The Financial Soundness of Selected Banks in South Africa

THE FINANCIAL SOUNDNESS OF SELECTED BANKS IN SOUTH AFRICA:

A CAMELS RATING SYSTEM APPROACH

Rushil Manga

211079359

Treatise submitted in partial fulfilment of the requirements for the degree of Master of Commerce in Economics (Course Work and Research)

The Faculty of Business and Economic Sciences

Nelson Mandela University

Port Elizabeth

April 2019

Supervisor: Dr H. Khobai

DEPARTMENT OF ACADEMIC ADMINISTRATION EXAMINATION SECTION SUMMERSTRAND NORTH CAMPUS

PO Box 77000 Nelson Mandela University Port Elizabeth 6013 Enquiries: Postgraduate Examination Officer

DECLARATION BY CANDIDATE

FULL NAME: RUSHIL MOHAN MANGA STUDENT NUMBER: 211079359 QUALIFICATION: Master of Commerce in Economics (Course Work and Research) TITLE OF THESIS: THE FINANCIAL SOUNDNESS OF SELECTED BANKS IN SOUTH AFRICA: A CAMELS RATING SYSTEM APPROACH

DECLARATION

In accordance with Rule G4.6.3, I declare that this treatise titled ‘The financial soundness of selected banks in South Africa: A CAMELS rating system approach’ is my own work, that all the sources used or quoted have been identified and acknowledged by means of appropriate referencing, and that I have not previously su0bmitted this dissertation for assessment to another university or for any other qualification.

Signature:

Date: 30 November 2018

0ABSTRACT Bank failure continues to feature in South Africa and although it is not uncommon, nor limited to any single country, it has the potential to have significant systemic risks. It is, therefore of the utmost importance to mitigate bank failure where possible. Bank supervision plays a key role in ensuring that individual banks, and the banking sector, remain sound. This study analysed seven selected banks in South Africa namely, ABSA, African Bank, Capitec Bank, FirstRand Bank, Nedbank, Standard Bank and VBS Mutual Bank.

The CAMELS rating system was applied to evaluate the component and composite ratings for each selected bank. The empirical evidence exhibited that the CAMELS model has been used world-wide and proved valuable in its simplicity and reliability. The results showed that all banks achieved a rating of three or fair, with the exception being African Bank. African Bank, rated four or marginal, continues to struggle to regain market confidence since its cu0ratorship and restructuring.

The study further showed that among the selected banks, management quality and liquidity were two components that consistently showed critical weaknesses, which posed concerns for formal supervision. The study utilised One-way ANOVA (Analysis of Variance) to analyse the results of the CAMELS model. It was found that there was no significant difference in the financial soundness of the selected banks as a measure of the CAMELS model.

The study further recommended that the banks invest and focus on developing human resource departments to attain and retain high quality managers in terms of qualifications and experience. The banks’ internal policies need to align, not only with the company’s business targets, but also the personal contentment and fulfilment of employees and managers. This will help reduce frictional unemployment in the banking sector. It must be noted that Capitec was the only bank to avoid a marginal or weak rating in the management quality component. To address the poor rating awarded to the liquidity component in all selected banks, it is recommended that senior management, regulators and supervisors need to work together to implement sound liquidity management practices.

The CAMELS model presents a clear depiction of the financial soundness of a bank and can be comparable to other competitive banks within a country. For this reason, the model would be easily understandable, not only to supervisors and senior management, but also investors, stake-holders, their customers and the general population. It is therefore recommended that the SARB publishes a detailed annual report, which analyses all banks in South Africa by way of the CAMELS model.

KEYWORDS CAMELS model, CAMELS ratings system, banks, South Africa, ANOVA, Basel, supervision, financial soundness, risk, ratings

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DEDICATION

This treatise is dedicated to my loving parents, Giles and Manjoo Manga, for giving me the opportunity to further my studies. None of this would have been possible without your love and support.

ACKNOWLEDGEMENTS

I would like to express my sincere gratitude to the following individuals who contributed and supported me:

• My supervisor, Dr H. Khobai, for his support, guidance and understanding throughout my research paper. Your input has made my treatise a success and enjoyable, for which I am grateful. • My parents, Giles and Manjoo Manga, for their love and support throughout my academic career and giving me the opportunity to grow, develop and follow my dreams. • My brother, Ashvin Manga, for supporting and motivating me. • My girlfriend, Davanitha Moodley, whose love, patience and words of encouragement kept me focused and motivated throughout my candidature. I further extend my gratitude to you for assisting me with the layout and presentation of my work. • Dr B. Ismail, for his guidance and support during the initial phase of my research. • Mr F. Geel and Mr R.G. Doraswami, for their diligence and time in the editing of my work. • My friends and colleagues, Merioboroghene Mowoe and Joseph Mhango, for their friendship and support throughout my studies.

I would also like to express my gratitude to the Nelson Mandela University Research Capacity Development for awarding me the Postgraduate Research Scholarship to fund my studies.

TABLE OF CONTENTS Page DECLARATION i ABSTRACT ii DEDICATION iv ACKNOWLEDGEMENTS v TABLE OF CONTENTS vi LIST OF TABLES ix LIST OF FIGURES xi LIST OF ABBREVIATIONS xii

CHAPTER ONE

INTRODUCTION 1.1 Background to the study 1 1.2 Problem statement 2 1.3 Objectives of the study 4 1.4 Purpose of the study 4 1.5 Research hypotheses 5 1.6 Research methodology 6 1.7 Significance of the Study 7 1.8 Organisation of the study 7 1.9 Summary 8

CHAPTER TWO OVERVIEW OF BANK SUPERVISION AND THE SELECTED

BANKS OF THE STUDY 2.1 Introduction 9 2.2 Bank supervision 9 2.2.1 On-site bank inspection 10 2.2.2 Off-site bank inspection 12

2.3 Basel Accords- Basel Committee on Banking Supervision (BCBS) 13 2.3.1 Basel I 13 2.3.2 Basel II 14 2.3.3 Basel III 14

2.3.4 Basel IV 15 2.4 Strategic overview of the selected banks 17 2.4.1 Absa Bank Limited (ABSA) 17 2.4.2 African Bank Limited 17 2.4.3 Capitec Bank Limited 19 2.4.4 FirstRand Bank Limited 20 2.4.5 Nedbank Limited 20 2.4.6 Standard Bank 21 2.4.7 VBS Mutual Bank 21 2.4.8 Summary of total assets and other indicators 22 2.5 Summary 22

