The impacts of China's shadow banking regulation on bank lending—An empirical analysis based on textual analysis and machine learning

Abstract

China's financial regulatory authority has strengthened shadow banking regulation in recent years, the impact of which has attracted the attention of academics and policymakers. We develop China's shadow banking regulation intensity change index using textual analysis and machine learning methods and explore the impacts of the regulation on bank lending. Using a fixed effect model and a dataset of 177 commercial banks in China from 2007 to 2020, we find that strengthened shadow banking regulation results in shrinkage in bank loan scale and lower loan growth. Mechanism analysis reveals that the asset-reallocation motives and profit-seeking motives generated by banks faced with strengthening regulation lead to a decline in bank lending. The credit tightening effect mainly exists in joint-stock, city, and rural commercial banks while insignificant in state-owned banks. This effect can be moderated by ample capital, abundant deposits, and high loan issuance efficiency. Strengthened regulation also affects loan composition, with an increase in the proportion of corporate lending and a decrease in mortgage lending. Our results have novel implications for both regulators and banks and are robust to several tests.