Yizhen Xie

Working papers

AI and Demand-Based Option Listing

Job Market Paper

Single-stock options account for over half of U.S. option contracts, yet 40% of single-stock option contracts listed during August 2021-July 2022 never had any trading volume. This paper studies option listing choice, focusing on Nasdaq's August 2022 adoption of an AI-based system that predicts contract-level market-wide demand and adjusts strike listings on Nasdaq-affiliated options exchanges. Using U.S. options transaction data around this adoption, I find that AI listing optimization increased the number of listed contracts by 11.8% and total lifetime trading volume by 30.4% in a difference-in-differences design. Relative to matched stocks listed on other exchanges, Nasdaq's AI-based option listing has the largest effects for low-volatility stocks, whose prices fluctuate relatively little. For these stocks, Nasdaq adds listings near current stock prices. The AI also reduces inactive listings for contracts with weekly expiration dates. I then develop a stylized strike-listing model in which an exchange maximizes fee revenue from volume, market makers bear inventory risk from quoted contracts, and investors value the utility-maximizing attainable portfolio. The model shows that volume-maximizing strikes differ from those that investors value the most. AI-based listing optimization can raise market activity and reduce stale listings, but volume maximization does not imply greater welfare or market completeness.

Conferences: American Economic Association Annual Meeting (ASSA), Lightning Session, 2027; American Finance Association (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, Southern Finance Association (SFA), 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)