README: Exploiting the Favorite-Longshot Bias on Polymarket

1. Project Context & Perspective

• Course Framework: Behavioral Finance Group Project [2].
• Chosen Perspective: A portfolio management/quant team developing a new trading strategy [1].
• Target Platform: Polymarket (utilizing on-chain public ledger data for transaction-level analysis).
• Proof-of-Concept Foundation: "Makers and Takers: The Economics of the Kalshi Prediction Market" (CEPR Discussion Paper DP20631) [4].

2. The Behavioral Anomaly: The Favorite-Longshot Bias

Definition of the Anomaly: The "favorite-longshot bias" is a pricing distortion commonly found in betting and prediction markets where low-probability events (longshots) are systematically overpriced, and high-probability events (favorites) are underpriced [5, 6].

Behavioral Drivers: According to recent research on the Kalshi prediction market, this anomaly is driven by a combination of market microstructure and behavioral biases [5, 7, 8]:

• Overestimating Small Probabilities: Traders display a systematic cognitive bias where they overweight the likelihood of unlikely events occurring [9, 10].
• Belief Disagreement & Sorting: The market consists of "Makers" (who post limit orders) and "Takers" (who accept them). Takers typically have more extreme, biased beliefs and are willing to pay higher prices for longshot contracts [8, 9].

Why Market Forces Haven't Eliminated It:

1. Small Volumes: Prediction markets often lack the deep liquidity of traditional financial markets, making it difficult for institutional arbitrageurs to deploy large amounts of capital to correct prices [11, 12].
2. High Risk/Variance: The asymmetric, all-or-nothing payoff structure of these contracts carries massive variance (e.g., standard deviations of returns around 33%), deterring rational investors [13].
3. Lack of Information: The exact mechanics of these biases and the differing returns between Makers and Takers are not widely understood by the retail participants populating these platforms [14].

3. Proposed Strategy

The Setup: While the foundational research was conducted on Kalshi, our team will analyze Polymarket. Polymarket offers a unique advantage because its transactions are recorded on a public blockchain ledger, allowing for transparent, granular data extraction of every trade, maker/taker dynamic, and wallet behavior.

The Trading Strategy: We propose an algorithmic market-making strategy that systematically exploits the favorite-longshot bias and Maker/Taker spread [5, 15].

• Avoid Taker Positions on Longshots: We will avoid acting as Takers on low-priced contracts (e.g., those priced <10¢), as Kalshi data shows these suffer from severe overpricing and average loss rates exceeding 60% [16, 17].
• Act as Makers on Favorites: We will act strictly as Makers (liquidity providers) for higher-priced, high-probability contracts (e.g., >50¢). By capturing the bid-ask spread and avoiding taker fees, Makers historically achieve a small but statistically significant positive rate of return (historically around 2.6% per contract on Kalshi) [16, 18, 19].

4. Evidence Supporting the Idea

Our strategy's thesis is heavily supported by transaction-level analysis of over 300,000 contracts on the Kalshi platform [5, 20]:

• Statistical Rejection of Rational Pricing: Mincer-Zarnowitz regressions firmly reject the idea that prediction market prices are unbiased estimators of reality [21, 22].
• Maker Outperformance: In prediction markets, Makers significantly outperform Takers. On Kalshi, average returns for Makers were -9.64%, while Takers absorbed massive losses of -31.46% [23].
• Pervasiveness: This anomaly is resilient and persists across market categories—including crypto, financials, politics, and entertainment [24].

5. Risks and Challenges

• Smart Contract & Protocol Risk: Because Polymarket operates on decentralized crypto rails, the strategy is exposed to smart contract vulnerabilities or stablecoin de-pegging risks not present on fiat-based platforms like Kalshi.
• Execution Risk: Acting as a Maker carries the risk of non-execution (limit orders not being filled) or adverse selection (being filled only when breaking news changes the true probability of the event) [23, 25].
• Capital Scalability: Because of order book thinness, attempting to compound the 2.6% return on favorites by deploying massive capital may squeeze the bid-ask spread, eroding the exact margin the strategy relies on [12].