CAMELS parameters’ impact on the risk of losing financial stability: The case of Russian banks

Elena G. Shershneva

DOI: 10.29141/2658-5081-2024-25-2-7 EDN: UXELPK JEL classification: G21, C53

Elena G. Shershneva Ural Federal University named after the first President of Russia B . N . Yeltsin,

Ekaterinburg, Russia

CAMELS parameters' impact on the risk of losing financial stability: The case of Russian banks

Abstract. The banking sector stability determines the financial immunity of a national economy. Current economic and political tensions precondition the need for predicative diagnosis of factors behind a decrease in a bank's financial stability taking into account national specificities. The paper aims to explore the impact of intrabank parameters on a risk of deteriorated financial stability of Russian banks. The methodological basis of the study is the theory of financial management as applied to the banking practice. The research methods include content analysis, multiple regression, and logit modelling. The evidence comes from the published financial statements of Russian banks for 2018-2023. The paper suggests an approach for rating banks according to their financial stability and develops logit models for evaluating the risk of losing financial stability based on the CAMELS parameters. The analysis demonstrates a noticeable positive impact of the return on assets and a noticeable negative effect of the overdue loans share on a bank's financial stability. At the same time, capital adequacy and current liquidity produce an ambiguous effect on the financial strength: they are significant only up to a certain point, after passing which they no longer exert any impact on the financial stability (the so-called "surplus paradox"). The study finds that the impact of the parameters differs for the medium-and long-term forecasting horizons: for a 6-month period, the return on assets is a more significant predictor of the financial instability risk, while the overdue loans share is more important for a 12-month period. The findings extend the understanding of the influence that bank's internal factors have on their financial stability and can be useful in building the algorithms for analysing and forecasting banking risks.

Keywords: bank's stability; commercial bank; financial sustainability; financial stability; risk; CAMELS parameters.

For citation: Shershneva E. G. (2024). CAMELS parameters' impact on the risk of losing financial stability: The case of Russian banks. Journal of New Economy, vol. 25, no. 2, pp. 130-152. DOI: 10.29141/2658-5081-2024-25-2-7. EDN: UXELPK.

Article info: received February 2, 2024; received in revised form March 10, 2024; accepted March 20, 2024

Е. Г. Шершнева Уральский федеральный университет им . первого Президента России Б . Н . Ельцина, г. Екатеринбург, РФ

Влияние CAMELS-nараметров

I V V

на риск потери финансовом устойчивости российских банков

Аннотация. Устойчивость банковского сектора является «финансовым иммунитетом» национальной экономики. В условиях экономико-политической напряженности повышается значимость многомерной предикативной диагностики факторов снижения финансовой стабильности банков с учетом национальных особенностей. Статья посвящена исследованию специфики влияния ряда параметров деятельности российских банков на риск потери их финансовой устойчивости. Методологической базой работы являются положения теории финансового менеджмента применительно к банковской практике. Методы исследования включают контент-анализ, многофакторный регрессионный анализ, логистическое моделирование. Информационную базу составляют данные публикуемой отчетности российских банков за 2018-2023 гг. Предложен методический подход к определению рейтинга финансовой устойчивости банка. Разработаны многомерные логит-модели оценки риска потери финансовой устойчивости на основе СЛМЕЬБ-переменных. Выявлено, что рентабельность активов существенно и положительно воздействует на финансовую устойчивость, а доля просроченной задолженности оказывает значительный негативный эффект на стабильность коммерческих банков. При этом индикаторы достаточности капитала и текущей ликвидности демонстрируют значимость до определенного уровня, выше которого теряют влияние на финансовую резистентность (так называемые парадоксы излишка). Установлено, что влияние анализируемых параметров для средне- и долгосрочных горизонтов прогнозирования различно: в течение 6 месяцев рентабельность активов является более значимым предиктором риска потери финансовой устойчивости, а уровень просроченных кредитов более значим для периода 12 месяцев. Выводы исследования расширяют научные представления о специфике влияния внутренних факторов банков на их финансовую устойчивость и могут быть использованы при разработке алгоритмов анализа и прогнозирования банковских рисков.

Ключевые слова: устойчивость банка; коммерческий банк; финансовая устойчивость; финансовая стабильность; банковский риск; СЛМЕЬБ-параметры .

Для цитирования: Shershneva E. G. (2024). CAMELS parameters' impact on the risk of losing financial stability: The case of Russian banks. Journal of New Economy, vol. 25, no. 2, pp. 130-152. DOI: 10.29141/2658-5081-2024-25-2-7. EDN: UXELPK.

Информация о статье: поступила 2 февраля 2024 г.; доработана 10 марта 2024 г.; одобрена 20 марта 2024 г.

Introduction

The strategic purpose of managing a bank's financial stability is twofold: ensuring its competitiveness in the market and maintaining the general stability of the banking system, which determines the national financial immunity. The strategic targets are achieved through tactical moves, among which the key ones are monitoring the financial condition and predicting risks.

Amidst global instability and systemic changes, bankers and researchers pay attention to the transformation of socioeconomic paradigm: modern business environment is characterised by a higher degree of financial and psychological tension as well as by uncertainty and fragility [Khalatur et al., 2021]. In such circumstances, maintaining the target state of financial stability is one of the major problems of financial management. Insufficient capital and liquidity, asset quality deterioration, reduced profits worsen the solvency of banks, which is transmitted to other sectors of economy: enterprises and individuals cannot receive money or make payments. Consequently, there are failures in the sphere of production and monetary circulation.

