Author: Mario @IOSG
Prediction markets are a type of speculative market that trades based on the results of future events. Their core function is to aggregate scattered information through contract prices. Under certain conditions, the price of a contract can be interpreted as a probability prediction of the event. Since the 1980s, a large number of studies have shown that prediction markets are very accurate, often better than traditional prediction methods such as polls or expert opinions. This predictive ability comes from the "wisdom of crowds": anyone can participate in the market, and traders with better information have an economic incentive to participate in transactions, thereby driving prices closer to the true probability. In short, a well-designed prediction market can efficiently integrate a large number of individual beliefs into a consensus estimate of future outcomes.
The origins of modern prediction markets can be traced back to some pioneering experiments in the late 1980s. The first academic prediction market was Iowa Electronic Markets (IEM), founded in 1988 at the University of Iowa.
IEM is a small real money market (regulators limit bets to about $500 per person) that focuses on the results of the US election. Despite its limited size, IEM has long demonstrated impressive forecasting accuracy. In the week before the election, the market predicted the Democratic and Republican candidates with an average absolute error of 1.5 percentage points, compared to 2.1 percentage points in the last Gallup poll during the same period. The chart below also shows that the forecast accuracy continues to improve as election day approaches and more information is absorbed by the market.
At the same time, some forward-looking ideas about using markets to predict uncertain events are gradually taking shape. Economist Robin Hanson proposed the concept of "Idea Futures" in 1990, which is to establish an institution for people to bet on scientific or social propositions. He believes that this can form a "visible expert consensus" and motivate honest contributions by rewarding accurate predictions and punishing wrong judgments. In essence, he sees the prediction market as a mechanism to resist bias and promote the revelation of the truth, which is applicable to scientific research, public policy and other fields. This concept (i.e., the "idea futures market") was very advanced at the time and laid the foundation for the theoretical expansion of the prediction market.
In the 1990s, some online prediction markets began to emerge, covering real money markets and "virtual currency" markets. The academic market at the University of Iowa is still running, while the "virtual currency" market has gained more attention among the public. For example, the Hollywood Stock Exchange (HSX), founded in 1996, is an entertainment prediction market that trades "shares" of movies and actors with virtual currency.
HSX has proven to be very good at predicting movie opening weekend box office and even Oscars, sometimes even more accurate than professional film critics. In 2007, HSX players accurately predicted 32 Oscar nominations in 39 major categories and hit 7 winners in 8 major categories. HSX is regarded as a classic example of a prediction market.
The basic mechanism of the prediction market is to create an incentive-compatible structure that motivates market participants to reveal their true information. Since traders bet with real money (or virtual currency), they tend to trade based on their true beliefs and private information.
From an economic perspective, a well-designed market should allow traders to maximize their expected returns by bidding prices that match their subjective probabilities.
In terms of preventing manipulation, academic research has found that prediction markets are highly resilient to price manipulation. Attempts to deviate prices from fundamentals often create arbitrage opportunities for other more rational traders, who will choose to trade on the opposite side, thereby pulling prices back to a more reasonable position. Empirical data shows that manipulation is often corrected quickly and even helps improve market liquidity. In other words, those who try to manipulate the market usually end up becoming "leeks" who "subsidize" smart traders, and market prices will ultimately reflect the true state of information.
Kalshi is a federally regulated prediction market exchange where users can trade on the outcomes of real-world events. It is the first exchange approved by the U.S. Commodity Futures Trading Commission (CFTC) to offer Event Contracts. Event Contracts are binary futures (yes/no) where the contract value is $1 if the event occurs and $0 if it does not occur.
Users can buy or sell “Yes” / “No” contracts at prices between $0.01 and $0.99, with the price representing the market’s implied expectation of the probability of an event occurring.
If the prediction is correct, the contract is settled at $1, and the trader can make a profit. Kalshi itself does not hold positions (unlike gambling websites, it does not act as a banker). It only acts as a matching platform to match long and short parties and makes profits from transaction fees.
#Proposal and Approval
New event markets (yes/no binary contracts) can be proposed by the Kalshi team or users through "Kalshi Ideas". Each proposal is subject to internal review and must meet CFTC regulatory standards, including clear event definitions, objective settlement conditions, and permitted event categories.
