The organization has accessed the prediction market, but is stuck at the third stage

Original Title: Prediction Markets: They Grow Up So Fast

Author: Alex Immerman ( @aleximm* )*

Compiled by: Odaily Planet Daily

Editor's Note: At the end of March this year, the prediction market, once considered a marginal field, reached a critical moment. Kalshi's research institution, Kalshi Research, held its first 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 niche to mainstream.

The conference opened with a conversation between Kalshi co-founders Tarek Mansour and Luana Lopes Lara, hosted by Bloomberg reporter Katherine Doherty. This article extracts and organizes the key points 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"—the U.S. elections, the Super Bowl, March Madness. These events dominate the news cycle and naturally consume most of the trading volume, leading outsiders to mistakenly believe that the value of prediction markets is limited to these events.

However, this impression is being shattered. Just as the conference was taking place, the weekly trading volume for sports predictions had just approached $3 billion, accounting for about 80% of Kalshi's total trading volume. While it seems to be a standout, a more critical trend is hidden behind it: the share of sports is actually at a historical low.

In other words, all other categories are growing faster. Entertainment, crypto, politics, culture, and other fields are bringing stronger user growth and more stable retention. Sports are more like an entry product—it's intuitive, emotionally driven, and has a clear rhythm, making it suitable for attracting public participation. At the same time, the long-tail markets, which account for over 20% of total trading volume, are growing rapidly and 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, co-head of global equities at Goldman Sachs, stated that predictions related to macro events and CPI are currently the most focused categories on Wall Street; CNBC's growth head Sally Shin mentioned that she has been using predictions related to the Federal Reserve Chair and non-farm payroll data as narrative tools; Tradeweb's co-head Troy Dixon described a future where large investment banks will establish dedicated prediction market trading departments, with financial contracts as core products.

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

Why Kalshi Attracts Wall Street's Attention

The efficiency of traditional financial markets largely relies on the existence of recognized 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, or the outcome of a Supreme Court case), there has previously been almost no widely recognized and dynamically updated "benchmark."

Prediction markets have changed this. Now, almost any event's future 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 that price or hedge other risks in their portfolios. This makes the event itself a directly tradable object.

As Tradeweb's Troy Dixon said: "If we go back to when Trump was first elected, many people were hedging in the stock market, such as shorting the S&P, because they believed his election would lead to a market decline. But that was a wrong trade. The question is, how should these events be priced? Where is the benchmark?"

Tarek also mentioned that one of his motivations for founding Kalshi stemmed from his previous work at Goldman Sachs, providing trading advice around the 2024 election and Brexit. Without prediction markets, institutions hedging political or macro events through related assets actually need to bear two layers of judgment—judging the outcome of the event itself and judging the relationship between that event and the traded asset, with the latter carrying a separate risk of failure.

When the event itself has a direct price benchmark, the originally dispersed dual risks are consolidated into a single judgment. As Tarek said, the market has begun to price various events.

Three Stages Toward Institutional Adoption

It is still too early to assert that Wall Street institutions have massively participated in Kalshi trading. Currently, most institutions still primarily use it as a data reference rather than for actual trading.

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

• The first stage is data integration: allowing prediction market prices to enter the daily workflows of institutions, such as enabling Goldman Sachs investment managers to view Kalshi's odds like they view the VIX index. This stage has already been partially achieved. Jonathan Wright, a professor at Johns Hopkins University and former Federal Reserve official, stated that Kalshi is almost the only reference source for Federal Reserve decisions, unemployment rates, and GDP;
• The second stage is system integration: including compliance approvals, legal confirmations, technical integrations, and internal education, integrating prediction markets into the usable financial tool system;
• The third stage is actual trading: institutions begin to hedge risks on the platform, with trading volume and liquidity gradually accumulating, forming positive feedback. More hedgers attract more speculators, tighter spreads attract more hedgers, and benchmark prices are continuously reinforced.

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

An important reason hindering institutions from entering the third stage is that current prediction market trading requires full margin; a $100 position requires a corresponding deposit of $100. This is acceptable for retail investors, but for hedge funds or banks that rely on leverage and capital efficiency, it is a clear limitation. As Tarek said, if you want to hedge $100, you must put in $100, which is too costly for institutions, and firms like Citadel or Millennium will not adopt this method. Kalshi has now obtained approval from the National Futures Association and is working with the Commodity Futures Trading Commission to introduce a margin trading mechanism.

What Will Happen Next?

Michael McDonough, head of Bloomberg's market innovation, provided the most direct judgment, the hallmark of success is that these things become boring. He compared prediction markets to the options market of the 1970s, which also faced controversies over manipulation and regulatory uncertainty, but these issues were ultimately digested and evolved into an infrastructure that requires almost no additional thought.

AQR partner Toby Moskowitz expressed his willingness 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 from Vote Hub described the ultimate form, where 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 large.

The normalization of prediction markets is already happening.

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

And all of this was almost nonexistent two years ago. At that time, the most successful traders on Kalshi were still seen as amateurs. But now, the situation has changed, and it is even difficult to define them with that term.

In a roundtable, four traders shared their paths; some spent eleven years studying the Billboard charts, while others have been continuously participating in prediction markets since 2006—when it was still a capital-less, somewhat geeky interest area. They do not come from financial backgrounds but from diverse fields such as music, politics, and poker. However, they all agree that what this platform truly rewards is deep domain knowledge, not resumes.

Summary

Prediction markets have come a long way. They were once seen as academic experiments, later became a brief hotspot during election cycles, and were once regarded as an extension 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 and diverse application scenarios from retail investors to large institutions.