Global Merger-Arbitrage Forecasting with Language Models

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[Submitted on 10 Jul 2026]

Title:Global Merger-Arbitrage Forecasting with Language Models

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Abstract: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.

Comments: Accepted to ICML 2026

Subjects:

Computation and Language (cs.CL)

Cite as: arXiv:2607.09921 [cs.CL]

(or arXiv:2607.09921v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2607.09921

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Hinal Jajal [view email] [v1] Fri, 10 Jul 2026 19:16:03 UTC (112 KB)

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