#Contract Authentication
After approval, the event will be officially launched under Kalshi’s Designated Contract Market (DCM) framework, and the document will list the contract specifications, trading rules and settlement standards.
#Online Trading
After the event market goes live, U.S. users can trade through Kalshi’s app, official website, or integrated platforms with brokerages such as Robinhood and Webull.
#Order Book Mechanism
When a new market is launched, the order book is empty and any user (market maker or ordinary trader) can place a limit order (for example: buy "yes" at $0.39, or sell "no" at $0.61).
#Market Making Incentives
To encourage liquidity, makers usually do not charge transaction fees, but some specialty markets charge extremely low fees.
#PriceDiscovery
Prices change dynamically with supply and demand, reflecting the market's consensus on the probability of an event. For example, if someone buys "yes" at $0.60 and another person sells "no" at $0.40, the system matches them and the contract is created. Both parties invest $0.60 and $0.40, with a total of $1.
#Confirm the result
Event outcomes are judged based on pre-specified authoritative data sources (e.g., government reports, official sports results).
#Automatic Settlement
If an event occurs, users holding the “Yes” contract will automatically receive $1 in profit for each contract; otherwise, the “No” party wins and the losing party’s contract will be reset to zero. There is no additional settlement fee.
Setting: P = contract price, C = contract quantity
Polymarket Overview: Polymarket is a distributed prediction market platform built on Polygon, where users can trade binary outcome tokens (Yes/No Tokens) corresponding to event outcomes. It uses the Conditional Token Framework (CTF) to fully collateralize each pair of outcome tokens with stablecoin (USDC), and the trading mechanism uses a hybrid central limit order book (CLOB) for efficient compliance. Market settlement is completed through UMA's Optimistic Oracle, a controversial distributed resolution system.
Polymarket uses Gnosis's Conditional Token Framework to represent each market outcome as a conditional token, deployed on the Polygon chain. For a binary market, two ERC-1155 tokens will be generated, such as Yes Token and No Token, with the same amount of USDC as collateral.
Splitting 1 USDC will generate 1 Yes + 1 No Token, and merging the Yes/No Tokens can unlock and return 1 USDC, ensuring that each pair of tokens is fully collateralized. When the event ends, only the token corresponding to the correct result is worth 1 US dollar, and the token corresponding to the wrong result loses value.
Polymarket uses a hybrid architecture called Binary Limit Order Book (BLOB) to integrate offline order management and on-chain transactions. Users sign orders offline, and the operating node searches for matching orders. If there are matching orders, the on-chain economic exchange is completed through the smart contract.
Unlike traditional exchanges that use internal rulings or data sources, Polymarket forms consensus through the community through UMA's Optimistic Oracle.
Polymarket's trading volume for the entire platform (including politics, technology, entertainment and other markets) in June was US$1.16 billion, slightly higher than Kalshi's approximately US$800 million.
A recent peer-reviewed study, Gambling on Crypto Tokens?, provides strong evidence of a link between crypto assets and gambling behavior. Using Google Trends as a proxy for retail investor attention, the researchers revealed several notable patterns:
Google Search Volume Index (SVI_{i,d}) represents the attention that an initial coin offering (ICO) or NFT project i received at the time of its release in a specific market area (DMA) d. The key coefficient β₁ in the regression model measures the impact of the gambling tendency in the region on the attention of crypto assets. f(X_d) represents the regional control variable that may affect the attention of ICOs or NFTs.
The main explanatory variable of X_d in the model is: lottery sales per capita, and includes interaction terms with token characteristics.
The regression results show that those U.S. DMAs with higher per capita lottery sales show significantly higher Google search activity in the following areas:
This suggests a high degree of overlap in behavior between gamblers and retail crypto investors.
Table 7 of the paper uses the time differences in the legalization of sports betting in various US states as a natural experiment. The results show that when gambling becomes legal in a state, crypto-related attention decreases significantly in areas with high lottery sales:
This means that there is a “substitution effect” between crypto tokens and gambling behavior - once legal gambling channels emerge, speculative interest in crypto assets will decline.
The conclusion of the entire paper is very clear: "Gambling preferences can strongly predict retail investors' interest in crypto markets."
Crypto trading is not only “like gambling,” to some users, it is gambling.
These findings further validate the contrast and highlight the existence of this category of users: those with a high appetite for risk and who pursue speculative excitement in casinos or platforms like Coinbase.
