A study of the factors influencing the capital structure choice for russian financial organizations

Size of the organization

The results of the model reveal that the size (interest revenue) is a significant factor, that is related to the level of leverage in the organization; according to the model, it has high statistical significance for all three leverage types, but the economic significance is rather small - the coefficients represent that the companies tend to decrease long-term debt and increase short-term debt level (because of the negative L1 coefficient, results are interpreted inverse) with increase of the size, but not much. The null hypotheses for relationships between size and leverage level was that those relationships are direct for long-term as well as for short-term debt, therefore, for the short-term debt the null hypothesis is confirmed, while for the long-term debt it is rejected. The results obtained for the short-term debt are in accordance with trade off and signaling theory, while the results for the other types of debt are in accordance with the pecking order theory. Overall, it can be concluded that the results are similar to the results obtained by Titman and Wessels on a sample of international companies and E. D. S. Silva et al. on a sample of Russian companies (1988, 2016).

Assets tangibility

The coefficient for the collateral value of assets is economically and statistically significant. According to the model, tangibility of assets have weak inverse relationships with the long-term and total debt. This result is not consistent with trade off or pecking order theory (both suggest positive relationships), moreover, previous studies revealed results that were in accordance with those theories (Ivashkovskaya and Solntceva, 2007; Frank and Goyal, 2007). The result is surprising firstly because it contradicts the theories and previous research results, and, secondly, because for the banks it was expected to reveal strong relationships between debt and the asset quality (asset risk), not with assets tangibility. This phenomena can be explained by the unique asset structure of banks - the value-adding assets for banks are not tangible, therefore, in order for the research to be consistent, the inverse ratio of assets tangibility should be implemented (1-fixed assets/total assets). If this correction is implemented, it can be concluded that the obtained results are consistent with the classic theories as well as with the previous research results - the relationships between the leverage and assets tangibility (1-fixed assets/total assets) is positive.

Earnings volatility

Earnings volatility coefficient is significant for the short term and, consequently, the total leverage level. The results supported the null hypothesis - the relationships between leverage and volatility of earnings are negative, what implies that the higher is the probability of bankruptcy, the smaller is the short-term leverage level, it may represent the managerial decisions of balancing the organization's financial health by balancing the amount of the short-term leverage in the capital structure. Those results are consistent with the results, obtained by Bradley and Gregg (1984). However, Titman and Wessels proven that volatility of earnings is not related to any measure of leverage (1988). This results are also supported by the economic conditions in Russia in the period 2013-2018 - many banks were closed in this period due to inability to meet its obligations, therefore it is crucial for banks to decrease its earnings volatility before raising the leverage level in order to make sure that they are safe and can face the unexpected volatility in the political and economical situation.

Macroeconomic factors

Inflation rate, unlike the interest rate, is statistically and economically significant factor for the level of short-term leverage according to the constructed model. Increase in inflation rate has a strong positive relationships with the proportion of the debt financing, it is consistent with the null hypothesis.

The result, obtained for the interest rate may be explained by the specific economic situation in the Russian Federation - after the interest rate (the key rate of the Central Bank) was first introduced in 2013 at a rate of 5.5%, a year after it was raised up to the 17% in 2014 and in the next 4 years it was gradually declining, therefore, the results revealed for the interest rate might not be precise as banks leverage experienced fluctuations, while the interest rate was declining for almost all the observed period.

The result for the inflation rate is consistent with the trade off theory but contradict the result obtained by E. D. S. Silva et al. (2016).

Total taxes paid

The resulting coefficient for the total taxes paid variable is economically and statistically significant only for the short-term leverage level. It may imply that balancing the short-term debt level is as instrument to quickly answer to the changes in the economic environment, for instance - increase the tax shield benefits in case of increase in the taxation.