Another important aspect is the specifics of a bank's reputation: the loss of customer trust inevitably leads to a decrease in financial stability, and conversely, financial problems negatively affect business reputation. In this context, ensuring the reliability and stability of a bank's activities is of particular relevance.

Thus, improving information and analytical tools to support managerial decision-making that ensures the regulation of financial flows of a commercial bank is a relevant research objective. Promoting the efficiency of financial analysis and the reliability of risk forecasting allows adjusting the current functioning of a bank's financial mechanism.

In addition, there is little research in Russian academic space and SCOPUS / Web of Science databases devoted to assessing the impact of various factors on the financial stability of Russian banks. Therefore, there is a need to advance the scientific understanding of the 'financial metabolism' specificity in the Russian banking sector.

The purpose of this study is to examine how intrabank parameters influence the risk of Russian banks losing the financial stability. To achieve this purpose, we set the following objectives: 1) to explore the impact of key indicators of capital, assets, liquidity and profitability on the financial stability of Russian banks and direct specific attention to the strength and direction of this influence; 2) to design a method for predicting the probability of a bank's financial stability loss based on intrabank CAMELS parameters.

The following hypotheses are formulated:

H1: Capital adequacy ratio has a significant positive impact on a bank's financial stability.

H2: Liquidity and return on assets have a significant positive impact on a bank's financial stability.

H3: Share of overdue debt has a significant negative impact on a bank's financial stability.

H4: Degree of factors' influence varies in the mid and long term when forecasting the risk of financial stability loss.

The paper is structured as follows. The next section reviews literature for interpretations of a bank's financial stability, as well as approaches and methods for analysing and forecasting the risk of financial instability. Then, the research method and mathematical tools for the analysis and econometric modelling are defined. The section afterwards presents a testing of predicative logit models, the assessment of the probability of a financial instability risk, and results of testing the hypotheses. Further section discusses the findings. The final section contains conclusions, points to the limitations and directions of future research.

Literature review

Research viewpoints on the understanding of a bank's financial stability and its factors. An

overview of scientific articles exploring the theoretical basis of the financial stability reveals that although authors often equate terms "stability", "reliability" and "sustainability", they approach these concepts differently. These approaches are discussed below.

From the perspective of a resource approach, scientists believe that a sustainable bank has sufficient financial resources to carry out stable activities. Mishkin [1999] defines financial stability as a balance of financial flows, the availability of own funds to function for a certain time period. Vostrikova and Panina [2020] characterise the bank's financial stability in terms of achieving goals, taking into account available financial resources. Chen [2022] looks at a bank's financial reliability through the prism of capital adequacy and the optimal ratio of own and borrowed resources. Ozili and Iorember [2023] determine a bank's financial stability as the ability to transform resources (human, financial, information and others) with maximum efficiency in risky and competitive environment.

A resilience-based approach focuses on a bank's ability to withstand negative factors. Miah and Uddin [2017] consider financial stability as the ability to withstand adverse internal and external economic and financial shocks and the ability to fulfill obligations without external interference. Papanikolaou [2018] identifies the financial stability as the state of an organisation when it is able to withstand shocks and eliminate imbalances without external help. Gorskiy, Reshulskaya and Rudakov [2020] think that financial stability is the immunity of an economic entity to the effects of internal and external negative factors. Rahman, Chowdhury and Tania [2021] note that reliable banks are able to optimally allocate capital, reduce investment risk and prevent disasters through the self-correction processes. According to Baranova, Vlasenko and Poberezhets [2022], financial stability is a state of organisation in which the effective allocation and use of financial resources helps mitigate external and internal risks and ensure sustainable development.

When adopting a complex approach, researchers urge to consider financial stability comprehensively, including customers' trust, maintenance of financial indicators, adaptation to changing external and internal factors, risk management and retaining business reputation. For example, to ensure financial stability, the Basel Committee on Banking Supervision formulates recommendations on improving quality and quantity of capital, and sets standards for liquidity and stable financing [Asghar, Rashid, Abbas, 2022]. Pukhov [2012] defines the bank's financial stability as a systemic concept reflecting the prospects for its development and including a range of indicators: state of capital, level of liquidity, quality of assets and liabilities, profitability, and quality of management. Barra and Zotti [2022] describe the bank's stability as a complex characteristic reflecting the interrelation of different aspects of bank activities, which allows maintaining solvency, liquidity and profitability. Halaj, Martinez-Jaramillo and Battiston [2024] present the financial stability as an integrated concept reflecting the prospects for the development and the ability to withstand systemic risks (for example, the macro-shock of the COVID-19).

We believe that in conditions of high uncertainty the complex approach is more relevant since a bank's financial stability is explored comprehensively: financial indicators, risk management, adaptation to changing external and internal factors, business reputation are all accounted for.

The similarity between the definitions is that the authors focus on the bank's ability to maintain solvency and perform its functions, while the differences relate to specific aspects and indicators of financial sustainability taken into consideration.

Financial stability criteria are a set of indicators used to assess the financial position of a bank. Indicators allow determining how well the bank is functioning and whether it is able to withstand economic shocks. The dominant yardsticks for a bank's financial stability are indicators of Capital, Assets, Management, Earnings, Liquidity and Sensitivity to risk (CAMELS) which are presented in Table 1.

