Peer-reviewed work and working papers by Philipp D. Dubach across quantitative finance, machine learning, and computational economics. Recent papers examine prediction-market microstructure, order-flow imbalances in options, attention dynamics in online communities, and glycemic modeling. Drafts and working papers are listed alongside published work; each entry links to the canonical source and, where relevant, to the editorial commentary on the blog.
Research
Working papers and published research at the intersection of quantitative finance, machine learning, and market microstructure.
Google Scholar · ResearchGate · arXiv · SSRN
2026
Synthetic Consumer Panels Without Microdata: Iterative Proportional Fitting from Open Aggregate Statistics
Preprint, SSRN, 2026.
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
Preprint, arXiv 2604.24366, 2026.
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.
Commentary: Read on the blog
Code: GitHub
The Online Gambling Fairness Paradox: Cryptographic Verification, Behavioral Harm, and Consumer Protection
Preprint, SSRN, 2026.
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.
Commentary: Read on the blog
Code: GitHub
Volatility Regime Prediction via Causal Discovery in Option Markets
Draft, 2026.
Dubach applies causal discovery methods to high-frequency option market data to predict transitions between volatility regimes.
Data: GitHub
Option Order Flow Imbalances, Informed Trading, and Short-Term Market Returns
Draft, 2026.
Dubach analyzes 17 years of high-frequency SPY options data to identify informed trading patterns through order flow imbalances.
Data: GitHub
2025
Attention Dynamics in Online Communities: Power Laws, Preferential Attachment, and Early Success Prediction on Hacker News
Preprint, SSRN, 2025.
Dubach studies attention dynamics in online communities through 38 million Hacker News submissions, examining power laws in content visibility and early predictors of engagement.
Commentary: Read on the blog
Modeling Postprandial Glycemic Response in Non-Diabetic Adults Using XGBRegressor
Preprint, SSRN, 2025.
Dubach applies a machine-learning approach to predict individual postprandial glycemic responses using continuous glucose monitoring data and meal composition.
Commentary: Read on the blog
Code: GitHub
2021
A Python Integration of Practical Asset Allocation Based on Modern Portfolio Theory and Its Advancements
Econometric Modeling: Capital Markets - Asset Pricing eJournal, Vol. 15, No. 3, 2021.
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.
Commentary: Read on the blog
For papers I'm reading, see reading.philippdubach.com. The full bibliography is on Google Scholar.