CHAPTER THREE LITERATURE REVIEW 3.1 Introduction 24 3.2 Fundamentals of the CAMELS rating system 24 3.2.1 The CAMELS rating system 24 3.2.2 Capital adequacy 27 3.2.3 Asset quality 29 3.2.4 Management quality 31 3.2.5 Earning ability 32 3.2.6 Liquidity 34 3.2.7 Sensitivity to market risk 35 3.2.8 Summary of the CAMELS component ratings system approach 36 3.3 Stress Testing 37 3.4 The significance of the CAMELS rating system approach 38 3.5 Empirical evidence of previous studies 39 3.6 Summary 43

CHAPTER FOUR RESEARCH METHODOLOGY 4.1 Introduction 44 4.2 Research design 44 4.3 Research method 45 4.4 Data collection 47 4.5 Data analysis 47

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4.5.1 The CAMELS model 47 4.5.2 One-way ANOVA 48 4.5.3 Hypotheses of the study 49 4.6 Summary 51

CHAPTER FIVE EMPIRICAL FINDINGS 5.1 Introduction 52 5.2 The CAMELS rating system model 52 5.3 Analysis of Variance, ANOVA 64 5.3.1 Capital adequacy ratio 64 5.3.2 Asset quality ratio 65 5.3.3 Cost to income ratio 66 5.3.4 Return on assets ratio 67 5.3.5 Return on equity ratio 67 5.3.6 Total loans to total deposits ratio 68 5.3.7 Total securities to total assets ratio 69 5.3.8 Financial soundness of selected banks 69 5.4 Summary of hypotheses testing 71 5.4.1 Null hypotheses decisions 71 5.4.2 Alternative hypotheses decision 72 5.5 Summary 73

CHAPTER SIX CONCLUSION AND RECOMMENDATIONS 6.1 Introduction 76 6.2 Conclusion 76 6.3 Recommendations 79 6.4 Limitations of the research 81 6.5 Scope for further research 81

REFERENCES 83

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LIST OF TABLES Table 1.1 List of failed banks 3 Table 2.1 Generic features of on-site bank inspections 11 Table 2.2 Generic features of off-site bank inspections 13 Table 2.3 Total assets and other indicators for Absa Bank 17 Table 2.4 Total assets and other indicators African Bank 18 Table 2.5 Total assets and other indicators Capitec Bank 19 Table 2.6 Total assets and other indicators FirstRand 20 Table 2.7 Total assets and other indicators Nedbank 21 Table 2.8 Total assets and other indicators Standard Bank 21 Table 2.9 Total assets and other indicators VBS Bank 22 Table 2.10 Summary of total assets and other indicators for all selected banks 22 Table 3.1 The CAMELS composite rating interpretation 27 Table 3.2 Capital adequacy ratios 28 Table 3.3 Asset quality ratios 30 Table 3.4 Management quality ratios 31 Table 3.5 Earning ability ratios 33 Table 3.6 Liquidity ratios 34 Table 3.7 Sensitivity to market risk ratios 35 Table 3.8 Summary of the CAMELS component rating system approach 37 Table 4.1 Number of banks in South Africa 485 Table 4.2 Breakdown of the CAMELS model 47 Table 4.3 Criteria and ratios ratings 48 Table 5.1 Banks’ sratios for 2015 (%) 54 Table 5.2 Banks’ sratios for 2016 (%) 55 Table 5.3 Aggregate banks’ sratios for 2015 and 2016 (%) 56 Table 5.4 Aggregated CAMELS component ratings for 2015 and 2016 (%) 59 Table 5.5 Summary of best and worst performing banks for each ratio 60 Table 5.6 Banks’ CAMELS composite rating 62 Table 5.7 Banks’ rating according to the CAMELS rating system 63 Table 5.8 Banks’ capital adequacy ratio for 2015 and 2016 (%) 65 Table 5.9 One-way ANOVA – analysis of capital adequacy ratio 66 Table 5.10 Banks’ asset quality ratio for 2015 and 2016 (%) 66 Table 5.11 One-way ANOVA – analysis of asset quality ratio 67 Table 5.12 Banks’ cost to income ratio for 2015 and 2016 (%) 68 Table 5.13 One-way ANOVA – analysis of cost to income ratio 68

Table 5.14 Banks’ return on assets ratio for 2015 and 2016 (%) 69 Table 5.15 One-way ANOVA – analysis of return on assets ratio 69 Table 5.16 Banks’ return on equity ratio for 2015 and 2016 (%) 70 Table 5.17 One-way ANOVA – analysis of return on asset ratio 71 Table 5.18 Banks’ total loans to total deposits ratio for 2015 and 2016 (%) 72 Table 5.19 One-way ANOVA – analysis of total loans to total deposits ratio 73 Table 5.20 The CAMELS composite rating awarded to each selected bank 74

LIST OF FIGURES

Figure 2.1 Structure of the Basel Committee on bnking surpervision (BCBS) 16 Figure 4.1 BASEL III framework 45 Figure 4.2 Research method to evaluate bank soundness 46 Figure 4.3 Analysis of variance technique 49 Figure 5.1 The CAMELS composite rating 53 Figure 5.2 Banks’ aggregated component ratings for 2015 and 2016 (%) 61 Figure 5.3 Banks’ capital adequacy raito for 2015 and 2016 (%) 71

LIST OF ABBREVIATIONS

AB - African Bank Limited ABSA - ABSA Bank Limited ANOVA - Analysis of variance BAGL - Barclays Africa Group Limited BCBS - Basel Committee on Banking Supervision C/I - Cost/Income CAR - Capital adequacy ratio CB - Capitec Bank DEA - Data Envelopment Analysis FDIC - Federal Deposit Insurance Corporation FRB - FirstRand Bank FSB - Financial Stability Board NED - Nedbank Limited NPL/TL - Non-performing loans/Total loans ROA - Return on assets RoB - Registrar of Banks ROE - Return on equity RWA - Risk-weighted asset SARB - South African Reserve Bank SB - Standard Bank TL/TD - Total loans/Total deposits TS/TA - Total securities/Total assets VBS - VBS Mutual Bank

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CHAPTER ONE INTRODUCTION

1.1 Background to the study The banking sector in a country’s economy plays an important role of connecting each industry through monetary transactions and finance. By virtue, the banking sector acts as the backbone of the economy. Gondesi (2016) stated that a banking system acts as a catalyst, contributing to socio-economic transformation and economic growth. Emphasis is therefore placed on the financial soundness and stability of a bank, and ultimately the entire financial system, which is based on both external and internal factors, and is of key importance to the growth and sustainability of any economy.