Table 1. CAMELS yardsticks of a bank's financial stability

Таблица 1. CAMELS-параметры финансовой устойчивости банка

Parameter Description Key indicator, %

Capital Bank's own funds (share capital, funds, profit), which can be used to cover losses incurred as a result of risky transactions and other adverse factors' influence Equity capital adequacy ratio

Assets Bank's assets are a set of profitable and liquid financial resources (cash, loans, investments, other assets). Assets are analysed based on riskiness, liquidity and profitability Ratio of overdue loans

Management A characteristic reflecting the quality of the bank's management. The main criterion for quality is the achievement of a bank's goals in terms of profitability, capitalisation and market position Operational efficiency ratio

Earnings/ Profitability A relative measure of banking business profitability. High earnings prove the effectiveness of a bank's activities and the ability to generate sufficient funds to maintain financial stability Ratio of net profit to total assets or equity (ROA, ROE)

Liquidity The bank's ability to meet its financial obligations in full. It is a factor of financial stability, since lack of liquidity leads to financial problems Current liquidity ratio

Sensitivity to risk A characteristic reflecting quality of risk management (credit risk, liquidity risk, currency risk, interest rate risk and others) Credit risk ratio

Having examined the research from different countries devoted to the impact of CAMELS factors on financial stability, we identified the following points.

Capital adequacy and its quality have a positive impact on a bank's financial stability. Siddika and Haron [2020] found that an increase in the capital adequacy ratio significantly reduces bank risk, while higher regulatory pressure corresponds to a higher level of bank risk. The authors characterised the regulatory paradox: the tightening of regulatory requirements, on the one hand, is aimed at increasing the financial stability of a banking system, and on the other hand, complicates the financial mechanism of banks, which creates conditions for instability. Bouheni and Hasnaoui [2017] concluded that rising capital can contribute to financial stability. Joudar et al. [2023] detected a strong positive correlation between the capital adequacy ratio and the financial stability of Islamic banks.

Asset characteristics affect financial stability in different ways: an increase in assets volume helps strengthen the bank's financial position, while an increase in overdue debts and loan reserves leads to the financial deterioration. According to Adusei [2015], Ali and Puah [2018], Rupeika-Apoga et al. [2018] the bank's stability depends on the size of a bank measured by natural logarithm of total assets. They found that banks with larger assets are more stable. The study by Audi, Kassem and Roussel [2021] also showed that large banks are less vulnerable to the risk of default (considering the example of banks from the MENA region). Ozili and Outa [2017] discovered inverse correlation between loan reserves and a bank's financial stability since the growth of reserves signals a credit risk and may cause an increase in overdue loans in future.

Quality of management affects financial stability in the following logic: competent goal setting, effective planning and analysis, and proper motivation lead to the intended financial results.

Therefore, the quality of management can be measured by the indicators of operational efficiency and income diversification. Shahriar et al. [2023] proved the hypothesis that improving operational efficiency contributes to the stability of banks. They demonstrated that net interest margin and non-interest income are positively related to a bank's stability, but long-term debt has a negative impact on a bank's resistance. Lepetit et al. [2008] revealed that income diversification, as a rule, affects the bank stability: a variety of business areas makes it possible to increase the efficiency and stability. However, the researchers noted that during times of crisis a high business diversification could create managerial difficulties for banks.

Profitability (earnings) is a key aim of a bank's business and allows it to maintain financial viability. It was found that return on assets has positive and significant impact on the stability of banks [Rupeika-Apoga et al., 2018; Kasri, Azzahra, 2020]. Mkadmi, Baccari and Ncib [2021] concluded that net interest margin and non-interest income have a positive insignificant effect on bank sustainability.

Liquidity is a balance of assets and liabilities in terms of maturity and amounts, as a result of which the bank remains solvent. The researchers conclude that liquidity has a positive impact on a bank's financial sustainability [Rupeika-Apoga et al., 2018; Shershneva, Bakr Hasan, Al Hadabi, 2020]. However, it is noted the negative influence of liquidity surplus, as low-yielding assets reduce profitability and capitalisation growth.

Sensitivity to risk is studied by various aspects: researchers mainly focus on the analysis of credit risk, operational risk, and interest rate risk. It was found that risk level adversely impacts on a bank's financial durability [Chiaramonte, Casu, 2017; Alaminos, del Castillo, Fernandez, 2018; Joudar et al., 2023].

In addition to the above-mentioned intrabank criteria, researchers investigate the influence of economic environment factors. There are studies showing an impact of the competition on a bank's reliability. In the papers of Uhde and Heimeshoff [2009], Kasman and Carvallo [2014] it is concluded that tightened competition strengthens financial stability. The authors explain this effect by stating that competition allows improving banking services and promotes the introduction of new banking products. As a result, banks can earn more profit and take a larger market share.

On the contrary, Yuan et al. [2022] found that there is ambiguous relationship between banking competition and stability (on the example of American banks). According to them, excessive competition in the banking sector ('invisible hand') may have been one of the main drivers of a financial crisis. In addition, oligopoly and monopolism increase risk appetite and displace medium and small banks, and this leads to inefficiency and fragility.

Ghosh [2016] discovered different effects of the foreign banks' presence in domestic markets. On the one hand, the increased banking globalisation reduces profits and cost efficiency, and raises information asymmetry in host markets. On the other hand, domestic banks are assimilating more advanced technologies and management practices. Greater globalisation of the banking sector has a positive impact on profits and stability only in emerging markets and in countries where the foreign banks' share accounts for more than 50 % of the market. Yin [2019] revealed that the entry of foreign banks could negatively impact the stability of national banks due to an increase in credit risk in the host country (based on a sample of 129 countries over the period 1995-2013).

A separate area of research focuses on the analysis of efficiency and financial stability of Islamic banks, considering the specifics of their activities. A number of studies have concluded that Islamic banks are more financially stable than traditional banks [Cihák, Hesse, 2010; Louhichi, Boujelbene, 2016; Daoud, Kammoun, 2020]. Cihák and Hesse [2010] showed that small Islamic banks are more financially stable than small traditional banks, but large traditional

banks are stronger than large Islamic banks. It is noted that financial stability of Islamic banks is achieved by high credit quality, sufficient capitalisation, reserve characteristics, non-aggressive credit policy and investments in the real assets [Nosheen, Rashid, 2019].

