Conference Agenda

Protective regulation aims to safeguard consumers yet may impose frictions that inhibit entrepreneurial activity. I study this tension in the context of U.S. state cryptocurrency licensing laws using novel data linking regulatory announcements, startup formation, investment, hiring, and patenting. Markets respond negatively to protective legislation, but regulated states subsequently experience more entrepreneurial activity. Difference-in-differences estimates exploiting staggered adoption show that licensing requirements increase startup entry, capital raised, patent applications, and hiring -- especially in engineering and compliance roles. To account for this divergence between market reactions and real outcomes, I develop a model in which regulatory intensity generates asymmetric responses by firm quality and size. Consistent with the model, the evidence indicates that protective regulation creates a certification channel that disproportionately benefits high-quality entrants while discouraging lower-quality competitors.

We provide the first large-sample evidence that decentralized voting is widespread within mutual fund families. Contrary to the view that families vote as unified blocs, we find that at least 40% of families exhibit evidence of decentralized voting, starting as early as 2006. We measure decentralization using voting disagreement within the family, which is low unconditionally due to the high volume of routine proposals, but rises substantially for controversial proposals, environmental and social issues, and when proxy advisors recommend voting “against.” Decentralized voting is more prevalent in families with more active funds and greater stewardship resources, and funds within a family vote more similarly when they share management structures and characteristics. Decentralization has consequences for governance and fund investors. First, it weakens the monitoring effectiveness of institutional investors—a result we corroborate using Vanguard's 2019 adoption of decentralized voting as a quasi-natural experiment. Second, funds that deviate from their family’s voting stance charge higher fees without delivering higher returns for clients. Yet, funds that deviate attract higher inflows.

Prior studies view investors as passive recipients of credit ratings who make portfolio decisions accordingly. In this paper, we identify circumstances in which institutional investors act as “rating-movers”, strategically shaping rating outcomes to favor their portfolio positions and managerial compensation. Using a proprietary dataset of Collateralized Loan Obligations (CLOs), we examine the incentives of CLO managers to resist rating downgrades that could trigger overcollateralization (OC) test failures. We develop a novel measure capturing managers’ incentives to suppress downgrades, based on the significance of a loan within a manager’s portfolio and the extent to which its downgrade would affect OC compliance. We find that loans facing greater resistance from CLO managers exhibit a significantly lower likelihood of subsequent downgrades. This effect is more pronounced for loans issued by private firms and when managers are approaching their incentive fee hurdles, highlighting the economic motivation behind the rating influence. Our results demonstrate that investors, much like fixed-income issuers, can exert strategic pressure on rating agencies, revealing a new source of conflicts of interest in the credit rating industry.

This paper examines the role of repo lending between counterparties affiliated with the same bank holding company (BHC). Using confidential transaction-level data, we find that Treasury repo rates between affiliated entities are significantly higher than those between unaffiliated parties. This affiliation premium is more pronounced when dealers face tighter balance sheet constraints, suggesting that regulatory capital requirements raise the cost of external borrowing and enhance the value of internal funding. During a temporary period of regulatory relief—when leverage requirements were relaxed—the affiliation premium nearly disappeared, only to re-emerge once the regulations were reinstated. Our findings underscore a unique competitive advantage for dealers within large BHCs: the ability to access internal liquidity from affiliated banks. This internal liquidity channel also facilitates the distribution of liquidity from banks with high level of reserves to the rest of financial system.

Active exchange-traded funds (AETFs) have grown rapidly despite the decline of poor-performing active mutual funds (AMFs). AETFs’ growth, however, is not due to superior performance. In fact, AETFs have significantly worse performance than AMFs. Rather, AETFs are taking advantage of the attention-driven trading behavior of retail investors. Similar to equity investors, AETF investors chase extreme short-term returns, both positive and negative, while long-term flows have no response to underperformance. Managers respond to these payoffs by taking high risks to generate extreme returns. Overall, our results show how active management has responded to the decline of their traditional distribution

channel.

This paper documents substantial asset `reclassification' in the mutual fund industry, exceeding $450 billion in 2021. These reclassification events do not involve investor flows; instead, mutual fund assets are simply converted into twin investment vehicles, such as separate accounts or collective investment trusts. Analyzing the implications of asset reclassification for the mutual fund literature, we find that these events distort inferred mutual fund flows without reflecting actual asset movements at the investment product level. Failing to account for asset reclassification in flow-based regression analyses can lead to biased estimates, as it resembles a non-classical measurement error. We first analyze scenarios in which mutual fund flows serve as a dependent variable, focusing on flow-performance sensitivity. A regression utilizing reclassification-adjusted quarterly flows demonstrates a 40-100% greater flow-performance sensitivity for mutual funds with twin vehicles than one employing unadjusted flows. We then examine cases when flows serve as an independent variable, as in `smart money' tests, where measurement error artificially inflates true estimates.