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arXiv cs.CL
arXiv cs.CL
7/14/2026
Global Merger-Arbitrage Forecasting with Language Models

Global Merger-Arbitrage Forecasting with Language Models

Short summary

Researchers present an LLM-based forecasting system for merger arbitrage that predicts outcomes of announced M&A deals—closing at announced terms, a higher bid, or termination. The system combines expert-guided context engineering with finetuning on hindsight-guided reasoning traces over hundreds of pages of technical documents. On 400+ deals across 42 countries, it achieves a Brier score of 0.151, outperforming market-implied probabilities, XGBoost, and frontier LLMs by 19-42%.

  • •LLM system predicts M&A deal outcomes (close, higher bid, or termination) using long-context reasoning over technical documents
  • •Combines expert context engineering with finetuning on hindsight-guided reasoning traces from historical deals
  • •Outperforms market-implied probabilities by 24%, XGBoost by 19%, and frontier LLMs by 25-42% on 400+ deals across 42 countries

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Read full article at arXiv cs.CL

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