Thus, it can be concluded that a bank's financial stability is a multi-dimensional concept that depends on a variety of factors.

Research methods for analysing and forecasting risk of a bank's financial instability. The review of literature allows identifying three groups of methods for predicting bank financial stability loss: statistical models, artificial intelligence models, and ensemble models.

1. Statistical models (Z-scoring, discriminant analysis, correlation and regression analysis, stochastic frontier analysis, data envelopment analysis, logit and probit models, stress testing). The earliest models for predicting bank failures using financial ratios and Z-scoring were proposed by Beaver [1966], Altman [1968], Meyer and Pifer [1970], Sinkey [1975], Ohlson [1980], Scott [1981], Hardy and Pazarbasioglu [1999]. In modern conditions these models are the foundation for advanced algorithms for early warning of a bank's financial risks.

Discriminant analysis is widely used in models with multiple variables and allows assigning the object to a specific class. For example, Shar, Shah and Jamali [2010] proposed a model called Bankometer which evaluates financial stability (S) by 6 parameters: capital adequacy ratio, capital to assets ratio, equity to total assets, non-performing loans ratio, cost-to-income ratio, loan-to-asset ratio. Points are assigned depending on the value of the parameter. The criteria are as follows: if S > 70 points, then bank is solvent and termed as a super sound bank (favourable financial status); if S < 50 points, then bank is termed as insolvent (high risk of financial distress); if 50 < S < 70 is true then bank can be classified as a gray area due to the susceptibility to classification errors.

Stochastic frontier analysis (SFA) is used to assess the impact of various factors on the financial condition. Sanchez Gonzalez, Restrepo-Tobon and Ramirez Hassan [2021] applied the SFA and Bayesian approach to analyse the impact of inefficiency on the time before the bankruptcy of American banks.

Data envelopment analysis (DEA) is a nonparametric predictive method. Its essence is to build the efficiency boundary (envelope hypersurface, which is based on performance indicators of the object of analysis). Optimal objects lie on this boundary. This method was first proposed by Barr, Seiford and Siems [1993]. In their study, the efficiency was associated with an indicator of financial stability, and DEA was a tool for determining the quality of management. Inefficiency was considered as the cause of bank failures and the authors concluded that inefficient banks are more likely to be financially unstable or bankrupt than efficient ones. The results of DEA-prediction by Li, Feng and Tang [2022] also showed that banks with lower performance indicators are more likely to face a financial collapse.

Logit modelling is based on the separation of objects into hyperplanes and logit regression, which makes it possible to determine the probability of financial stability loss. Kolari et al. [2002] concluded that logit regression is an effective method of failures predicting: it allows accurately recognising the classification signs of financial problems. Based on a logit regression it was found that the probability of a bank's financial stability loss and financial crisis decreases as liquidity reserves increase, while capital adequacy ratios are significant only for large banks [Chiaramonte, Casu, 2017]. Alaminos, del Castillo and Fernandez [2018] used logit modelling to design bankruptcy forecasting models for Asia, Europe and America, as well as a global model.

Stress testing is an analysis of the impact of various shocks on banking activities and is a multi-stage procedure for assessing losses as a result of the implementation of a stress scenario. Stress scenarios are divided into three types: historical, hypothetical and hybrid; predictive

tools use a wide variety of modelling methods from a simple regression to nonlinear panel data [Bidzhoyan, 2020].

2. Artificial intelligence models (artificial neural networks, feature recognition models, support vector machines, decision trees and other). Neural networks as a nonparametric method are often used in research on predicting bank failures. It was first introduced by Tam [1991]. He investigated the effectiveness of 59 pairs of bankrupt and non-bankrupt banks and came to the conclusion that accuracy of forecasting neural networks is higher than parametric algorithms.

Feature recognition models include two stages of processing: learning and object recognition. At the learning stage, knowledge acquisition methods are used to automatically generate object recognition rules and function hints from the training data. At the recognition stage, these hints and rules are used to analyse the details of objects and determine their internal structure [Dimov, Brousseau, Setchi, 2007]. For example, in the article of Samitas, Kampouris and Kenourgios [2020] the intelligent recognition model is proposed to identify features of a financial crisis on the example of 33 countries. The results have shown that the stock volatility is an important predictor of the financial crisis, while cross-country financial relationships are channels of financial contagion.

Support vector machines (SVM) are a set of similar teacher-learning algorithms used for classification and regression analysis tasks. The content of SVM method consists in finding the hyperplane or line separating the classes in the best way. Samples which are the closest to the separating hyperplane are called support vectors. The best class separation would be the one that maximises the distance between the support vectors and the separating hyperplane. In the works of Min and Lee [2005], Erdogan [2013], there were made attempts to apply SVM-method to the bank bankruptcy forecasting.

3. Ensemble models (combine several models with improvement approaches and bundling into packages).

Citterio [2024] conducted a comparative analysis of methods for predicting financial insolvency of banks and concluded that statistical methods based on the logit model and discriminant analysis provide the best compromise between accuracy and interpretability, while simple classifiers are a reliable alternative to more advanced approaches, especially when the interpret-ability of the analysis is important.

Thus, the literature review has demonstrated that the widely used bank financial stability diagnostic models are based on the financial coefficient analysis and logistic regression. Following this logic, the below is the result of our original modelling.

