Knowledge that Transforms

Using the exclusion of business development companies (BDCs) from stock indexes, this paper studies the effectiveness of market discipline in the direct lending space. Amid share sell-offs by institutional investors, a drop in BDCs’ valuations limits their ability to raise new equity capital. Following this funding shock, BDCs do not adjust their capital structure. At the same time, they are reducing the risk exposure of their portfolios. We document a greater reduction in risk for BDCs subject to stronger market discipline from their debtholders. BDCs pass through the capital shock to their portfolio firms by reducing their investment intensity.

The real estate market is highly intermediated, with 90% of buyers and sellers hiring an agent. However, low barriers to entry and fixed commission rates result in large market share for inexperienced intermediaries. Using micro-level data on 8.5 million listings and a novel research design, we show that house listings by inexperienced agents have a lower probability of selling, and this effect is strongest during the housing bust. We estimate that 3.7% more listings would have been sold in a flexible commission equilibrium. Eighty percent of this improvement comes from competition and the remainder from commission variation across experience.

The classic view assumes banks prioritize immediate repayment by selling assets until default. We endogenize run frequency and study how general settlement rules trade off liquidity provision net of fire sale losses against induced run incentives. Panic runs are eliminated when all illiquid assets are sold under orderly resolution, but liquidity provision in a run is minimal. When suspension after some fire sales is followed by immediate liquidation, run frequency falls then rises in suspension delay. Thus, optimal suspension may require some sale of illiquid assets, in contrast to MMF norms. Ex post discretion induces excessive liquidation and more frequent runs. (JEL D8, G21)

We develop an algorithm for solving a large class of nonlinear high-dimensional continuous-time models in finance. We approximate value and policy functions using deep learning and show that a combination of automatic differentiation and Ito’s lemma allows for the computation of exact expectations, resulting in a negligible computational cost that is independent of the number of state variables. We illustrate the applicability of our method to problems in asset pricing, corporate finance, and portfolio choice and show that the ability to solve high-dimensional problems allows us to derive new economic insights.

We study how consumer spending responds to digital payments, using the differential switch to digital payments across consumers induced by the sudden 2016 Indian Demonetization for identification. Digital payment use rose by 2.94 percentage points and monthly spending increased by 2.38% for an additional 10 percentage points in prior cash dependence. Spending remained elevated even when cash availability recovered. Robustness analyses show that the spending response is not driven by purchase substitution, income shocks, credit supply, or price changes. We provide causal evidence that digital payments increase consumer spending due to subdued endowment effects.

We argue that institutional brokerage networks facilitate liquidity provision and mitigate the price impact of large non-information-motivated trades. Using commissions, we map trading networks of mutual funds (institutions) and their brokers. Central funds (institutions) tend to outperform their peripheral counterparts in terms of return gap (execution shortfall). This outperformance is more pronounced when funds experience large outflows and for large trades in less liquid stocks. Central brokers can deliver superior trade execution compared to peripheral brokers, but mainly for central institutions. We use the collapse of Lehman Brothers as a quasi-natural experiment to establish the likely causality of our findings.

We provide a systematic empirical assessment of the Minsky hypothesis that business fluctuations stem from irrational swings in expectations. Using predictable firm-level forecast errors, we build an aggregate index of irrational expectations and use it to provide three sets of results. First, we show that our index predicts aggregate credit cycles. Next, we show that these predictable credit cycles drive cycles in firm-level debt issuance and investment and similar cycles between financially constrained and unconstrained firms, as Minsky predicts. Finally, we show more pronounced cycles in firm-level financing and investment for firms with ex ante more optimistic expectations. (JEL G31, G32, G40, E32, E44)

We propose a loglinear present-value identity in which investment (“scale”), profitability (“yield”), and discount rates determine a firm’s market-to-book ratio. Our identity reconciles existing influential market-to-book decompositions and facilitates novel insights from three empirical applications: (1) Both investment and profitability are important contributors to the value spread and stock return news variance. (2) Any cross-sectional return predictability has a mirror image in cash-flow fundamentals, providing asset pricing theories with additional moments to match. (3) The investment spread significantly improves the predictability of time-series variation in the value premium and justifies the poor performance of value in recent years.

Health care costs for U.S. employers have tripled over the past 20 years. Using firm-specific health expense data, I show that firms negatively adjust capital expenditures and R&D expenses in response to increases in health care costs. The effects are more pronounced for firms that are financially constrained, employ more high-skilled workers, and have less bargaining power relative to insurers. Furthermore, policy uncertainty surrounding health care costs is substantial and discourages capital investment. These findings suggest that an elevated level of health care costs and the associated uncertainty limit a firm’s ability to expand physically or through innovation.

Hedge fund teams with heterogeneous educational backgrounds, academic specializations, work experiences, genders, and races, outperform homogeneous teams after adjusting for risk and fund characteristics. An event study of manager team transitions, instrumental variable regressions, and an analysis of managers who simultaneously operate solo- and team-managed funds address endogeneity concerns. Diverse teams deliver superior returns by arbitraging more stock anomalies, avoiding behavioral biases, and minimizing downside risks. Moreover, diversity allows hedge funds to circumvent capacity constraints and generate persistent performance. Our results suggest that diversity adds value in asset management. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online