📌 Key facts
- When: Start anytime. Applications are open!
- How to apply: Send us an email (see contact below) with your CV, grade report, and a short note on your research interest.
Generative AI is rapidly reshaping the information workflow of professional investors, from earnings call analysis to portfolio monitoring and report generation. At AXIA, we are actively building and evaluating AI-driven tools in our own research and portfolio management process. This thesis offers the rare opportunity to work directly with a practicing investment team, with access to real workflows, proprietary research, and a live implementation environment.
- 📌 Key facts
- 💡 Background
- 🦾 Who We Are
- 🎯 Goals and Topics
- 🎓 Profile - what we value
- ➕ Additional Info
- 📝 How to Apply
- 📬 Contact
💡 Background
The integration of large language models and AI-based automation into professional asset management is no longer speculative. It is happening in real workflows, and the practical implications for analysts, portfolio managers, and client reporting remain underexplored academically. Questions around information extraction from unstructured financial documents, signal generation from alternative data, AI-augmented portfolio construction, and the reliability of model outputs in high-stakes investment decisions are both academically interesting and immediately relevant to practitioners. This thesis aims to bridge that gap by combining academic rigor with a real investment environment.
🦾 Who We Are
AXIA Asset Management GmbH is an independent asset manager and family office headquartered in Dortmund, with additional offices in Vellmar, Oldenburg, Leer, and Frankfurt. We design and steer comprehensive wealth structures for private clients, entrepreneurial families, and institutional partners. Our work spans discretionary portfolio management, family office services, in-house fund management, B2B/white-label structures, and real estate governance. AXIA is actively engaged with AI in financial markets through several mandates with explicit AI exposure. The student joining this thesis will work directly with the portfolio management team and gain a hands-on view of how investment decisions are made and implemented in practice.
🎯 Goals and Topics
The thesis sits at the intersection of applied AI, financial analysis, and investment practice. The following topics reflect areas where we believe a thesis can produce both academic insight and directly applicable value for our investment process. Topics can be combined, scoped down, or refined together with the student.
- AI-Driven Fundamental Equity Analysis. Build a structured pipeline using LLMs and complementary methods to extract, normalize, and evaluate information from financial documents (annual reports, earnings transcripts, investor presentations, regulatory filings) and support fundamental equity research at scale.
- Scenario Analysis for Securities, Portfolios, and Commodities. Develop an AI-driven framework to project the impact of defined macro or sector scenarios (interest rate shifts, geopolitical events, supply chain shocks, commodity price moves) on individual positions and full portfolios, including commodity-specific transmission channels.
- Automated Portfolio Construction and Evaluation. Design AI-supported approaches to portfolio construction, ongoing evaluation, and rebalancing under defined constraints (mandate, risk profile, regulatory or sustainability requirements), including drift detection and identification of rebalancing candidates.
- Real-Time Monitoring and Event-Driven Alerting. Build an AI-based monitoring layer for a portfolio of equities and funds that continuously processes incoming information (news, filings, earnings, regulatory events) and surfaces timely, classified alerts with a first-pass directional read (bullish vs. bearish) and an action recommendation for human review.
- Alpha Discovery from Alternative Data Sources. Systematically harvest and evaluate trading-relevant signals from platforms outside the traditional financial data stack (QuiverQuant for congressional and insider trades, X for breaking sentiment, Reddit for positioning, Polymarket for event probabilities) and assess which sources carry genuine information edge.
- Mining and Exploration Sector Intelligence. Apply AI to the systematic analysis of technical mining documents (NI 43-101 / JORC reports, drill result announcements, resource updates, geological surveys) to build scalable intelligence on junior miners and explorers, a segment where sell-side coverage is sparse and information depth is rewarded.
We are open to both empirical research projects and applied implementation projects with academic framing. The topics above are only suggestions. If you have your own research idea that you believe could be valuable for AXIA, we are happy to hear it and discuss it with you.
🎓 Profile - what we value
We are looking for a student who combines intellectual curiosity with rigorous, independent work. You should have: A strong academic background in finance, economics, information systems, computer science, data science, or a related field. Genuine interest in financial markets, investment analysis, and AI as a working tool rather than a buzzword. Hands-on experience with Python and basic comfort working with LLM APIs and unstructured text. A self-driven, communicative working style. Solid English. German is a plus but not required.
Prior experience in asset management or financial analysis is welcome but not a precondition.
➕ Additional Info
Both Bachelor and Master theses are welcome. We can provide access to proprietary research materials, internal research workflows, practitioner interviews, and selected data infrastructure as appropriate to the topic. The work can be conducted largely remote, with regular touch points with the AXIA portfolio management team. Where appropriate, we aim to translate the findings into practice and, with the student's agreement, present results in suitable practitioner formats.
📝 How to Apply
If you are interested, please contact Leonard Kummer at AXIA Asset Management by email. Use the subject line: Application AI Thesis – [Your Name]. Please include your CV, grade report, a short outline of your tentative research direction, and your ideal start date.
We look forward to hearing from you.
📬 Contact
Leonard Kummer, Portfoliomanager (AXIA Asset Management GmbH)
kummer@axia-am.de
cc:
Simon Hochstraßer (Chair for Strategy and Organisation)