The Economist (2018) posited that owing to poor economic growth, low demand for credit, changes in financial sector policy, and a more demanding environment for sovereign debt, banks in South Africa will continue to be challenged, which may increase funding costs. In addition, South Africa continues to face an uncertain political climate and fundamental challenges with state-owned enterprises. Given current levels of economic and political uncertainty, South Africa’s financial markets are likely to be exposed to higher levels of volatility (PWC 2017). Although the South African banking system is well developed and strictly regulated, these factors negatively affect businesses, retail and investor confidence in South Africa and requires modification to see improvements in the national economy (The Economist 2018 & Mchunu 2018). Should a bank fail, the downside risk on the economy is far greater than in other types of business firms (Makhubela 2006). In the context of this study, ‘bank failure’ refers to the inability of a bank to repay its depositors and creditors and/or a situation where the market value of the bank’s assets declines below the market value of its liabilities (Makhubela 2006). Owing to the risks associated with bank failure, banks are generally subjected to two types of regulations supervised by the South African Reserve Bank (SARB) and Registrar of Banks (RoB) as defined below (Okeahalam 1998):

1 Economic regulation to ensure social welfare rights, which encourages higher competition, less collusion and lower industry concentration, and 2 Prudential regulations to ensure depositors’ funds are secure and the financial system is not compromised.

Overall responsibility for bank supervision lays with the SARB, and the RoB is responsible for the day-to-day operational prudential regulation (Okeahalam, 1998). The RoB can use early warning models to evaluate the likelihood of bank failure and take necessary action to manage risk and/or avoid bank failure completely (Okeahalam 1998).

This chapter introduces the study and states the problem statement, research hypotheses and significance of the study. In addition, the research objectives and methodology are also stated in this chapter, followed by the structure of the study.

1.2 Problem statement Bank failures are not uncommon to certain countries, nor is it limited to some countries only (Basu 2003). However, South Africa continues to experience bank failures (Basu 2003). The cost of bank failures can be high and has the potential to cause instability in a country’s financial system (Basu 2003). In turn, this affects domestic and international business confidence, which adversely impacts on the country’s growth rate. The failure of African Bank, for example, was a shock to the South African markets and induced significant systematic risk in the financial system (Mare, Sanderson & de Jongh 2017). Owing to backward and forward linkages of the banking sector, bank failure has a significant impact on individuals, communities and other sectors of the economy. The banking sector is fundamental to an economy and any weaknesses must be addressed, supervised and improved to ensure soundness and stability in the financial markets. Table 1.1 comprises a list of all failed banks in South Africa since 1994. The table further shows the primary causes for each bank’s failure. Makhubela (2006) defined the types of bank risks as follows:

• Credit risk: Credit risk is the risk associated with the portion of credit advanced to borrowers that will not be repaid in accordance to the terms at the outset. • Market risk: Market risk is the risk associated with changes in prices determined by market-related factors. Therefore, it is the portion of total risk that is not unique to a bank. • Liquidity risk: The bank’s risk of being in a position of being unable to repay depositors on demand, or as and when due, because of holding insufficient cash or liquid assets.

• Systemic risk: Systemic risk refers to the risk associated with the failure of a particular financial institution, which leads to the collapse of other institutions or financial systems in the absence of measures aimed at preventing contagious effects.

Table 1.1: List of failed banks

Institution Year Primary cause of failure Prima Bank 1994 Liquidity risk, Credit risk, Market risk Sechold Bank 1994 Market risk African Bank 1995 Liquidity risk, Credit risk, Market risk Community Bank 1996 Liquidity risk, Credit risk Islamic Bank 1997 Liquidity risk, Credit risk New Republican Bank 1999 Liquidity risk, Reputational risk, Credit risk FBC Fidelity Bank 1999 Liquidity risk, Reputational risk, Credit risk Regal Treasury 2001 Liquidity risk, Reputational risk, Credit risk Saambou 2002 Liquidity risk, Reputational risk BOE Bank 2002 Liquidity risk African Bank 2014 Operational risk, Liquidity risk VBS Mutual Bank 2018 Operational risk, Liquidity risk Source: Adapted from Makhubela (2006) and Business Report (2018)

Dube and Kaya (n.d), designed South Africa’s first systemic risk ranking model, which provides qualitative insight and identifies the systemic risk of each financial institution (UCT 2017). The study focused on identifying which financial institution would have the greatest negative impact on the financial system should failure occur. Foggitt, Heymans, van Vuuren & Pretorius (2017) conducted research which also measured the systemic risk in the South African banking sector but found that the financial system exhibited moderate systemic risk. The study done by Dube and Kaya found that Standard Bank (25.56%) posed the greatest systemic risk, followed by Barclays Africa Group (13%) and FirstRand Group (12.94%). The results of the study were significant as it indicated that only three banks formed 51.5% of all systemic risk in South Africa (UCT 2017).

To manage the mix and complexities of the banks’ exposure to different types of risk, it is of critical importance to regularly evaluate the overall performance of banks by implementing a regulatory banking supervisory framework (Dang 2011). The CAMELS model has been used as such as a framework to assess financial soundness and performance of banks (Dang 2011). Through the implementation of regular inspection of banks, formal and informal supervision could be better directed and controlled. While the CAMELS rating system has been used internationally and by the SARB to evaluate performance, there are limited research papers that apply the model to South African banks. This current research aims to explain the CAMELS model and its applicability to South African banks.

1.3 The purpose of the study The purpose of this study, using the CAMELS rating system, is to investigate the financial soundness of selected banks in South Africa. Through the individual examination of the selected banks, recommendations will be made with an aim to ensure banks remain financially sound and the banking sector remains stable. Furthermore, this study could be used as a reference for researchers wishing to investigate other areas of South Africa’s banking sector.

1.4 Objectives of the study The primary objective of the research is to assess the financial soundness of the selected banks in South Africa according to the CAMELS rating system. The secondary objectives include the following:

1 To determine whether the CAMELS rating system can be used as an early-warning system to identify banks that need urgent formal supervision; 2 To determine the key components of the selected banks in South Africa that require urgent attention by internal senior management and/or supervisors; and 3 To test if there is a significant difference in the financial soundness of the selected banks as a measure of the CAMELS model.

1.5 Research hypotheses To critically evaluate the performance and financial soundness of the selected banks by way of the CAMELS model, the following hypotheses are stated:

Hypothesis I (I) H0 : There is no significant difference in the financial soundness of the selected banks as a measure of the CAMELS model. (I) Ha : There is a significant difference in the financial soundness of the selected banks as a measure of the CAMELS model.

Hypothesis II (II) H0 : There is no significant difference in the capital adequacy ratio of the selected banks under the CAMELS model. (II) Ha : There is a significant difference in the capital adequacy ratio of the selected banks under the CAMELS model.

