Knowledge that Transforms

Intermediaries reduce agency problems through monitoring, but credible monitoring requires sufficient retention until the loan matures. We study credit markets when intermediaries cannot commit to retention. Two structures are examined: investors lending alongside an all-equity bank and investors lending through the bank via short-term debt. With a commitment to retention, they are equivalent. Without commitment, the all-equity bank sells loans and reduces monitoring over time. Short-term debt encourages the intermediary to retain loans and incentivizes monitoring. Our analysis provides a novel mechanism for intermediaries’ reliance on short-term debt—the constant repricing of debt creates incentives that resolve the commitment problem in loan retention and monitoring.

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.

Lenders can exploit households’ payment data to infer their creditworthiness. When households value privacy, they then face a tradeoff between protecting such privacy and attaining better credit conditions. We study how introducing an informationally more intrusive digital payment vehicle affects households’ cash use, credit access, and welfare. A tech monopolist controls the intrusiveness of the new payment method and manipulates information asymmetries among households and oligopolistic banks to extract data contracts that are more lucrative than lending on its own. The laissez-faire equilibrium entails a digital payment vehicle that is more intrusive than socially optimal, providing a rationale for regulation.

We explore indicators of market power in a data market. Markups cannot measure competition, because most data products’ marginal cost is zero, making the markup infinite. Yet, data monopolists may not exert monopoly power because they cannot commit to restricting data sales to future customers. This limited commitment and strategic substitutability of data undermine sellers’ monopoly power. But data subscriptions restore this monopoly power. Evidence from online data markets supports the model’s insight that subscriptions indicate market power. Model and evidence reveal that data subscriptions are better for consumers because they sustain the incentive to invest in high-quality data.

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.

Dark pools can restrict access for specific trader types. We compare execution outcomes between dark pools that restrict high frequency trader access and those that do not. We find that trades executed in dark pools with more access restrictions have less order flow information leakage, adverse selection risk and post-trade order imbalances than trades in less restricted pools. Evidence from exogenous dark pool closures demonstrates that these differences are causal. The ability to segment order flow can benefit investors because it allows them to make trade-offs between execution risk and information leakage across different dark venues.

This paper presents a framework to study how investors’ social concerns affect technology choices. Consequentialist preferences (disutility from aggregate harm) influence outcomes only if investors coordinate, unless internalized harm is independent of an investor’s mass. Non-consequentialist preferences (disutility from stockholdings) affect outcomes regardless of coordination. Both preferences have stronger impact when risk-sharing consequences of technology supply are small (e.g., highly correlated returns), and their effects cannot be inferred from cost-of-capital differences. When harm is stochastic, polluting firms may appear less risky to social investors. Depending on type and strength of social preferences, this can support or hinder the green transition.

We construct real-time machine learning strategies based on a “universe” of fundamental signals. The out-of-sample performance of these strategies is economically meaningful and statistically significant, but considerably weaker than those documented by prior studies that use curated sets of signals as predictors. Strategies based on a simple recursive ranking of each signal’s past performance also yield substantially better out-of-sample performance. We find qualitatively similar results when examining past-return-based signals. Our results underscore the key role of feature engineering and, more broadly, inductive biases in enhancing the economic benefits of machine learning investment strategies.

We propose a framework to study regulatory complexity, based on concepts from computer science. We distinguish different dimensions of complexity, classify existing measures, develop new ones, compute them on three examples — Basel I, the Dodd–Frank Act, and the European Banking Authority’s reporting rules — and test them using experiments and a survey on compliance costs. We highlight two measures that capture complexity beyond the length of a regulation. We propose a quantitative approach to the policy trade-off between regulatory complexity and precision.

Financial benchmarks such as LIBOR underpin the pricing of trillions of dollars of contracts around the world. We evaluate the quality of benchmark prices using a state-space model to separate information from noise. Applying the method to LIBOR benchmarks and their replacements, we find that alternative reference rates (ARRs) are less noisy in four of the five currencies. However, the USD ARR is considerably more noisy, resulting in billions of dollars of noise-related wealth transfers between contract counterparties. We show that benchmark reforms such as expanding the reference market and using a trimmed mean can reduce noise in ARRs.