Materials and methods

Our method for predicting the probability of a bank's financial instability consist of two stages. At the first stage, an aggregated financial stability indicator and a rating scale are developed. At the second stage, multidimensional logit models to predict the risk of financial stability loss for periods of 6 and 12 months are designed.

The method will allow testing the four hypotheses formulated above.

First stage. Design of a financial stability indicator and a bank's rating. To analyse financial stability, we will select parameters that can be unambiguous predictors of the risk of financial insolvency of Russian banks. Based on the analysis of literary sources, it can be argued that among the CAMELS parameters, the most significant are Capital, Assets, Earnings (Profitability) and Liquidity. Besides, management effectiveness and risks are not reported in the published statements of banks, therefore, Management and Sensitivity to risk are not accepted for evaluation procedures.

To diagnose a bank's financial stability, we propose to use the following group of indicators calculated according to methodology of the Bank of Russia1:

1) Indicator of capital (Cpm);

2) Indicator of assets (IAssets);

3) Indicator of profitability (Inability);

4) Indicator of liquidity (hiquidity).

The indicator for each group is estimated based on several coefficients (from 3 to 8) using a point-weight method and is rated on a scale: 1 - good, 2 - satisfactory, 3 - doubtful, 4 - unsatisfactory.

For a generalised assessment we will calculate an original aggregated financial stability indicator (FSI) using the formula:

FSI = "\J I Capital X I Assets X I Profitability X I Liquidity- (1)

As a result of the FSI calculation, the bank's financial stability can be characterised as high, medium (satisfactory), instability or financial insolvency. Depending on the level of financial stability the bank may be assigned an "A", "B" or "C" rating (Table 2).

Table 2. Rating scale of a bank's financial stability

Таблица 2. Рейтинговая шкала финансовой устойчивости банка

Financial stability level Financial stability rating FSI value

High A rating 1 < FSI < 1.5

Medium (satisfactory) B rating 1.5 < FSI < 2.5

Instability or financial insolvency C rating 2.5 < FSI < 4

A "C" rating means that a bank has serious financial difficulties which could lead to the inability to pay deposits and conduct other operations. The deterioration of the financial situation may be the reason for the rehabilitation or revocation of license of the Bank of Russia.

Table 3 demonstrates the result of calculating the indicators of capital, assets, profitability, liquidity and aggregated FSI for some Russian banks using the 2023 data. Depending on the size of the assets, banks are grouped into the largest (assets > 10 trillion rubles), large (1 < assets < 10 trillion rubles), medium (1 trillion rubles < assets < 100 billion rubles) and small (assets < 100 billion rubles).

Table 3. Diagnostic indicators and the rating of banks in 2023 Таблица 3. Диагностические индикаторы и рейтинг банков за 2023 г.

Indicator The largest banks Large banks Medium banks Small banks

Sberbank VTB Alfabank Tinkoff Zenit Sinara Fora-Bank Rusnarbank

Capital 1.12 1.08 1.07 1 1.23 1.5 1.7 1.9

Assets 1.3 1.68 1.65 1.3 1.8 1.95 1.9 3

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Profitability 1.8 1.9 2 2 2.1 2.1 2.2 2.5

Liquidity 1.07 1.15 1.1 1 1.16 1 1.15 1.16

FSI 1.33 1.41 1.4 1.27 1.51 1.57 1.69 2

Rating A А А А В B В В

Source: Own calculation based on the data from banks' published financial statements for Q4 2023 retrieved from the portal BANKI.RU. www.banki.ru.

1 Instruction of the Bank of Russia of April 3, 2017 no. 4336-U "On the assessment of the economic situation of banks". https://base.garant.ru/71682362/. (In Russ.)

Table 3 shows that the largest and large banks have an "A" rating, which means they are more stable. This confirms the conclusions of researchers that the bank size is positively correlated with financial stability level. In general, all banks are financially stable, but they have a satisfactory profitability indicator. Also, medium and small banks have worse asset quality compared to larger banks.

However, in modern conditions, it is necessary to pay attention not only to the analysis of the current state of a bank's financial stability, but also to the development of early warning models that will identify financial problems at an early stage or hidden threats. The introduction of such models will allow a commercial bank to identify possible problems in a timely manner and take measures to strengthen the financial viability of banks, which helps prevent future losses.

Second stage. Logit-model design for predicting the probability of a bank's financial stability loss. Management decisions are made under uncertainty, which requires to operate with probabilistic categories that allow making optimal decisions. A logit model was chosen to estimate the probability of a bank's financial stability loss.

The feature of logit models is that the dependent variable is binary: it takes the value 1 if there is a high probability of the bank's financial stability loss, and 0 if there is no probability of financial instability. With this approach, the probability of financial deterioration is in the interval [0; 1]. The logit model is as follows:

^ ~ ^ + g-(Po + № + ... + P„x„)' (2)

where P is the probability of a bank's financial condition falling into a doubtful category ("C" rating) and P E [0; 1]; e is a natural logarithm; p0 and Pi are regression coefficients; the are financial coefficients.

The data fits into a linear regression model, which is then acted upon by a logistic function. This function describes the target categorical dependent variable.

The essence of logistic regression is that the set of initial data can be divided by the hyperplane equation into two or more classes. To define a class, it is necessary to set a boundary value.

The following financial coefficients were selected to model the probability of financial stability loss:

1) x1 - capital adequacy ratio (ratio of a bank's equity to risk assets);

2) x2 - share of overdue loans over 90 days (ratio of overdue loans to total loans);

3) x3 - return on assets (ratio of profit to assets);

4) x4 - current liquidity ratio (proportion of current assets to current liabilities).

