Knowledge that Transforms

Open banking (OB) empowers bank customers to share their financial transaction data with fintechs and other banks. New cross-country data shows 49 countries adopted OB policies, privacy preferences predict policy adoption, and adoption spurs fintech entry. UK microdata shows that OB enables: (i) consumers to access both financial advice and credit; (ii) SMEs to establish new lending relationships. In a calibrated model, OB universally improves welfare through entry and product improvements when used for advice. When used for credit, OB promotes entry and competition by reducing adverse selection, but higher prices for costlier or privacy-conscious consumers partially offset these benefits.

Most online marketplaces are peer-to-peer. Credit ones, however, are not and they have resurrected many features of traditional financial intermediaries. To understand why, we use online credit as a laboratory to investigate the value of financial intermediation. We develop a structural model of online debt crowdfunding and estimate it on a novel database. We find that abandoning the peer-to-peer paradigm raises lender surplus, platform profits, and credit provision, but exposes investors to liquidity risk. A counterfactual where the platform resembles a bank by bearing liquidity risk can generate larger lender surplus and credit provision when liquidity is low and lenders are risk averse.

We study debt and equity valuation when investors have private information and may exhibit differences of opinion. Our model generates several predictions that are consistent with empirical evidence but difficult to reconcile with traditional models. Belief dispersion relates to expected equity and debt returns in opposite directions. Similarly, expected debt (equity) returns typically increase (decrease) with default risk, though these relationships reverse for firms close to bankruptcy. Firms’ capital structures affect their valuations even without classical capital structure frictions (e.g., tax shields, distress costs) – when liquidity is higher in the equity than in the debt market, leverage can raise firm value.

For variables with a term structure, the standard difference-in-differences (DiD) model is predisposed toward misspecification, even under random assignment, because of heterogeneity over the maturity spectrum and imperfect matching between treated and control units . Estimated treatment effects that are false, biased, or hard to interpret become a concern. Neither unit fixed effects nor standard term-structure controls resolve the problem. Solutions that overcome imperfect matching involve estimating the term structure of hypothesized treatment, which is also what is economically interesting (regardless of matching efficiency). These issues are not unique to DiD analysis, but are generic to group-assignment settings.

We present a model in which firms compete for workers who value nonpecuniary job attributes, such as purpose, sustainability, political stances, or working conditions. Firms adopt production technologies that enable them to offer jobs with varying levels of these desirable attributes. Firms’ profits are higher when they cater to workers with extreme preferences. In a competitive assignment equilibrium, firms become polarized and not only reflect but also amplify the polarized preferences of the general population. More polarized sectors exhibit higher profits, lower average wages, and a reduced labor share of value added. Sustainable investing amplifies firm polarization.

Using global mutual fund and American Depositary Receipt (ADR) data, we test if funds strategically trade cross-listed firms’ equity shares in the most liquid trading location. We find that especially funds that score high on traditional skill measures exhibit a liquidity-based trading venue preference. We identify an informed trading motive as the most likely driver for such behaviour rather than preference based on geographic, economic, cultural, or governance motives. Thus, liquidity picking is associated with fund outperformance and stock selection ability that is not limited to only cross-listed firms. Our tests directly support theories of informed trading in a multi-market setting.

We leverage the capabilities of GPT-3 to generate historical business descriptions for over 63,000 global firms . Utilizing these descriptions and advanced embedding models from OpenAI, we construct time-varying business networks that represent business links across the globe. We showcase the performance of these networks by studying the lead–lag effect for global stocks and predicting target firms in M&A deals. We demonstrate how masking firm-specific details can mitigate look-ahead bias concerns that may arise from the use of embedding models with a recent knowledge cutoff, and how to differentiate between competitor, supplier, and customer links by fine-tuning an open-source language model .

We develop a novel measure of job-worker allocation quality ( JAQ ) by exploiting employer-employee data with machine learning techniques. Based on our measure, the quality of job-worker matching correlates positively with individual labor earnings and firm productivity, as well as with market competition, non-family firm status, and employees’ human capital . Management plays a key role in job-worker matching: when managerial hirings and firings persistently raise management quality , the matching of rank-and-file workers to their jobs improves. JAQ can be constructed from any employer–employee data set including workers’ occupations, and used to explore research questions in corporate finance and organization economics.

We propose a model in which arbitrageurs act strategically in markets with entry costs. In a repeated game, arbitrageurs choose to specialize in some markets, which leads to the highest combined profits. We present evidence consistent with our theory from the options market, in which suboptimally unexercised options create arbitrage opportunities for intermediaries. We use transaction-level data to identify the corresponding arbitrage trades. Consistent with the model, only 57% of these opportunities attract entry by arbitrageurs. Of those that do, 49% attract only one arbitrageur. Finally, we detail how market participants circumvent a regulation devised to curtail this arbitrage strategy.

We model the competition between digital forms of fiat money and private digital money. Countries digitize their currencies–by upgrading existing or launching new payment systems (including CBDCs)–to compete with foreign fiat currencies and private digital money. A pecking order emerges: less dominant currencies digitize earlier, reflecting a first-mover advantage; dominant currencies delay digitization until they face competition; the weakest currencies forgo digitization. However, delayed digitization allows private digital money to gain widespread adoption, eventually weakening fiat money’s role. We highlight how geopolitical considerations, stablecoins, and interoperability between fiat and private digital money shape the dynamics of currency competition.