a16z: From retail traders to top institutions, market predictions are heading toward maturity

Author: Alex Immerman、Santiago Rodriguez, a16z Growth Fund Partner; Source: a16z; Compiled by: Shaw Golden Finance

The financial industry is segmented into countless sub-sectors, and each sub-sector has its own widely recognized top benchmark research conference. Every year, leaders in healthcare services, payment providers, and biotechnology gather in San Francisco to attend the JPMorgan Healthcare Conference. Meanwhile, major players in the global macro space and figures from the political world travel to Switzerland’s Alps each year to take part in the Davos Forum. Technology media, telecommunications, real estate, industrial sectors, financial services, and industries of all kinds all have their own signature summits.

At the end of March, Kalshi Research—Kalshi’s research-focused division for academic and institutional work—held its first research conference in New York, bringing together academic experts, Wall Street executives, former political figures, and traders who influence market directions. The composition of the attendees on site precisely confirms that “this industry is moving toward maturity.”

The conference opening featured a conversation between Kalshi co-founders Tarek Mansour and Luana Lopes Lara and Bloomberg reporter Katherine Doherty. Below are the core viewpoints on industry development that emerged in this discussion and in the subsequent roundtable segments.

1. Life (and markets) are not just about elections and sports

There is often a pattern in major news cycles: a single hot event (such as the 2024 election, the Super Bowl, or the recent NCAA March Madness basketball tournament) dominates headlines, driving a surge in prediction market trading volume, and giving people a false impression: “Prediction markets can only do these things.”

However, although early views generally held that prediction markets are only viable during election cycles, Kalshi has achieved significant growth in other areas.

During the research conference, weekly trading volume in sports-related markets was just shy of $3 billion, accounting for about 80% of Kalshi’s total trading volume, which was mainly driven by NCAA March Madness. Tarek Mansour and Luana Lopes Lara defined the dominance of sports as a cyclical phenomenon.

More telling data is this: even though the absolute trading volume in sports hit a historic high, its share of total trading volume is actually at a historic low. The growth rates of all other categories are faster.

The two pointed out that entertainment, cryptocurrencies, politics, and culture show stronger user growth, and trading volume retention performs better than sports. Sports plays the role of a mass-market catalyst—an audience-attracting product that people are familiar with, with fixed schedules and strong emotion-driven demand.

But the company has also seen substantial growth in long-tail markets. This portion accounts for more than 20% of Kalshi’s remaining trading volume, and is crucial for institutional hedging and information markets.

Then an institutional-focused panel discussion further validated this view from the demand side:

• Cyril Goddeeris, Co-Head of Global Equities at Goldman Sachs, said that prediction categories tied to macro events and CPI data are the most watched areas on Wall Street.

• Sally Shin, EVP of CNBC Growth Business, said she has already used market forecasts related to the Federal Reserve Chair and non-farm employment data as narrative tools.

• Troy Dixon, Co-Head of Global Markets at Tradeweb, described a future like this: large Wall Street banks will set up dedicated prediction market trading divisions, with financial contracts as the core product.

2. Why Kalshi got the attention of Wall Street

There are many reasons traditional financial markets can function smoothly, and one important reason is that each major asset class has a widely recognized benchmark: the S&P 500 index represents the average performance of 500 stocks; and crude oil has benchmark prices set by the Intercontinental Exchange (ICE).

But with respect to political and economic events (such as who wins an election, whether a tariff bill can pass, or the outcome of a particular case at the Supreme Court), before this, there were almost no benchmarks that were broadly recognized and could be updated dynamically. Prediction markets changed that. Now, for nearly any event, there is a real-time, liquid market benchmark for its future outcome.

Once an event has a credible price—such as a probability quote of 30% for a tariff bill passing—institutional trading counterparties can transact at that price. This creates a mechanism to trade directly on the event or to hedge downside risks of other parts of an investment portfolio. As Troy Dixon of Tradeweb put it:

“Think back to when Trump was elected the first time. There was a lot of hedging in the stock market. The strategy was to short the S&P 500 because it was widely believed that a Trump victory would cause stocks to fall. In the end, that trade lost a lot. The key question was: how do you price events like this? And what is the benchmark?”

Tarek Mansour also mentioned a similar original motivation for founding Kalshi: he had worked in the trading division at Goldman Sachs, where the team recommended trades tied to the 2024 U.S. election and Brexit. Without prediction markets, for institutions to hedge political or macro event risk through related assets is essentially like placing two bets at the same time: one bet on the event’s outcome itself, and another bet on the correlation between the event and the trading asset. The second layer of betting could fail independently just as easily.