These factors characterise the financial health of a bank, as they have a high weight in the group indicator and reflect the quality of capital and assets, as well as the state of liquidity and profitability.

The next methodological step is the compilation of regression equation Z = f (x1, x2, x3, х4), where FSI indicator is used as a predictive metric:

1) if FSIE [1; 2.5], then Z = 0;

2) if FSI E (2.5; 4], then Z = 1.

So, the linear regression model is as follows:

Z = P0 + P1X1 + P2 X2 + P3 X3 + P4 X4. (3)

Data of medium and small Russian banks with different financial conditions for 2018-2023 were used as a training data set. The data of the largest and a number of large banks were not

used because they have serious government support and the ability to use refinancing loans from the Bank of Russia. As shown in Table 3, such banks have a stable financial position ("A" rating). According to 500 observations, the following two equations were obtained:

1) for a bank's financial stability loss prediction model for 6 months (6M):

Z6M = -0.115 + 0.0067x1 + 0.0082x2 - 0.0173x3 + 0.0031x4; (4)

2) for a bank's financial stability loss prediction model for 12 months (12M):

Z12M = -0.001127 + 0.0144x1 + 0.1352x2 - 0.0168x3 + 0.0148x4. (5)

Regression models demonstrate that the most significant variables are the level of overdue loans (x2) and the return on assets (x3). Capital adequacy (x1) has a positive and non-significant impact on a bank's financial stability. The independent variables of multiple regression are not multicollinear, which was verified using the Excel analysis by the method of paired correlation.

The obtained models allow concluding that for periods of 6 and 12 months, the significance of financial stability factors is different. For 6 months, the return on assets is a more significant factor of financial stability, and for a 12-month period, the share of overdue loans is more significant. It can be explained by the fact that overdue debts and loan reserves accumulated for a year can create losses.

Substituting regressions (4) and (5) into formula (2) we get the final logit-models:

P(ZJ ~ , -(-0.115 + 0.0067*1 + 0.0082X2 - 0.0173.*, + 0.0031*,)' (6)

1 + e

p(Z ) =_-__(7)

V 12M/ ^-(-0.001127+ 0.0144*1 + 0.1352*2 - 0.0168*3 + 0.0148*4)" V '

To assess the quality of logit-model P(Z), the McFadden-R2 formula is used:

McFadden -R2=l--In plausible function Z--(8)

In plausible function Z with zero parameters

For our logit-models McFadden-R2 = 0.31 and 0.39 (what is acceptable). Set the boundary value for logit models (6) and (7):

1) if P(Z) < 0.5 then bank is classified as positive;

2) if P(Z) > 0.5 then bank is classified as negative (high risk of loss of financial stability). The proposed multidimensional risk-models make it possible to eliminate subjectivity and

promote the efficiency of management decisions in the analytical activities of commercial banks.

Research results

In this section, we will test logit-models for predicting the risk of financial stability loss on the example of Rusnarbank. It was selected as an object of assessing the probability of financial deterioration to "C" rating because it showed the worst rating result (Table 3). Rusnarbank's data as of the beginning of 2024 is presented in Table 4. We can see that financial indicators x2 and x3 do not correspond to the recommended ones: an increased level of overdue loans and low return on assets.

Table 4. Rusnarbank's data for predicting the risk of a deterioration in rating

Таблица 4. Данные Руснарбанка для прогнозирования риска снижения рейтинга

Financial coefficient Recommended value of coefficient Rusnarbank's data

xi Capital adequacy ratio > 0.08 0.10

x2 Share of overdue loans < 0.10 0.21

xs Return on assets > 0.02 0.009

x4 Current liquidity ratio > 0.50 1.53

Based on the logit-models (6) and (7) we can determine the probability of transition to "C" rating for Rusnarbank:

P(Z6M) =---« 0.473 (< 0.5);

j ^-(-0.115 + 0.0067 x 0.10 + 0.0082 x 0.21 -0.0173 x 0.009 + 0.0031 x 1.53) 4 "

P(.Z12m) -(-0.001127 + 0.0144 x 0.10 + 0.1352 x 0.21 - 0.0168 x 0.009 + 0.0148 x 1.53) 0.513 0.5).

1 + e

The estimation has shown that for the next 6 months Rusnarbank is classified as positive (stable), but the probability of transition to "C" rating is at high level. For the time horizon of 12 months Rusnarbank is classified as negative (unstable): a high level of overdue debt and low profitability create conditions for losing the financial stability. When the quality of loans is low, the bank is obliged to form sufficient reserves, this reduces profits, increases risks and pressure on capital. Despite the significant liquidity reserve (x4 = 1.53), Rusnarbank's financial position may be under threat within 12 months. Therefore, the liquidity factor is not significant for long term.

The obtained results are as follows. H1 is not supported: there is no positive impact of the capital adequacy on financial stability. Since capital plays a protective function, the importance of capital adequacy increases in instability times.

H2 is partially supported: profitability (return on assets) has a significant positive impact on a bank's financial stability, but liquidity does not have a significant positive impact. This is a liquidity trap paradox: on the one hand, liquidity provides the bank with a reserve of solvency, but on the other hand, excessive liquidity reduces profitability (banks store assets in low-yield financial instruments). As a result, commercial bank loses its revenue, its market share, and the operational efficiency goes down.

H3 is supported: the share of overdue loans has a significant negative impact on a bank's financial stability. The level of overdue loans can depreciate assets and lead to losses, which increases the instability risk. Moreover, bad loans carry a potential threat of a new wave of crisis in banking sector.

