Working Papers

Job Market Paper

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.
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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.''
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Recent Publications

Forthcoming JFE

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In JLFA

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.
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In VLBR

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.
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In SLR

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.
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Recent Posts

15 min read

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 \[y_{it} = \alpha_i + \alpha_t + \tau_{it} + \epsilon_{it}\] where \(\alpha_i\) are unit fixed effects drawn from \(\sim N(0, 1)\), \(\alpha_t\) are period fixed effects, also drawn from \(\sim N(0, 1)\), and the white-noise error term \(\epsilon_{it}\) is drawn from \(\sim N\left(0, \left(\frac{1}{2}\right)^2\right)\).
18 min read

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.
29 min read

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.
16 min read

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.
26 min read

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.
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Recent & Upcoming Talks

Sep 22, 2020 12:00 AM — 12:00 AM San Francisco / Melbourne

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.
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Mar 10, 2020 12:00 AM — 12:00 AM San Francisco

Figuring out how to make sense of difference-in-differences analysis when treatment is staggered across units and time.
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Sep 6, 2019 12:00 AM — Sep 7, 2019 12:00 AM New York

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.
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May 20, 2016 12:00 AM — 12:00 AM Cambridge, Massachusetts

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Experience

 
 
 
 
 

PhD Candidate

Stanford GSB

Aug 2016 – Present California
 
 
 
 
 

Summer Associate

Cravath, Swaine & Moore

Jun 2016 – Aug 2016 New York
 
 
 
 
 

Enforcement Intern

Securities & Exchange Commission

Jun 2015 – Aug 2015 New York
 
 
 
 
 

Research Fellow

Stanford Law School

Jun 2013 – Jun 2013 Stanford
 
 
 
 
 

Senior Consultant

Navigant Economics

Mar 2011 – May 2013 Oakland

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Contact

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