Hypothesis III (III) H0 : There is no significant difference in the asset quality ratio of the selected banks under the CAMELS model. (III) Ha : There is a significant difference in the asset quality ratio of the selected banks under the CAMELS model.

Hypothesis IV (IV) H0 : There is no significant difference in the cost to income ratio of the selected banks under the CAMELS model. (IV) Ha : There is a significant difference in the cost to income ratio of the selected banks under the CAMELS model.

Hypothesis V (V) H0 : There is no significant difference in the return on assets ratio of the selected banks under the CAMELS model. (V) Ha : There is a significant difference in the return on assets ratio of the selected banks under the CAMELS model.

Hypothesis VI (VI) H0 : There is no significant difference in the return on equity ratio of the selected banks under the CAMELS model. (VI) Ha : There is a significant difference in the return of equity ratio of the selected banks under the CAMELS model.

Hypothesis VII (VII) H0 : There is no significant difference in the total loans to total deposits ratio of the selected banks under the CAMELS model. (VII) Ha : There is a significant difference in the total loans to total deposits ratio of the selected banks under the CAMELS model.

Hypothesis VIII (VIII) H0 : There is no significant difference in the total securities to total assets ratio of the selected banks under the CAMELS model. (VIII) Ha : There is a significant difference in the total securities to total assets ratio of the selected banks under the CAMELS model.

1.6 Research methodology As previously stated in section 1.3, the purpose of the study is to assess the financial soundness of selected banks in South Africa. The data collected will be applied to the CAMELS model to award component ratings, according to a set of financial ratios, and thereafter, a composite rating to each bank will be awarded. The analysis of variance (ANOVA) will be used for the hypotheses testing, to test the significant differences in each financial ratio and ultimately, the financial soundness of the selected banks as a measure of the CAMELS model. Data for the study will be collected from the Banker Database and ABSA’s annual consolidated and separate financial statements. The study is constrained by the limitations of secondary data available and is not a representation of the entire banking system of South Africa. The assessment of the banks will be limited to the data collected, which could be more clearly defined with a greater scope of data. Chapter four will elaborate further on the research methodology of the study.

1.7 Significance of the study Over the past few decades, both developed and developing markets experienced periods of turbulence, which stemmed from the banking sector (Gondesi 2016). The most recent and severe financial crisis originated in United State of America during 2007 to 2009, which saw the collapse of major players in the banking system. The collapse was a result of high risk-taking, inadequate capital and liquidity levels, and weaknesses in the prudential framework within the American banking industry (Bank for International Settlements 2018.) As previously stated in the Problem Statement (Section 1.2), South Africa has a significantly high level of systemic risk. A typical way to support the banking sector is to tighten prudential supervision and apply the tools that facilitate timeous and early warning signals to supervisors as well as the internal management of banks (Gondesi 2016).

Effective on-site and off-site supervision of banks is imperative to efficiently manage the risks associated with bank failure. Bank supervisors need to regularly collect data from all banks to critically evaluate their financial soundness. Dang (2016) stated that on-going and up-to-date inspections help to identify matters of concern sufficiently early for supervisory attention before bank failure occurs. Muralidhara and Lingam (2017), Gondesi (2016) and Rostami (2015) argued that the CAMELS rating system is an effective and useful tool for assessing the financial soundness and identifying potential internal risks of a bank. This study is significant for several reason. Firstly, it applies the trusted CAMELS model to South African banks and uses ANOVA to analyse the results. This validates the methodology and applicability of research on South African banks as there is currently limited research papers produced, using the CAMELS rating system approach. Secondly, the study is significant as it identifies, analyses and interprets bank-specific factors and financial ratios to assess financial soundness of the selected banks in South Africa. Thirdly, this study aims to contribute to the research in this field.

1.8 Organisation of the study • Chapter One introduces the study • Chapter Two provides an overview of bank supervision and the selected banks identified for the study

• Chapter Three explains the CAMELS rating system followed by a review of the relevant literature • Chapter Four will discuss the methodology of the research • Chapter Five will present and discuss the main findings, and • Chapter Six comprises the conclusion and recommendations.

1.9 Summary Chapter One provides an introduction to the study. It first emphasises the significance of the banking system in a country’s economy and follows with an overview of bank failure. This section further provides a list of failed banks in South Africa since 1994 and their respective primary causes.

The problem statement highlights the significance of bank failure and its impact on the economy. Furthermore, this section provides a description of several types of bank risks. Thereafter followed a description of the purpose of the study, which is the investigation and assessing of the financial soundness of selected banks in South Africa.

The objectives of this study are then outlined, and the research hypotheses are defined. The most significant of them being H0 and H01. The null hypothesis states that there is no significant difference in the financial soundness of the selected banks as a measure of the CAMELS model. The alternative hypothesis states that there is a significant difference in the financial soundness of the selected banks as a measure of the CAMELS model.

The research methodology is outlined and explains the use of two methods applied to assess the banks. The first being the application of the CAMELS rating system and the second, the application of results to ANOVA. The use of ANOVA analyses the results for significant differences among the selected banks as a measure of the CAMELS model. The significance of the study is then stated followed by a description of how the study is organised. Chapter Two provides an overview of bank supervision and explains the Basel Accords. It also provides on overview of the selected banks for the study.

CHAPTER TWO OVERVIEW OF BANK SUPERVISION AND THE SELECTED BANKS OF THE STUDY

2.1 INTRODUCTION This chapter provides an overview of bank supervision in South Africa and highlights the strategic direction of the selected banks. Section 2.2 provides an overview of bank supervision and its role in facilitating financial stability. Moreover, this section introduces on-site and off-site bank inspections and explains its respective functions. Section 2.3 outlines the Basel Accords and provides an explanation of the Basel I, II, III and IV frameworks. Section 2.4 presents the banks selected for the study and highlights the strategic direction of each bank. In addition, it briefly sketches the background to the selected banks and discusses several indicators and significant events that have recently taken place in the banking sector.

2.2 Bank supervision The SARB plays an important role in maintaining financial soundness and stability of the banking sector (Meiring 2012). The banks in South Africa are required to undergo various forms of on-going inspections by regulatory authorities (Meiring 2012). This includes on-site bank supervision and off-site banking surveillance (Hafeman & Randle 2009). Furthermore, banks are required by legislation to present, on a routine basis, financial and other reports, which are regularly analysed to identify any detrimental or negative developments, such as potential default trends (Meiring 2012).

