Jesse Schmolze

Learning Time-Inhomogeneous Markov Dynamics in Financial Time Series via Neural Parameterization

with Jan Rovirosa  ·  advised by Prof. Alejandra Quintos

Preprint, arXiv:2605.04690. Presented at the UW–Madison Undergraduate Research Symposium (April 2026) and the ASA Midwest Regional Conference on Data Science and Statistics (May 2026).

This paper develops a neural network framework for modeling the dynamics of equity returns as a time-inhomogeneous Markov chain, estimating regime-dependent transition matrices as a function of macroeconomic and fundamental signals. The approach addresses the well-documented failure of the standard Markov property in financial time series, offering a flexible and interpretable alternative to static transition models. Extended to a 10-asset US bank panel covering 24 years and 6,183 trading days per asset — spanning the dot-com bust, the financial crisis, COVID, and the 2022–23 rate shock — the framework shows that conditioning on realized volatility rather than returns improves internal consistency by 5.6% and out-of-sample fit in 9 of 10 assets, with high-volatility regimes compressing next-step distributions regardless of the current state.

arXiv:2605.04690 Read the paper (PDF) Multi-asset extension (PDF)

Who Captures Trade-Network Growth? Institutional Capacity and the Absorption of Foreign Shocks

Final draft complete. Publication to follow.

This paper uses Double Machine Learning on a panel of 111 countries from 1996 to 2023 to estimate how a receiving country's political stability moderates its absorption of exogenous growth shocks transmitted through bilateral trade networks. Instrumenting via Leave-i-Out and commodity terms-of-trade shocks provides causal evidence that the absorption rate is near zero for politically unstable countries and approaches one-to-one for the most stable, with the baseline DML estimates shown to be a conservative lower bound.

Autonomous Race Strategy via Reinforcement Learning

Badger Solar Racing, UW–Madison

This project develops a real-time race strategy system for a competitive solar car in the American Solar Challenge, a ten-day, 1,000-mile cross-country race. A Proximal Policy Optimization agent is trained on a neural network surrogate of a high-fidelity MATLAB/Simulink vehicle simulation, learning to issue speed commands every five minutes that maximize distance while keeping the battery alive across a full race day.