用大模型预测并购交易结局，准确率远超市场和传统方法。
Global Merger-Arbitrage Forecasting with Language Models
- 结合专家设计的长文档上下文与历史交易回溯推理数据微调模型。
- 在400多笔跨国并购案上，预测误差比市场预期低24%。
- 适合金融量化、投行风控等需要长文本分析的场景。
我们提出一种基于语言模型的并购套利预测系统，针对需长期上下文推理的高风险金融场景。不同于以往聚焦短文本和泛领域基准的LLM判断预测研究，本工作处理的是数百页技术文件的长文本推理任务。系统通过专家指导的上下文工程，结合从历史交易中提取的回溯式推理轨迹进行微调。面对已公告的并购案，输出三类互斥结果的概率分布：按原条款完成、更高报价、交易终止。在涵盖42个国家、超过400笔大型并购的样本外测试中，微调后系统表现最佳，类别平衡的Brier得分降至0.151，较校准后的市场隐含概率低24%，较XGBoost低19%，较前沿语言模型低25%-42%。消融实验表明，基于回溯监督和专家设计的上下文对成功至关重要。
原文摘要 · Abstract (English)
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.
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