Deterring Algorithmic Manipulation

Does the existing anti-manipulation framework effectively deter algorithmic manipulation? With the dual increase of algorithmic trading and the occurrence of “mini-flash crashes” in the market linked to manipulation, this question has become more pressing in recent years. In the past thirty years, the financial markets have undergone a sea change as technological advancements and innovations have fundamentally altered the structure and operation of the markets. Key to this change is the introduction and dominance of trading algorithms. Whereas initial algorithmic trading relied on preset electronic instructions to execute trading strategies, new technology is introducing artificially intelligent (“AI”) trading algorithms that learn dynamically from data and respond intuitively to market changes. These technological developments have exposed significant shortcomings in the effectiveness of anti-manipulation laws, particularly regarding one of their fundamental goals: deterring market manipulation. Preventing manipulation remains a key feature of the legal regime governing the financial markets. Rampant manipulation undermines the viability of the market and, in the case of algorithmic manipulation, increases systemic risks within the market. Deterring algorithmic manipulation is thus essential to the viability and stability of the market. But credible and effective deterrence of wrongdoing requires certainty of punishment, which is increasingly unattainable with respect to algorithmic manipulation under the existing legal regime. Specifically, the law of manipulation tethers liability to scienter, which algorithms cannot legally form. Further, deciphering the intent of the human behind the algorithm can be a near-impossible task in all but the most egregious cases. The scienter-focused nature of the anti-manipulation framework therefore diminishes the disciplinary power of the law, weakening deterrence and incentivizing algorithmic manipulation. This Article demonstrates that the scienter-centric analysis undergirding anti-manipulation laws creates gaps in the detection and punishment of algorithmic manipulation that weaken the current legal regime’s deterrent effect. The acute failure of the law to punish algorithmic manipulation incentivizes potential wrongdoers to utilize algorithms to cloak their misdeeds, exposing the markets to significant systemic harm. Notably, unlike other scholars and policymakers that view transparency as the ultimate solution to increase accountability for algorithms, this Article highlights the potential limitations of relying primarily on transparency. Rather, the Article urges changes to the legal framework to modernize its applicability: eschew the scienter requirement and, instead, focus on the resulting harm of the algorithm on the market. Together, these proposals are likely to credibly deter algorithmic manipulation, safeguarding the viability, efficiency, and stability of the markets

Benchmark Regulation

Benchmarks are metrics that are deeply embedded in the financial markets. They are essential to the efficient functioning of the markets and are used in a wide variety of ways—from pricing oil to setting interest rates for consumer lending to valuing complex financial instruments. In recent years, benchmarks have also been at the epicenter of numerous, multi-year market manipulation scandals. Oil traders, for example, deliberately execute trades to drive benchmarks lower artificially, allowing the traders to capitalize on the manipulated benchmarks. This ensures that later trades relying on the benchmarks will be more profitable than they otherwise would have been. Such manipulative practices have far-reaching and, in some instances, destabilizing effects on the financial markets. In responding to these benchmark manipulation scandals, regulators have relied on the existing anti-manipulation framework, which is based solely on ex post prosecution of wrongdoers. The current framework treats benchmark manipulation as just another form of market manipulation. But, as more benchmark manipulation schemes come to light, they cast doubt on the effectiveness of this traditional approach to curbing a modern-day form of manipulation. This Article provides the first in-depth analysis of the differences between benchmark manipulation and traditional forms of market manipulation. This analysis demonstrates that regulators cannot adequately address benchmark manipulation through ex post enforcement actions alone. In failing to recognize how benchmark manipulation differs from traditional manipulation, regulators miss a prime opportunity to oversee a key facet of the financial markets and safeguard market integrity. By focusing on the unique attributes of benchmarks that make them susceptible to manipulation, this Article puts forward a comprehensive prescriptive regulatory framework aimed at detecting and minimizing benchmark manipulation, rather than merely punishing wrongdoers after the fact

Hazardous Hedging: The (Unacknowledged) Risks of Hedging with Credit Derivatives

Is hedging with credit derivatives always beneficial? The benefit of hedging with credit derivatives, such as credit default swaps, is presumed by the Dodd-Frank Act, which excludes hedge transactions from much of the new financial regulation. Yet, significant new risks can arise when credit derivatives are used to manage risks. Hedging, therefore, should be defined not only in relation to whether a transaction offsets risks, but also whether, on balance, the risks that are mitigated—as well as any new risks that arise—are outweighed by the potential benefits. Regulators of the derivatives markets must consider the risks of hedging with credit derivatives and the inability of firms to account for those risks, as well as the value to firms of mitigating risks with credit derivatives and the costs arising from their use. Among other proscriptions, the types of credit derivatives transactions that can be classified as a hedge should be limited, as well as the size of those positions. In addition, margin and collateral requirements for credit derivatives should take account of the greater risks arising from their use. Firms using credit derivatives to hedge often fail to account for the full costs associated with using those instruments. There are numerous risks that can arise. Informational asymmetries and negative externalities, however, make it difficult for firms to accurately assess those risks. Consequently, the far-reaching exemptions for hedge activity provided by the Dodd-Frank Act are inappropriate. Credit derivative hedges must be subject to regulatory oversight, rather than exemptio