The Bank Supervision Department within the SARB is mandated to promote the safety and soundness of the banking system (SARB 2018). This department ensures the effective application of international regulatory and supervisory standards (SARB, 2018). In addition, Makhubela (2006) stated that the Bank Supervision Department’s approach is risk focused. Botha and Makina (2011) explained that there are three main elements of a financial or banking crisis within a bank:

1 Incentive structures are not motivational enough to promote efficiency; 2 Management and internal controls are weak and ineffective, and 3 Regulations, monitoring and supervision are lacking or inadequate.

The Bank Supervision Department is responsible for the prudential supervision of banks (SARB Research Department: Information Division 2005). The desired outcome of supervision is to prevent potentially harmful risk taking by regulating capital adequacy and promoting sound risk management practices (SARB Research Department: Information Division 2005 and Botha & Makina 2011). The Bank Supervision Department’s focus is to ensure appropriate entry and exit to the banking system, through strict licencing requirements (SARB 2016). Furthermore, emphasis is placed on banks’ compliance with prudential standards and supervisory requirements (SARB 2016).

In August 2017, the Financial Sector Regulation (FSR) Act 9 of 2017 was passed and signed into law (SARB 2018). The FSR Act brings about three crucial changes to the regulation of South Africa’s financial sector (SARB 2018):

1 The FSR Act explicitly mandates the SARB to maintain and enhance financial stability; 2 Creates a prudential regulator located within the SARB, and 3 Establishes a market conduct regulator located outside of the SARB.

In addition, there are a several key principles that form part of the FSR Act. These include the need to strengthen the positive impact that the financial sector has on society and the economy; minimise the social and fiscal impact of bank and other failures; promote financial inclusion and transformation; and support competition, innovation and diversity in the industry (SARB 2018).

2.2.1 On-site bank inspection On-site bank inspections assess the accuracy of the financial statements, accounting records, internal controls and the compliance with law and regulatiuons (Dang 2011). In addition, on-site bank inspections assess the methodology applied by the bank in the processess and internal controls (SARB 2016). Hafeman and Randle (2009) stated that on-site bank inspections are usually conducted on a regular basis - at least once a year. However, as part of the planning process, supervisory authorities will need to consider the situation of the bank, the market, and past observations and experiences in previous on-site inspections (Hafeman & Randle 2009). Supervisors make an

overall assessment of the bank and provides a true and fair outlook of the bank’s financial position and management practices (Sahajwala & Van den Bergh 2000).

Supervisors need to plan and determine the frequency of on-site bank inspections according to the riskiness of a specific bank (Hafeman & Randle 2009). It is important to note that on-site bank inspection is an ongoing process and even if the inspection is successful, concerns and/or weaknesses in one year should not reappear in the following inspection in the next year (Hafeman & Randle 2009). Additionally, on-site bank inspections provide a bank profile, by assessing the most recent quantitative and qualitiative data, which generates a timely warning to supervisors if there is any reason for concern (Sahajwala & Van den Bergh 2000). Hafeman & Randle (2009) posited that supervisors understand that management, products, internal programmes and systems, financial market conditions and the economy constantly change as new conditions and challenges arise. Gilbert, Meyer & Vaughan (2002) argued that even though the on-site bank inspections are the most effective tool for identifying safety- and-soundness problems, the process is costly and takes up valuable time from management and employees of a bank. However, it may be highly beneficial as the results from the on-site bank inspection may be shared with the management of the bank, which could point out areas that can be improved. Table 2.1 below, shows the generic features of the on-site bank inspection capability. The table shows that on-site bank inspections are highly significant in assessing current financial conditions of banks. Furthermore, the focus is on qualitative assessments and the outcome of these types of inspections reveals the strong determination of supervisory concern.

Table 2.1: Generic Features of on-site bank inspections

Use of Assessment Forecasting quantitative Relation to Inclusion of Focus on of curent future analysis formal qualitative risk financial financial and supervisory assessments categories condition condition stististical action procedures

On-site bank       inspection

 Not significant Significant Highly significiant Source: Adapted from Sahajwala and Van den Bergh (2000)

2.2.2 Off-site bank inspection As previously stated, the financial market is constantly changing and banks need to adapt to new conditions in which to operate. As the key factors, such as management, products, market conditions and the economic environment are not constant, the supervisory authority should be flexible as to when inspections of a bank are to take place (Hafeman & Randle 2009). For this reason, the supervisory authority may need to rely more heavily on off-site inspections to complement the on-site inspection (Dang 2011). However, off-site inspections highlight the level of risk exposure based on annual and quarterly financial information (Dang 2011).

Dang (2011) explained that there are two commonly used off-site examination tools, namely:

1 Supervisory screens, which comprise financial ratios that are extracted from balance sheets and income statements, and 2 Econometric models that are used to deduce information from the financial ratios.

Sahajwala and Van den Bergh (2000) maintained that off-site bank inspections involve the analysis and review of annual financial statements and other reports to examine the financial profile of the bank. This allows supervisors to study the business holistically and determine the frequency of on-site bank inspections. The results from off-site bank inspections are usually kept confidential and used internally by supervisors (Sahajwala & Van den Bergh 2000). Table 2.2 shows generic features of the off-site bank inspection capability. As explained previosuly, off-site bank inspections generally focus on the anlysis of financial statements to assess the current financial condition of a bank. Furthemore, significant application of qualitiative and quantitive assessments take place in this process.

Table 2.2: Generic features of off-site bank inspections

Off-site bank       inspection

 Not significant Significant Highly significiant Source: Adapted from Sahajwala and Van den Bergh (2000)

2.3 Basel Accords – Basel Committee on Banking Supervision (BCBS) 2.3.1 Basel I The Basel framework was implemented to improve the safety and financial soundness of the international banking system and set the terms for banking regulation (Mishi 2015). The Basel I framework was significant as it implemented a minimum ratio of capital to risk-weighted assets of 8% (BIS 2018). Roy, Kohli & Khatkale (2013) stated that the Basel I framework did very well to address credit risk, which was the main risk of the banks. Owing to evolving financial environments, new financial institutions entering the market, and innovative products and services, more and more shortcomings of Basel I were being observed (Shakdwipee & Mehta 2017). Shortcomings of the Basel I framework includes the following (Mishi 2015):

• Basel I adopted a “one size fits all” approach, which lacked differentiation between all banking institutions. Developing, emerging and developed economies were required to comply with the minimum capital adequacy ratios. Therefore, the framework did not consider the fact that countries have different degrees of access to capital markets. • The Basel I framework only focused on credit risk and did not pay attention to market and operational risk. • Capital was easily transferred to less regulated categories allowing capital arbitrage to occur, and • There were discrepancies regarding the Tier I and Tier II capital. This was owing to the lack of capital available in the event of debt instruments, such as longer maturity bonds, lose value because of interest rates and liquidity risk.

