This study examines the standardization of venture capital (VC) contracts since the release of the National Venture Capital Association (NVCA) model charter in 2003. Using nearly 5,000 charters issued in connection with a startup’s Series A financing, the paper finds a significant increase in the model’s adoption from less than 3% of charters in 2004 to nearly 85% by 2022. Adoption of the Delaware-oriented charter has also been accompanied by the growing dominance of Delaware incorporation, with Delaware charters growing from 54% of sample charters in 2004 to 100% in 2022. High adoption rates among the six most active law firms servicing U.S. startups largely explain the success of the standardization project.While cosine similarity analysis reveals charters are overall more similar in 2022 than in 2004, the capital structures of Series A startups have also become substantially more complex. Series A charters authorizing only a single class of common stock and a single series of “Series A” preferred stock constituted 86% of charters in 2004 but constituted just 5% of 2022 charters, while 30% of 2022 charters had either 2 classes of common stock or 3 or more series of preferred stock. The additional complexity arises almost entirely from multiple securities reflecting prior seed stage financing. In contrast, efforts to add founder-friendly capital securities—such as dual class common stock and founder preferred stock—have made only modest inroads. Overall, the story of VC contracting over the past two decades is largely one of standardization, albeit with growing complexity around startup capital structures due to the increasing importance of seed stage capital and changing expectations regarding what constitutes a “Series A” startup.

Starting in February 2021, surging Berkshire Hathaway-A volume mystified market watchers. Averaging 375 shares for a decade, daily volume rose to over 20,000 shares, then plummeted nine-fold in June 2024. We demonstrate that the increased volume was nonexistent-the result of reporting rules, retail trading, and fractional shares. Phantom volume created dislocations in BRK.A's relationship to BRK.B, missed arbitrage opportunities, higher trading costs, and incentivized manipulation. Its subsequent reduction was also due to regulatory change. We argue that short-sighted regulations impose a new "limit to arbitrage", and improving transparency in the national market system is the real mystery to solve.

New approaches to corporate purpose have emerged in recent years that hold out the promise of addressing concerns about corporate social responsibility (CSR) through shareholder governance, rather than in spite of it. The seminal such approach—enlightened shareholder value—posits that treating other stakeholders well can ultimately redound to long-term shareholder value. However, two more recent proposals reconceptualize shareholder interests in more holistic ways and urge that it is shareholders’ welfare, not shareholder value per se, that managers should pursue. In particular, the “shareholder social preferences” view incorporates into the corporate objective the degree to which the firm’s operations align with the social views of shareholders. The “portfolio value maximization view,” in contrast, argues that corporate fiduciaries should maximize the value of diversified shareholders’ portfolios by considering the externalities of the firm’s operations on those portfolios.Shifting to shareholder welfare as the corporate objective, however, would do little to improve corporate conduct and would entail substantial costs. The social preferences of shareholders are conflicted, muted, and often prefer less protection of stakeholder interests than provided by law. Shareholders’ portfolio value captures only a small portion of the externalities like pollution that its proponents hope to address and risks motivating anticompetitive conduct. And neither corporate managers nor shareholders would have the information and incentives needed to pursue these additional shareholder welfare considerations. On the contrary, by distracting management from their core competencies, shareholder welfarism would ultimately lower shareholder welfare.The future of CSR, as with its past, is instead with enlightened shareholder value (ESV). But the existing law-and-economics literature on ESV has been stunted by key misconceptions, which we attempt to dispel. The increasing use by various actors in the corporate system of normative arguments that sound in ESV terms may lead to new pathways for achieving social progress.

This paper investigates fractional share trading. We develop a latency-based method for identifying a large sample of fractional share trades. We find that high-priced stocks, meme stocks, IPOs, SPACs, and popular retail stocks exhibit considerable numbers of these tiny trades. We surmise that this reflects dollar-based order entry, with many tiny trades being fractional components of larger orders. We show that our fractional trade measure is predictive of future liquidity and volatility, suggesting a new metric to capture the information in retail trades. We identify how data and reporting protocols preclude knowing the extent of fractional share trading, inflate volume data, and provide censured samples of these off-exchange trades.

