Fixed Income Portfolio Manager · Quantitative Researcher · Art-Market Analyst
Jan Serwart is a Fixed Income Portfolio Manager and quantitative researcher at Zurcher Kantonalbank. He holds a Ph.D. in Economics and Finance from the University of St. Gallen (HSG), awarded summa cum laude, and a Master of Science in Economics from the London School of Economics and Political Science. His doctoral dissertation, Quantitative Sentiment Analysis: Applications in Finance and Contemporary Art, was supervised by Prof. Francesco Audrino and received the Prize for Best Chapter in Quantitative Economics.
Before joining ZKB, Jan founded NAMAC GmbH, a consulting firm that built proprietary software to analyze art-market data, developed statistical models to predict artist success, and served clients including art funds and collectors. His research bridges quantitative finance and cultural economics: his paper on yield curve trading strategies exploiting sentiment data has been published in the North American Journal of Economics and Finance, and his dissertation introduced novel methods for dynamically quantifying artist reputation through network analysis. This platform is the product of that research — making art-market network intelligence accessible to collectors, galleries, and investors.
| 2023 – present | Fixed Income Portfolio Manager Zurcher Kantonalbank Asset Management, Zurich |
| 2020 – 2024 | Ph.D. in Economics and Finance (summa cum laude) University of St. Gallen (HSG) Prize for Best Chapter in Quantitative Economics |
| 2020 – 2023 | Founder, NAMAC GmbH Art-market consulting & quantitative analytics |
| 2020 – 2023 | Doctoral Research Assistant Chair of Statistics, University of St. Gallen |
| 2017 – 2018 | M.Sc. Economics London School of Economics and Political Science |
| 2014 – 2017 | B.A. Economics (GPA 5.85/6, Rank 1/125) University of St. Gallen (HSG) |
A central chapter of Jan's doctoral dissertation addresses a question of direct relevance to art collectors and investors: can the dynamics of an artist's reputation predict the future financial returns of their artworks?
The research maps the global art community as a network of over 2.8 million Instagram accounts, where each node represents a gallery, museum, curator, collector, media outlet, or art advisor. The importance of each node is measured by its indegree centrality — the number and quality of incoming connections it receives. Using this network, the study constructs a status-weighted attention score for each artist: a dynamic measure of how much attention the artist receives from high-status actors in the field.
The key finding is that changes in collector attention are predictive of future artist returns. If an artist experiences a significant increase in status-weighted collector attention between two consecutive periods, the median price per square centimeter of their paintings tends to rise in the following year. This insight is applied in a trading framework: artists are ranked by the differential in their collector attention scores, and the top-ranked group is compared against the bottom-ranked group and a random selection.
The results are striking. The mean returns of artists selected using the collector attention differential are 60% to 80% higher than those in the bottom group, and the differences are statistically significant. The attention-based portfolio also outperforms a randomly constructed portfolio by as much as 30% to 50%. The methodology equally helps to avoid losses: artists with declining collector attention tend to underperform, making the signal useful not only for identifying opportunities but also for risk mitigation.
This research provides a quantitative, data-driven foundation for art investment decisions — moving beyond subjective taste toward measurable indicators of market momentum. It is this same analytical framework that powers this platform.
Published in the North American Journal of Economics and Finance. This paper investigates the predictive power of macro sentiment indicators for the U.S. Treasury yield curve and develops a bond butterfly trading strategy based on yield curve shape change predictions. Joint work with Francesco Audrino.
Ph.D. Dissertation no. 5416, University of St. Gallen (2024). Contains three papers: yield curve trading strategies, quantifying artist reputation through network analysis, and optimal art investment using collector attention dynamics.
Dr. oec. (HSG) Jan Serwart
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