使用语言模型进行全球并购套利预测
Global Merger-Arbitrage Forecasting with Language Models
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中文总结 AI 辅助
研究使用语言模型预测并购套利结果，结合专家指导上下文工程与基于事后推理痕迹的微调，在超400笔跨国大交易的样本外数据集上性能最佳，证明该方法在专业长上下文金融工作流中成功，相关因素起关键作用。
中文摘要 AI 辅助
我们提出了一种用于并购套利的语言模型预测系统，并购套利是一种专业的高风险金融场景，任务是预测已宣布的并购交易结果。与之前使用语言模型进行判断性预测的工作不同，我们研究的场景需要对数百页技术文档进行长上下文推理。我们的系统将专家指导的上下文工程与基于事后推理痕迹的微调相结合。给定已宣布的交易，它会输出三种互斥结果的概率分布。在跨越42个国家的400多笔大型交易的样本外数据集上，我们的微调系统取得了最佳性能，将类别平衡的布里尔分数降至0.151。这些结果表明基于语言模型的预测可以在专业的长上下文金融工作流程中取得成功，基于事后的监督和专家设计的上下文起着关键作用。
英文摘要
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.