Growth opportunities

The growth opportunities variable is statistically and economically significant only for the total leverage level, it does not influence one of the leverage types separately. The sign of the variable is negative, it is consistent with the null hypothesis for the long-term leverage level. Such result may imply that firms with higher growth opportunities tend to decrease the level of leverage, in each case choosing the unique mix of the long-term and short-term leverage amount. Obtained result is in accordance with the trade off theory and consistent with the results, obtained by Myers and Fama and French - the less growth opportunities the organization has, the more it favors debt financing (1977, 2002). The results for the short-term and long term debt are different, and it is consistent with studies of Rajan and Zingales and Titman and Wessels, however, the obtained coefficients are not statistically significant (1995, 1988).

Table 8

Obtained results

Variable	Hypothesis (LtD)	Result (LtD)	Hypothesis (StD)	Result (StD)
Profitability	-	None	-	None
Tangibility of assets	-	-	-	None
Size	+	-	+	-
Earnings volatility	-	None	-	-
Interest rate	+	None	+	None
Inflation rate	+	None	+	+
Total taxes paid	+	None	+	+
Growth opportunities	-	None	+	None
Asset risk (asset quality)	-	None	-	None

Conclusion

This study was conducted in order to investigate the factors, determining the capital structure for the certain type of companies. The main objective of the study was to test the consistency of the theory that the capital structure decisions for the financial organizations have the underlying hypotheses that differ from the most of the existing modern capital structure theories, as they were developed primarily for the non-financial organizations. Nine factors were tested on two types of leverage separately (long-term debt share and short-term debt share) and on the total leverage level in the constructed models, the empirical evidences provide that there are differences between the behavior of the Russian financial institutions and the behavior of the non-financial organizations of the developed countries, which are most commonly used as a sample for the capital structure theories test. According to the results, all types of leverage levels are strongly influenced by the size of the organization - smaller banks tend to have more long-term and short-term leverage. Amount of taxes paid have relationships only with the short-term leverage, it implies that increase of taxation of the bank is followed by increase of the short-term leverage level. Earnings volatility and inflation rate are not correlated with the long-term leverage, only with short-term and total leverage. The long-term leverage is only related to the size of the organization and the assets tangibility, it can be explained by the fact that the long-term leverage level and its increase or decrease is a strategic decision that should-not be influenced by fluctuations in factors like earnings volatility or total amount of taxes paid. However, it is interesting that the growth opportunities variable, that was expected to have some influence on the long-term leverage is not statistically significant for any type of leverage.

These findings suggest the need for the future analysis in order to find explanations for the revealed controversies. Future analysis of the capital structure determinants should consider another variables in the model (for instance, try to use different measure for the growth opportunities). Furthermore, future study may include not only book values, but market figures as well, it will allow to relax the limitations present in this research and to test new variables (such as market-to-book ratio).

Contribution and practical implications

As it was stated in the Chapter 1 - the problem of balancing sources of financing is important for three classes of economists: financial specialists of the company concerned with financing firms, managerial economists concerned with capital budgeting and economic theorists concerned to explain investment behavior at both micro and macro levels. Additionally, understanding the relationships between the leverage level in banks and macroeconomic factors might be very important for the Central bank of the country in order to understand the consequences of volatility in the inflation rate and interest rate on the financial health of the country.



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Appendix

Table 9

Heteroskedasticity test

TD	Breusch-Pagan / Cook-Weisberg test for heteroskedasticity Ho: Constant variance Variables: fitted values of TD   chi2(1) = 2513.62 Prob > chi2 = 0.0000
	White's test for Ho: homoskedasticity against Ha: unrestricted heteroskedasticity   chi2(65) = 195.96 Prob > chi2 = 0.0000
LtD	Breusch-Pagan / Cook-Weisberg test for heteroskedasticity Ho: Constant variance Variables: fitted values of LtD   chi2(1) = 6127.96 Prob > chi2 = 0.0000
	White's test for Ho: homoskedasticity against Ha: unrestricted heteroskedasticity   chi2(65) = 264.97 Prob > chi2 = 0.0000
StD	Breusch-Pagan / Cook-Weisberg test for heteroskedasticity Ho: Constant variance Variables: fitted values of StD   chi2(1) = 4963.87 Prob > chi2 = 0.0000
	White's test for Ho: homoskedasticity against Ha: unrestricted heteroskedasticity   chi2(65) = 184.96 Prob > chi2 = 0.0000



