What drives short-term credit spreads: credit risk, liquidity risk, or both? We investigate this issue using the structural approach to credit risk modeling and a novel data set of secondary market transaction prices for Chinese commercial papers (CPs). In particular, we propose and test a structural model with jump risk and exogenous market illiquidity under which the predicted yield spreads can be decomposed into a credit component and a liquidity component. We find that credit risk and, especially liquidity risk, are important determinants of short-term yield spreads. Our model-based decomposition results show that, on average, credit risk and market liquidity account for about 25% and 52% of CP yield spreads, respectively. For comparison, we also examine the drivers of the US CP yield spreads using security-level data. We find that credit risk accounts for a small fraction of the observed yield spreads but liquidity contributes a much greater proportion.

We surveyed all Chinese public firms in 2019 and 2022 to examine the real effects of financial markets. Over 90% of firms say they actively monitor the stock market, and the most common reasons they provide are that they learn new information from the price and that they depend on the price for financing. Focusing on the learning channel, we examine how the responses relate to firm characteristics and actions. Firms that indicate learning have characteristics that suggest greater benefit from market information. They also exhibit higher investment-to-price sensitivity. We provide results on what dimensions of information firms learn about.