Editor’s Note: For a long time, prediction markets have been regarded as a “niche product”—first as academic experiments, then as a tool for public opinion during election seasons, and later as an extension of sports betting. They have always seemed to rely on high-profile contexts, yet rarely been understood as genuine financial infrastructure.
However, in the author’s view, prediction markets are evolving from a niche, election- and sports-focused “event trading tool” into a financial infrastructure capable of pricing uncertainty.
The author notes that key industry shifts are evident at three levels: First, application scenarios are expanding—while sports remains a traffic entry point, longer-tail markets such as entertainment, macroeconomics, and CPI are growing faster and beginning to accommodate institutional demand; second, prediction markets have, for the first time, provided a tradable price benchmark for the "events themselves," enabling institutions to hedge political or macro risks directly rather than through secondary bets on related assets; third, institutional adoption is progressing—from using data as a reference (observing odds) to system integration and ultimately active trading—though the industry remains in its early stages.
Prediction markets are undergoing a process similar to the early stages of options markets—professionalization, institutionalization, and infrastructure development. Once liquidity, leverage, and regulation gradually improve, they could become a core market instrument connecting retail and institutional participants, used to hedge and price real-world uncertainties.
Finance is a highly "vertically layered" world, where nearly every niche has its own widely recognized "annual pilgrimage." Leaders in healthcare providers, payers, and biotech companies gather annually in San Francisco for the J.P. Morgan Healthcare Conference. Major figures in global macroeconomics and government officials from around the world travel to the Swiss Alps to attend the World Economic Forum Annual Meeting (Davos). TMT, real estate, industrial, financial services, and virtually every other industry you can think of also have their own flagship conferences.
At the end of March this year, Kalshi Research, Kalshi’s academic and institutional research division, hosted its first research conference in New York, bringing together academics, Wall Street executives, former policymakers, and the traders who truly drive the markets. The composition of attendees clearly reflects a trend: this industry is “maturing.”
The day’s events opened with a conversation between Kalshi co-founder Tarek Mansour and Luana Lopes Lara, moderated by Katherine Doherty. Below are some key industry insights drawn from this panel and subsequent roundtable discussions:
The market and life are about more than just elections and sports.
During major news cycles, a familiar pattern often emerges: a large-scale event—such as the 2024 election, the Super Bowl, or more recently, March Madness college basketball—dominates the vast majority of media headlines and consequently drives the bulk of trading volume in prediction markets. This can easily create the impression that “the value of prediction markets is only evident during these events.”
However, although early narratives often portrayed prediction markets as tools relevant only during election cycles, Kalshi has also seen significant growth in other areas.
At the time of the research meeting, weekly trading volume in sports markets had just approached $3 billion, accounting for approximately 80% of Kalshi’s total trading volume, primarily driven by March Madness. Tarek and Luana viewed this high concentration as a temporary phenomenon.
A more insightful metric is that although the absolute volume of sports trading has reached an all-time high, its share of total trading volume is at a historical low. This indicates that all other categories are growing at a faster rate.
The two founders noted that categories such as entertainment, crypto, politics, and culture are demonstrating stronger user growth and better trading retention structures than sports. Sports functions more like a mass-market "gateway" product—characterized by high familiarity, clear timing rhythms, and strong emotional engagement—making it a classic entry point.
Meanwhile, the company has also observed significant growth in more niche markets. These markets currently account for over 20% of Kalshi’s trading volume and will play an increasingly critical role in future institutional hedging and information markets.
A subsequent institutional roundtable confirmed this assessment from the demand side.
Cyril Goddeeris, Co-Head of Global Equities at Goldman Sachs, said that forecasts related to macroeconomic events and CPI data are currently the most closely watched category on Wall Street. Sally Shin, Executive Vice President of Growth Business at CNBC, noted that she has already used prediction markets for topics such as “the Fed Chair’s tenure” and “non-farm payrolls” as narrative tools for content. Troy Dixon, Co-Head of Global Markets at Tradeweb, went further, painting a future vision in which major investment banks will establish dedicated prediction market trading desks centered on financial contracts.
Why Kalshi is attracting Wall Street's attention
A key reason traditional financial markets function is that each major asset class has a widely recognized benchmark: the S&P 500 Index represents the overall performance of 500 stocks, while crude oil has benchmark pricing systems such as ICE.
However, for political and macroeconomic events—such as who wins an election, whether tariffs are passed, or the outcome of Supreme Court cases—there has long been a lack of widely accepted, dynamically updated "pricing benchmarks." Prediction markets have changed this—today, almost any future event can have a real-time, liquid "price anchor."
