Research

SSRN Author Page

Publications

Finance topics

[1] “Mutual fund tax implications when investment advisors manage tax-exempt separate accounts” (with William Beggs and Austin Hill-Kleespie), Journal of Banking & Finance, 134, 106313, 2022.

• Abstract: Asset management firms often operate investment vehicles, such as separate accounts and private funds, side-by-side with their mutual funds. This study investigates the tax implications for mutual fund investors subject to these arrangements. We find that a substantial presence of tax-exempt separate account clients for an asset management firm adversely impacts the tax burdens placed on the taxable shareholders of its mutual funds. Our results are consistent with spillover effects from the of presence non-mutual fund clientele impacting mutual fund manager decisions.

[2]“Employment Protection and Tax Aggressiveness: Evidence from Wrongful Discharge Laws” (with Douglas Fairhurst and Xiaoran Ni), Journal of Banking & Finance, 119,10597, 2021.

• Abstract: We examine whether labor market frictions affect firms’ tax aggressiveness. Exploiting the adoption of U.S. state-level Wrongful Discharge Laws as a quasi-exogenous shock to a firm's firing costs, we document a decline in tax aggressiveness for firms located in states that increase employment protection. We further show that greater employment protection increases distress risk. The decline in tax aggressiveness is more pronounced for firms that are more vulnerable to financial distress and constrained from external financial markets. Our results imply that firms avoid risky tax positions in order to mitigate increased distress risk due to more rigid labor costs.

[3] "Robots, Labor Market Frictions, and Corporate Financial Policies",

European Financial Management, forthcoming

• Abstract: We construct a novel firm-level measure of robot exposure using the International Federation of Robotics (IFR) dataset and new robot patent data. We find that the use of robots leads to higher leverage and lower cash holdings. Using an instrumental variable based on the comparative advantage of robots in specific tasks, we find that the effect is likely to be causal and driven by the reduced operating leverage. The effect is stronger when firms are hit by negative shocks including minimum wage hikes and foreign competition. Firms with more robots pay out more and use fewer corporate hedging contracts.

FinTech topics

[1] "SAE-FiRE: Enhancing Earnings Surprise Predictions Through Sparse Autoencoder Feature Selection" (with Huopu Zhang and Mengnan Du), arxiv: 2505.14420, 2025.

• Abstract: We propose the Sparse Autoencoder for Financial Representation Enhancement (SAE-FiRE) framework to address these limitations by extracting key information while eliminating redundancy. SAE-FiRE employs Sparse Autoencoders (SAEs) to efficiently identify patterns and filter out noise, and focuse specifically on capturing nuanced financial signals that have predictive power for earnings surprises. Experimental results indicate that the proposed method can significantly outperform comparing baselines. 

Working Papers (* denotes co-author presentation)

FinTech topics

[1] Machine learning and hedge fund (with Dantong Yu, Huopu Zhang)

We apply machine-learning methods to predict hedge fund return and performance.

[2] Machine learning and merger and acquisition (with Dantong Yu, Muntasir Shohrab, Zhibo Ye)

Under review

We apply machine-learning methods to analyze companies' M&A decisions.

Finance topics

[1] Municipal Bond (with Han Xia, Zihan Ye, and Luo Zuo)

[2] Conference call (with Allen Hu and Can Huang)

[2] "Environmental Risk and Green Innovation: Evidence from Evacuation Spills", with Yongqiang Chu and Xuan Tian

[3] "Financial Policies of Organized Labor in the 21st Century" with Ryan Williams and David Yin

• Georgia State University Seminar*, Université Paris-Dauphine Seminar*, University of Lille Seminar*, ESCP Business School Seminar*, University of Arizona Ph.D. Seminar, Nanjing University Seminar*, 2020 Finance Management Association Conference.

[4] "Creditor at the Gate: How access to debt changes firm’s disclosure readability" with Wenyao Hu

Work-In-Progress

[5] Machine learning projects in analyzing companies' annual reports, earnings conference calls, patent text, and business news. 

We apply machine-learning methods to analyze companies' annual reports, earnings conference calls, patent text, and business news, and use machine-learning methods to predict mutual fund performance, stock crash risk, and so on.