We show current market practices relating to odd-lot quotes create a large “inside” market where better prices routinely exist relative to the National Best Bid or Offer. We show that odd-lot quotes play a price discovery role, and these quotes provide valuable information to traders with access to proprietary data feeds. Using a XGBoost machine learning algorithm that uses odd-lot data to predict future prices, we demonstrate a simple and profitable trading strategy. We argue the SEC’s proposed round-lot redefinition reduces—but does not eliminate—the high incidence of superior odd-lot quotes within the NBBO.

U.S. fair-lending law prohibits lenders from making credit determinations that disparately affect minority borrowers if those determinations are based on characteristics unrelated to creditworthiness. Using an identification under this rule, we show risk-equivalent Latinx/Black borrowers pay significantly higher interest rates on GSE-securitized and FHA-insured loans, particularly in high-minority-share neighborhoods. We estimate these rate differences cost minority borrowers over $450 million yearly. FinTech lenders’ rate disparities were similar to those of non-Fintech lenders for GSE mortgages, but lower for FHA mortgages issued in 2009–2015 and for FHA refi mortgages issued in 2018–2019.

AbstractUsing City of Oakland data during COVID-19, we document that small-business components of survival capabilities (i.e., revenue resiliency, labor flexibility, and committed costs) vary by firm size. Nonemployer businesses rely on low-cost structures to survive. Microbusinesses (1–5 employees) depend on 14% greater revenue resiliency. Enterprises (6–50 employees) use labor flexibility to survive but face 10%–20% higher residual closure risk from committed costs. The evidence argues for size targeting of financial support programs, including committed costs and revenue-based lending programs. Supporting the capabilities mapping, we find that the Paycheck Protection Program (PPP) increased medium-run survival probability by 20.5% specifically for microbusinesses.

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Using unique City of Oakland data during COVID-19, we document that small business survival capabilities vary by firm size as a function of revenue resiliency, labor flexibility, and committed costs. Nonemployer businesses rely on low cost structures to survive 73% declines in own-store foot traffic. Microbusinesses (1-to-5 employees) depend on 14% greater revenue resiliency. Enterprises (6-to-50 employees) have twice-as-much labor flexibility, but face 11%-to-22% higher residual closure risk from committed costs. Finally, inconsistent with the spirit of Chetty-Friedman-Hendren-Sterner (2020) and Granja-Makridis-Yannelis-Zwick (2020), PPP application success increased medium-run survival probability by 20.5%, but only for microbusinesses, arguing for size-targeting of policies.

We examine the common and growing misuse of Tobin’s q as a proxy for firm value within the law and finance literatures. We trace the history of Tobin’s q, beginning with its original role as a mean-reverting construct that macroeconomists used to model investment policy. We document how the original version of q morphed into the simplified market-to-book ratio version that law and finance scholars regularly use today to examine regulatory policy, corporate governance, and other economic phenomena. Whereas macroeconomists rejected this simplistic version of q because of measurement error problems, law and finance scholars embraced it as a proxy for firm value. In addition, we demonstrate empirically why the simplistic version of q is so problematic. Many of the problems arise because regressions that have as their dependent variable a ratio with book value in the denominator are likely to produce biased estimates, due to both omitted assets and time-varying, firm-specific characteristics that can systematically alter a firm’s book value. As a result, the simplistic version of q produces non-classical measurement error in regression specifications that seek to estimate the relationship between firm value and various corporate and regulatory phenomena. We also confirm, consistent with macroeconomists’ view of the original Tobin’s q, that the market-to-book estimate of q is mean-reverting in terms of stockholder returns. Finally, we suggest a new approach. We replicate the details of one leading study that was based on the simplistic version of q and then show how its results differ when we employ several alternative approaches. We propose that scholars should use these alternative approaches, including direct estimates of firm value instead of the simplistic market-to-book ratio, and, when possible, should supplement the popular fixed effects estimator with the first difference estimator. Overall, our message is straightforward: scholars should view with suspicion any assertions about corporate governance and regulation that are based on the use of market-to-book ratios as the dependent variable in regressions.

