{"slug": "global-merger-arbitrage-forecasting-with-language-models", "title": "Global Merger-Arbitrage Forecasting with Language Models", "summary": "Researchers developed a language-model forecasting system for merger arbitrage that predicts M&A deal outcomes using long-context reasoning over hundreds of pages of technical documents. The finetuned system outperformed market-implied probabilities and other models, achieving a class-balanced Brier score of 0.151 on over 400 large deals across 42 countries.", "body_md": "arXiv:2607.09921v1 Announce Type: new\nAbstract: We present a language-model forecasting system for merger arbitrage, a specialized high-stakes financial setting in which the task is to predict the outcome of announced M\\&A deals. Unlike prior work on judgmental forecasting with LLMs, which has focused on broad mixed-topic benchmarks and short context such as news snippets, we study a setting that requires long-context reasoning over hundreds of pages of technical documents. Our system combines expert-guided context engineering with finetuning on hindsight-guided reasoning traces derived from historical deals. Given an announced deal, it outputs a probability distribution over three mutually exclusive outcomes: closing at announced terms, a higher bid, or deal termination. On an out-of-sample set of more than 400 large deals spanning 42 countries, our finetuned system achieves the best performance of any method we evaluate, reducing class-balanced Brier score to 0.151. This is 24\\% below calibrated market-implied probabilities, 19\\% below XGBoost, and 25-42\\% below frontier language models. These results, together with ablation studies, show that LLM-based forecasting can succeed in specialized, long-context financial workflows, with hindsight-based supervision and expert-designed context playing a critical role.", "url": "https://wpnews.pro/news/global-merger-arbitrage-forecasting-with-language-models", "canonical_source": "https://arxiv.org/abs/2607.09921", "published_at": "2026-07-14 04:00:00+00:00", "updated_at": "2026-07-14 04:33:30.802858+00:00", "lang": "en", "topics": ["large-language-models", "artificial-intelligence", "machine-learning", "natural-language-processing"], "entities": ["arXiv"], "alternates": {"html": "https://wpnews.pro/news/global-merger-arbitrage-forecasting-with-language-models", "markdown": "https://wpnews.pro/news/global-merger-arbitrage-forecasting-with-language-models.md", "text": "https://wpnews.pro/news/global-merger-arbitrage-forecasting-with-language-models.txt", "jsonld": "https://wpnews.pro/news/global-merger-arbitrage-forecasting-with-language-models.jsonld"}}