diverse backgrounds like music, politics, and poker. However, they unanimously agreed that the platform truly rewards deep domain knowledge, not resumes.

Summary

Prediction markets have come a long way. They were once seen as academic experiments, later became fleeting hotspots during election cycles, and were also viewed as extensions of sports betting.

The message conveyed by this conference is already very clear: prediction markets are gradually evolving into an infrastructure for pricing uncertainty, serving a wide range of participants from retail investors to large institutions and diverse application scenarios.