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

Battle Count: 83

Highly relevant to quantitative trading. The paper directly addresses lead-lag relationship discovery and trading strategy construction in prediction markets. The core insight—that LLMs can serve as semantic risk managers to filter statistically fragile relationships and reduce downside losses—is directly applicable to pairs trading, statistical arbitrage, and risk management in broader financial markets. The trading-based evaluation framework and signal-triggered protocol are practical and implementable. The focus on loss reduction rather than win-rate improvement aligns with risk-managed trading philosophy.

Moderate complexity. The statistical stage (Granger causality via VAR models, ADF testing, log-odds transformation) is well-established and straightforward to implement. The LLM semantic filtering stage requires API access to GPT-5-nano and careful prompt engineering. The trading protocol is simple and mechanical. Main challenges include: (1) proper stationarity testing and preprocessing, (2) designing effective prompts for economic mechanism evaluation, (3) managing the rolling-window evaluation framework, and (4) accessing and processing prediction market data. No custom model training is required.

The paper provides detailed methodology including Granger causality setup, log-odds transformation, trading protocol, hyperparameters (K=100, M=20, lag lengths 1-5, holding horizons 1-21 days), and prompt template (Figure 3). Uses Kalshi Economics data (Oct 2021 - Nov 2025, 554 markets). However, no code repository is mentioned, and the LLM prompt details are shown only as a figure. The use of GPT-5-nano as a proprietary API model limits full reproducibility.