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Market-Based Solutions to Vital Economic Issues


Kenan Institute 2022 Annual Theme: Stakeholder Capitalism
Market-Based Solutions to Vital Economic Issues



The data-generating process of productivity growth includes both trend and business-cycle shocks, generating many counterfactuals for prices under full-information. In practice, agents cannot immediately distinguish between the two shocks, leading to "rational confusion": each shock inherits properties of its counterpart.

We study the relation between trade credit, asset prices, and production-network linkages. Empirically, firms extending more trade credit earn 7.6% p.a. lower risk premiums and maintain longer relationships with customers.

Kenan Institute Research Director Christian Lundblad navigated the cognitive dissonance provided by another strong jobs report when considered alongside more negative indicators during the institute’s latest economic briefing July 8. The virtual event took place at 9 a.m. after the release of the latest monthly employment numbers. Lundblad also answered questions from the audience, including limitations on the Federal Reserve in addressing core consumer price issues, the differences among regional labor markets, and the probability of an actual recession vs. a technical recession occurring this year.

We coin the term credit market fluidity to describe the intensity of credit reallocation, whose properties and implications we study within the commercial loan market in France over the period 1998 through 2018. We base our analysis on credit register data and thus provide a more complete account of gross credit flows across and within bank loan portfolios.

This study uses passage of the Dodd-Frank Act as a setting to examine whether changes in legal liability exposure faced by credit rating agencies affect the number of financial statement information signals required before rating changes. For upgrades, we predict and find that the greater legal exposure after the Act incentivized rating agencies to require more information signals, i.e., a greater number of prior quarters in which upgrades were implied by financial statement information.

We document that there is commonality in the loan fees that short sellers pay, and the common component of loan fees explains a significant amount of loan fee variation. While the top principal component of stock returns only explains 28.3% of their variation, we find that the top principal component of loan fees explains 45.6% of their variation.

The psychology literature documents that individuals derive current utility from their beliefs about future events. We show that, as a result, investors in financial markets choose to disagree about both private and price information. When objective price informativeness is low, each investor dismisses the private signals of others and ignores price information. In contrast, when prices are sufficiently informative, heterogeneous interpretations arise endogenously: most investors ignore prices, while the rest condition on it.

Using United States tax return data containing the universe of individual taxable stock sales from 2008 to 2009, we examine which individuals increased their sale of stocks following episodes of market tumult. We find that the increase was disproportionately concentrated among investors in the top 1% and top 0.1% of the overall income distribution, retired individuals and individuals at the very top of the dividend income distribution. Our estimates suggest that, following the day when Lehman Brothers collapsed, taxpayers in the top 0.1% sold $1.7 billion more in stocks than individuals in the bottom 75%. This difference is equal to 89% of average daily sales by taxpayers in the top 0.1%.

Financial markets reveal information which firm managers can utilize when making equity value-enhancing investment decisions. However, for firms with risky debt, such investments are not necessarily socially efficient. Despite this friction, we show that learning from prices improves investment efficiency.

Join us to hear from Seth Lloyd, Professor of Mechanical Engineering and Physics at MIT, as he shares his findings on quantum algorithms for analyzing financial data and predicting time series

We introduce a new framework that facilitates term structure modeling with both positive interest rates and flexible time-series dynamics but that is also tractable, meaning amenable to quick and robust estimation. Using both simulations and U.S. historical data, we compare our approach with benchmark Gaussian, stochastic volatility, and shadow rate models, where the latter enforces positive interest rates.

Participants include Jim Goldman, Assistant Professor of Financial Economics, University of Toronto; Eva Steiner, Associate Professor of Real Estate, Penn State University; Jay R. Ritter, Joseph B. Cordell Eminent Scholar, Warrington College of Business, University of Florida; Allyson Tucker, Chief Investment Officer, Washington State Investment Board; Michael Elio, Partner, StepStone; Christian Lundblad, Richard Levin Distinguished Professor of Finance, Director of Research, Kenan Institute of Private Enterprise; Matt Harvey, Managing Director, Head of Direct Lending, PGIM Private Capital; and David Sambur, Apollo Global Management, Inc.