The disproportionate burden of COVID-19 among communities of color, together with a necessary renewed attention to racial inequalities, have lent new urgency to concerns that algorithmic decision-making can lead to unintentional discrimination against members of historically marginalized groups. These concerns are being expressed through Congressional subpoenas, regulatory investigations, and an increasing number of algorithmic accountability bills pending in both state legislatures and Congress. To date, however, prominent efforts to define algorithmic accountability have tended to focus on output-oriented policies that may facilitate illegitimate discrimination or involve fairness corrections unlikely to be legally valid. Worse still, other approaches focus merely on a model’s predictive accuracy—an approach at odds with long-standing U.S. antidiscrimination law. We provide a workable definition of algorithmic accountability that is rooted in the caselaw addressing statistical discrimination in the context of Title VII of the Civil Rights Act of 1964. Using instruction from the burden-shifting framework, codified to implement Title VII, we formulate a simple statistical test to apply to the design and review of the inputs used in any algorithmic decision-making processes. Application of the test, which we label the input accountability test, constitutes a legally viable, deployable tool that can prevent an algorithmic model from systematically penalizing members of protected groups who are otherwise qualified in a legitimate target characteristic of interest. * I. Michael Heyman Professor of Law & Faculty Co-Director of the Berkeley Center for Law and Business UC Berkeley School of Law. ‡ Soloman P. Lee Chair in Business Ethics and Associate Professor – UC Berkeley Haas School of Business. † Professor & Lisle and Roslyn Payne Chair in Real Estate Capital Markets, Co-Chair, Fisher Center for Real Estate and Urban Economics – UC Berkeley Haas School of Business. ° Professor & Kingsford Capital Management Chair in Business Economics – UC Berkeley Haas School of Business. ALGORITHMIC DISCRIMINATION AND INPUT ACCOUNTABILITY UNDER THE CIVIL RIGHTS ACTS

The disproportionate burden of COVID-19 among communities of color and a necessary renewed attention to racial inequalities have lent new urgency to concerns that algorithmic decision-making can lead to unintentional discrimination against members of historically marginalized groups. These concerns are being expressed through Congressional subpoenas, regulatory investigations, and an increasing number of algorithmic accountability bills pending in both state legislatures and Congress. To date, however, prominent efforts to define algorithmic accountability have tended to focus on output-oriented policies that may facilitate illegitimate discrimination or involve fairness corrections unlikely to be legally valid. Worse still, other approaches focus merely on a model's predictive accuracy—an approach at odds with long-standing U.S. anti-discrimination law.We provide a workable definition of algorithmic accountability that is rooted in case law addressing statistical discrimination in the context of Title VII of the Civil Rights Act of 1964. Using instruction from the burden-shifting framework codified to implement Title VII, we formulate a simple statistical test to apply to the design and review of the inputs used in any algorithmic decision-making process. Application of the test, which we label the Input Accountability Test, constitutes a legally viable, deployable tool that can prevent an algorithmic model from systematically penalizing members of protected groups who are otherwise qualified in a legitimate target characteristic of interest.

Using new data from the two U.S. securities information processors (SIPs) between August 6, 2015 and June 30, 2016, we examine claims that high-frequency trading (HFT) firms use direct feeds to exploit traders who rely on SIP prices. Across $3.7 trillion of trades in the Dow Jones 30, the SIPs report quote updates from exchanges 1,128 mu s after they occur. However, the SIP-reported National Best Bid and Offer (NBBO) matches the NBBO calculated without reporting latencies in 97% of all SIP-priced trades. Liquidity-taking orders gain on average $0.0002/share when priced at the SIP-reported NBBO rather than the instantaneous NBBO, but aggregate gross profits are just $14.4 million. These findings indicate that direct feed arbitrage is not a meaningful source of HFT profits, nor can it explain the arms race for trading speed. (C) 2019 Elsevier B.V. All rights reserved.

