How capital structure and bank liquidity affect bank performance: Evidence from the Bayesian approach

Figures & data

Table 1. Summary statistics

Variables	Obs	Mean	Standard deviation	Min	Max
ROA	463	0.0105	0.0089	−0.0599	0.0557
ROE	463	0.1063	0.0809	−0.5633	0.4449
EPS	463	0.1332	0.1134	−0.4572	0.6691
TDTA	463	0.9137	0.0321	0.8340	0.9546
BFD	463	0.5589	0.1003	0.1876	0.7546
LGR	463	0.4350	0.9553	−0.4157	11.3173
LogSIZE	463	31.7379	1.5414	26.0908	34.9553
LIQ	463	0.9351	0.4054	0.3719	7.2959
RGDP	463	0.0614	0.0104	0.0291	0.0755
M2GR	463	0.2314	0.1055	0.0440	0.4610

Table 2. Summary statistics for the political connection variable (POC)

	With Connection	Without Connection	With and without Connection^3	Total
Numbers of Banks	18	11	8	37
Percent	50	29	21	100

Table 3. Bayesian correlation matrix

	TDTA	BFD	LGR	LogSIZE	LIQ	POC	RGDP	M2GR	VIF
TDTA	—								2.05
BFD	−0.322*** [2.181e+10]	—							1.21
LGR	−0.144 [9.094]	0.197*** [889.509]	—						1.10
LogSIZE	0.661*** [3.197e+59]	−0.316*** [7.573e+9]	−0.278*** [1.840e+7]	—					1.97
LIQ	−0.294*** [2.086e+8]	0.089 [0.390]	0.103 [0.760]	−0.266*** [3.329e+6]	—				1.14
POC	0.011 [0.058]	0.009 [0.058]	−0.049 [0.101]	0.151 [16.002]	−0.017 [0.061]	—			1.10
RGDP	0.000 [0.057]	0.062 [0.144]	0.132 [3.961]	−0.201*** [1312.826]	0.071 [0.196]	.025[.065] [0.065]	—		1.67
M2GR	0.146 [8.443]	−0.038 [0.081]	−0.270*** [2.147e+6]	0.275*** [3.776e+6]	−0.183*** [140.348]	.033[.075] [0.075]	−.319***[3.011e+9] [3.011e+9]	—	1.52

Note: Bayes factors (BF) in brackets, * BF₁₀ > 10, ** BF₁₀ > 30, *** BF₁₀ > 100.

Table 4. MCMC diagnostics for the models (ROA)

Models	Model 1		Model 2		Model 3
Independent variables	Efficiency	GR diagnostic	Efficiency	GR diagnostic	Efficiency	GR diagnostic
TDTA	1.0000	1.0000	1.0000	1.0000	0.9929	1.0001
BFD	1.0000	1.0000	1.0000	1.0000	1.0000	1.0000
LGR	0.9934	1.0002	1.0000	1.0000	1.0000	1.0000
LogSIZE	1.0000	1.0000	0.9819	1.0000	0.9872	1.0000
LIQ	0.9901	1.0000	1.0000	1.0001	1.0000	1.0000
POC	1.0000	1.0000	1.0000	1.0000	0.9934	1.0002
RGDP	—	—	0.9856	1.0000	1.0000	1.0000
M2GR	—	—	1.0000	1.0000	1.0000	1.0000
COVID19	—	—	—	—	0.9716	1.0000
_cons	1.0000	1.0001	0.9833	1.0000	0.9907	1.0001
var	0.9631	1.0002	0.9507	1.0000	0.9729	1.0000

Table 5. MCMC diagnostics for the models (ROE)

Models	Model 1		Model 2		Model 3
Independent variables	Efficiency	GR diagnostic	Efficiency	GR diagnostic	Efficiency	GR diagnostic
TDTA	1.0000	1.0000	0.9748	1.0000	1.0000	1.0001
BFD	1.0000	1.0001	0.9820	1.0000	1.0000	1.0000
LGR	1.0000	1.0000	0.9897	1.0000	1.0000	1.0000
LogSIZE	1.0000	1.0000	0.9883	1.0001	1.0000	1.0001
LIQ	1.0000	1.0000	0.9882	1.0000	1.0000	1.0001
POC	0.9752	1.0000	1.0000	1.0000	0.9843	1.0002
RGDP	—	—	0.9860	1.0000	1.0000	1.0001
M2GR	—	—	1.0000	1.0001	1.0000	1.0000
COVID19	—	—	—	—	1.0000	1.0000
_cons	1.0000	1.0000	0.9888	1.0001	1.0000	1.0001
var	0.9614	1.0001	0.9809	1.0000	0.9836	1.0001

Table 6. MCMC diagnostics for the models (EPS)

