Research

SSRN Author Page

Publications

[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.

• 2020 Finance Management Association Conference*, 2020 Southern Finance Association Conference*

• 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.

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 financial risk (with Yi Chen, Jiaheng Xie, Ryan Liu)

We apply machine-learning methods to analyze companies' annual reports and earnings conference calls to predict financial risk.

[3] 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.

[4] Machine learning and start-up exit (with Dantong Yu, Muntasir Shohrab, Zhibo Ye)

We apply machine-learning methods to analyze start-up companies' exit decisions.

Finance topics

[1] Municipal Bond (with Zihan Ye)

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

• Abstract: Exploring evacuation spill information from the U.S. Coast Guard’s National Response Center database, we examine how firms alter their green innovation activities and strategies in response to environmental spills occurring near their headquarters. We find that, in response to nearby environmental spills, firms increase both environmental innovation input and output. Increases in managers’ perceived risk and compliance cost, in part due to public outrage and reprobation, enhanced local environmental regulations, as well as human capital redeployment, are three plausible underlying economic channels through which environmental risk affects firms’ green innovation.

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

• The use of robots mitigates labor market frictions, and thus enables firms to use high leverage and maintain lower cash holdings.

• Cambridge University Seminar, University of Arizona Seminar, 2021 American Economic Association (AEA) Annual Meeting, 2020 Northern Finance Association Conference, 2020 Financial Intermediation Research Society Conference (accepted and on conference program but conference canceled), 2020 Midwest Finance Association Conference, 2020 Finance Management Association Conference, 2020 Finance Management Association Doctoral Consortium, 2020 Eastern Finance Association Conference, 2020 Southwestern Finance Association Conference, Best Paper Award in Corporate Finance, 2020 Southern Finance Association Conference.

• Abstract: Using a novel dataset on industrial robots from the International Federation of Robotics (IFR), 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 effect is likely to be causal. Further analyses show that the effect is driven by the decreases in operating leverage when firms use more robots. We also find that the effect is stronger when firms are hit by negative shocks, suggesting that the use of robots mitigates labor market frictions and increases operating flexibility.

[4] "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.

• Abstract: Although frequently considered an independent and opposing force to capital, modern unions face many of the same pressures as businesses. Using a novel sample of union financial statements, we document that unions behave like corporations in a variety of ways. Unions often have well-compensation teams of executives whose incentives may become misaligned with members. Their relative cash and leverage choices are well-explained by extant corporate finance theories. Unions invest in capital expenditures and public security markets. They appear to be prone to agency problems. More competitive environments are correlated with leaner financial policies and lower executive compensation. Misalignment in incentives between members and the union is related to higher executive compensation, worse performance, and more unrelated investment.

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

• Abstract: This paper studies how the ease of repossessing collateral in bankruptcy affects corporate disclosure policy. Using a plausibly exogenous variation of the ability to repossess assets generated by state anti-recharacterization laws, we find that the anti-recharacterization laws, which make collateral repossession easier for secured lending, improve corporate disclosure readability. Consistent with the argument that firm with capital needs to reduce financial statement complexity to borrow more debt, we show that the effect of ARLs on disclosure readability is less pronounced on high-growth firms and more profitable firms.

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.

[6]“Credit Rights and the Cost of Capital: Evidence from Fraudulent Transfer Laws” (with Sandy Klasa)

• Abstract: Fraudulent transfer laws remove the burden of proof from creditors attempting to claw back funds that were transferred out of failing business. They are particularly important for entrepreneurs whose personal assets are commingled with those of their firm. While most states adopted such laws in the early part of the 20th century, a subset of states adopted the laws more recently. This allows us to examine the effect of the staggered adoption of these laws on entrepreneurship. We show that after the adoption of fraudulent transfer laws, the cost of capital decreases since the protection of creditor rights harms shareholders’ interest. We also find that the adoption of these laws increases bankruptcy risk and leads firms to reduce their leverage. Further, the adoption of the laws reduces firm innovation and growth rates and increases employee turnover.

[7]“Playing It Safe? Evidence from Hurricane Strikes” (with Mitch Towner)

• Abstract: We examine managers’ incentive to play it safe by studying how managers respond to hurricane events when their firms are located in the neighborhood of the disaster area. We find that firms hold more cash and undertake more diversifying acquisitions in response to nearby hurricane events. Over time, the perceived risk decreases, and the bias disappears.

[8]“Robots, Product Lifecycle, and Corporate Acquisition: A Text-Based Measure of Product Lifecycle” (with Zhipeng Chen)

• Abstract: We use machine learning methods to construct product lifecycle measures from 10-Ks and exploit the financial crisis as an identification strategy. We show that robots reduce the impact of negative macro-economic shocks on firms’ aging. Also, firms with more robots make more value-adding acquisitions.

[9]“Robots and Workplace Safety" (with Wesley Deng)

• Abstract: We examine how robots adoption affects workplace safety. We show that after the adoption of robots, the workplace safety improves since robots replace workers from dangerous jobs and improve operating efficiency. We also show that firms with more robots are more likely to adopt safety clauses in CEO compensation contracts, are rated higher in workplace safety culture, invest more in safety and management is more likely to discuss safety issues during earnings conference call.