This document contains supporting materials for the article How Rigged Are Stock Markets? Evidence from Microsecond Timestamps by Robert P. Bartlett, III and Justin McCrary. The paper to which this Appendix applies is available at the following URL: https://ssrn.com/abstract=2812123

Prevailing research in market microstructure posits that liquidity providers bypass queue lines on exchanges by offering liquidity in dark venues with de minimis sub-penny price improvement, thus exploiting an exception to the penny quote rule. We show that (a) the SEC enforces the quote rule to prevent sub-penny queue-jumping in dark pools unless trades are "pegged" to the NBBO midpoint and (b) the documented increase in dark trading due to investor queue-jumping stems from increased midpoint trading. Although encouraging pegged midpoint orders can subject traders to direct feed arbitrage, we estimate that less than 2% of shares traded per year present exploitable trading opportunities for this form of latency arbitrage, yielding annual gross potential profits of less than $20 million.

This paper uses cash flow statements to study leveraged buyouts of large publicly traded U.S. firms by private equity funds between 1980 and 2006. Presenting the origin, ownership and use of cash in these transactions, once controlled by private equity funds, firms exhibit a significant decline in investment and growth, as debt is used to motivate managers and forces the release of excess free cash flows. I do not find evidence of value creation, as the profitability of the underlying assets does not increase under private equity control. In addition, I offer an alternative explanation for why LBOs in the U.S. experience a period of contraction, while those completed France, studied in Boucly, Sraer, and Thesmar (2011), experience growth: sample selection. Finally, cash flow statements are also used to evaluate free cash flow proxies frequently used in the literature. I illustrate that the significant gains in free cash flows are driven by a reduction in investment post-LBO and this is strongly correlated with declining future growth rates.

Using a sample of 388 securities fraud lawsuits filed between 2002 and 2017 against foreign issuers, we examine the effect of the Supreme Court's decision in Morrison v. National Australia Bank Ltd. We find that the description of Morrison as a steamroller, substantially ending litigation against foreign issuers, is a myth. Instead, we find that Morrison did not significantly change the type of litigation brought against foreign issuers, which, both before and after this case, focused on foreign issuers with a U.S. listing and substantial U.S. trading volume. Although dismissal rates rose post-Morrison, we find no evidence that this was related to the decision. Settlement amounts and attorneys' fees remained unchanged post-Morrison. We use these findings to theorize that Morrison was primarily a preemptive decision about standing that firmly delineated the exposure of foreign issuers to U.S. liability in response to the Vivendi case, which sought to expand the scope of liability for foreign issuers whose shares traded primarily in non-U.S. venues. When Morrison is placed in its true context, it is justified as a decision in line with administrative and court actions that have historically aligned firms' U.S. liability to be proportional to their U.S. presence. Although Morrison had this defining effect, it did not change the litigation environment for foreign issuers, which was the oft-cited import of the decision. More generally, our analysis of Morrison underscores how the decision has been mistakenly characterized as a case primarily about extraterritoriality rather than standing.

Pragmatic and effective research on corporate governance often turns critically on appreciating the legal institutions surrounding corporate entities – yet such nuances are often unfamiliar or poorly specified to economists and other social scientists without legal training. This chapter organizes and discusses key legal concepts of corporate governance, including statutes, regulations, and jurisprudential doctrines that “govern governance” in private and public companies, with concentration on the for-profit corporation. We review the literature concerning the nature and purpose of the corporation, the objects of fiduciary obligations, the means for decision making within the firm, as well as the overlay of state and federal law pertaining to how that decision-making authority is exercised within publicly traded companies. A core feature of this analysis is that while the basic structures pertinent to corporate law and governance are familiar and in some ways predictable, they are also in a constant state of flux, shaping and being shaped by institutional adaptations of firms, regulators and courts. This chapter is most appropriate for social science researchers and/or students who are new to the legal dimensions of firm governance.