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From Regression to Reasoning: Predicting M&A Announcement Returns With Large Language Models

Maximilian Schreiter et al.

European Financial Management2026https://doi.org/10.1111/eufm.70059article
AJG 3ABDC A
Weight
0.50

Abstract

This study investigates whether large language models (LLMs) can predict short‐term market reactions to M&A announcements. We prompt OpenAI's latest reasoning models (o3, GPT‐5, and GPT‐5.1) to forecast whether the combined market value of acquirer and target will increase or decrease, drawing on deal‐, firm‐, and macroeconomic data for large domestic U.S. transactions (2012–2022). Our analysis shows that LLMs outperform logistic regression and naive buy‐all benchmarks in predictive accuracy. Their forecasts further translate into portfolios with superior risk‐adjusted performance. These findings highlight the transformative potential of generative AI for empirical finance and M&A decision‐making.

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https://doi.org/https://doi.org/10.1111/eufm.70059

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@article{maximilian2026,
  title        = {{From Regression to Reasoning: Predicting M&A Announcement Returns With Large Language Models}},
  author       = {Maximilian Schreiter et al.},
  journal      = {European Financial Management},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1111/eufm.70059},
}

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From Regression to Reasoning: Predicting M&A Announcement Returns With Large Language Models

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.