Knowledge that Transforms

We study the valuation of collateral by comparing spreads on loans by the same bank, to the same borrower, at the same origination date, but backed by different types of collateral. Pledging collateral reduces borrowing costs by 23 BPS on average. The effect varies across different types of collateral, with marketable securities being most valuable, and real estate and accounts receivables and inventory being more valuable than fixed assets and a blanket lien. Further, the rate reduction from pledging collateral is sensitive to the value of the underlying collateral, and collateral tends to be more valuable for smaller and private firms and for loans with longer maturity.

Pricing is integral to insurance design, directly influencing firm behavior and moral hazard, though its effects are insufficiently understood. I study a quasi-experiment in which deposit insurance premiums were changed for U.S. banks with unequal timing, generating differentials between banks in both levels and risk-based “steepness” of premiums. I find evidence that differentials in premiums resulted in distortions, including regulatory arbitrage, but also provided strong incentives to curb moral hazard. I find that firms that faced stronger pricing incentives to become (or remain) safer were more likely to subsequently do so than similar firms that faced weaker pricing incentives.

The literature provides conflicting arguments and mixed results regarding whether capital markets punish managerial myopia. Using managers cutting research and development (R&D) investments to meet short-term earnings goals as a research setting, this study reveals that capital markets penalize managerial myopia, especially for firms with high investor sophistication. Moreover, the negative market reactions to managerial myopia are weaker for firms with overinvestment problems than for those without such problems. Overall, the results support the notion that security markets are not shortsighted. In further analysis, we document that compensation, especially earnings-based compensation, may cause managers to behave myopically. Our study contributes to the literature, reconciling previously mixed findings by capturing managers’ myopic behavior in a more targeted way and showing that markets punish myopic R&D cutting.

This article presents an augmented deep factor model that generates latent factors for cross-sectional asset pricing. The conventional security sorting on firm characteristics for constructing long–short factor portfolio weights is nonlinear modeling, while factors are treated as inputs in linear models. We provide a structural deep-learning framework to generalize the complete mechanism for fitting cross-sectional returns by firm characteristics through generating risk factors (hidden layers). Our model has an economic-guided objective function that minimizes aggregated realized pricing errors. Empirical results on high-dimensional characteristics demonstrate robust asset pricing performance and strong investment improvements by identifying important raw characteristic sources.

We confirm prior evidence that bonds on average are offered at prices below their immediate post-offer secondary market prices. However, in cases where banks lead–manage their own bond offerings the underpricing is significantly less as compared with other non-self-marketed offerings. These findings are robust across various matched samples and selection models. Our results suggest that the bond offering process is characterized by substantive agency conflicts between shareholders of corporations (issuers) and underwriters.

We use the 2015 Chinese stock market crash to study the effects of government stock purchases. The Chinese government purchased stocks to stabilize the markets through state-owned financial institutions known as the “National Team.” We find that the intervention led to reduced volatility and price informativeness. These impacts are driven by the disclosure of government portfolios. Consistent with investors having a stronger incentive to acquire government intervention information instead of fundamental news, we find reduced information production and information asymmetry following intervention disclosure. The article suggests that government stock purchases involve a trade-off between stability and informational efficiency.

I develop a model revealing the interplay between a stock’s liquidity and the policies and value of the issuing firm. The model shows that bid-ask spreads increase not only the firm’s cost of capital but also the opportunity cost of cash, then lowering cash reserves, increasing liquidation risk, and reducing firm value. These outcomes are stronger when internalized by liquidity providers, simultaneously leading to a wider bid-ask spread. A two-way relation between the firm and the liquidity of its stock arises, implying that shocks arising within the firm or in the stock market have more complex implications than previously understood.

We view innovation investments as real options and explore the implications of risk (volatility) as well as a newly defined outcome independent measure of ambiguity (Knightian uncertainty) for innovation decisions. The empirical analysis uses stock returns to compute an implementable measure of ambiguity. We also control for risk and other determinants of innovation. We find a consistently significant negative effect of ambiguity on R&D, patents, and citations, as predicted. The effect of risk on R&D is positive and significant, but the corresponding effect on patents and citations is negative and significant. Ambiguity matters more for high-tech firms, consistent with intuition.

By employing a dynamic model with two limit order books, we show that fragmentation is associated with reduced competition among liquidity suppliers and lower picking-off risk of limit orders. Due to these countervailing channels, the impact of fragmentation on liquidity and welfare differs with asset volatility: When volatility is high (low), liquidity and aggregate welfare in a fragmented market are higher (lower) than in a single market. However, fragmentation always shifts welfare away from agents with exogenous trading motives and toward intermediaries. We empirically corroborate our model’s predictions about liquidity. Our model reconciles the mixed results in the empirical literature.

During the first half of 2020, the difference in savings from mortgage refinancing between high- and low-income borrowers was 10 times higher than before. This was the result of two factors: high-income borrowers increased their refinancing activity more than otherwise comparable low-income borrowers and, conditional on refinancing, they captured slightly larger improvements in interest rates. Refinancing inequality increases with the severity of the COVID-19 pandemic and is characterized by an underrepresentation of low-income borrowers in the pool of applications. We estimate a difference of $5 billion in savings between the top income quintile and the rest of the market.