With a direct benchmark for the event itself, these two bets merge into one. As Tarek Mansour said: “This group of market participants is now pricing all kinds of events.”

3. Three stages of large-scale institutional adoption

If large Wall Street institutions are already trading big volumes on Kalshi, that would still be premature: today, most institutions still mainly view Kalshi as a data source rather than a trading platform.

But Luana Lopes Lara said the path for broader Wall Street access to this market is already very clear, and can be summarized into the following three stages:

• Stage 1: Data application. Integrate price data into institutional workflows, until Goldman’s portfolio managers become accustomed to browsing Kalshi’s probability quotes the way they check the Volatility Index (VIX). To a certain extent, this stage has already become reality. As Jonathan Wright, a professor at Johns Hopkins University and a former Fed official, said: “For data like Federal Reserve decisions, unemployment, and GDP, Kalshi is almost the only reference source.”

• Stage 2: System integration. Complete compliance and legal approvals, integrate technical systems, and train internal staff—meaning the full process of introducing a new financial instrument.

• Stage 3: Real-world deployment. Risk hedging is actually executed on the exchange, and trading volume and market depth begin to form a positive feedback loop. In this stage, more hedging orders attract more speculative orders; narrower bid-ask spreads attract more hedgers; and the market benchmark is thus reinforced through self-acceleration.

At present, most institutions are still in Stage 1. A significant number have entered Stage 2, while only a small number have reached Stage 3.

A key reason why more institutions have not made it to Stage 3 is that: currently, trading prediction market contracts requires posting the full nominal value as margin— for a $100 position, $100 must be deposited with the clearing institution. This is manageable for retail traders, but it is a significant constraint for hedge funds or banks that operate based on leverage ratios and return on capital.

Tarek Mansour said: “If you want to hedge $100, you have to deposit $100 with the clearinghouse. That’s too costly for institutions. Companies like Castle Investment and Millennium Management wouldn’t do that.” Kalshi has just obtained a license from the National Futures Association (NFA) and is working with the U.S. Commodity Futures Trading Commission (CFTC) to roll out margin trading.

4. Future development directions

Michael McDonough, Bloomberg’s head of market innovation, put it most plainly: “Success means these products become boring.”

He compared it to the options market in the 1970s: back then, people also worried about market manipulation and regulatory uncertainty, but ultimately these issues were gradually resolved as infrastructure improved, becoming routine—something no one would deliberately pay attention to.

Toby Moskowitz, a partner at AQR Management, said that he “puts real money on” the view that prediction markets will become a mature tool for institutions within five years—possibly even sooner.

Garrett Herren of the voting platform Vote Hub described the final mature state: “The future question won’t be whether we should use prediction markets, but how we should use them. Once people start asking that question, it means they’ve become indispensable.”

In fact, although prediction markets are still small in scale today, the underlying hedging market is extremely large:

In reality, the normalization of prediction markets has already begun.

During the political-themed panel discussion, former U.S. Representative Mondaire Jones pointed out that senior congressional leaders from both parties—including President Trump, House Minority Leader Jeffries, and Senate Minority Leader Schumer—have already cited Kalshi’s probability data publicly. Scott Tranter of DDHQ confirmed that prediction market data has now become a standard reference metric within party committees. Vote Hub also announced that it will directly integrate Kalshi data into its midterm election prediction models.

All of this was unimaginable two years ago. Two years ago, the most successful traders on Kalshi were still just a group of amateurs. Now, the situation is different—calling them “amateurs” is no longer appropriate.

In the Kalshi “Traders Behind the Market” panel discussion, four traders shared their experiences of making this their career. Their professional habits are the same as those of professional traders—some have spent eleven consecutive years focusing on data from the Billboard charts; others entered prediction markets as early as 2006, when it was still just a “nerdy niche hobby” with no real money involved. All four panelists have no background in finance; they come from music, politics, and poker, but they agree that what the platform truly rewards is deep domain expertise—not flashy credential certificates.

Prediction markets have come a long way. They started as a novelty concept in academia, then became a popular topic during elections, and later came to be seen as a derivative of sports betting. This conference makes clear that: Prediction markets are moving toward maturity, becoming a foundational infrastructure that provides uncertainty pricing, serving participants ranging from retail traders to top institutions, and with an expanding range of use cases.