[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-85761-en":3,"doc-seo-85761-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},85761,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Global Merger-Arbitrage Forecasting with Language Models","Global Merger-Arbitrage Forecasting with Language Models presents an LLM-based forecasting system for merger arbitrage, predicting the outcome of announced M&A deals under long-context, high-stakes conditions. The method couples expert-guided context engineering with finetuning on hindsight-guided reasoning traces from historical transactions. For each announced deal, it outputs a probability distribution across three mutually exclusive outcomes: closing at announced terms, a higher bid, or termination. On 400+ large deals across 42 countries, it achieves a class-balanced Brier score of 0.151, outperforming market-implied calibration, XGBoost, and frontier language models, with ablations showing key contribution from each component.","Global Merger-Arbitrage Forecasting with Language Models  \nHinal Jajal 1 Michal Mucha 1 Charles Sweat 1 Chris Pulman 1 Charlie Flanagan 1 Peter Anderson 1  \narXiv :2607 .0992 1v 1 [ cs .CL] 10 Jul 2026  \nAbstract  \nWe 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 hindsightguided 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.  \n1 Introduction  \nJudgmental forecasting—probabilistic prediction of future events based on documents and contextual reasoning, rather than explicit statistical models—is increasingly being studied in the context of large language models. Recent work shows that LLMs can produce calibrated probability forecasts approaching the accuracy of some human forecasters  \n1Balyasny Asset Management. Correspondence to: Hinal Jajal \u003C[bamappliedai@bamfunds.com](bamappliedai@bamfunds.com) >.  \nProceedings of the 43 rd International Conference on Machine Learning, Seoul, South Korea. PMLR 306, 2026 . Copyright 2026 by the author(s) .  \n(Halawi et al., 2024 ; Schoenegger et al., 2024 ; Alur et al., 2025) . However, this literature largely focuses on broad, mixed-topic question banks, posed with only shallow context such as news headlines and blurbs (Karger et al., 2025) . It remains unclear whether LLM-based forecasting can add value in specialized, high-stakes domains that require analyzing long and highly technical texts.  \nWe focus on a financial domain: forecasting the outcomes of announced mergers and acquisitions (M&A) . Such forecasts are actively monetized in merger-arbitrage strategies, where an investor buys the target firm’s stock after a deal is announced (possibly against a short position in the acquirer) to capture the spread between the current price and the deal consideration. The investor earns this spread if the deal closes and faces losses if the deal terminates. Therefore, accurate probability forecasts of deal closure and alternative resolutions are central to position sizing, risk management, and portfolio construction.  \nArriving at these probability forecasts requires processing large volumes of complex text. Analyzing a single deal can require reading a hundred-page merger agreement; assessing competitive overlaps, regulatory regimes and political considerations across jurisdictions to anticipate regulatory responses; comparing against outcomes in similar past transactions; scrutinizing shareholder bases, voting histories, and public statements; reviewing risks in the acquirer’s financing and balance sheet, and more. At the same time, analysts must integrate this information with a continuous stream of regulatory filings, press releases, and expert analyses.  \nWe build an LLM-based forecasting system tailored to this setting. First, retrieval-augmented research agents each gather and analyze a specific dimension of deal context. Second, an ensembled","cbCaijv7HU3aUxuq","https://ap.wps.com/l/cbCaijv7HU3aUxuq","pdf",454419,1,13,"English","en",105,"# Abstract\n# Introduction\n## Merger-arbitrage forecasting problem\n## LLM forecasting system design\n## Experimental results and ablations","[{\"question\":\"What does the system predict in merger arbitrage forecasting?\",\"answer\":\"It generates a probability distribution over three mutually exclusive outcomes for an announced M\\u0026A deal: closing at announced terms, a higher bid, or deal termination.\"},{\"question\":\"How does the model use long-context documents to forecast outcomes?\",\"answer\":\"It combines expert-guided context engineering with retrieval-augmented research agents that gather deal-specific information, then uses finetuning on hindsight-guided reasoning traces derived from historical deals.\"},{\"question\":\"What performance does the finetuned system achieve on the evaluation set?\",\"answer\":\"On a held-out test set of 404 large deals across 42 countries, the best class-balanced Brier score is 0.151, improving calibration and outperforming market-implied probabilities, XGBoost, and frontier language 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does the system predict in merger arbitrage forecasting?","Question",{"text":74,"@type":75},"It generates a probability distribution over three mutually exclusive outcomes for an announced M&A deal: closing at announced terms, a higher bid, or deal termination.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How does the model use long-context documents to forecast outcomes?",{"text":79,"@type":75},"It combines expert-guided context engineering with retrieval-augmented research agents that gather deal-specific information, then uses finetuning on hindsight-guided reasoning traces derived from historical deals.",{"name":81,"@type":72,"acceptedAnswer":82},"What performance does the finetuned system achieve on the evaluation set?",{"text":83,"@type":75},"On a held-out test set of 404 large deals across 42 countries, the best class-balanced Brier score is 0.151, improving calibration and outperforming market-implied probabilities, XGBoost, and frontier language 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