Table 10

Hausman test

Long-term leverage

Leverage (long-term)
Coefficients
	(b)	(B)	(b-B)	sqrt(diag(V_b-V_B))
	fixed	random	Difference	S.E.
ROE	.0583349	.0484829	.009852	.0090675
ROA	-.0436308	-.0057943	-.0378365	.0284407
LS	-.0017715	.0209136	-.022685	.007011
LAT	.0043909	-.0003712	.0047621	.0015427
LVol	-.0287572	-.0026984	-.0260589	.0079694
Int	-.2872451	-.27562	-.011625	.019282
Inf	.3219604	.284752	.0372084	.0117008
Tot_tax	2.59e-08	3.67e-08	-1.08e-08	3.77e-09
GO	-.0091481	-.0025769	-.0065713	.0022726
LAR	.0023631	.0013602	.0010029	.0005551
b = consistent under Ho and Ha; obtained from xtreg
B = inconsistent under Ha, efficient under Ho; obtained from xtreg
Test: Ho: difference in coefficients not systematic
chi2(9) = (b-B)'[(V_b-V_B)^(-1)](b-B)
= 24.59
Prob>chi2 = 0.0035

Table 11

Leverage (short-term)
Coefficients
	(b)	(B)	(b-B)	sqrt(diag(V_b-V_B))
	fixed	random	Difference	S.E.
ROE	.5340251	.5489861	-.014961	.0375801
ROA	-1.955445	-2.009319	.0538736	.1308448
LS	.0084434	-.000461	.0089044	.01632
LAT	-.0159187	-.0145361	-.0013826	.0049763
LVol	-.0430595	-.0265412	-.0165183	.0195504
Int	-.8085674	-.7891283	-.019439	.0631288
Inf	.6118119	.5871131	.0246989	.0384722
Tot_tax	2.58e-08	4.42e-09	2.13e-08	1.52e-08
GO	.0646864	.0624375	.0022489	.0070067
LAR	-.0027093	.00317	-.0058793	.0021236
b = consistent under Ho and Ha; obtained from xtreg
B = inconsistent under Ha, efficient under Ho; obtained from xtreg
Test: Ho: difference in coefficients not systematic
chi2(9) = (b-B)'[(V_b-V_B)^(-1)](b-B)
=13.30
Prob>chi2=0.1495

Table 12

Total leverage

Leverage (total)
Coefficients
	(b)	(B)	(b-B)	sqrt(diag(V_b-V_B))
	fixed	random	Difference	S.E.
ROE	.59236	.5751323	.0172277	.035768
ROA	-1.999076	-1.958245	-.0408308	.1223353
LS	.006672	.0254741	-.0188021	.0179758
LAT	-.0115278	-.018999	.0074711	.0050353
LVol	-.0718168	-.0264554	-.0453614	.0212447
Int	-1.095812	-1.03483	-.0609827	.0650637
Inf	.9337723	.8546809	.0790914	.0396764
Tot_tax	5.17e-08	6.18e-08	-1.01e-08	1.45e-08
GO	.0555382	.0615006	-.0059624	.0070997
LAR	-.0003462	.0013884	-.0017346	.0020432
b = consistent under Ho and Ha; obtained from xtreg
B = inconsistent under Ha, efficient under Ho; obtained from xtreg
Test: Ho: difference in coefficients not systematic
chi2(9) = (b-B)'[(V_b-V_B)^(-1)](b-B)
=9.71
Prob>chi2=0.3748