To gauge the size of the market, consider the rise of crypto-native gambling platforms (which only support cryptocurrency deposits) such as Stake.com.
In just a few years, Stake has captured the huge demand for high-risk, high-return entertainment among global users and achieved explosive growth:
Although some modern bookmakers also offer early settlement functions, there are limitations as follows:
Stake is just one example of the crypto gambling market. According to industry reports, the total gambling revenue of crypto casinos exceeded $81 billion in 2024, and even with stricter regulations in many countries, it still cannot stop its growth.
This figure represents the gross revenue generated by cryptocurrency betting, indicating that the intersection of "crypto × gambling" has formed an extremely large user and capital pool, which currently mainly flows to offshore or unregulated platforms.
The success of Stake.com clearly demonstrates that:
There is a huge market group who:
These people are essentially the core user group of the prediction market, but they are currently concentrated on non-regulatory platforms such as Stake.
A regulated exchange such as Kalshi, with its sound market structure and compliant supervision, is expected to attract a portion of this user group while providing a compliant and exciting experience. The scale of Stake’s growth shows that if these users migrate to legal platforms, the annual total addressable market (TAM) of “crypto-flavored” gambling activities in the US market can reach billions of dollars.
This is not just a theoretical overlap. Multiple studies and questionnaires have confirmed that cryptocurrency speculators and gamblers have a high degree of overlap in key demographic and behavioral characteristics.
In many cases, they are the same type of people, or even the same person. Here are a few prominent points of overlap:
Therefore, the target group that Kalshi can target is precisely those retail users who are keen on speculation, who span the crypto trading community and the gambling community. These users are already engaged in similar behaviors, and the key is to provide a product that can still provide a similar stimulating experience under a compliant and secure framework. The overlap of behaviors also shows that Kalshi's marketing should emphasize both the pleasure of predicting the market (to impress gambling users) and its financial and rational trading attributes (to impress investors' self-awareness).
In the past few years, the overall narrative of the crypto industry has changed dramatically, from "unregulated" free innovation to institutional-led, regulatory compliance. This shift has brought a favorable competitive environment to regulated platforms such as Kalshi, and also highlighted the strategic value of its regulatory moat (CFTC approval).
Kalshi is very cautious in brand positioning, deliberately avoiding the "gambling" label, and instead shaping itself as a new type of trading platform: providing investment tools for "event contracts" and having regulated exchange qualifications. This strategy is very forward-looking:
CEO Tarek Mansour even questioned: "If we consider it gambling, then the entire financial market should be considered gambling."
This passage summarizes Kalshi's positioning logic: treat event contracts as financial derivatives, such as buying a contract for "whether July will set a record high temperature", just like buying crude oil futures or earnings options, rather than rolling the dice. This language conversion strategy is very effective, not only optimizing regulatory cognition, but also making users more willing to regard it as an "investment" rather than a "bet."
Even in its partnership with Robinhood, the language used in its promotional materials focuses on “bringing event trading to the masses” rather than using any gambling terms. This sophisticated branding is both a response to regulation and an attempt to dispel the stigma of gambling among users. By packaging the prediction market as a “trading experience,” Kalshi attracts a wider user base—including those who like to speculate but want to think they are “investing rationally.”
The line between gambling and trading has always been blurred. As a student of quantitative finance, we are often taught that market fluctuations are random walks and Brownian motion. But this also raises a question: if trading is driven by volatility, speculation and emotions, is it really different from gambling? If you bet on a high-risk event with a positive expected value, is it still gambling? According to the "Gambler's Ruin Theory", a gambler who continues to increase his bets but does not reduce his bets when he loses will eventually go bankrupt, even if the expectation of each bet is positive. So, are we all "high-level gamblers"?
The data is clear: crypto retail investors are not only similar to gamblers, they are often the same people.
Kalshi does not require a choice between these two types of users, it can serve both at the same time. Whether it is a "smart trader" executing an arbitrage strategy, or a "recreational player" predicting sports results, Kalshi provides a regulated, legal market environment where these behaviors can coexist.
By abstracting the label of "gambling" and embracing the language of "market", Kalshi is connecting two large user groups: those who want to play and those who want to trade. In this process, it does not sacrifice either party, but strengthens the identity and behavioral value of both.