The Rise of Prediction Markets: From Kalshi to the Evolution of Encryption Gambling

Prediction markets are a speculative market based on trading future event outcomes, with the core function of aggregating dispersed information through contract prices. Under specific conditions, contract prices can be interpreted as probability forecasts for event occurrences. A large body of research indicates that the accuracy of prediction markets typically surpasses traditional forecasting methods, such as polls or expert opinions. This predictive capability stems from "collective intelligence": anyone can participate in the market, and traders with more information have economic incentives to engage in trading, driving prices closer to true probabilities. In short, well-designed prediction markets can efficiently integrate a large number of individual beliefs to form consensus estimates of future outcomes.

The modern prediction market traces its origins back to groundbreaking experiments in the late 1980s. The first academic prediction market was the Iowa Electronic Markets established in 1988 at the University of Iowa(IEM). IEM is a small-scale real-money market that primarily focuses on the outcomes of U.S. elections. Despite its limited size, IEM has demonstrated impressive predictive accuracy over the long term. In the week leading up to the election, the market's average absolute error in predicting the vote shares of Democratic and Republican candidates was 1.5 percentage points, while the last Gallup poll during the same period had an error of 2.1 percentage points. As election day approached and more information was absorbed by the market, the predictive accuracy continued to improve.

At the same time, some forward-looking concepts about using the market to predict uncertain events are gradually taking shape. Economist Robin Hanson proposed the concept of "Idea Futures" in 1990, which involves establishing an institution that allows people to bet on scientific or social propositions. He believed this could create a "visible expert consensus" and incentivize honest contributions by rewarding accurate predictions and punishing incorrect judgments. Essentially, he viewed prediction markets as a mechanism to counter bias and promote the revelation of truth, applicable in areas such as scientific research and public policy. This concept was very ahead of its time and laid the foundation for the theoretical expansion of prediction markets.

In the 1990s, some online prediction markets began to emerge, covering real money markets and the "virtual currency" market. The academic market at the University of Iowa continues to operate, while the "virtual currency" market has gained more attention among the public. For example, the Hollywood Stock Exchange(HSX), established in 1996, is an entertainment prediction market that trades "shares" of movies and actors using virtual currency.

HSX has proven to be very good at predicting weekend box office figures for movies and even Oscars, sometimes with accuracy exceeding that of professional film critics. In 2007, HSX players accurately predicted 32 of the 39 Oscar nominations and hit 7 of the 8 major award winners. HSX is regarded as a classic case of a prediction market.

Theoretical Basis and Market Mechanism Design

The basic mechanism of prediction markets lies in creating an incentive-compatible structure that motivates market participants to reveal their true information. Since traders have to bet with real money ( or virtual currency ), they are inclined 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 quoting prices that align with their subjective probabilities.

In terms of preventing manipulation, academic research has found that prediction markets are highly resilient to price manipulation behaviors. Attempts to deviate prices from fundamentals typically create arbitrage opportunities for other more rational traders, who choose to trade on the opposite side, thereby pulling the price back to a more reasonable position. Empirical data shows that manipulation behaviors are often quickly corrected, and even help to enhance market liquidity. In other words, those who attempt to manipulate the market usually end up becoming the "subsidized" "leeks" for smart traders, and market prices will ultimately still reflect the true state of information.

Kalshi

Kalshi is a federally regulated prediction market exchange where users can trade on the outcomes of real-world events. It is the first exchange to receive approval from the U.S. Commodity Futures Trading Commission to offer event contracts. Event contracts are binary futures ( yes/no ); if the event occurs, the contract is worth $1; if it does not occur, it is worth $0.

Users can buy or sell "Yes"/"No" contracts with prices between $0.01 and $0.99, where the price represents the market's implied expectation of the probability of the event occurring.

If the prediction is correct, the contract will settle at $1, and the trader can profit from it. Kalshi itself does not hold positions; it only serves as a matching platform for both long and short sides, earning profits from trading fees.

market creation process

Proposal and Approval

The new event market ( is a yes/no binary contract ) that can be proposed by the Kalshi team or users through "Kalshi Ideas". Each proposal must undergo internal review and comply with CFTC regulatory standards, including clear event definitions, objective settlement conditions, and permissible event categories.

Contract Certification

After approval, the event will officially launch under Kalshi's designated contract market framework, with the document detailing the contract specifications, trading rules, and settlement standards.

Online Trading

After the launch of the event market, American users can trade through Kalshi's app, official website, or integrated platforms with brokerages like Robinhood and Webull.

Initial Liquidity and Pricing

Order Book Mechanism

When a new market is launched, the order book is empty, and any user (, whether a market maker or an ordinary trader ), can place limit orders (. For example: buy "yes" at $0.39, or sell "no" at $0.61 ).