Once an event—such as “Will a 30% tariff be approved?”—has a credible price, institutions can trade directly against that price. This enables trading on the event itself, as well as hedging risks associated with other assets in a portfolio. As Troy Dixon of Tradeweb said: “Going back to Trump’s first election, there was massive hedging in the stock market; the logic was to short the S&P because if Trump won, the market would surely fall. But that trade failed. The question is: how do you price these events? What’s the benchmark?”
Tarek also mentioned that this was one of the motivations behind founding Kalshi. During his time at Goldman Sachs, his trading desk recommended trades based on the 2024 election and Brexit. Without prediction markets, institutions hedging against political or macro events through related assets are effectively betting on two things at once: whether the event occurs, and the correlation between the event and the traded asset. The second judgment can easily be wrong on its own.
When the event itself has a direct price benchmark, these two layers of risk are compressed into one. As Tarek said: “Now, this market is beginning to price everything.”
The three stages of institutional adoption of prediction markets
It is still too early to say that major Wall Street institutions are trading extensively on Kalshi. Currently, most institutions still treat it primarily as a data source rather than a trading platform.
However, Luana notes that the path for institutional adoption of this market is clear and can be divided into three stages:
The first phase is data integration: incorporating predictive prices into institutional workflows. For example, enabling Goldman Sachs portfolio managers to routinely check Kalshi’s odds data as naturally as they check the VIX index. This phase is already partially underway. Professor Jonathan Wright of Johns Hopkins University and former Federal Reserve official stated: “In areas such as Federal Reserve decisions, unemployment rates, and GDP, Kalshi is nearly the only reference source.”
The second phase is system integration: including compliance and legal approvals, technical integration, and internal education—essentially the process of introducing a new financial instrument.
The third stage is actual trading: institutions begin hedging risk directly on the platform, with trading volume and market depth gradually building. At this point, increased hedging demand attracts speculators, tighter spreads draw in more hedgers, and a benchmark price forms a self-reinforcing positive feedback loop.
Currently, most institutions remain in the first stage, some have progressed to the second stage, and very few have truly entered the third stage. A major obstacle is that trading prediction markets currently requires full margin. For example, a $100 position requires $100 in margin. While this may be acceptable for individual investors, the mechanism is too costly for hedge funds or banks that rely on leverage and capital efficiency.
As Tarek said: “If you want to hedge $100, you have to put up $100 at the clearinghouse. That’s too expensive for institutions. Institutions like Citadel or Millennium won’t do this.” Kalshi has now obtained a license from the National Futures Association (NFA) and is working with the Commodity Futures Trading Commission (CFTC) to introduce margin trading.
What happens next?
Michael McDonough, Head of Market Innovation at Bloomberg, summed it up most directly: “The sign of success is when these things become boring.” He compared prediction markets to the options market of the 1970s, which was similarly fraught with manipulation and regulatory uncertainty, but ultimately evolved into an infrastructure so established that few think about it today.
AQR partner Toby Moskowitz said he is "willing to put his money where his mouth is," predicting that markets will become a viable institutional tool within five years, and possibly even sooner.
Garrett Herren of Vote Hub described the end state: “The question is no longer whether to use prediction markets, but how to use them. Once it reaches this point, it becomes indispensable.”
In fact, although the current size of prediction markets is still limited, the hedging market itself is a massive sector.
In fact, the normalization of prediction markets is already taking place.
During a roundtable discussion on political themes, former Congressman Mondaire Jones noted that top leaders from both parties—including President Trump, House Minority Leader Jeffries, and Senate Minority Leader Schumer—have begun citing Kalshi’s odds data in public remarks. Scott Tranter of DDHQ also confirmed that prediction market data has now become a standard input within party committees. Meanwhile, Vote Hub announced that it has directly integrated Kalshi’s data into its midterm election forecasting model.
Yet all of this didn’t exist two years ago. At that time, the most successful traders on Kalshi were still primarily considered “amateurs.” Today, that label is no longer even accurate.
At Kalshi’s “The People Behind the Markets” panel, four traders shared their career paths—paths that sound no different from those of traditional professional traders: one spent 11 years studying the Billboard music charts, another honed their skills in prediction markets since 2006, when it was still a “slightly geeky, nearly unprofitable hobby.” Notably, none of the panelists came from traditional finance; instead, they hailed from music, politics, and poker. Yet they all agreed that what this platform truly rewards is deep domain expertise, not a glamorous resume.
Prediction markets have come a long way. From being initially viewed as academic experiments, to later being regarded as a "novel tool" during elections, and then briefly categorized as "sports betting-like products," their positioning has continuously evolved. The clear message conveyed at this conference is that prediction markets are now emerging as infrastructure—for pricing uncertainty and serving a wide range of participants, from retail traders to large institutions, across diverse use cases.
