Knowledge that Transforms

Moreira and Muir question the existence of a strong risk‐return trade‐off by showing that investors can improve performance by reducing exposure to risk factors when their volatility is high. However, Cederburg et al. show that these strategies fail out‐of‐sample, and Barroso and Detzel show they do not survive transaction costs. We propose a conditional multifactor portfolio that outperforms its unconditional counterpart even out‐of‐sample and net of costs. Moreover, we show that factor risk prices generally decrease with market volatility. Our results demonstrate that the breakdown of the risk‐return trade‐off is more puzzling than previously thought.

We introduce a new measure of the price charged by financial intermediaries for connecting mortgage borrowers with capital market investors. Based on administrative lender pricing data, we document that the price of intermediation reacts strongly to variation in demand, reflecting capacity constraints of mortgage originators. This positive comovement of price with quantity reduced the pass‐through of quantitative easing. We also find a notable upward trend in this price between 2008 and 2014, likely due to increased legal and regulatory burden in the mortgage market. The trend led to an implicit cost to borrowers of nearly $100 billion over this period.

Based on automated credit lines to vendors trading on Alibaba's online retail platform and a discontinuity in the credit decision algorithm, we document that a vendor's access to FinTech credit boosts its sales growth, transaction growth, and the level of customer satisfaction gauged by product, service, and consignment ratings. These effects are more pronounced for vendors characterized by greater information asymmetry about their credit risk and less collateral, which reveals the information advantage of FinTech credit over traditional credit technology.

Process automation reduces racial disparities in credit access by enabling smaller loans, broadening banks' geographic reach, and removing human biases from decision making. We document these findings in the context of the Paycheck Protection Program (PPP), where private lenders faced no credit risk but decided which firms to serve. Black‐owned firms obtained PPP loans primarily from automated fintech lenders, especially in areas with high racial animus. After traditional banks automated their loan processing procedures, their PPP lending to Black‐owned firms increased. Our findings cannot be fully explained by racial differences in loan application behaviors, preexisting banking relationships, firm performance, or fraud rates.

We examine the timing of returns around the publication of anomaly trading signals. Using a database that captures when information is first publicly released, we show that anomaly returns are concentrated in the first month after information release dates, and these returns decay soon thereafter. We also show that the academic convention of forming portfolios in June underestimates predictability because it uses stale information, which makes some anomalies appear insignificant. In contrast, we show many anomalies do predict returns if portfolios are formed immediately after information releases. Finally, we develop guidance on forming portfolios without using stale information.

We show that utility tokens can limit the rent‐seeking activities of two‐sided platforms with market power while preserving efficiency gains due to network effects. We model platforms where buyers and sellers can meet to exchange services. Tokens serve as the sole medium of exchange on a platform and can be traded in a secondary market. Tokenizing a platform commits a firm to give up monopolistic rents associated with the control of the platform, leading to long‐run competitive prices. We show how the threat of entrants can incentivize developers to tokenize and discuss cases where regulation is needed to enforce tokenization.

Much of the extant literature predicts market returns with “simple” models that use only a few parameters. Contrary to conventional wisdom, we theoretically prove that simple models severely understate return predictability compared to “complex” models in which the number of parameters exceeds the number of observations. We empirically document the virtue of complexity in U.S. equity market return prediction. Our findings establish the rationale for modeling expected returns through machine learning.

I present a model of consumption and savings for a multiperson household in which members are imperfectly altruistic, derive utility from both private and shared public goods, and share wealth. I show that, despite having standard exponential time preferences, the household is time‐inconsistent: Members save too little and overspend on private consumption goods. The household remains time‐inconsistent even when members save separately, because the possibility of voluntary transfers or joint contribution to the public good preserves the dynamic commons problem. The household will choose to share wealth when the risk‐sharing benefits outweigh the utility cost of overconsumption.

We decompose mutual fund value added by the length of funds' holdings using transaction‐level data. We motivate our decomposition with a model featuring horizon‐specific investment ideas, where short‐term ideas are less scalable because the associated trades cannot be spread over time. Fund turnover correlates negatively with the horizon over which value is added and positively with price impact costs. As predicted, holdings of high‐turnover funds add a substantial amount of value in the first two weeks, of which more than 80% is earned on Federal Open Market Committee (FOMC) and earnings announcement days. Holdings of low‐turnover funds add value only over longer horizons.

The prevalence of pay based on risky firm outcomes for nonexecutive workers presents a puzzling departure from conventional contract theory, which predicts insurance provision by the firm. When workers at the same firm compete against each other for promotions, the optimal contract features pay based on firm outcomes as insurance against promotion risk. The model's predictions are consistent with many observed phenomena, such as performance‐based vesting and overvaluation of equity pay by nonexecutive workers. It also generates novel predictions linking a firm's hierarchy to its workers' pay structure.