Yizhen Xie

Working papers

AI and Demand-Based Option Listing

Job Market Paper

This paper studies how AI-based option listing optimization affects trading volume and bid-ask spreads. Under conventional grid-based listing, over 80% of single-stock option contracts trade zero or one contract per day, reflecting a misallocation of listings relative to demand. In August 2022, Nasdaq introduced an AI algorithm that predicts option-level demand and reallocates listings toward contracts that investors are most likely to trade. Using transaction data from all U.S. options exchanges (2021-2025), I find that the AI-based listings increase trading volume by 11.8%-14.0% for stocks whose options trade on multiple competing exchanges using a difference-indifferences design, without significant changes in bid-ask spreads. I develop a structural model in which option volume is determined by a matching function of listings and latent demand, identified nonparametrically via quantile estimation exploiting the conditional exogeneity of pre-AI listings. The model decomposes volume changes into a matching effect and a demand shift, showing that AI-based listings improve allocative efficiency by better aligning contract availability with investor demand. More broadly, the results demonstrate how AI-driven product curation can enhance market outcomes and reshape competition in platform settings with capacity constraints and uncertain demand.

Conferences: AFA PhD Poster Session, 2026; Southwestern Finance Association (SWFA), 2026; North American Summer Meeting 2026; Econometric Society Interdisciplinary Frontiers (ESIF) conference on Economics and AI+ML 2026

Presentations: CMU Finance Brown Bag, 2025; CMU Economics Lunch Seminar, 2025; Inter-Finance PhD Seminar, 2025

Information Frictions and Index ETF

with Darcy Pu (Peking University)    Updated version coming soon (data: 2025/04)

We provide evidence for information frictions to explain the lack of the flow-return-fee sensitivity in index ETFs, for both sophisticated investors and unsophisticated investors. We rationalize the findings with a dynamic model with switching costs.

Presentations: LBS Transatlantic Doctoral Conference* , LBS*, CMU, 2021; Inter-Finance Ph.D. Seminar, PKU Economics and Finance Alumni Forum, 2022
(*  by coauthor)

Publications

Structural Estimation of Executive Compensation

with Robert A. Miller (Carnegie Mellon University)    Foundations and Trends in Accounting, 2025

Structural estimation of executive compensation combines cross-sectional and longitudinal data relating firm performance to the wages, grants and wealth holdings of managers to quantify principal-agent models characterized by asymmetric information. The estimated models are used to measure the importance of information asymmetries, such as the degree of conflict between executives and the firms they manage, the role of human capital in mitigating conflicting interests, and the social welfare loss from moral hazard. Following a brief guide to this survey and a short review of related literature, we begin by describing the data used to estimate these models, explain the theory behind a simple static model of moral hazard, and provide a first approach to estimating them, before analyzing identification in more depth. The latter sections then show how the simplest models of moral hazard can be extended to account for other sources of hidden information and dynamic considerations that arise from the life cycle aspirations of managers.

Presentations: I have used the notes from this paper as the basis for a lecture on this topic in a Ph.D.-level Advanced Econometrics course. The material has also been adopted by my coauthor in upcoming summer schools on Dynamic Structural Estimation at the University of Michigan and São Paulo School of Economics – EESP, as well as lectures at Columbia University and the University of Notre Dame.

Teaching

Solo-Instructor, Undergraduate
Principles of Microeconomics  (Rating: 4.0 / 5)

Teaching Assistant, MBA

Machine Learning for Business Applications (Prof. Ben Collier)

Debt Markets  (Prof. Chester Spatt)

Real Estate  (Prof. Chester Spatt)

Trade and Investment Strategy  (Prof. Robert A. Miller)

Options  (Prof. Duane Seppi;  Prof. Zeigham Khokher)

Teaching Assistant, PhD
Macroeconomics  (Prof. Liyan Shi;  Prof. Ali Shourideh)

Econometrics  (Prof. Robert A. Miller)
Advanced Econometrics  (Prof. Robert A. Miller)