Although the impacts of air pollution on individual health are well documented, it is unclear whether and how air pollution affects firm strategy on employee treatment. This study examines this issue in the context of China and presents strong evidence that air pollution significantly enhances employee treatment. To establish causality, we further instrument for air pollution using thermal inversions, introduce a regression discontinuity approach relying on the Huai River boundary, and a falsification test. Monetary compensation, safety security, and career training jointly determine our findings. Two plausible mechanisms are corporate brain drain and public attention. Our results are more pronounced for non-state-owned firms, and firms with less financial constraints, intensive R&D or competition, and increased job market opportunities. Overall, we highlight that air pollution is an important noneconomic factor driving firms' human capital and employee treatment strategy.

We model competition for liquidity provision between high-frequency traders (HFTs) and slower execution algorithms (EAs) designed to minimize investors’ transaction costs. Under continuous pricing, EAs dominate liquidity provision by using aggressive limit orders to stimulate HFTs’ market orders. Under discrete pricing, HFTs dominate liquidity provision if the bid-ask spread is binding at one tick. If the tick size (minimum price variation) is not binding, EAs choose between stimulating HFTs and providing liquidity to non-HFTs. Transaction costs increase with the tick size but can be negatively correlated with the bid-ask spread when all traders can provide liquidity.

This study investigates whether and to what extent bank control over firms by their representation on boards of directors and by equity holdings through trust business may affect corporate environmental responsibility. Using a large sample of listed firms in the United States from 2004 to 2016, we find that banker directors with equity affiliation improve firms’ environmental performance scores and such impact is associated with affiliated bank’s shareholdings, investment horizon, and environmental orientation. Additionally, we document that the effects of bank control on firm’s environmental investments is stronger for firms with more short-term institutional investors but weaker for firms with more analyst coverage. Moreover, we find that when firms are financially constrained, banker directors reduce environmental investments. Finally, product-market competition matters for a firm’s environmental strategies as the relation of bank control and environmental investments is more profound under conditions of greater industry competition.

This paper uses loan application-level data from a peer-to-peer lending platform to study the risk-taking channel of monetary policy. By employing a direct ex-ante measure of risk-taking and estimating the simultaneous equations of loan approval and loan amount, we provide evidence of monetary policy's impact on a nonbank financial institution's risk-taking. We find that the search-for-yield is the main driving force of the risk-taking effect, while we do not observe consistent findings of risk-shifting from the liquidity change. Monetary policy easing is associated with a higher probability of granting loans to risky borrowers and greater riskiness of credit allocation. However, these changes do not necessarily relate to a larger loan amount on average.

How do corporate bond mutual funds manage liquidity to meet investor redemptions? We show that during tranquil market conditions, these funds tend to reduce liquid asset holdings to meet redemptions, temporarily increasing relative exposures to illiquid asset classes. When aggregate uncertainty rises, however, they tend to scale down their liquid and illiquid assets proportionally to preserve portfolio liquidity. This fund-level dynamic management of liquidity appears to affect the broad financial market: Redemptions from the corporate bond fund sector lead to more corporate bond selling during high-uncertainty periods, which generates price pressures and predicts strong return reversals.

Disagreement measures are known to predict cross-sectional stock returns but fail to predict market returns. This paper proposes a partial least squares disagreement index by aggregating information across individual disagreement measures and shows that this index significantly predicts market returns both in- and out-of-sample. Consistent with the theory in Atmaz and Basak (2018), the disagreement index asymmetrically predicts market returns with greater power in high-sentiment periods, is positively associated with investor expectations of market returns, predicts market returns through a cash flow channel, and can explain the positive volume-volatility relationship.

We propose a novel high-frequency decomposition of daily stock returns into news- and non-news-driven components, and uncover evidence of pervasive stock market underreaction to firm news. Prices tend to drift in the same direction as the initial market response for several days after the news arrival without reversals. A trading strategy exploiting the return drift generates high abnormal returns and remains profitable after transaction costs. To understand the economic mechanism, we find that the return drift is stronger when investors are distracted. Analysts’ slow adjustments of market expectations following firm news also contribute to the market underreaction.

We develop a dynamic asset pricing model of cryptocurrencies/tokens that allow users to conduct peer-to-peer transactions on digital platforms. The equilibrium price of tokens is determined by aggregating heterogeneous users’ transactional demand, rather than discounting cash flows as is done in standard valuations models. Endogenous platform adoption builds on user network externality and exhibits an -curve: it starts slow, becomes volatile, and eventually tapers off. The introduction of tokens lowers users’ transaction costs on the platform by allowing users to capitalize on platform growth. The resultant intertemporal feedback between user adoption and token price accelerates adoption and dampens user-base volatility.