Breusch-Pagan Lagrange multiplier test

Exhibit 1

Total debt test

TD[Bank,t]=Xb+u[Bank]+e[Bank,t]
Estimated	results:
	Var	sd=sqrt(Var)
TD	.1012289	.3181649
e	.0386677	.1966411
u	.0567805	.2382865
Test: Var(u) = 0
chibar2 (01) = 1464.75
Prob > chibar2 = 0.0000

Exhibit 2

Total debt test

StD[Bank,t]=Xb+u[Bank]+e[Bank,t]
Estimated	results:
	Var	sd=sqrt(Var)
TD	.0560018	.236647
e	.0304668	.1745474
u	.0263352	.1622814
Test: Var(u) = 0
chibar2 (01) = 594.56
Prob > chibar2 = 0.0000

Results

Exhibit 3

Short-term debt results

Arellano-Bond dynamic panel-data estimation	Number of obs = 1259
Group variable: Bank		Number of groups = 361
Time variable: Year
				Obs per group: min = 1
				avg = 3.487535
						max = 4
Number of instruments = 21		Wald chi2(11) = 29.52
				Prob > chi2 = 0.0019
One-step results
			(Std. Err. Adjusted for clustering on Bank)
	Robust
StD	Coef.	Std.Err.	z	P>|z|	[95%Conf.Interval]
StD
L1.	-.1837724	.2586786	-0.71	0.477	-.6907731	.3232282
L1ROE	.5320321	.4501448	1.18	0.237	-.3502355	1.4143
L1ROA	-1.590542	1.371686	-1.16	0.246	-4.278998	1.097915
LS	-.0371089	.019097	-1.94	0.052	-.0745383	.0003204
LAT	-.0072583	.0044782	-1.62	0.105	-.0160355	.0015188
LVol	-.0735543	.0208758	-3.52	0.000	-.1144701	-.0326385
Int	.0285643	.2268752	0.13	0.900	-.416103	.4732316
Inf	.5323628	.2511726	2.12	0.034	.0400736	1.024652
Tot_tax	4.02e-08	1.93e-08	2.08	0.038	2.30e-09	7.81e-08
L1GO	-.0098407	.0098955	-0.99	0.320	-.0292356	.0095542
LAR	.0032864	.0042365	0.78	0.438	-.005017	.0115898
_cons	.1528767	.2227298	0.69	0.092	-.2836657	.5894191
Instruments for differenced equation
	GMM-type:	L(2/.).StD
	Standard:	D.L1ROE D.L1ROA D.LS D.LAT D.Lvol D.Int D.Inf D.Tot_tax D.L1GO D.LAR
Instruments for level equation
	Standard:_cons

Exhibit 4

Long-term debt results

Arellano-Bond dynamic panel-data estimation	Number of obs = 1261
Group variable: Bank		Number of groups = 361
Time variable: Year
				Obs per group: min = 1
				avg = 3.493075
						max = 4
Number of instruments = 20		Wald chi2(11) = 513.90
				Prob > chi2 = 0.0000
One-step results
			(Std. Err. Adjusted for clustering on Bank)
	Robust
LtD	Coef.	Std.Err.	z	P>|z|	[95%Conf.Interval]
LtD
L1.	0.969782	.0625947	21.88	0.000	1.247099	1.492465
L1ROE	.0060377	.0666359	0.09	0.928	-.1245662	.1366416
L1ROA	.1074757	.2613835	0.41	0.681	-.4048266	.619778
L1LS	-.0258142	.0119005	-2.17	0.030	-.0491388	-.0024897
LAT	-.0085277	.0049506	-1.72	0.085	-.0182307	.0011754
LVol	-.0055791	.0140873	-0.40	0.692	-.0331897	.0220315
Int	-.0457904	.1424257	-0.32	0.748	-.3249396	.2333587
Inf	-.0151433	.0865722	-0.17	0.861	-.1848217	.1545351
Tot_tax	-8.88e-09	2.64e-08	-0.34	0.737	-6.07e-08	4.29e-08
L1GO	.001853	.0120883	0.15	0.878	-.0218397	.0255456
LAR	-.0005301	.002725	-0.19	0.846	-.0058711	.0048109
_cons	.270835	.1579053	1.72	0.086	-.0386536	.5803237
Instruments for differenced equation
	GMM-type:	L(2/.).LtD
	Standard:	D.L1ROE D.L1ROA D.L1LS D.LAT D.LVol D.Int D.Inf D.Tot_tax D.L1GO D.LAR
Instruments for level equation
	Standard:_cons