Models	Model 1		Model 2		Model 3
Independent variables	Efficiency	GR diagnostic	Efficiency	GR diagnostic	Efficiency	GR diagnostic
TDTA	1.0000	1.0000	0.9851	1.0000	1.0000	1.0001
BFD	1.0000	1.0000	1.0000	1.0000	1.0000	1.0001
LGR	1.0000	1.0001	1.0000	1.0000	1.0000	1.0000
LogSIZE	0.9619	1.0001	1.0000	1.0000	1.0000	1.0000
LIQ	1.0000	1.0001	1.0000	1.0000	1.0000	1.0000
POC	0.9927	1.0001	1.0000	1.0001	0.9868	1.0000
RGDP	—	—	0.9773	1.0000	1.0000	1.0002
M2GR	—	—	1.0000	1.0001	1.0000	1.0000
COVID19	—	—	—	—	1.0000	1.0001
_cons	1.0000	1.0001	1.0000	1.0001	0.9954	1.0000
var	0.9917	1.0000	0.9900	1.0000	1.0000	1.0001

Table 7. The impacts of capital structure and bank liquidity on the performance of commercial banks

Models	ROA		ROE		EPS
Independent variables	Mean	Probability of mean	Mean	Probability of mean	Mean	Probability of mean
TDTA	−0.0584	1.0000*	−0.3759	0.9932*	0.1926	0.5774**
	[−0.0774;		[−0.6741;		[−1.8020;
	−0.0396]		−0.0786]		2.1610]
BFD	0.0105	0.9836**	0.1169	0.9978**	0.1276	0.5524**
	[0.0008;		[0.0378;		[−1.8649;
	0.0204]		0.1960]		2.0833]
LGR	0.001	0.9735**	0.0546	0.9979**	0.0974	0.5401**
	[0.0000;		[0.0176;		[−1.8646;
	0.0020]		0.0915]		2.0624]
LogSIZE	0.0190	0.9164**	1.1782	1.0000**	0.7148	0.7629**
	[−0.0081;		[0.9119;		[−1.2359;
	0.0464]		1.4430]		2.6765]
LIQ	0.0006	0.7028**	0.1174	1.0000**	0.2065	0.5817**
	[−0.0017;		[0.0739;		[−1.7556;
	0.0029]		0.1607]		2.1540]
POC	0.001	0.8523**	−0.0115	0.9481*	0.139	0.5526**
	[−0.0009;		[−0.0254;		[−1.8290;
	0.0030]		0.0024]		2.0877]
RGDP	0.1489	0.9785**	1.2132	0.8912**	0.0052	0.5038**
	[0.0048;		[0.2806;		[−1.9682;
	0.2923]		2.1692]		1.9583]
M2GR	−0.0191	0.9999*	−0.0774	0.9583*	0.0512	0.5218**
	[−0.0287;		[−0.1649;		[−1.9059;
	−0.0094]		0.0102]		1.9909]
COVID19	−0.0041	0.5047*	−0.0178	0.9308*	−0.0006	0.5002*
	[−1.9678;		[−0.1214;		[−1.9646;
	1.9582]		0.0354]		1.9474]
_cons	−0.0156	0.6374*	−3.8131	1.0000*	0.2043	0.5806**
	[−0.1023;		[−4.5961;		[−1.7795;
	0.0701]		−3.0269]		2.1881]
var	0.0001	—	0.0027	—	2,891,902	—
	[0.0001;		[0.0023;		[2,536,297;
	0.0001]		0.0032]		3,291,556]

Notes: *, ** is probability of mean < 0, and > 0, respectively; 95%-credible interval in brackets.

Table 8. The impacts of capital structure and bank liquidity on the performance of commercial banks

Models	ROA		ROE		EPS
Independent variables	Coefficient	P>|z|	Coefficient	P>|z|	Coefficient	P>|z|
TDTA	−0.0833***	0.0000	−0.3969***	0.0010	−0.7215***	0.0000
BFD	0.0077***	0.0060	0.0555**	0.0690	0.0463	0.2380
LGR	0.0054***	0.0000	0.0335**	0.0290	0.0319	0.1060
LogSIZE	0.0938***	0.0000	1.1999***	0.0000	1.9546***	0.0000
LIQ	0.0077***	0.0000	0.1010***	0.0000	0.1183***	0.0000
POC	−0.0013***	0.0070	−0.0204***	0.0000	−0.0273***	0.0000
RGDP	0.0012**	0.0190	0.0649**	0.0145	0.0803***	0.0030
M2GR	−0.0063**	0.0300	−0.0707**	0.0390	−0.0745*	0.0920
COVID19	−0.0019**	0.0200	−0.0116	0.2050	−0.0252**	0.0210
_cons	−0.2530***	0.0000	−3.7939***	0.0000	−6.1271***	0.0000
Firm fixed effect	Yes		Yes		Yes
Prob > chi2	0.0000		0.0000		0.0000
Obs	463		463		463

Notes: ***, **, and * indicate significance at 1%, 5%, and 10%, respectively.

