Information Games: Strategic Crowding and Firm Repositioning in Language-Model Space

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Highly relevant for understanding how firm similarities (peers) evolve over time. It provides a framework for distinguishing between shared market movements and idiosyncratic strategic responses, which is crucial for factor investing, pairs trading, and risk model updates. The use of LLM embeddings for firm positioning is a modern quantitative technique.

High complexity. Requires implementing a multi-agent Nash equilibrium solver (projected extragradient), handling distribution-valued positions, computing Wasserstein distances on high-dimensional embeddings, and managing complex sampling protocols for news articles. The theoretical underpinnings (variational inequalities, contest theory) are also advanced.

The paper provides detailed mathematical derivations, specific parameter settings for the computational experiment (Tables 6-8), and specifies the use of Qwen3-Embedding-4B. It mentions replication materials and Lean 4 formal proofs for key claims, enhancing reproducibility. However, the specific corporate news dataset (Nasdaq firm-news asset) availability is not explicitly detailed as a public download link in the extract.