This paper models the precautionary motive for a firm's cash holdings. A two-period investment model shows that the cash holdings of financially constrained firms are sensitive to cash flow volatility because financial constraints create an intertemporal trade-off between current and future investments. When future cash flow risk cannot be fully diversifiable, this intertemporal trade-off gives constrained firms the incentives of precautionary savings: they increase their cash holdings in response to increases in cash flow volatility. However, there is no systematic relationship between cash holdings and cash flow volatility for unconstrained firms. We test the empirical implications of our theory using quarterly information from a sample of U.S. publicly traded companies from 1997 to 2002, and find that the empirical evidence supports our theory.

This paper compares the predictive power of credit scoring models based on machine learning techniques with that of traditional loss and default models. Using proprietary transaction-level data from a leading fintech company in China, we test the performance of different models to predict losses and defaults both in normal times and when the economy is subject to a shock. In particular, we analyse the case of an (exogenous) change in regulation policy on shadow banking in China that caused credit conditions to deteriorate. We find that the model based on machine learning and non-traditional data is better able to predict losses and defaults than traditional models in the presence of a negative shock to the aggregate credit supply. This result reflects a higher capacity of non-traditional data to capture relevant borrower characteristics and of machine learning techniques to better mine the non-linear relationship between variables in a period of stress.

Abstract Using a unique dataset of more than 2 million Chinese firms that received credit from both an important big tech firm (Ant Group) and traditional commercial banks, this paper investigates how different forms of credit correlate with local economic activity, house prices, and firm characteristics. We find that big tech credit does not correlate with local business conditions and house prices when controlling for demand factors, but reacts strongly to changes in firm characteristics, such as transaction volumes and network scores used to calculate firm credit ratings. By contrast, both secured and unsecured bank credit react significantly to local house prices, which incorporate useful information on the environment in which clients operate and on their creditworthiness. This evidence implies that the wider use of big tech credit could reduce the importance of the collateral channel but, at the same time, make lending more reactive to changes in firms’ business activity.