2.3.2 Basel II To improve on Basel I, the Basel Committee introduced the Basel II framework in 1999, which was gradually implemented until 2006 (Mishi 2015). Basel II was designed to overcome the shortcomings of Basel I. Basel II focused on three main aspects (BIS 2018):

1 Minimum capital requirements; 2 Supervisory review monitoring bank’s capital adequacy and internal assessment process, and 3 Market discipline by enforcing disclosure to encourage sound banking practices.

The Basel II framework allowed the banks, mainly the large banks and those with complex risk management systems, to apply internally developed models to measure risk levels (Mishi 2015). Basel II provided a more comprehensive approach to regulate bank capital compared to the Basel I framework (Sadien 2017). However, one of the main criticisms of Basel II pertains primarily to pro-cyclicality (Mishi 2015). This means that Basel II capital regulation promotes substantial credit growth in economic booms and discourages credit extension in recessions (Liu & Molise 2018). Basel II posed several challenges to the banks, including: general skill shortage, the quality of data, systems constraints and new systems requirements (Mishi 2015). These challenges were encountered on a global scale with variations in reactive measures to address them from country to country (Mishi 2015). To further improve on the weaknesses exhibited in the Basel II framework, Basel III was introduced in 2010, with the main goal of achieving financial stability (Liu & Molise 2018).

2.3.3 Basel III The financial crisis of 2008 was the main reason for introducing Basel III to further strengthen the banking system (Roy et al. 2013). Basel III introduced new capital, leverage, and liquidity standards to support regulation, supervision, and risk management of the financial sector (Mishi 2015). The Basel III framework, therefore, comprised new rules and a global liquidity standard, which encouraged banks to raise high-quality liquid assets and more stable funding (Shakdwipee & Mehta 2017). Furthermore, the Basel III framework has been effective in reducing systemic risk and the cyclical effects of Basel II (Shakdwipee & Mehta 2017).

2.3.4 Basel IV On 7 December 2017, the Basel Committee on Banking Supervision (BCBS) published a set of proposed reforms for the global regulatory framework of the banking industry (Deutsche Bank 2018). The main aim of the proposed new reforms was to make the capital framework more resilient and to improve confidence in the financial system (Deutsche Bank 2018). However, owing to higher risk sensitivity, this change would have an impact on individual products, portfolios and business areas (PwC 2017). Furthermore, banks would be challenged to adapt to the requirements and regulations of the new framework. Kock, Schneider, Schneider & Schrock (2017) that the impact of Basel IV will vary depending on the location, bank type and business model of each bank. The reforms include adjustments in the calculations made to measure capital requirements with the aim of making outcomes more comparable to banks on a global scale (Deutsche Bank 2018).

There are four significant issues that arise from the proposal of the new framework (KPMG 2018):

1 Some banks will be challenged to face significantly higher minimum capital requirements. 2 Many banks will be challenged to upgrade their data, systems as well as internal and external reporting. 3 Banks will be tasked to make important decisions regarding the application of internal model approaches to adjust asset portfolios in response to changes in risk weightings, improving capital ratios through issuing new capital, retaining earnings, or a reduction in Risk Weighted Assets (RWAs). 4 The impact of the revised standards needs to be assessed in the broader context, taking into consideration the market developments that demand adaptability from banks.

Figure 2.1 explains the structure of the BCBS. It further identifies each group represented by either the SARB or the Banking Supervision Department or neither.

Figure 2.1: Structure of the Basel Committee on banking supervision (BCBS)

Source: SARB (2018)

2.4 Strategic overview of the selected banks 2.4.1 Absa Bank Limited (ABSA) ABSA is fully owned by Barclays Africa Group Limited (BAGL) (Gloabal Credit Rating Co. 2018). Barclays Africa Group incorporates a strategy that strives to double the company’s share of revenue in Africa and has three strategic priorities (Barclays 2017). These three priorities are as follows (Barclays 2017):

• To create a thriving organisation; • Restore leadership in core businesses, and • Build pioneering new propositions.

Table 2.3 reveals a slightly more than 22 per cent growth in total assets from 2014 to 2017 for ABSA ban, and that in 2016 it had the second largest customer base in South Africa.

Table 2.3: Total assets and other indicators for ABSA Bank

Total assets (R millions) Number of Number of Number of employees customers ATMS 2014 2015 2016 2017 (2016) (2016) (2016) 804 271 926 526 914 669 983 378 30 739 9 425 714 8 885 Source: SARB (2015, 2017), The Banker Database (2018)

2.4.2 African Bank Limited African Bank Limited’s mission is to be a successful retail bank, offering a diverse range of products and services to the consumers of South Africa (African Bank 2016). On 30 August 2014, advocate J.F. Myburgh was appointed as Commissioner in terms of s69A (1) of the Banks Act, 94 of 1990 (Banks Acts) by the Registrar of Banks (RoB). The main purpose of this appointment was to investigate the business, trade, dealings, affairs or assets and liabilities of African Bank Limited or its associates (van Wyk 2016). The executive summary of the Myburgh Report (2016) highlighted the following events that led to the rapid decline in African Bank Limited:

• In the annual reports for the financial year 2013, African Bank Limited disclosed a loss of R4,5 billion;

• On 2 May 2014, the bank was expected to show a headline loss of approximately R2 billion due to an increase in specific provisions totalling R600 million; • On 6 August 2014, the bank presented an anticipated basic and headline loss of more than R4,6 billion. On the same date, the CEO at the time, Mr Kirkinis resigned; • The SARB reported that African Bank Limited was regarded as systematic to the banking sector of South Africa. This was owing to the bank’s substantial client base of over 3 million customers, its role in financial inclusion, the potential impact on the socio-political environment, and the effect and representation on foreign investor confidence; • The jobs of 5 700 employees were placed at risk; • The aggregate amounts of loans were increasing while the bank was producing poor results in 2013 and 2014, and • By August 2014, the market had lost confidence in African Bank Limited and it was set to face a capital and liquidity crisis.

An important part of the bank’s downfall was the purchase made in 2007 of Ellerines, a furniture retail business, for over R9 billion without conducting a due diligence or complete board approval (Rabana 2016). Adding to the pressure, African Bank Limited lent Ellerines a total of R1,4 billion, which was unsecured and saw little to no prospect of being repaid (Peacock 2016). African Bank Limited was placed under curatorship on 10 August 2014 (Myburgh 2016). To avoid liquidation the Reserve Bank had agreed to implement a rescue plan (Cameron 2014). Since these events, African Bank continued to face a competitive environment. Table 2.4 shows the decline in total assets of the bank, from 2015 when the bank was placed under curatorship to 2017.

