Knowledge that Transforms

We exploit the information content of option prices to construct a novel measure of bank tail risk. We document a persistent increase in tail risk for the U.S. banking industry following the global financial crisis, except for banks designated as systemically important by the Dodd–Frank Act. We show that this post-crisis difference in tail risk for large and small banks is consistent with the too-big-to-fail (TBTF) status of large banks being reinforced by the Dodd–Frank designation: Naming the banks whose failure could threaten the financial stability of the U.S. gave investors a list of banks the government deemed as TBTF.

We propose a novel measure of the market return tail risk premium based on minimum-distance state price densities recovered from high-frequency data. The tail risk premium extracted from intra-day S&P 500 returns predicts the market equity and variance risk premiums and expected excess returns on a cross section of characteristics-sorted portfolios. Additionally, we describe the differential role of the quantity of tail risk, and of the tail premium, in shaping the future distribution of index returns. Our results are robust to controlling for established measures of variance and tail risk, and of risk premiums, in the predictive models.

The onset of COVID-19 was characterized by voluminous, negative news. Higher narrativity news topics (measured by textual proximity to articles describing the 1987 stock market crash and textual distance from Federal Reserve communications) were systematically associated with contemporaneous market responses, which were larger on high volatility days (hypersensitivity), and with markets–news feedback. Hypersensitive news topic-market pairs were associated with next-day reversals. A test using the news–markets relationship identifies a mid-March 2020 structural break, which was knowable by the end of April. Post break, markets and news became considerably less coupled, and hypersensitivity and reversals abated.

Big data on job postings reveal multiple facets of the impact of COVID-19 on corporate hiring. Firms disproportionately cut new hiring for high-skill positions, with financially constrained firms reducing skilled hiring the most. Applying machine learning methods to job-ad texts, we find that firms have skewed their hiring toward operationally-core functions. New positions display greater flexibility regarding schedules and tasks. While job posting levels show signs of recovery starting in late-2020, changes to job descriptions and skill profiles persist through early-2022. Financial constraints amplify these changes, with constrained firms’ new hires witnessing greater adjustments to job roles and employment arrangements.

We view innovation investments as real options and explore the implications of risk (volatility) as well as a newly defined outcome independent measure of ambiguity (Knightian uncertainty) for innovation decisions. The empirical analysis uses stock returns to compute an implementable measure of ambiguity. We also control for risk and other determinants of innovation. We find a consistently significant negative effect of ambiguity on R&D, patents, and citations, as predicted. The effect of risk on R&D is positive and significant, but the corresponding effect on patents and citations is negative and significant. Ambiguity matters more for high-tech firms, consistent with intuition.

We find that male participants in Harvard Business School’s New Venture Competition who were randomly exposed to more venture capital (VC) investors on their panel were substantially more likely to start a VC-backed startup post-graduation, indicating that access to investors impacts fundraising independent of the quality of ideas. However, female participants experience no benefit from exposure to male or female venture capitalists (VCs), which appears related to a reduced propensity to reach out to VCs to whom they were exposed. Our results therefore also demonstrate gender-based differences in the degree to which increased exposure to investors can address networking frictions in venture capital.

By employing a dynamic model with two limit order books, we show that fragmentation is associated with reduced competition among liquidity suppliers and lower picking-off risk of limit orders. Due to these countervailing channels, the impact of fragmentation on liquidity and welfare differs with asset volatility: When volatility is high (low), liquidity and aggregate welfare in a fragmented market are higher (lower) than in a single market. However, fragmentation always shifts welfare away from agents with exogenous trading motives and toward intermediaries. We empirically corroborate our model’s predictions about liquidity. Our model reconciles the mixed results in the empirical literature.

I introduce two dimensions of uncertainty, about the upside and the downside of an asset, in a model of asset valuation under asymmetric information. This justifies capital structures with equity and risky debt for information revelation purposes. However, a capital structure with only one information-sensitive security, equity, can be optimal when investors are less informed about the dimension that matters more for valuation. This is relevant for innovative firms with a large upside subject to strong information asymmetries, which often have abnormally low leverage, and for firms at an intermediate stage of their life cycle that do not issue risky debt.

During the first half of 2020, the difference in savings from mortgage refinancing between high- and low-income borrowers was 10 times higher than before. This was the result of two factors: high-income borrowers increased their refinancing activity more than otherwise comparable low-income borrowers and, conditional on refinancing, they captured slightly larger improvements in interest rates. Refinancing inequality increases with the severity of the COVID-19 pandemic and is characterized by an underrepresentation of low-income borrowers in the pool of applications. We estimate a difference of $5 billion in savings between the top income quintile and the rest of the market.

In business research, firm size is both ubiquitous and readily measured. Complexity, another firm-related construct, is also relevant, but difficult to measure and not well-defined. As a result, complexity is less frequently incorporated in empirical designs. We argue that most extant measures of complexity are one-dimensional, have limited availability, and/or are frequently misspecified. Using both machine learning and an application-specific lexicon, we develop a text solution that uses widely available data and provides an omnibus measure of complexity. Our proposed measure, used in tandem with 10-K file size, provides a useful proxy that dominates traditional measures.