3. Liquidity ratios

quick liquidity ratio = high liquid assets (1 day)/ liabilities without term

current liquidity ratio = liquid assets (30 days) / current liabilities (30 days)

long-term liquidity ratio = credits with maturity date > 1 year / equity and liabilities with maturity date > 1 year

According to Instruction of Central Bank of Russia №139-I «About required standards», 03.12.2012 banks are required to have minimum or maximum level for liquidity ratios:

H2 quick liquidity ratio = min 15%

H3 current liquidity ratio = min 50%

H4 long-term liquidity ratio = max 120%

4. Profitability ratios

Return on equity (ROE) = Net income/total equity

ROE equals net income divided by average total stockholders’ equity and measures the percentage return on each dollar of stockholders’ equity. It is the aggregate return to stockholders before paying cash dividends.

Return on assets (ROA) = Net income/total assets

ROA equals net income divided by average total assets and thus measures net income per dollar of average assets owned during the period.

5. Regression analysis

In statistical modeling, regression analysis is a statistical process for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables (or 'predictors'). More specifically, regression analysis helps one understand how the typical value of the dependent variable (or 'criterion variable') changes when any one of the independent variables is varied, while the other independent variables are held fixed. Most commonly, regression analysis estimates the conditional expectation of the dependent variable given the independent variables – that is, the average value of the dependent variable when the independent variables are fixed. Less commonly, the focus is on a quantile, or other location parameter of the conditional distribution of the dependent variable given the independent variables. In all cases, the estimation target is a function of the independent variables called the regression function. In regression analysis, it is also of interest to characterize the variation of the dependent variable around the regression function which can be described by a probability distribution. The performance of regression analysis methods in practice depends on the form of the data generating process, and how it relates to the regression approach being used. Since the true form of the data-generating process is generally not known, regression analysis often depends to some extent on making assumptions about this process. These assumptions are sometimes testable if a sufficient quantity of data is available [3, p.27].

3. Setting up the model

The first step in running regression analysis in Excel is verifying that the software has the capabilities to perform the calculations.

1. Gather and upload data - gather all the requisite data needed to perform the calculations. All information will be gathered from the website of Central Bank of Russian Federation: https://www.cbr.ru/credit/transparent.asp

Banks	quick liquidity ratio,%	current liquidity ratio,%	long term liquidity ratio,%	ROE,%	after tax profit	equity
Sberbank	110	151	165	10,15	236256	2328156
VTB	61	99	58	3,79	48580	1282028
Gazprombank	50	151	52	-8,33	-34365	412370
VTB 24	84	118	78	0,26	461	179100
Otkritie	274	113	79	1,83	2303	126034
Rosselhoz	148	285	67	-29,61	-69207	233712
Alphabank	132	162	54	21,89	49591	226554

Table 1: Data on 01.01.2016, million rubles

2. Run the regression - after uploading data go to the data tab and select Data Analysis to bring up the Data Analysis ToolPak. Select Regression in the list of options for analysis tools, and click OK. Use the Regression tool to input your X and Y ranges for the data sets. Select and output the range for the results of the regression. Depending on the options you select using the regression tool, there are multiple tables of output, and potentially charts as well. The resulting output is the regression analysis;

SUMMARY OUTPUT

Регрессионная статистика
Множественный R	0,69292231
R-квадрат	0,48014133
Нормированный R-квадрат	-0,0397173
Стандартная ошибка	16,368749
Наблюдения	7

Дисперсионный анализ
	df	SS	MS	F	Значимость F
Регрессия	3	742,3967	247,4655656	0,9236	0,525278194
Остаток	3	803,8078	267,9359442
Итого	6	1546,205

	Коэффициенты	Стандартная ошибка	t-статистика	P-Значение	Нижние 95%	Верхние 95%	Нижние 95,0%	Верхние 95,0%
Y-пересечение	19,6646	24,1387	0,8147	0,4749	-57,155	96,4847	-57,155	96,4847
quick liquidity ratio	0,0064	0,0890	0,0723	0,9469	-0,2768	0,2897	-0,2768	0,2897
current liquidity ratio	-0,1704	0,1079	-1,5794	0,2124	-0,5137	0,1730	-0,5137	0,1730
long term liquidity ratio	0,0735	0,1704	0,4312	0,6955	-0,4690	0,6159	-0,4690	0,6159