Table 2.4: Total assets and other indicators for African Bank

Total assets (R millions) Number of Number of Number of employees customers ATMS (2016) 2014 2015 2016 2017 (2016) (2016)

51 341 54 800 36 460 31 356 - 1 250 000 -

Source: SARB (2015, 2017), The Banker Database (2018)

2.4.3 Capitec Bank Limited Capitec Bank is focused on the objective of continuing to create sustainable value in the long term for the customers of the bank. Capitec defines each of its strategic objectives as follows (Capitec Bank Holdings Limited 2013):

• To provide a unique service; • Enhance the product offering; • Grow transaction income; • Manage the cost of credit to clients, and • Responsible management of regulatory and compliance risk.

In addition, Capitec has economic performance objectives that are geared to maintain the return on equity, focus on cost efficiency and a lower cost to income ratio (Capitec Bank Holdings Limited 2013). The company’s objectives have proven to be effective as the bank has experienced rapid growth year-on-year in total assets and has significantly expanded in terms of its number of employees and customers over the past number of years (2014 – 2018). Capitec has been successful in extensively growing its client base, acquiring nearly a third of the employed adult population in South Africa as at March 2018 (Capitec Bank Holdings Limited 2018).

Table 2.5: Total assets and other indicators for Capitec Bank

Total assets (R millions) Number of Number of Number of employees customers ATMS (2016) (2016) (2016) 2014 2015 2016 2017

51 877 61 970 71 729 87 033 11 440 7 269 000 1 236

Source: SARB (2015, 2017), The Banker Database (2018)

Capitec came under attack in January 2018 when the Viceroy Research Group (2018) publicly posted a report titled “Capitec: A Wolf in Sheep’s Clothing”. The report accused Capitec of being a loan shark and declared the bank “uninvestable”. The Viceroy Research Group (2018) further stated that the SARB and Minister of Finance should place Capitec under curatorship. This report was responded to by the National Treasury (2018), describing the allegations as reckless and untrue. The SARB

assured the National Treasury that Capitec is well capitalised, liquid, solvent, and satisfies all prudential requirements (National Treasury 2018).

2.4.4 FirstRand Bank Limited The long-term objective of FirstRand Bank is to deliver superior and sustainable economic returns to shareholders within acceptable levels of volatility and maintain balance sheet strength (FirstRand 2013).

FirstRand Bank has shown strength in its annual growth in total assets since 2014 and employs the second largest number of individuals amongst the banks in South Africa.

Table 2.6: Total assets and other indicators for FirstRand

Total assets (R millions) Number of Number of Number employees customers of ATMS (2016) (2016) (2016) 2014 2015 2016 2017

856 911 979 920 1 016 761 1 120 747 45 100 - 7 335

Source: SARB (2015, 2017), The Banker Database (2018)

2.4.5 Nedbank Limited Nedbank aspires to be one of the most admired banks in Africa and has several strategic focus areas (Nedbank Group 2018): • Delivering innovative market-leading client experiences; • Growing the transactional banking franchise at a faster rate than the market; • Being operationally excellent; • Managing scarce resources to optimise economic outcomes, and • Providing the bank’s clients with access to the best financial services network in Africa.

Table 2.7 shows Nedbank’s growth in total assets, which has slowed down since the increase in total assets from 2014 to 2015.

Table 2.7: Total assets and other indicators for Nedbank

Total assets (R millions) Number of Number of Number employees customers of ATMS 2014 2015 2016 2017 (2016) (2016) (2016) 714 408 813 287 869 362 892 006 32 401 7 700 000 -

Source: SARB (2015, 2017), The Banker Database (2018)

2.4.6 Standard Bank Standard Bank’s desired outcome of its strategy is to deliver superior return on equity and sustainable growth in earnings (Standard Bank 2015). Standard Bank dominates the banking sector in South Africa, boasting the highest figure for total assets, number of employees and customers. This is shown in Table 2.8 below.

Table 2.8: Total assets and other indicators for Standard Bank Number Total assets (R millions) Number of Number of of employees customers ATMS 2014 2015 2016 2017 (2016) (2016) (2016) 1 099 713 1 213 528 1 234 575 1 254 849 54 767 16 816 000 8 822 Source: SARB (2015, 2017), The Banker Database (2018)

2.4.7 VBS Mutual Bank VBS Mutual Bank, established in 1993, is a wholly black-owned specialist corporate finance and retail bank (VBS Mutual Bank 2018). VBS Mutual Bank strives to maintain the highest standards of governance, local empowerment and ethics (VBS Mutual Bank 2018). However, on 11 March 2018, VBS Mutual Bank was placed under curatorship and advocate T. Motau was appointed as the investigator in terms of section 134 of the Financial Sector Regulations Act 9 of 2017, to conduct a forensic investigation into the business affairs of the company (SARB 2018). The SARB (2018) stated that the main purpose of the investigation is to determine the following: • Whether the bank intended to defraud depositors or other creditors of the bank, or for any other fraudulent purpose; • Whether the bank’s business conduct displayed questionable and/or reckless business practices or material non-disclosure, with or without the intention to defraud depositors and other creditors, and

• If there was any irregular conduct by the bank’s shareholders, directors, executive management, staff, stakeholders and/or related parties.

Thompson (2018) stated that the investigation has revealed criminal conduct in the affairs of VBS. Moreover, 53 persons of interest, natural and juristic, benefited by almost R2 billion between March 2015 and June 2018. The investigation is still ongoing and has not yet been concluded. Table 2.9 shows steady growth of total assets under management and indicate a growing company. However, owing to corruption and fraud, business was swiftly halted in 2018.

Table 2.9: Total assets and other indicators for VBS Mutual Bank Number of Number of Total assets (R millions) Number of employees customers ATMS (2016) 2014 2015 2016 2017 (2016) (2016) 362 717 1 543 2 401 87 - - Source: SARB (2015, 2017), The Banker Database (2018)

2.4.8 Summary of total assets and other indictors Table 2.8 provides a summary of each bank’s total assets from 2014 to 2017 and shows the number of employees, customers and ATMS for the year 2016. According to total assets from 2014 to 2017, Standard Bank dominates the scene. Furthermore, Standard Bank has the highest number of employees and customers.

