Global Merger-Arbitrage Forecasting with Language Models
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 term...
| Authors | Hinal Jajal, Michal Mucha, Charles Sweat et al. |
|---|---|
| Primary category | cs.CL |
| Published | 10 July 2026 |
| Last updated at source | 10 July 2026 |
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