High-bargaining-power (low-bargaining-power) customer (supplier) firms borrow (lend) more trade credit according to the literature. We study whether this bargaining power effect strengthens or weakens when credit tightens. We construct a Nash bargaining model of trade credit and show that the effect weakens if the suppliers' financing costs increase more than that of the customers and strengthens if the opposite is true. We find support for model predictions using a unique database of listed firms in China that discloses firms' transaction information with important customers and suppliers. Interest-rate sensitive suppliers, proxied by a non-state ownership, a high debt rollover risk, and a high financial constraint index, reduce trade credit to their high-bargaining-power customers during credit contractions, while interest-rate sensitive and high-bargaining-power customers, proxied by similar indices, borrow more trade credit.

We document that leased capital constitutes about 30% of the total productive physical assets used by US public firms. We develop an analytical framework to demonstrate how the neglect of leases leads to an overestimation of productivity. This overestimation can be decomposed into two distinct channels: one arises from the mismeasured factor share, and the other from the omitted-leased-capital channel. Empirically, we find that the overestimation of aggregate productivity is substantial, has been increasing over time, and exhibits strong countercyclicality. In the cross-section, the decomposition of overestimation presents asymmetric patterns for firms of different sizes and levels of financial constraint. Our findings highlight the critical importance of explicitly accounting for the “unmeasured” leased capital in studies on productivity measurements.

This paper investigates the impact of institutional herding on stock price formation, conditional on firms’ credit ratings, using 13F data from 1986 to 2019. In line with the current literature, we find herding intensity is driven by past returns consistent with momentum trading; however, we also find that herding is more sensitive to past returns for non-investment grade (NIG) stocks than investment grade (IG) stocks, resulting in a market bifurcation. We then examine the price impact of these trades and find that herding in NIG equities enhances price discovery. One plausible explanation is that information gradually diffuses within non-investment grade stocks, and herding behavior strengthens information discovery. Finally, we show both momentum-triggered herding and non-momentum-triggered herding contribute to price discovery among non-investment grade stocks.

We provide for the first time the emerging market variance risk premium (EMVRP) from 2006 to 2023, based on nine emerging stock and option markets—Brazil, China, India, South Korea, Mexico, Poland, Russia, South Africa, and Taiwan. The EMVRP significantly predicts international stock returns and currency appreciation rates, especially for horizons longer than six months. This is in sharp contrast with the predictive pattern of the developed market variance risk premium (DMVRP), which is more important over horizons shorter than six months. These findings are consistent with an illustrative model incorporating partial market integration and heterogeneous economic uncertainty.

During the 2015 stock market crisis, the Chinese government used hundreds of billions of dollars to purchase shares directly in the secondary market. We find that compared with non-rescued firms, rescued firms have significantly lower liquidity after being rescued. Policy uncertainty regarding subsequent interventions better explains the reduction in liquidity than the liquidity dry-up and bad firm signaling hypotheses. Inconsistent with the potential moral hazards associated with government bailouts, the investment policies of rescued firms become more conservative after being rescued. Our evidence warns of the unintended consequences of direct purchase rescue programs.