Impact of Fintech on Bank Risk-Taking: Evidence from China
Yüklə 1,3 Mb. Pdf görüntüsü
|
- Bu səhifədəki naviqasiya:
- Table 1.
| Table 1.
Variable selection and definition. Variable Type Variable Name Variable Symbol Variable Meaning Explained variable Z Z-Score
ln (Z + 1) Volatility of return on assets SDROA ln (SDROA + 1) Core explanatory variable
Fintech level Fintech 1 ln (the coverage breadth dimension of regional digital finance index) Fintech 2 Refer to
Li et al. ( 2020 ) Instrumental variable Urban innovation index Innovation Refer to Kou and Liu ( 2017
) Intermediary variables Net interest margin NIM
Net interest income/average interest-bearing assets
× 100
Management ability Governance ln (governance fees) Bank competition intensity HHI Regional Herfindahl index Household propensity to save PSaving
Total savings of local residents/district population Bank-level control variables Bank size Size
ln (total bank assets) Asset-liability ratio DAR Total bank liabilities/total bank assets × 100
Capital adequacy ratio CAR
Net bank capital/risk-weighted assets × 100 Net operating margin Netprf
Net profit/operating revenues × 100 Cost-to-income ratio CIR
Operating cost/operating revenue × 100 Proportion of non-interest income NIRR
Non-interest income/operating revenue × 100 Liquidity of funds SAR
Bank balance/total bank assets × 100 City-level control variables Level of economic development PGDP
ln(GDP per capita) Degree of financial development FinDev (Total regional deposits + total regional loans)/total wages of all employees Macro-level control variables Monetary Policy Trend M2 Broad money growth rate 4.2.5. Descriptive Analysis Table
2 reports the basic statistical characteristics of the variables. After logarithmic processing of Z-Score, the maximum value of the bank’s Z-Score is 0.613, and the minimum value is 0.001, but the standard deviation is small, and the average value is very close to the median. This shows that there is a large gap in the risks undertaken by small and medium banks in each region, but the overall distribution is more concentrated, and there is no obvious leftward or rightward trend. The maximum value of Fintech 1 without logarithmic processing is 248.5, the minimum value is 14.76, and the standard deviation is large, which indicates that the development of Fintech in different regions of China is uneven, and regional differences are obvious. However Fintech 1 after logarithmic processing has a smaller standard deviation and a smoother change, which shows that the method of taking the logarithm of the index is reasonable. 1 This article introduces the cross-multiplying terms of internet penetration and financial technology development to examine the moderating effect of regional internet penetration on the correlation between financial technology and bank risk. |