LLM as a Risk Manager: LLM Semantic Filtering for Lead-Lag Trading in Prediction Markets

2026-02-04

AI Summary

This study addresses the challenge of identifying robust lead-lag relationships in prediction markets, where event-level time series are often confounded by spurious statistical correlations. The authors propose a two-stage causal screening framework: first, Granger causality tests are applied to market-implied probability sequences to generate candidate relationships; second, a large language model (LLM) evaluates the semantic plausibility of the underlying economic transmission mechanisms based on event descriptions, enabling a refined re-ranking of candidates. This approach uniquely integrates LLMs as semantic risk managers within prediction markets, combining statistical signals with domain knowledge to effectively filter fragile associations. Empirical results on the Kalshi Economics market demonstrate improved trading performance, with win rates increasing from 51.4% to 54.5% and average losses decreasing from $649 to $347, with consistent robustness across multiple configurations.

Technology Category

• Machine Learning: Causal Learning
• Game Theory and Economic Paradigms: Auctions and Market-Based Systems
• Reasoning under Uncertainty: Causality

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Prediction markets provide a unique setting where event-level time series are directly tied to natural-language descriptions, yet discovering robust lead-lag relationships remains challenging due to spurious statistical correlations. We propose a hybrid two-stage causal screener to address this challenge: (i) a statistical stage that uses Granger causality to identify candidate leader-follower pairs from market-implied probability time series, and (ii) an LLM-based semantic stage that re-ranks these candidates by assessing whether the proposed direction admits a plausible economic transmission mechanism based on event descriptions. Because causal ground truth is unobserved, we evaluate the ranked pairs using a fixed, signal-triggered trading protocol that maps relationship quality into realized profit and loss (PnL). On Kalshi Economics markets, our hybrid approach consistently outperforms the statistical baseline. Across rolling evaluations, the win rate increases from 51.4% to 54.5%. Crucially, the average magnitude of losing trades decreases substantially from 649 USD to 347 USD. This reduction is driven by the LLM's ability to filter out statistically fragile links that are prone to large losses, rather than relying on rare gains. These improvements remain stable across different trading configurations, indicating that the gains are not driven by specific parameter choices. Overall, the results suggest that LLMs function as semantic risk managers on top of statistical discovery, prioritizing lead-lag relationships that generalize under changing market conditions.

Problem

• prediction markets
• lead-lag relationships
• spurious correlations
• causal discovery
• semantic filtering

Innovation

• LLM-based semantic filtering
• lead-lag trading
• Granger causality
• prediction markets
• causal screening