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

TL;DR

This paper introduces a hybrid approach combining statistical causality and LLM-based semantic filtering to improve lead-lag detection in prediction markets, resulting in more robust trading strategies and reduced losses.

Contribution

It presents a novel two-stage causal screener that integrates Granger causality with LLM semantic assessment to filter statistically fragile lead-lag relationships.

Findings

Hybrid approach outperforms statistical baseline in profit and loss metrics.

Win rate increases from 51.4% to 54.5%.

Average loss per trade decreases from 649 USD to 347 USD.

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.