Resonanz Spotlight Martin Brückner – Cracking the Code of Merger Arbitrage
15 snips
Sep 11, 2025 Martin Brückner, Co-founder and CIO of First Private Investment Management, dives into the world of systematic merger arbitrage. He reveals how data and algorithms can transform deal investing into a repeatable strategy, moving away from instinctual judgment. The conversation covers their four-step process of screening to execution, the criteria for selecting deals, and how they quantify qualitative risks through data. Brückner emphasizes the fusion of data science and human oversight while addressing skepticism in the investment community.
AI Snips
Chapters
Transcript
Episode notes
Merger Arbitrage As A Classification Task
- Merger arbitrage is essentially a binary classification problem: deals either close or fail.
- Machine learning excels at such classification tasks and can outperform gut judgment on repeatable patterns.
Follow A Structured Four‑Step Process
- Screen all announced, investable deals and forecast success, duration, downside and expected return.
- Rank deals by an attractiveness score and feed forecasts into dynamic portfolio construction and execution.
Data Quality And Global Breadth Matter
- High-quality, investable data is the primary filter for deal consideration.
- Mid-cap and non-US deals often hide attractive opportunities overlooked by US-focused managers.