Market Making Incentives

To encourage liquidity, makers are usually exempt from fees, but some special markets may charge very low fees.

price discovery

Prices fluctuate dynamically based on supply and demand, reflecting the market's consensus on the probability of events. For example: if someone buys "yes" at $0.60 and another person sells "no" at $0.40, after the system matches them, a contract is created, with both parties contributing $0.60 and $0.40, totaling $1.

market settlement mechanism

Result Confirmation

The event results are determined based on pre-specified authoritative data sources.

Automatic Settlement

If the event occurs, users holding the "Yes" contract automatically receive a profit of $1 per share; conversely, if "No" wins, the losing party's contract becomes null. No additional settlement fees.

fee structure

Setting: P = contract price, C = contract quantity

• Buying fee: C * P * 2%
• Selling Fee: C * (1-P) * 2%

Polymarket

Polymarket Overview: Polymarket is a decentralized prediction market platform built on Polygon, where users can trade binary outcome tokens corresponding to event results ( Yes/No Tokens ). It uses the Conditional Token Framework ( CTF ), which fully collateralizes each pair of outcome tokens with stablecoin ( USDC ). The trading mechanism employs a hybrid centralized limit order book ( CLOB ) for high efficiency matching. Market settlement is carried out through UMA's Optimistic Oracle, which is a disputeable decentralized resolution system.

Conditional Token Framework ( CTF ) and Result Token

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 Token + 1 No Token. Merging the Yes/No Tokens will unlock a refund of 1 USDC, ensuring that each pair of tokens is fully collateralized. When the event ends, only the tokens corresponding to the correct outcome are worth 1 dollar, while the tokens for the incorrect outcome are worthless.

hybrid order book architecture ( CLOB/BLOB )

Polymarket adopts a hybrid architecture called Binary Limit Order Book(BLOB), which integrates offline order management and on-chain transactions. Users sign orders offline, and the operating nodes search for matching orders; if a match is found, the on-chain economic exchange is completed via a smart contract.

order lifecycle

1. The user signs the EIP-712 order ( off-chain as Buy YES @$0.62)
2. Operating nodes maintain offline order books
3. When there are matching orders, the operating node will submit the matching results to the Exchange.sol contract.
4. On-chain execution of atomic swap to complete settlement

Atomic Swap example

• Buy vs Sell: One person buys Yes for $0.40, while another person sells Yes for $0.40, exchanging USDC and Yes token through the contract.
• Buy vs Buy: Two people mutually buy Yes/No, the contract extracts a total of 1 dollar from both parties and splits it into Yes/No tokens for the two users.
• Sell vs Sell: Two people sell Yes/No at the same time, merging tokens to return nearly the collateral.
• Supports partial trades, multi-order matching, can deduct price, integrated trading, and completes atomic execution.

UMA Optimistic Oracle

Unlike traditional exchanges that rely on internal adjudication or data sources, Polymarket forms consensus through the community via UMA's Optimistic Oracle.

• After the event ends, anyone can submit the result disclosure for this market and place a margin to enter the dispute period.
• If there is no dispute, this result is accepted; if there is a dispute, it is resolved through UMA community voting.

Kalshi vs Polymarket: Structure and Technology Comparison

Polymarket's total platform ( includes markets such as politics, technology, and entertainment. In June, the trading volume was $1.16 billion, slightly higher than Kalshi's approximately $800 million.

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Market Expansion Strategy / Growth Momentum

) Academic Insight: "The Gambling Tendencies of Encryption Tokens"

A recent peer-reviewed study "The Gambling Inclination of Crypto Tokens?" provides strong evidence of a correlation between crypto assets and gambling behavior. The researchers used Google Trends to proxy the attention of retail investors, revealing several significant patterns:

1. Per capita lottery sales can predict the attention toward encryption tokens.

The Google search volume index represents the level of attention a particular initial coin offering or NFT project receives at the time of its release within a specific market region. The key coefficients in the regression model measure the impact of the gambling tendency in that region on the attention given to encryption assets. The main explanatory variable in the model is: per capita lottery sales, and it includes interaction terms with the characteristics of the tokens.

The regression results show that areas in the United States with higher per capita lottery sales exhibit significantly higher Google search activity in terms of initial coin offerings: the regression coefficient ranges from 6.28 to 6.88, with a p-value < 0.01, indicating statistical significance.

This indicates a high overlap in behavior between gamblers and encryption retail investors.

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)# 2. Wallet Attention and Investment Results

• In regions with higher per capita lottery sales, the activity of wallets will quickly rise after the release of ICOs or NFTs.
• During the ICO/NFT release period, the search volume related to wallets surged more noticeably in regions with high lottery ticket sales.
• These regions also show higher funding amounts, more participants, and faster NFT minting speeds.

3. Legalization of gambling as a natural experiment

The paper utilizes the time differences in the legalization of sports betting across various states in the U.S. as a natural experiment. The results show that when gambling becomes legal in a state, in areas with high lottery sales.