Table

Variables	Symbol	Definition
Dependent variables
Return on asset	ROA	Net profit/Average Total Assets.
Return on equity	ROE	Net profit (after taxes)/Average Total Equity.
Earnings per share	EPS	We follow Siddik et al. (2017) to estimate EPS as the ratio of Net income divided by the total number of outstanding shares.
Independent variables
Total debt to total asset	TDTA	Following Al-Qudah and Jaradat (2013), the TDTA is Total debt over Total asset
Bank Funding Diversity	BFD	We use the formula of Vo (2020): $\mathrm{B}\mathrm{F}\mathrm{D}=1-\left[{\left(\frac{EQU}{Fund}\right)}^2+{\left(\frac{GOV}{Fund}\right)}^2+{\left(\frac{IBD}{Fund}\right)}^2+{\left(\frac{CD}{Fund}\right)}^2+{\left(\frac{DER}{Fund}\right)}^2+{\left(\frac{LTF}{Fund}\right)}^2+{\left(\frac{OTH}{Fund}\right)}^2\right]$
Loans Growth	LGR	According to Ozili (2015), LGR is calculated using the following formula: (Total Bank’s Loan in year t - Total Bank’s Loan in year t-1)/(Total Bank’s Loan in year t-1)
Size of the Bank	LogSIZE	Following Anbar and Alper (2011), the size of the bank is the Natural logarithm of total assets.
Liquidity	LIQ	Following Al-Qudah and Jaradat (2013), the LIQ variable is calculated as Total loan debt divided by Total customer deposits.
Political Connection	POC	Following Rehman et al. (2016) and Duong et al. (2022), POC is the dummy variable with a value of 1 if the CEO or chairman is a member of the Communist Party of Vietnam and 0 otherwise. According to The constitution of the Socialist Republic of Viet Nam, section 04, The Communist Party of Vietnam is the only ruling party in Vietnam.
Economic Growth	RGDP	The annual growth rate of real GDP (Khan et al. (2017)
Money Supply Growth Rate	M2GR	M2 yearly growth rate (Hameed, 2010)
COVID-19 pandemic	COVID19	The dummy variable takes the value of 1 if the year 2020 and 0 otherwise.

Siddik, M., Alam, N., Kabiraj, S., & Joghee, S. (2017). Impacts of capital structure on performance of banks in a developing economy: Evidence from Bangladesh. International Journal of Financial Studies, 5(2), 13. https://doi.org/10.3390/ijfs5020013

 Web of Science ®Google Scholar

Al-Qudah, A. M., & Jaradat, M. A. (2013). The impact of macroeconomic variables and banks characteristics on Jordanian Islamic banks profitability: Empirical evidence. International Business Research, 6(10), 153. https://doi.org/10.5539/ibr.v6n10p153

 Google Scholar

Vo, X. V. (2020). The role of bank funding diversity: Evidence from Vietnam. International Review of Finance, 20(2), 529–536. https://doi.org/10.1111/irfi.12215

 Web of Science ®Google Scholar

Ozili, P. K. (2015). How bank managers anticipate non-performing loans. Evidence from Europe, US, Asia and Africa. Applied Finance and Accounting, 1(2), 73-80 73–80. https://doi.org/10.11114/afa.v1i2.880

 Google Scholar

Anbar, A., & Alper, D. (2011). Bank specific and macroeconomic determinants of commercial bank profitability: Empirical evidence from Turkey. Business and Economics Research Journal, 2(2), 139–152. https://ssrn.com/abstract=1831345

 Google Scholar

Rehman, R. U., Zhang, J., & Ahmad, M. I. (2016). Political system of a country and its non-performing loans: A case of emerging markets. International Journal of Business Performance Management, 17(3), 241–265. https://doi.org/10.1504/IJBPM.2016.077243

 Google Scholar

Duong, K. D., Vu, D. N., Le, K. D., & Van Nguyen, D. (2022). Do political connections and bank funding diversity increase non-performing loans: New evidence from the Bayesian approach. Montenegrin Journal of Economics, 18(4), 81–94. https://doi.org/10.14254/1800-5845/2022.18-4.8

 Web of Science ®Google Scholar

Khan, M. S., Scheule, H., & Wu, E. (2017). Funding liquidity and bank risk taking. Journal of Banking & Finance, 82, 203–216. https://doi.org/10.1016/j.jbankfin.2016.09.005

 Web of Science ®Google Scholar

Hameed, D. (2010). Impact of monetary policy on gross domestic product (GDP). Interdisciplinary Journal of Contemporary Research in Business, 3(1), 1348–1361. https://doi.org/10.2139/ssrn.1857413

 Google Scholar

Data availability statement

The [Excel File] data used to support the findings of this study are available from the corresponding author upon request.