The Effects of Going Public on Bank Risks: Evidence from China

ABSTRACT

Using data from China’s banks between 1995 and 2017, we employ propensity score matching and difference in differences approaches to investigate the effects of going public on bank risks, including insolvency risk, capital risk, liquidity risk, asset quality, credit risk, and prudential behavior, and obtain the following results. First, bank risks (except for insolvency risk) are improved after going public. Second, going public has stronger effects if a bank is listed on more than one stock exchange. Finally, both the single-listing effects and cross-listing effects of going public on bank risks are stronger in state-owned banks than in non-state-owned banks.

KEY WORDS:

• Bank risks
• China
• going public
• state ownership

JEL:

• G21
• G32
• G34

Acknowledgments

We are grateful for Iftekhar Hasan, Chunxia Jiang, Cheng Li, Vikas Mehrotra, Mingming Zhou, and seminar participants at the University of Alberta and Xi’an Jiaotong University for their helpful comments. We are also thanks for Rui Ma and Qian Guo from Xi’an Jiaotong University for the data collection and processing. Maoyong Cheng acknowledges financial support from the National Science Foundation of China (Grant Nos. 71403202 and 71472148) and China Postdoctoral Science Foundation (Grant Nos. 2015M572532, 2016T90897, and 2018M631128), Postdoctoral Science Foundation of Shaanxi Province, and the Chinese Ministry of Finance (Accounting Master Training Project, 2015). All errors are our own.

Declaration of Interest Statement

We declare that we have no any actual or potential conflict of interest including any financial, personal or other relationships with other people or organizations within three years of beginning the submitted work that could inappropriately influence, or be perceived to influence, their work.

Supplementary material

Supplementary data can be accessed here

Notes

1. Due to the lack of data periods, we start with t = 1998.

2. Based on the existing literature and our data structure, we employ the three year overlapping average values.

3. The logistic regression results for every year are not shown for reasons of brevity but are available upon request from the authors.

4. We delete the bank that first went public in 1991 in our sample. Therefore, we obtain a sample in which the treatment group has 37 listed banks between 1995 and 2017.

5. According to our data set, we choose 4-year periods to compute the average value. However, some banks have less than 4 years of observations before or after going public. We use as many year observations as we can obtain to compute their average value. Additionally, we also employ 3- and 2-year periods as robustness tests, and we obtain similar results.

6. We delete the bank that first went public in 1991 in our sample.

Additional information

Funding

This work was supported by the China Postdoctoral Science Foundation [2015M572532,2016T90897,2018M631128];Postdoctoral Science Foundation of Shaanxi Province;the Chinese Ministry of Finance (Accounting Master Training Project, 2015);National Natural Science Foundation of China [71403202,71472148].