University of applied sciences bfi vienna

master program

banking and finance

lecture:

m a s t e r t h e s i s

Relationship between liquidity ratios and profitability in Russian banks using regression analysis

Subject area:

Submitted by:      

Matriculation number:      

Supervisor:      

Providing body: Fachhochschule des BFI Wien GmbH

Wohlmutstraße 22

1020 Wien

Wien,      

Table of contents

1. Introduction

1.1. Methodology

1.2. Assumptions

2. Basic definitions

2.1. Bank liquidity risk

2.2. Liquidity risk management

2.3. Liquidity ratios

2.4. Profitability ratios

2.5. Regression analysis

3. Setting up the model

3.1. Gathering the data

3.2. Regression analysis with use of MO Excel

4. Conclusion

Abstract

This work investigated the impact of liquidity management on the profitability of banks in Russia. The work is necessitated by the need to find ways of increasing sufficiency of liquidity management in Russian banking industry. Liquidity management is defined by the structure of bank assets such as cash assets, loans and securities. The relationship between the structure of bank assets and bank profitability defined with help of regression analysis to test the hypothesis. The results of this study would show that it’s important for banks to find optimal structure of assets that allow to avoid lack of liquidity and it’s excess. In this case the effective liquidity risk management leads to the rising of bank profitability.

1. Introduction

Research question (which question shall be answered by the thesis?):

1. What is nature of the relationship between liquidity level and bank profitability?

2. How the relationship between liquidity level and bank profitability in period of stable economic situation in a country differ from that in period of liquidity crisis?

1. Methodology

A sample design – stratified random sampling and simple random sampling. Stratified random sampling - a method of sampling that involves the division of a population into smaller groups known as strata. In stratified random sampling, the strata are formed based on members' shared attributes or characteristics.  It can be used probability stratified random sampling because the population (amount of banks) is defined and divided into three strata: federal, regional and local banks. The strata are formed based on the size of banks. After determination of each strata it would be applied simple random sampling to define 10 banks typical for each strata.

Simple random sampling -­ a subset of a statistical population in which each member of the subset has an equal probability of being chosen. A simple random sample is meant to be an unbiased representation of a group. It can be used because the population is defined. There are 647 banks in Russian Federation on 18.01.2017.

Data collection method: an every bank is required to disclosure annual reports. This study made use documentary secondary data from annual report of commercial banks, bulletin of Central Bank of Russia and information from official website of Central Bank of Russia.

For calculating the efficiency and quality of liquidity management it would be used analysis of banks financial documents and the methodology of Russian central bank of evaluating the quality of liquidity risk management based on the methodology of Basel Committee on Banking Supervision.

Liquidity analysis coefficients that would be used in this study:

The liquidity ratio = liquid assets / total assets

Cash ratio = cash / total assets

Loan / deposit ratio = loans / deposits

Loan / liabilities ratio = loans / liabilities

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

Method of analysis is the 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.

The function for this study is given as:

Y = b0 + b1X1 + b2X2 + b3X3 + e

Where:

Y = Profitability representing the dependent variable;

b0, b1, b2, b3 are regression parameters;

X1 , X2 , X3 are independent variables;

X1 – quick liquidity ratio;

X2 – current liquidity ratio;

X3 – long-term liquidity ratio