H4 is supported: the degree of factors' influence varies for medium and long-term when forecasting the financial risk. For 6 months, the return on assets is a more significant factor of financial stability, and the share of overdue loans is more significant for 12 months. If we compare capital and liquidity, then capital is more significant for 6 months, and their significance is the same for 12 months. Overall, the hypotheses testing results are summarised in Table 5.

Table 5. Hypothesis testing results

Таблица 5. Результаты проверки гипотез

Hypothesis Description Result

H1 Capital adequacy ratio has a significant positive impact on a bank's financial stability Not supported

H2 Liquidity and return on assets have a significant positive impact on a bank's financial stability Partially supported

H3 Share of overdue debt has a significant negative impact on a bank's financial stability Supported

H4 Degree of factors' influence varies in the mid and long term when forecasting the risk of financial stability loss Supported

Diagnosing a bank's financial stability loss will allow managers and owners to identify challenges in advance and take timely actions to return the bank to normal. The proposed approach is an analytical tool for decision-making in conditions of a multiplicity of intrabank risk factors. Firstly, the indicator of a bank's financial stability (FSI) allows determining the risk category of a bank - its rating. Rating enables the bank to adjust its activities in the following areas: provisions management, cost management, decision-making on the loan allocation and the identification of problem loans, determining the value of loans taking into account risks, monitoring loan portfolios. Secondly, multidimensional logit-models make it possible to predict the probability of a bank's financial condition deterioration. Anticipating problematic consequences is an important component of crisis management and operational restructuring of the bank business.

Discussion

The study shows that the exploration of a bank's financial stability is in the focus of attention of scientists from different countries. Some analyse internal factors of financial stability such as capital, assets, liquidity, profitability, risks, management efficiency. Others examine external aspects such as competition, national and global conditions. At the same time, scholars unanimously agree that financial stability is a multidimensional characteristic and is affected by many endogenous and exogenous factors.

This study contributes to expanding the scientific understanding of how endogenous factors influence the financial stability of Russian banks. Hypotheses about the impact of key indicators of capital, assets, profitability and liquidity on the aggregated financial stability indicator are tested. Using regression analysis and logit modelling, an approach to assessing the risk of the financial stability loss is proposed. The multidimensional predictive model that simultaneously takes into account several intrabank parameters enables the assessment of the probability of a bank's financial deterioration.

The findings appear consistent with the researchers' opinion concerning the noticeable impact of the asset quality and profitability on the financial stability. A substantial negative impact of overdue loans and credit reserves' growth, as well as a significant positive impact of return on assets on a bank's financial position has been supported. The works of authors from different countries, for example Kolari et al. [2002], Adusei [2015], Ozili and Outa [2017], Kryazheva and Ivanova [2017], Audi, Kassem and Roussel [2021], emphasise the negative impact of a large share of overdue loans and reserves on a bank's financial position. The scholars are unanimous in stating that reducing the volume of overdue loans is a priority task, which allows reducing loan reserves and, as a result, maximising the bank profit. To improve the effectiveness of credit

management it is necessary to refine the early diagnosis procedures of financial problems and elements of the credit policy.

Profitability indicators are the key predictors of the financial stability. The experience of different countries shows a significant positive impact of both return on assets and net interest margin on the financial stability of banks. This result is obtained in the empirical works of Rupeika-Apoga et al. [2018], Kasri and Azzahra [2020], Mkadmi, Baccari and Ncib [2021], Shahriar et al. [2023]. The bank's profit is the basis for further existence of bank and one of the signs of effective management. Other important conditions for ensuring an appropriate level of profitability include optimising the structure of income and expenses, determining the minimum interest margin, identifying trends in the profitability of credit operations, planning the minimum margin yield.

Having applied the developed financial stability indicator and rating scale we have found that larger banks have a higher financial stability level. Assessment has shown differences in the financial position of Russian banks: the largest and large banks remain at a high level of stability ('A' rating), medium and small banks are at the average level ("B" rating). Large banks shape the risk landscape of the banking market, and the importance of their financial stability for national economy is crucial. This observation allows us to conclude that the value of a bank's assets (bank size) has a positive impact on the financial position. A similar finding is contained in the studies by Adusei [2015], Ali and Puah [2018], Rupeika-Apoga et al. [2018], Audi, Kassem and Roussel [2021].

Unexpected results were also obtained. The hypothesis about positive correlation between capital adequacy ratio and bank financial stability has not been supported. This study explains the n-shaped impact of capital adequacy on the financial stability. It might be assumed that up to a certain value-point, an increase in capital adequacy contributes to a bank's stability, and after passing this value, on the contrary, leads to a reduction in the operational efficiency. There are two reasons. Firstly, the increase in capital does not in itself affect the growth of business efficiency. Secondly, capital growth may indicate an increase in risks, since banks are required to create capital at risks as a financial buffer. Therefore, too high capital adequacy ratio is economically inexpedient and banks with an increased level of capital adequacy ratio (> 16 %) are not more stable. Consequently, there is a dualism of researchers' opinions regarding the impact of capital adequacy on a bank financial position. It can be assumed that the difference of opinion is explained by the influence of national characteristics, regulatory requirements, and differences in business models.

With regard to the impact of the current liquidity ratio on financial stability, the liquidity surplus paradox has been revealed: normal liquidity guarantees solvency, but excessive liquidity reduces profitability. Therefore, the impact of liquidity on the bank financial stability is n-shaped: above a certain value-point the current liquidity ratio does not contribute to ensuring the financial durability. Normal liquidity is a significant positive factor behind the financial stability, but excessive liquidity reduces profitability and causes the risk of losses.

