We hand-collect a time-series database of business closures and related restrictions for every county in the United States since March 2020. We then relate these policies to future growth in deaths due to COVID-19. To our knowledge, ours is the most comprehensive database of U.S. COVID-19 business policies that has been assembled to date. Across specifications, stay-at-home orders, mandatory mask requirements, beach and park closures, restaurant closures, and high risk (Level 2) business closures are the policies that most consistently predict lower 4- to 6- week-ahead fatality growth. Read more.
“Millionaires Speak: What Drives Their Personal Investment Decisions?” by Svetlana Bender, James J. Choi, Danielle Dyson (UBS), and UBS Adriana Robertson (University of Toronto - Faculty of Law)
We survey 2,484 U.S. individuals with at least $1 million of investable assets about how well leading academic theories describe their financial beliefs and decisions. The most important factors determining portfolio equity share are professional advice, time until retirement, personal experiences, rare disaster risk, and health risk. Beliefs about how expected returns vary with stock characteristics often differ from historical relationships, and more risk is not always associated with higher expected returns. Read more.
“Climate Finance” by Stefano Giglio, Bryan T. Kelly, Johannes Stroebel (New York University)
We review the literature studying interactions between climate change and financial markets. We first discuss various approaches to incorporating climate risk in macro-finance models. We then review the empirical literature that explores the pricing of climate risks across a large number of asset classes including real estate, equities, and fixed income securities. In this context, we also discuss how investors can use these assets to construct portfolios that hedge against climate risk. We conclude by proposing several promising directions for future research in climate finance. Read more.
“(Re-)Imag(in)ing Price Trends” by Jingwen Jiang (University of Chicago), Bryan T. Kelly, and Dacheng Xiu (University of Chicago)
We reconsider the idea of trend-based predictability using methods that flexibly learn price patterns that are most predictive of future returns, rather than testing hypothesized or pre-specified patterns (e.g., momentum and reversal). Our raw predictor data are images—stock-level price charts—from which we elicit the price patterns that best predict returns using machine learning image analysis methods. The predictive patterns we identify are largely distinct from trend signals commonly analyzed in the literature, give more accurate return predictions, translate into more profitable investment strategies, and are robust to a battery of specification variations. They also appear context-independent: Predictive patterns estimated at short time scales (e.g., daily data) give similarly strong predictions when applied at longer time scales (e.g., monthly), and patterns learned from US stocks predict equally well in international markets. Read more.
“Shadow Banks and Optimal Regulation” by Christopher Clayton, Andreas Schaab (Harvard University)
We develop a new framework to study regulatory policy in the presence of unregulated financial institutions ("shadow banks'') when there are pecuniary externalities. Using sufficient statistics, we show that optimal regulation in the presence of shadow banks is scaled by a "regulatory arbitrage multiplier.'' This multiplier only depends on aggregate shadow banking activity. Our framework provides guidance on how to regulate currently unregulated financial institutions and sectors. To first order, the marginal welfare gain of regulating a shadow bank is large when a notion of its intermediary activity substitution effects across its activities is large. We further characterize optimal activity-based regulation whereby the planner regulates a particular activity across all shadow banks, e.g. a tax on debt. To first order, gains from activity regulation are large when average substitution effects across intermediaries are large for the regulated activity. We show how our results extend to broader classes of non-pecuniary externalities. Read more.
“Adverse Selection Dynamics in Privately-Produced Safe Debt Markets” by Nathan Foley-Fisher (Board of Governors of the Federal Reserve System), Gary B. Gorton, Stephane Verani (Board of Governors of the Federal Reserve System)
Privately-produced safe debt is designed so that there is no adverse selection in trade. This is because no agent finds it profitable to produce private information about the debt's backing and all agents know this (i.e., it is information-insensitive). But in some macro states, it becomes profitable for some agents to produce private information, and then the debt faces adverse selection when traded (i.e., it becomes information-sensitive). We empirically study these adverse selection dynamics in a very important asset class, collateralized loan obligations, a large symbiotic appendage of the regulated banking system, which finances loans to below investment-grade firms. Read more.
“Modeling Corporate Bond Returns” by Bryan T. Kelly, Diogo Palhares (AQR Capital Management, LLC) and Seth Pruitt (Arizona State University)
We propose a new conditional factor model for returns on corporate bonds. The model has four factors with time-varying factor loadings that are instrumented by observable bond characteristics. We have three main empirical findings. The first is that our factor model excels in describing the risks and returns of corporate bonds, improving over previously proposed models in the literature by a large margin. Second, using bond characteristics to instrument evolving bond risk exposures significantly improves not only our model, but also previously proposed models of observable corporate bond factors. Third, our no-arbitrage model recommends a systematic bond investment portfolio that significantly outperforms leading corporate credit investment strategies. However, also we find that a ``pure alpha'' bond portfolio---which is orthogonal to factor risk---is incrementally profitable when combined with the no-arbitrage strategy. Read more.
“What Explains Differences in Finance Research Productivity During the Pandemic?” by Brad M. Barber (University of California, Davis), Wei Jiang (Columbia University), Adair Morse (University of California, Berkeley), Manju Puri (Duke University), Heather Tookes, Ingrid M. Werner (The Ohio State University)
How has COVID-19 impacted faculty productivity? Does it differ by characteristics such as gender and family structure? To answer these questions, we conduct a survey of American Finance Association (AFA) members. Overall, faculty respondents report lower research productivity with less time allocated to research and more time allocated to teaching. There is also heterogeneity: 14.5% of respondents report an increase in productivity. We find the negative effects on research productivity are particularly large for women and faculty with young children regardless of gender. Thus, the pandemic has the effect of widening the gender gap for women and creates a “family gap” in productivity for both men and women with young children. Lower research productivity for faculty with young children is explained, to a large extent, by increased time spent on childcare. Our results suggest the need for deliberate policy to factor in these underlying mechanisms. We caution that a one-size-fits-all tenure-clock extension can have unintended negative consequences of increasing disparity. Read more.