Knowledge that Transforms

Although algorithmic trading now dominates financial markets, some exchanges continue to use human floor traders. On March 23, 2020 the NYSE suspended floor trading because of COVID‐19. Using a difference‐in‐differences analysis around the closure of the floor, we find that floor traders are important contributors to market quality. The suspension of floor trading leads to higher spreads and larger pricing errors for treated stocks relative to control stocks. To explore the mechanism, we exploit two partial floor reopenings that have different characteristics. Our finding suggests that in‐person human interaction facilitates the transfer of valuable information that algorithms lack.

We model the “feedback effect” of a firm's stock price on investment in projects exposed to a systematic risk factor, like climate risk. The stock price reflects information about both the project's cash flows and its discount rate. A cash‐flow‐maximizing manager treats discount rate fluctuations as “noise,” but a price‐maximizing manager interprets such variation as information about the project's net present value. This difference qualitatively changes how investment behavior varies with the project's risk exposure. Moreover, traditional objectives (e.g., cash flow or price maximization) need not maximize welfare because they do not correctly account for hedging and risk‐sharing benefits of investment.

At‐scale field experiments at major U.K. banks show that automatic enrollment into “just‐in‐time” text alerts reduces unarranged overdraft and unpaid item charges 17% to 19% and arranged overdraft charges 4% to 8%, implying annual market‐wide savings of £170 million to £240 million. Incremental benefits from “early‐warning” alerts are statistically insignificant, although economically significant effects are not ruled out. Prior to the experiments, over half of overdrafts could have been avoided by using lower‐cost liquidity available in savings and credit card accounts. Alerts help consumers achieve less than half of these potential savings.

Uniswap is a system of smart contracts on the Ethereum blockchain and is the largest decentralized exchange with a liquidity balance worth up to 4 billion USD and daily trading volume of up to 7 billion USD. It is a new model of liquidity provision, so‐called automated market making. For this new market form, we characterize equilibrium in the liquidity pools. We collect all 95.8 million Uniswap interactions and compare this automated market maker (AMM) to a centralized limit order book. We document absence of long‐lived arbitrage opportunities, and show conditions under which the AMM dominates a limit order market.

We study the asset market for pollutive plants. Firms divest pollutive plants in response to environmental pressures. Buyers are firms facing weaker environmental pressures that have supply chain relationships or joint ventures with the sellers. While pollution levels do not decline following divestitures, sellers highlight their sustainable policies in subsequent conference calls, earn higher returns as they sell more pollutive plants, and benefit from higher Environmental, Social, and Governance (ESG) ratings and lower compliance costs. Overall, the asset market allows firms to redraw their boundaries in a manner perceived as environmentally friendly without real consequences for pollution but with substantial gains from trade.

We investigate a seller's strategic choice between optimally structured negotiations with fewer bidders and an auction with more competing bidders when payments can have a contingent component, as is common in mergers and acquisitions (M&A), patent licensing, and employee compensation. The key factor favoring negotiations is that it allows the seller to set her preferred payment structure—that is, the revenue‐maximizing mix of cash and contingent pay; reserve prices are of secondary importance. Negotiations are more likely to dominate if synergies increase in bidders' productivity types (as with acquirer‐target complementarities in M&A). Higher dispersion and magnitude of bidders' private valuations also favor negotiations.

We study a search and bargaining model of over‐the‐counter markets for nonstandardized assets of heterogeneous quality. Once matched, investors privately learn their values positively correlated with asset quality. Bargaining results in delay that is hump‐shaped in quality and U‐shaped in asset turnover. We document these patterns in commercial real estate and corporate bonds markets. Extreme qualities are little affected by changes in asset standardization, while intermediate qualities are more susceptible. For nonstandardized assets, opacity ensures active trading of all assets, which explains why their trading is decentralized and suggests that trade centralization should come with greater standardization.

We assess racial discrimination in mortgage approvals using confidential data on mortgage applications. Minority applicants tend to have lower credit scores and higher leverage, and are less likely to receive algorithmic approval from race‐blind automated underwriting systems (AUS). Observable applicant‐risk factors explain most of the racial disparities in lender denials. Further, exploiting the AUS data, we show there are risk factors we do not observe, and these factors at least partially explain the residual 1 to 2 percentage point denial gaps. We conclude that differential treatment plays a more limited role in generating denial disparities than previous research suggests.

Portfolio allocation decisions increasingly incorporate social values. We develop a tractable framework to study how competition between investors to own socially valuable assets affects social welfare. Relative to the most common social‐investing strategies, we identify alternative strategies that result in higher impact and higher financial returns. We identify strategies for investors to have impact when impact is difficult to measure. From the firm's perspective, increasing profitability can have greater impact than directly increasing social value. We present new empirical evidence on the social preferences of investors that demonstrates the practical relevance of our theory.

We study the optimal design of stress scenarios. A principal manages the unknown risk exposures of agents by asking them to report losses under hypothetical scenarios before taking remedial actions. We apply a Kalman filter to solve the learning problem, and we relate the optimal design to the risk environment, the principal's preferences, and available interventions. In a banking context, optimal capital requirements cover losses under an adverse scenario, while targeted interventions depend on covariances among residual exposures and systematic risks. Our calibration reveals that information is particularly valuable for targeted interventions as opposed to broad capital requirements.