Within the framework of this article, the time factor was taken into account to predict the risk of financial instability. It has been revealed that the degree of factors' influence is different for medium- and long-term risk forecasting horizons. For a 6-month period, the return on assets is a more important factor of financial stability, and the share of overdue loans is more significant for a 12-month period. This can be attributed to the fact that overdue loans and loan reserves that have accumulated over the year may lead to losses. Banks should take appropriate measures to reduce their total loan debt in order to maintain stability.

Regarding the prospects for further research, we believe that in order to diagnose financial stability in the banking sector, there are at least three topics: risks of climate change, cyber risks

and risks of business models. Feridun and Güngor [2020] identify two types of climate risks for the banking sector: transition risks of low-carbon economy and physical risks of climate change, both of which may have negative consequences for banks. Cyber risks pose a serious threat to banks and their customers, as information and money are leaked as a result of cyber-attacks and unauthorised debiting of funds from accounts. The importance of business models as a revenue management mechanism and their impact on financial stability is beyond doubt. So, Lartey et al. [2022] consider the business models of banks as a factor that helps understand the specifics of profit formation and financial risks.

The overall picture seems to be that the financial health of the banking sector has fundamental importance in maintaining national and global financial stability.

Conclusion

The stable functioning of banks is not only of commercial, but also of social importance, and therefore, their financial viability must be consistent with both financial and social interests. As Russian and foreign practice has demonstrated, insufficient financial stability of banks leads to insolvency and financial crises. Hence, defining the boundaries of financial stability is one of the most vital issues of financial management.

To prevent financial deterioration in a timely manner, it is necessary to predict and analyse many factors and parameters of the bank's activities. Banks cannot influence the external factors such as market interest rates, foreign exchange rates, competitive climate, demand for loans. On the contrary, internal factors are manageable and can be adjusted. Accordingly, this study focused on the development of an approach for analysing and predicting the bank's financial stability based on the consideration of intrabank CAMELS indicators. As a result, the following conclusions are obtained.

First, the analysis of the existing research has revealed that a bank's financial stability is a multidimensional characteristic of its activities. Scientific viewpoints on the financial stability can be divided into a resource approach, a resilience-based approach and a complex approach. Plurality of viewpoints on the financial stability and its factors broadens the scientific understanding of financial mechanism features in the banking sector. For example, an interesting and unexpected conclusion of some scholars is that the non-interest Islamic banking is more stable compared to the traditional banking. This finding should be taken into account in traditional banks when developing investment products.

Second, the original approach to assessing the level of a bank's financial stability based on the aggregated financial stability indicator and the rating scale ("A", "B" and "C" ratings) is proposed. Calculation has shown that there are differences in the stability indicators of analysed Russian banks: the largest and large banks are more reliable ("A" rating), medium and small banks have taken the average level ("B" rating). This observation allows us to infer that the size of a bank's assets has a positive impact on the financial position. At the same time, all banks have a low level of profitability and this factor should be in the area of special attention of bank managers.

Third, the impact of internal indicators on a bank's financial stability has been determined using multiple regression models. Profitability (return on assets) has significant and positive impact on the financial reliability; overdue loans level has a significant and negative effect. The study has also found that the importance of financial stability factors differs in time: the return on assets is a more critical indicator for the period of 6 months, while the overdue loans ratio matters in the period of 12 months. The non-obvious conclusion is that the capital adequacy and the current liquidity have an n-shaped impact on financial stability. It might be assumed that up to a certain value, a growth of these indicators generates stability for a bank, and after passing this point, on the contrary, reduces the efficiency and durability. This situation can be described

as a liquidity surplus paradox: normal liquidity provides financial stability, but excess liquidity reduces profitability and increases financial risks.

Fourth, models for predicting the risk of a bank's financial stability loss based on multidimensional logistic regression are designed. The developed approach reflects the influence of the most important explanatory variables on the probability of financial stability deterioration and at the same time features high interpretability. On the example of a Russian bank, the risk assessment of transition to "C" rating (financial insolvency) is undertaken. The assessment suggests that for 6 months the analysed bank is classified as positive (stable), while for 12 months the bank is classified as negative, since a high level of overdue loans and low profitability create conditions for the financial stability loss.

Despite the relevant conclusions, certain limitations of this study must be reported. Firstly, the study focuses on data of a bank's published reports. Insider data should be included in analytical models, which will allow examining the intrabank parameters on the financial position in a more detailed way. Secondly, regression analysis is based on the data for 2018-2023. This period saw the serious global challenges such as the COVID-19 and the Russian financial shock of 2022. Russian banks also experienced serious financial consequences, which was reflected in their financial reports. Investigating the financial stability factors during economic well-being and during crises is a promising research area and will allow understanding the impact differences. Another option is to scrutinise reasons behind the instability of homogeneous groups of banks (asset size, regional location, business model).

It should be noted that future studies could fruitfully explore new reality risks such as climate risks, cyber risks, social responsibility risks, and business model risks. Scientists and bankers note the increasing influence of these risks on a bank's financial sustainability. In addition, the research should aim at developing methods of modelling such as machine learning models for predicting financial risks, since multidimensional intelligent models allow finding non-obvious relationships in data and ensure high predictive accuracy. Application of digital intelligent technologies has great potential in the development of early warning algorithms and forecasting of a bank's risks.

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Information about the author

Elena G. Shershneva, Cand. Sc. (Econ.), Associate Prof., Associate Prof. of Banking and Investment Management Dept. Ural Federal University named after the first President of Russia B. N. Yeltsin, Ekaterinburg, Russia. E-mail: e.g.shershneva@urfu.ru

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Источники

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