Global Merger-Arbitrage Forecasting with Language Models
arXiv:2607.09921v1 Announce Type: new 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 e...
arXiv cs.CL
·Hinal Jajal, Michal Mucha, Charles Sweat, Chris Pulman, Charlie Flanagan, Peter Anderson
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