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

Translation｜Odaily Planet Daily (@OdailyChina); Translator｜Asher (@Asher_ 0210)

Editor's Note: At the end of March this year, the once marginal field of prediction markets reached a pivotal moment. Kalshi's research arm, 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 sends 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, moderated 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 broken. Just as the conference was held, the weekly trading volume for sports predictions approached $3 billion, accounting for about 80% of Kalshi's total trading volume. It seems to stand alone, but behind it lies a more critical trend: the proportion of sports is actually at a historical low.

In other words, all other categories are growing faster. Entertainment, crypto, politics, culture, and more are driving stronger user growth and more stable retention. Sports are more like an entry product—it is intuitive, strongly emotion-driven, and its rhythm is clear, 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 verified by the institutional side. Cyril Goddeeris, co-head of global equity at Goldman Sachs, stated that predictions related to macro events and CPI are the categories currently of greatest interest on Wall Street; CNBC Growth Executive Sally Shin mentioned that she is already using market predictions related to the Federal Reserve Chair and non-farm payroll data as narrative tools; Tradeweb co-head Troy Dixon depicted a future where major investment banks will establish dedicated trading departments for prediction markets, using financial contracts as core products.

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

Why Kalshi Attracts Attention from Wall Street

The efficiency of traditional financial markets heavily 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 an ICE benchmark price. However, there has previously been virtually no widely recognized and dynamically updated “benchmark” for political and economic events (such as who will win the election, whether a certain tariff will pass, or the outcome of a Supreme Court case).

Prediction markets have changed this. Today, almost any event can have a real-time, liquid price benchmark. When the market can reliably price the “probability of a 30% tariff passing,” institutions can trade around that price or hedge against 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 the time of Trump’s first election, many were hedging in the stock market, such as shorting the S&P because people believed his election would lead to a market drop. But that was a wrong trade. The problem is, how do you price those events? Where is the benchmark?”

Tarek also mentioned that one of his motivations for founding Kalshi stemmed from his previous work at Goldman Sachs, advising on trades around the 2024 election and Brexit. In the absence of prediction markets, institutions face two layers of judgment when hedging political or macro events using related assets—they must judge not only the outcome of the event itself but also the relationship between that event and the traded asset, which carries 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 stated, the market has begun pricing a variety of events.

Three Stages Toward Institutional Adoption

It is still early to assert that Wall Street institutions have massively participated in Kalshi trading. Currently, most institutions use it primarily 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: incorporating prediction market prices into the daily workflows of institutions, such as allowing Goldman Sachs investment managers to view Kalshi odds like they view the VIX index. This stage has already been partially realized. Johns Hopkins University professor and former Federal Reserve official Jonathan Wright stated that with regard to Fed decisions, unemployment rates, and GDP, Kalshi is almost the only reference source;
• The second stage is system integration: including compliance approvals, legal confirmations, technological integrations, and internal education to incorporate prediction markets into the available financial instruments体系;
• 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 continually reinforced.

Currently, most institutions remain in the first stage, a portion has entered the second stage, and only a few have moved to the third stage.

One important reason hindering institutions from entering the third stage is that trading in prediction markets currently requires full margin, meaning a $100 position needs to be backed by a $100 deposit. This is acceptable for retail investors but poses a clear limitation for hedge funds or banks that rely on leverage and capital efficiency. As Tarek pointed out, if you want to hedge $100, you must commit $100, which is too costly for institutions; firms like Citadel or Millennium will not adopt this model. 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 market innovation at Bloomberg, provided the most straightforward judgment: the mark of success is when these things become boring. He compared prediction markets to the options market of the 1970s, which also faced controversy around manipulation and regulatory uncertainty, but these issues were ultimately digested, evolving into a type of infrastructure that requires little 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 of Vote Hub described the final form, stating that the question will no longer be 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 currently have a relatively small 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 high-level figures from both parties, including Trump, House Minority Leader Jeffries, and Senate Minority Leader Schumer, have already begun to publicly reference Kalshi odds. DDHQ's Scott Tranter also confirmed that prediction market data has now become an important input for party decision-making. Meanwhile, Vote Hub announced that it has directly integrated Kalshi data into its midterm election prediction model.

These developments were almost non-existent two years ago. At that time, the most successful traders on Kalshi were still regarded as amateurs. But now, the situation has changed, and it is even difficult to define them with that term.

During a roundtable, four traders shared their paths; some spent eleven years studying the Billboard charts, while others have participated in prediction markets since 2006—when it was still a niche field without funding and a somewhat geeky identity. They do not come from finance backgrounds but from various sectors such as music, politics, and poker. However, they unanimously believe that the platform truly rewards deep domain knowledge rather than resumes.

Conclusion

Prediction markets have come a long way. They were once seen as academic experiments, later became brief hotspots during election cycles, and were sometimes viewed as an extension of sports betting.

And 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 applications from retail investors to large institutions.

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