Semantic Trading: Agentic AI for Clustering and Relationship Discovery in Prediction Markets

• URL: http://arxiv.org/abs/2512.02436v1
• Date: Tue, 02 Dec 2025 05:45:48 GMT
• Title: Semantic Trading: Agentic AI for Clustering and Relationship Discovery in Prediction Markets
• Authors: Agostino Capponi, Alfio Gliozzo, Brian Zhu,
• Abstract summary: We present an agentic AI pipeline that autonomously clusters markets into coherent topical groups using natural-language understanding.<n>We identify within-cluster market pairs whose resolved outcomes exhibit strong dependence, including same-outcome (correlated) and different-outcome (anti-correlated) relationships.<n>Results show that agent-identified relationships achieve roughly 60-70% accuracy, and their induced trading strategies earn about 20% average returns over week-long horizons.
• Score: 6.329256068898982
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Prediction markets allow users to trade on outcomes of real-world events, but are prone to fragmentation through overlapping questions, implicit equivalences, and hidden contradictions across markets. We present an agentic AI pipeline that autonomously (i) clusters markets into coherent topical groups using natural-language understanding over contract text and metadata, and (ii) identifies within-cluster market pairs whose resolved outcomes exhibit strong dependence, including same-outcome (correlated) and different-outcome (anti-correlated) relationships. Using a historical dataset of resolved markets on Polymarket, we evaluate the accuracy of the agent's relational predictions. We then translate discovered relationships into a simple trading strategy to quantify how these relationships map to actionable signals. Results show that agent-identified relationships achieve roughly 60-70% accuracy, and their induced trading strategies earn about 20% average returns over week-long horizons, highlighting the ability of agentic AI and large language models to uncover latent semantic structure in prediction markets.

Related papers

• Forecasting Future Language: Context Design for Mention Markets [81.25011140991566]
  We study how input context should be designed to support accurate prediction in mention markets.<n>We find three insights: (1) richer context consistently improves forecasting performance; (2) market-conditioned prompting (MCP) treats the market probability as a prior and updates it using textual evidence, yields better-calibrated forecasts; and (3) a mixture of the market probability and MCP (MixMCP) outperforms the market baseline.
  arXiv  Detail & Related papers  (2026-02-04T12:43:31Z)
• PredictionMarketBench: A SWE-bench-Style Framework for Backtesting Trading Agents on Prediction Markets [0.0]
  PredictionMarketBench is a SWE-bench-style benchmark for evaluating algorithmic and LLM-based trading agents on prediction markets.<n>PredictionMarketBench standardizes (i) episode construction from raw exchange streams (orderbooks, trades, lifecycle, settlement), (ii) an execution-realistic simulator with maker/taker semantics and fee modeling, and (iii) a tool-based agent interface.<n>We release four Kalshi-based episodes spanning cryptocurrency, weather, and sports. Baseline results show that naive trading agents can underperform due to transaction costs and settlement losses, while fee-aware algorithmic strategies remain competitive in volatile episodes
  arXiv  Detail & Related papers  (2026-01-28T06:41:12Z)
• Magentic Marketplace: An Open-Source Environment for Studying Agentic Markets [74.91125572848439]
  We study two-sided agentic marketplaces where Assistant agents represent consumers and Service agents represent competing businesses.<n>This environment enables us to study key market dynamics: the utility agents achieve, behavioral biases, vulnerability to manipulation, and how search mechanisms shape market outcomes.<n>Our experiments show that frontier models can approach optimal welfare-- but only under ideal search conditions. Performance degrades sharply with scale, and all models exhibit severe first-proposal bias, creating 10-30x advantages for response speed over quality.
  arXiv  Detail & Related papers  (2025-10-27T18:35:59Z)
• The Invisible Handshake: Tacit Collusion between Adaptive Market Agents [16.94262518248427]
  We study the emergence of tacit collusion between adaptive trading agents in a market with endogenous price formation.<n>We show that, when agents follow simple learning algorithms to maximize their own wealth, the resulting dynamics converge to collusive strategy profiles.
  arXiv  Detail & Related papers  (2025-10-14T08:28:33Z)
• When Agents Trade: Live Multi-Market Trading Benchmark for LLM Agents [74.55061622246824]
  Agent Market Arena (AMA) is the first lifelong, real-time benchmark for evaluating Large Language Model (LLM)-based trading agents.<n>AMA integrates verified trading data, expert-checked news, and diverse agent architectures within a unified trading framework.<n>It evaluates agents across GPT-4o, GPT-4.1, Claude-3.5-haiku, Claude-sonnet-4, and Gemini-2.0-flash.
  arXiv  Detail & Related papers  (2025-10-13T17:54:09Z)
• When AI Meets Finance (StockAgent): Large Language Model-based Stock Trading in Simulated Real-world Environments [55.19252983108372]
  We have developed a multi-agent AI system called StockAgent, driven by LLMs. The StockAgent allows users to evaluate the impact of different external factors on investor trading. It avoids the test set leakage issue present in existing trading simulation systems based on AI Agents.
  arXiv  Detail & Related papers  (2024-07-15T06:49:30Z)
• A Network Simulation of OTC Markets with Multiple Agents [3.8944986367855963]
  We present a novel approach to simulating an over-the-counter (OTC) financial market in which trades are intermediated solely by market makers. We show that our network-based model can lend insights into the effect of market-structure on price-action.
  arXiv  Detail & Related papers  (2024-05-03T20:45:00Z)
• Equilibrium of Data Markets with Externality [5.383900608313559]
  We model real-world data markets, where sellers post fixed prices and buyers are free to purchase from any set of sellers. A key component here is the negative externality buyers induce on one another due to data purchases. We prove that platforms intervening through a transaction cost can lead to a pure equilibrium with strong welfare guarantees.
  arXiv  Detail & Related papers  (2023-02-16T00:57:49Z)
• Price Interpretability of Prediction Markets: A Convergence Analysis [6.623653707600985]
  We show that the limiting price converges to the geometric mean of agent beliefs in exponential utility-based markets. We also show that the limiting price can be characterized by systems of equations that encapsulate agent beliefs, risk parameters, and wealth.
  arXiv  Detail & Related papers  (2022-05-18T13:15:11Z)
• Learn to Match with No Regret: Reinforcement Learning in Markov Matching Markets [151.03738099494765]
  We study a Markov matching market involving a planner and a set of strategic agents on the two sides of the market. We propose a reinforcement learning framework that integrates optimistic value iteration with maximum weight matching. We prove that the algorithm achieves sublinear regret.
  arXiv  Detail & Related papers  (2022-03-07T19:51:25Z)
• Design and Analysis of a Synthetic Prediction Market using Dynamic Convex Sets [9.519772465536882]
  We present a synthetic prediction market whose agent purchase logic is defined using a sigmoid transformation of a convex semi-algebraic set. We show that the resulting synthetic prediction market can be used to arbitrarily approximate a binary function defined on a set of input data.
  arXiv  Detail & Related papers  (2021-01-05T21:11:13Z)
• A Sentiment Analysis Approach to the Prediction of Market Volatility [62.997667081978825]
  We have explored the relationship between sentiment extracted from financial news and tweets and FTSE100 movements. The sentiment captured from news headlines could be used as a signal to predict market returns; the same does not apply for volatility. We developed an accurate classifier for the prediction of market volatility in response to the arrival of new information.
  arXiv  Detail & Related papers  (2020-12-10T01:15:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.