I am an Assistant Professor at Berkeley Law School, and a recent PhD and law graduate from Stanford University. My research interests include corporate governance, empirical legal studies, and law and economics/finance.
Interests
Corporate Governance
Empirical Legal Studies
Law and Finance
Education
PhD in Business Administration, 2021
Stanford University
Juris Doctor, 2017
Stanford University
BSc in International Economics, 2009
Edmund Walsh School of Foreign Service, Georgetown University
We provide large-sample evidence on whether U.S. publicly traded corporations opportunistically use voluntary disclosures about their commitments to employee diversity. We document significant discrepancies between companies' disclosed commitments and their hiring practices and classify firms that discuss diversity more than their actual employee gender and racial diversity warrants as “diversity washers." We find diversity-washing firms obtain superior scores from environmental, social, and governance (ESG) rating organizations and attract investment from institutional investors with an ESG focus. These outcomes occur even though diversity-washing firms are more likely to incur discrimination violations and pay larger fines for these actions. Our study highlights the consequences of selective ESG disclosures on an important social dimension of employee diversity, equity, and inclusion.
In this paper I explore the relationship between the rise of hedge fund activism and firm outcomes, using a study design that explicitly takes into account how activists pick their targets. Contrary to much prior work, I find no evidence that activism is associated with increased firm operating performance or significant long-term returns once comparing to firms based on their similarity to the targets. However, activism does increase firm payouts to shareholders and decreases investment, consistent with the argument of many critics of activism. I also find that firm-level employment declines significantly following a targeting event, and that the subset of firms that experience an increase in operating performance also engage in higher levels of tax avoidance. The deregulation of proxy access rules, wholesale de-staggering of corporate boards, and the rise in importance of proxy advisory firms who frequently recommend voting for activist proposals have made firms more susceptible to aggressive activism over the past three decades. The results in this paper, coupled with the rhetorical shift in focus from short-term profits to sustainable growth by large institutional investors, suggest a re-framing of the public debate over the benefits of shareholder activism.
A longstanding debate over the impact of state antitakeover provisions has emerged among scholars in law and finance. Corporate law practitioners and researchers argue that the second generation of antitakeover statutes were made redundant by the affirmation of rights plans, while empirical scholars have found wide-ranging impacts from their adoption when used as an exogenous shocks to managerial entrenchment. This paper subjects the standard approach used in the empirical literature to a series of straightforward sensitivity analyses, consistent with the present best practice in panel data analysis. Contrary to the majority of published research, there is scant evidence for a consistent and reliable impact of antitakeover statute adoption on common firm outcome measures. These findings are consistent with the legal argument that takeover statutes provide little additional takeover deterrence in the presence of a ``shadow pill.''
We show that using modern estimation techniques (with penalized regression and cross-validation to select comparable peer firms) for event studies can both reduce expert witness discretion and produce more accurate stock return predictions.
This paper shows that, both conceptually and empirically, the exclusion of dual-class shares by index providers is unlikely to act as a deterrence mechanism.
This paper shows that the type of event studies commonly used in securities litigation fail during periods of market volatility, and proposes alternatives more suitable during such periods.
In this memo we will test empirically whether there are issues with the event-study DiD even if the dynamic treatment effects are constant across cohorts. To do this I will conduct a similar simulation to the one shown in my slides.
The data generating process is yit=αi+αt+τit+ϵit
where αi are unit fixed effects drawn from ∼N(0,1), αt are period fixed effects, also drawn from ∼N(0,1), and the white-noise error term ϵit is drawn from ∼N(0,(12)2).
In doing research for my dissertation I keep on running across models that control for somewhat arbitrary variables (or where at least there is no justification for their inclusion). This is common in applied corporate finance / managerial accounting papers. As a result, I snarked.
The issue here is that in regressions for some outcome - say firm valuation (the dreaded Q) - we want to look at the the change in the outcome variable around some treatment shock, but we want to control for some variables.
Introduction In this post I expand on the implications of recent econometric work on issues with difference-in-difference (DiD) designs with staggered treatment rollout. For a longer discussion of these issues, and the details of new proposed modifications to the standard two-way fixed effect regression-based DiD models, refer to my prior post here. Here I will demonstrate the practical importance of correcting for these issues with staggered DiD on a live policy issue - whether the adoption of legalized medical cannabis laws has a causal effect on opioid overdose mortality.
In recent years there has been a growing movement within certain factions of Congress, the judicial branch, and the legal academy to require all financial regulations to be subject to strict cost-benefit review (CBR). Rival commentators meanwhile argue that historically conceived CBR would be ill-advised for financial regulations due to the interconnected nature of financial markets, poor data quality, and questions of practical political economy. In this review I will explain the rationale behind agency-required CBR analysis, survey the arguments both for and against detailed CBR as applied to financial regulation, and explain how strictly implemented CBR would affect the viability of reforms like increased capital requirements.
Introduction In this methodological section I will explain the issues with difference-in-differences (DiD) designs when there are multiple units and more than two time periods, and also the particular issues that arise when the treatment is conducted at staggered periods in time.
In the canonical DiD set-up (e.g. the Card and Kreuger minimum wage study comparing New Jersey and Pennsylvania) there are two units and two time periods, with one of the units being treated in the second period.
The difference-in-differences (DiD) research design is popular method for testing changes in outcome variables across treated and untreated groups. While the set-up is intuitive and easy to implement in the canonical setting of two time periods and two groups, most modern research using DiD exploits the staggered implementation of treatment across many units and different time periods. Unfortunately, the common practice using unit and time fixed effects, along with an indicator variable for active treatment (the two-way fixed effects TWFE estimator) has known flaws that potentially biases the parameter estimates in most use settings. In this talk I’ll discuss the pitfalls of the common approach. Using simple simulation analyses I’ll show how the bias arises, and where the potential for bias is largest. In addition I will discuss new methods for conducting DiD analyses that overcome the flaws in the TWFE approach, and show the implications with an example from prior literature.
We investigate the use of modern statistical techniques in the application of event studies conducted on single securities for the purpose of securities litigation. Single-firm event studies are widely used in civil litigation, with billions of dollars in settlements hinging on the outcome of the exercise. Prior work has explored modifying the standard single-firm event study design to provide more robust statistical inference. But little work has been done to determine methods that can directly increase the precision of the excess return estimate. We take a prediction approach to the excess return calculation and find substantial performance improvement is possible using modern machine learning methods.