Exhibit 5

Total debt results

Arellano-Bond dynamic panel-data estimation	Number of obs = 1259
Group variable: Bank		Number of groups = 361
Time variable: Year
				Obs per group: min = 1
				avg = 3.487535
						max = 4
Number of instruments = 21		Wald chi2(11) = 34.18
				Prob > chi2 = 0.0003
One-step results
			(Std. Err. Adjusted for clustering on Bank)
	Robust
TD	Coef.	Std.Err.	z	P>|z|	[95%Conf.Interval]
TD
L1.	.0827979	.3736627	0.22	0.825	-.6495676	.8151634
L1ROE	.361234	.5571255	0.65	0.517	-.7307119	1.45318
L1ROA	-.9576324	1.711425	-0.56	0.576	-4.311963	2.396698
LS	-.0676185	.0345677	-1.96	0.050	-.1353699	.0001328
LAT	-.0143558	.0053775	-2.67	0.008	-.0248955	-.003816
LVol	-.1156959	.0332198	-3.48	0.000	-.1808056	-.0505863
Int	.1226569	.2769817	0.44	0.658	-.4202174	.6655311
Inf	.647829	.3762454	1.72	0.085	-.0895985	1.385257
Tot_tax	2.08e-08	3.14e-08	0.66	0.507	-4.08e-08	8.24e-08
L1GO	-.0251323	.0145938	-1.72	0.085	-.0537355	.003471
LAR	.0037127	.0048673	0.76	0.446	-.0058269	.0132523
_cons	.3373282	.3622836	0.93	0.083	-.3727346	1.047391
Instruments for differenced equation
	GMM-type:	L(2/.).TD
	Standard:	D.L1ROE D.L1ROA D.LS D.LAT D.LVol D.Int D.Inf D.Tot_tax D.L1GO D.LAR
Instruments for level equation
	Standard:_cons

Significance level

Table 13

Variables significance for short-term debt leverage

Leverage (StD)
Variable	active
StD
L1.	-.18377243
|
L1ROE	.53203213
L1ROA	-1.5905416
LS	-.03710894*
LAT	-.00725835
LVol	-.07355429***
Int	.02856427
Inf	.53236282**
Tot_tax	4.020e-08**
L1GO	-.00984071
LAR	.00328644
_cons	.15287672*
legend:	*p<.1;**p<.05;***p<.01

Table 14

Variables significance for long-term debt leverage

Leverage (LtD)
Variable	active
LtD
L1.	0.9697821***
|
L1ROE	.00603774
L1ROA	.1074757
LS	-.02581424**
LAT	-.00852767*
LVol	-.00557908
Int	-.04579045
Inf	-.01514331
Tot_tax	-8.878e-09
L1GO	.00185297
LAR	-.00053007
_cons	.27083501*
legend:	*p<.1;**p<.05;***p<.01

Table 15

Variables significance for total debt leverage

Leverage (Total debt)
Variable	active
TD
L1.	.0827979
|
L1ROE	.36123402
L1ROA	-.95763237
LS	-.06761855*
LAT	-.01435579***
LVol	-.11569591***
Int	.12265686
Inf	.64782901*
Tot_tax	2.083e-08
L1GO	-.02513227*
LAR

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