The Future of AI Accountability in the Financial Markets

Consumer interaction with the financial market ranges from applying for credit cards, to financing the purchase of a home, to buying and selling securities. And with each transaction, the lender, bank, and brokerage firm are likely utilizing artificial intelligence (AI) behind the scenes to augment their operations. While AI’s ability to process data at high speeds and in large quantities makes it an important tool for financial institutions, it is imperative to be attentive to the risks and limitations that accompany its use. In the context of financial markets, AI’s lack of decision-making transparency, often called the “black box problem,” along with AI’s dependence on quality data, present additional complexities when considering the aggregate effect of algorithms deployed in the market. Owing to these issues, the benefits of AI must be weighed against the particular risks that accompany the spread of this technology throughout the markets. Financial regulation, as it stands, is complex, expensive, and often involves overlapping regulations and regulators. Thus far, financial regulators have responded by publishing guidance and standards for firms utilizing AI tools, but they have stopped short of demanding access to source codes, setting specific standards for developers, or otherwise altering traditional regulatory frameworks. While regulators are no strangers to regulating new financial products or technology, fitting AI within the traditional frameworks of prudential regulation, registration requirements, supervision, and enforcement actions leaves concerning gaps in oversight. This Article examines the suitability of the current financial regulatory frameworks for overseeing AI in the financial markets. It suggests that regulators consider developing multi-faceted approaches to promote AI accountability. This Article recognizes the potential harms and likelihood for regulatory arbitrage if these regulatory gaps remain unattended and thus suggests focusing on key elements for future regulation—namely, the human developers and regulation of data to truly “hold AI accountable.” Therefore, holding AI accountable requires identifying the different ways in which sophisticated algorithms may cause harm to the markets and consumers if ineffectively regulated, and developing an approach that can flexibly respond to these broad concerns. Notably, this Article cautions against reliance on self-regulation and recommends that future policies take an adaptive approach to address current and future AI technologies

Corporate Racial Responsibility

The 2020 mass protests in response to the deaths of George Floyd and Breonna Taylor had a significant impact on American corporations. Several large public companies pledged an estimated $50 billion to advancing racial equity and committed to various initiatives to internally improve diversity, equity, and inclusion. While many applauded corporations’ willingness to engage with racial issues, some considered it further evidence of corporate capitulation to extreme progressivism at shareholders’ expense. Others, while thinking corporate engagement was long overdue, critiqued corporate commitment as insincere. Drawing on historical evidence surrounding the passage of Title II of the Civil Rights Act of 1964, this Article engages with the debate on corporate “racial” responsibility to demonstrate that corporate engagement on race is not new. Indeed, during the struggle to desegregate public accommodations, corporate social responsibility was invoked to encourage voluntary desegregation and avoid federal intervention. Segregation was good business for some; for others, maintaining white supremacy justified any pecuniary losses. While this Article argues that corporations have a role to play in achieving racial equity, it cautions against reliance on corporate social responsibility to advance racial equality. Past and current iterations of corporate racial responsibility have often represented a market-fundamentalist, value-extractive approach to racial equity that reifies existing racial hierarchies. By valuing racial equity in terms of its potential profitability, corporate racial responsibility can subordinate human dignity to wealth maximization. This Article argues for a more meaningful corporate racial responsibility that addresses the structures and laws undergirding racial inequities within corporations and our larger society

The Case for Climate Conscious, Low Carbon Federal Procurement

Purchasing practices are one of many contributors to the climate crisis. As the world’s largest purchaser of goods and services, the U.S. Federal Government is in a unique position to cut a significant portion of national emissions through the development of more responsible, sustainable, and—most importantly—climate-conscious supply chains. According to the Office of the Federal Chief Sustainability Officer, federal supply chain emissions associated with federal contracts are twice as high as Federal Scope 1 and Scope 2 emissions, combined. As such, reforming Federal procurement practices to limit direct emissions as well as emissions in supply chains can play a crucial role in reaching the goal of net-zero emissions by 2050. The Biden Administration has taken a strong stance on climate change, initiating, reinstating, and further developing necessary policy adjustments such as transitioning the government fleet to electric vehicles, supporting energy efficiency in buildings, and the uptake in renewable energy generation, and drafting a new Federal Sustainability Plan. The RCRC Committee has prepared recommendations relevant to Federal procurement practices to help achieve maximum emissions reductions at both the government and national levels

Grey matter in shadow banking: international organizations and expert strategies in global financial governance

Who controls global policy debates on shadow banking regulation? We show how experts secured control over how issues in shadow banking regulation are treated by examining the policy recommendations of the Bank of International Settlements, the International Monetary Fund and the Financial Stability Board. The evidence suggests that IO experts embedded a bland reformism opposed to both strong and ‘light touch’ regulation at the core of the emerging regulatory regime. Technocrats reinforced each other's expertise, excluded some potential competitors (legal scholars), co-opted others (select Fed and elite academic economists), and deployed measurement, mandate, and status strategies to assert issue control. In the field of shadow banking regulation, academic economists’ influence came from their credibility as arbitrageurs between several professional fields rather than their intellectual output. The findings have important implications for how we study the relationship between IO technocrats and experts from other professional field