'Autocorrelated Optimize-via-Estimate: Predict-then-Optimize versus Finite-Sample Optimality', a seminar by Zichun Wang (Hong Kong University of Science and Technology)

Autocorrelated Optimize-via-Estimate: Predict-then-Optimize versus Finite-Sample Optimality

By Zichun Wang from Hong Kong University of Science and Technology (Guangzhou)

Abstract

Models that directly optimize for out-of-sample performance in the finite-sample regime have emerged as a promising alternative to traditional estimate-then-optimize approaches in data-driven optimization. In this work, we compare their performance in the context of autocorrelated uncertainties, specifically, under a Vector Autoregressive Moving Average VARMA (p,q) process. We propose an autocorrelated Optimize-via-Estimate (A-OVE) model that obtains an out-of-sample optimal solution as a function of sufficient statistics, and propose a recursive form for computing its sufficient statistics. We evaluate these models on a portfolio optimization problem with trading costs. A-OVE achieves low regret relative to a perfect information oracle, outperforming predict-then-optimize machine learning benchmarks. Notably, machine learning models with higher accuracy can have poorer decision quality, echoing the growing literature in data-driven optimization. Performance is retained under small mis-specification.

About the speaker

Zichun Wang is currently a PhD student in Financial Technology at The Hong Kong University of Science and Technology (Guangzhou). She received her Master’s degree in Mathematics from the National University of Singapore. Her primary research interests lie in data-driven optimization, with a focus on finite-sample optimality and learning-based decision-making.