Knowledge that Transforms

Several influential studies show that transformations of implied volatilities calculated from options prices predict stock returns. This predictability is puzzling because market participants readily observe options prices. We find that this predictability is consistent with implied volatilities reflecting stock borrow fees that are known to predict stock returns. We derive a formula relating the option-implied volatility spread to the borrow fee. Motivated by this relation, we show that the return predictability from implied volatility spread and skew decreases by at least two-thirds if high-fee stocks are excluded. The patterns for other predictors computed from option implied volatilities are similar.

Using financial account data linking small businesses to their owner households, we examine how business owners’ consumption responded to changes in business revenues during the COVID-19 crisis. In the first two months following the National Emergency, business revenues declined by 40 percent, largely driven by national factors rather than local infection rates or policies. However, the pass-through of revenue losses to owner consumption was limited: each dollar of revenue loss resulted in only a 1.6-cent decline in consumption. This muted pass-through persisted through 2021, even after the introduction of COVID-19 vaccines. Our findings suggest that federal subsidies and pandemic-induced reductions in spending opportunities explain the limited impact.

We analyze the trade-offs of having intermediaries originate government-sponsored enterprise (GSE) mortgages using proprietary GSE data. We first find evidence of lenders pricing for observable and unobservable default risk independently of the GSEs. We then develop and estimate a model of competitive lending in which lenders have skin-in-the-game and conduct additional screening beyond the GSEs’ criteria. Lenders reduce costs via screening but also charge markups. On net, interest rates are higher compared to a counterfactual effectively without intermediaries. In an extension, the observed differences between banks and nonbanks are more consistent with differences in their skin-in-the-game rather than screening quality.

Houses with higher value uncertainty receive less mortgage credit: mortgages backed by these houses are more likely to be rejected, have higher interest rates, and have lower loan-to-price ratios. The relationship between house value uncertainty and credit availability is driven partly by a classic channel in which uncertainty lowers debt recovery rates, and partly by a novel channel where more uncertain appraisals make regulatory constraints on loan size more likely to bind. We build a structural model to quantify the effects of each channel, and show how a shift toward computerized asset appraisals could influence credit access.

Corporate earnings announcements unpack large bundles of public information that should, in efficient markets, trigger jumps in stock prices. Testing this implication is difficult in practice, as it requires noisy high-frequency data from after-hours markets, where most earnings announcements are released. Using a unique dataset and a new microstructure noise-robust jump test, we show that earnings announcements almost always induce jumps in the stock price of announcing firms. They also significantly raise the probability of price co-jumps in non-announcing firms and the market. We find that returns from a post-announcement trading strategy are consistent with efficient price formation after 2016.

Identifying firms’ bond-market-specific economic links through credit-rating comovement of their corporate bonds, a long-short strategy for stocks based on these links generates a risk-adjusted alpha of 0.45% per month, which cannot be explained by existing economic links in the literature. Market segmentation between the equity and bond markets appears to be the underlying mechanism: (i) The cross-return predictability is muted in the bond market; (ii) The cross-return predictability is mitigated in the presence of cross-holding investors; (iii) Equity analysts slowly incorporate information from rating-comovement links to their forecasts.

This article investigates predictors and benefits of corporate adaptation to crisis, adding a new dimension to studies of flexibility and resilience based on ex ante characteristics. We produce a unique sample of work-from-home announcements scraped from company websites during Covid-19. The announcers’ valuations increased by 3%–5% and risk declined versus matches, consistent with real-options theory under asymmetric information. We estimate characteristics, including subtle textual topics from 10-Ks, that predicted adaptation, show faster price response following Bloomberg coverage, and real advantages in subsequent operating performance. Corporate adaptation to crisis adds value and reduces risk, beyond information in firm characteristics.

We provide a spatial framework to study competition between banks and fintechs in the lending market and examine the impact on investment and welfare. Based on the key differences between banks and fintechs, we derive results consistent with the empirical evidence available. We find that fintechs with inferior monitoring efficiency can successfully enter because of their superior flexibility in pricing and that higher bank concentration leads to higher fintech loan volume. If fintechs and banks have similar funding costs, fintech borrowers pay lower loan rates and have higher default rates than bank borrowers with similar characteristics; however, the result will flip if fintechs have much higher funding costs than banks. The advantage of fintechs in offering convenience can also induce them to charge higher loan rates than banks. Fintech entry will improve welfare if fintechs have high monitoring efficiency and inter-fintech competition intensity is intermediate. Fintech entry may induce banks’ exit and reduce investment; however, it will increase investment if inter-fintech competition is intense enough.

We examine the adoption rates of responsible investment (RI) policies among university endowments. Adoption rates are higher among universities that face stakeholder pressure and are donation-dependent. Policy adoption predicts greater abnormal donations totaling 12 % of endowment assets, especially from “socially conscious” donors and during periods of higher media attention to climate change. Universities also experience greater student applications following adoptions. RI endowments have greater management costs, greater return volatility, and similar overall asset growth (donations plus net-of-cost investment income) compared to non-RI endowments. We conclude that RI policies are an important part of the optimal contract between universities and their stakeholders.

Distributed ledgers promise to enable the classical vision of money as a universal transaction record. But is it ever optimal to update a ledger through decentralized consensus? Analyzing an exchange economy with credit, we show that centralized updating is optimal when long-term rewards are more valued, minimizing redundant validation costs and maximizing economic surplus. Decentralization becomes preferable under weaker intertemporal incentives and when validators are drawn from market participants. We show how competing ledgers – anonymous or identified, permissioned or permissionless – can achieve socially optimal outcomes even in low-trust environments. Our framework provides a foundation for designing robust and efficient ledger systems.