Research
Quantitative research across markets, machine learning, and computational health.
2026
Synthetic Consumer Panels Without Microdata: Iterative Proportional Fitting from Open Aggregate Statistics
Dubach shows that a nationally representative synthetic consumer panel can be built from only freely published aggregate cross-tabulations via iterative proportional fitting, with no individual-level microdata seed. The method is instantiated for Switzerland from two free Federal Statistical Office cubes, producing 10,000 personas whose marginals reproduce the population register and validated against real Swiss human ratings from the ENERGYSCAPE panel.
The Anatomy of a Decentralized Prediction Market: Microstructure Evidence from the Polymarket Order Book
Dubach analyzes 30 billion Polymarket order-book events across 52 days to identify eight microstructure patterns, including a longshot spread premium and the finding that public-feed trade direction matches on-chain ground truth only 59% of the time. Under review at the Journal of Financial Markets.
The Online Gambling Fairness Paradox: Cryptographic Verification, Behavioral Harm, and Consumer Protection
Dubach presents a statistical analysis of 20,038 cryptocurrency crash game rounds, confirming cryptographic fairness while showing that mathematical fairness alone does not ensure consumer safety.
2025
Attention Dynamics in Online Communities: Power Laws, Preferential Attachment, and Early Success Prediction on Hacker News
Dubach studies attention dynamics in online communities through 38 million Hacker News submissions, examining power laws in content visibility and early predictors of engagement.
Modeling Postprandial Glycemic Response in Non-Diabetic Adults Using XGBRegressor
Dubach applies a machine-learning approach to predict individual postprandial glycemic responses using continuous glucose monitoring data and meal composition.
2021
A Python Integration of Practical Asset Allocation Based on Modern Portfolio Theory and Its Advancements
Dubach and Hilber present an open-source Python implementation of modern portfolio theory and its extensions, including Black-Litterman, risk parity, and hierarchical risk parity.