Table 2.10: Summary of total assets and other indicators for all selected banks

Total assets (R millions) Number of Number of Number Bank employees customers of ATMS 2014 2015 2016 2017 (2016) (2016) (2016) ABSA 804 271 926 526 914 669 983 378 30 739 9 425 714 8 885 African Bank 51 341 54 800 36 460 31 356 - 1 250 000 - Capitec Bank 51 877 61 970 71 729 87 033 11 440 7 269 000 1 236 FirstRand 856 911 979 920 1 016 761 1 120 747 45 100 - 7 335 Nedbank 714 408 813 287 869 362 892 006 32 401 7 700 000 - Standard 1 099 713 1 213 528 1 234 575 1 254 849 54 767 16 816 000 8 822 Bank VBS Mutual 362 717 1 543 2 401 87 - - Bank Source: SARB (2015, 2017), The Banker Database (2018)

2.5 Summary This chapter provided an overview and understanding of on-site and off-site inspections, bank supervision and the Basel Accords. Section 2.4 provided insights into the selected banks of the study and presented a summary of total asset growth from 2014 to 2017 accompanied by other indicators explaining the size of the banks compared to each other.

Among the selected banks, were included the African Bank and VBS Mutual Bank. Both these banks were placed under curatorship within the past five years. African Bank experienced critical losses that placed the bank under threat. Following these events, African Bank has been restructured and continues to operate as a licenced bank. VBS Mutual Bank is currently (2018) under investigation for fraud.

In January 2018, Capitec was under attack by Viceroy Research Group. The research group accused the company of being a loan shark and declared the bank “uninvestable”. However, the accusations were swiftly altered and corrected by the National Treasury that found no substantial evidence to support the accusations made. Standard Bank continues to dominate the industry in terms of total assets, number of employees and number of customers.

Chapter Three will explain the fundamentals of the CAMELS rating system approach and its method of assessing bank soundness, as well as providing a literature review.

CHAPTER THREE LITERATURE REVIEW 3.1 Introduction This chapter provides an explanation of the CAMELS rating system approach. In addition, it presents the empirical studies conducted on banks and/or banking sector, both internationally and locally, applying the CAMELS rating system approach. Section 3.2 explains each component of the CAMELS model and provides the financial ratios that are attributed to the components. Section 3.3 briefly discusses stress testing, followed by an overview of the significance of the CAMELS rating system approach in Section 3.4. Finally, Section 3.5 discusses previous empirical studies conducted.

3.2 Fundamentals of the CAMELS Rating System This section provides the definition and framework of the CAMELS rating system and its six components.

3.2.1 The CAMELS rating system The banking sector plays a pivotal role in its contribution to overall growth and the national economy (Dash & Das 2010). Therefore, it is imperative to evaluate, analyse and monitor the performance of the banking sector in a country (Dash & Das 2010). The CAMELS rating system is described as a performance evaluator often applied to the banking sector, which was originally developed by the Uniform Financial Institutions Rating System (UFIRS) (Desta 2016). Effinger (2017) explained that even though the financial system has undergone incredible change, the CAMELS rating system has remained relatively unchanged for almost 40 years. Moreover, Desta (2016) and Rostami (2015) argued that the CAMELS rating system serves as a reliable and effective supervisory tool for measuring the soundness of a financial institution. In 1988 the BCBS of the Bank of International Settlements (BIS) proposed that the CAMELS rating system model be used to assess financial institutions (Dash & Das 2010).

The CAMELS rating system is used in banking sectors across the world to evaluate performance and risk (Raiyani 2010). Rostami (2015) explained that the CAMELS

rating system allows for efficient and accurate performance evaluation in the banking sector and most importantly, to anticipate future and relative risk. In addition, the CAMELS rating system assists in assessing a financial institution’s exposure to operational, financial and market risks based on the composite rating assigned to a financial institution (Desta 2016). Klingelhöfer, Erasmus & Teka (2016) explained the six components of the CAMELS rating system of evaluating the soundness of a bank and/or banking sector. The six components comprise the following:

1 Capital adequacy 2 Asset quality 3 Management quality 4 Earnings 5 Liquidity, and 6 Sensitivity to market risks

Each of the six components are awarded a component rating whereby the assessment is relative to the financial institution’s size, the nature of its business, complexity of activities, and risk profile (Wachira 2010). In accordance with the CAMELS rating system, a financial institution is evaluated and assigned an overall composite rating ranging from 1 to 5 (FDIC 2014). A rating of 1 indicates the strongest performance, highest level of risk management practices and least concern for supervision (FDIC 2014). A rating of 5 is the lowest rating that indicates the weakest performance, poor risk management practices and highest level of concern for supervision (FDIC 2014). Dang (2011) explained that each component in the CAMELS rating system can be measured and assessed by using the CAMELS composite rating range, which is from 1 to 5.

The Federal Deposit Insurance Corporation (2014) defined each composite rating as follows: • Composite rating 1: Financial institutions in this rating category are sound in every aspect of the business and generally have components rated 1 or 2. Weaknesses are minor and pose very little risk that is capable to be effectively managed by the Board of Directors and management. Financial institutions with this rating, can withstand turbulent market conditions and macroeconomic shocks. Furthermore,

these financial institutions fully comply with the laws and regulations. Financial institutions with a rating of 1 display the strongest performance, efficient and effective risk management practices and raise no concern for supervision.

• Composite rating 2: Financial institutions in this rating category are fundamentally sound. For this rating to be allocated, there should be no component rating worse than 3. The institutions in this rating category exhibit moderate weaknesses, however well within the capabilities of the Board of Directors and management to correct. These financial institutions are financially sound and able to withstand market turbulence. The financial institutions exhibit substantial compliant practices with the laws and regulations. Ultimately, these financial institutions conduct satisfactory risk management practices and therefore, supervision is informal and limited.

• Composite rating 3: Financial institutions in this rating category display some level of concern for supervision in one or more component areas. Weaknesses in these financial institutions may range from moderate to severe. However, the severity of the weaknesses will not lead to a component to be rated worse than 4. The Board of Directors and management may struggle to effectively mitigate or resolve weaknesses if under time constraints. Financial institutions in this rating category are typically less capable of enduring business shocks and are more vulnerable to external influences than those financial institutions in the composite rating category 1 or 2.

• Composite rating 4: Financial institutions in this rating category typically display unsound and risky practices or conditions. There are severe financial or managerial weaknesses in the business, which result in unsatisfactory performance. The weaknesses are not being attended to by the Board of Directors or management. Financial institutions in this rating category are generally unable to survive business fluctuations; they may operate under serious noncompliance of laws and regulations. Risk management practices are generally unacceptable. To address the problems and challenges, formal supervision is required owing to high concern.

• Composite rating 5: Financial institutions in this rating category display extremely unsound and risky practices or conditions and exhibit critically weak performance, often conduct inadequate risk management practices and are the highest level of concern for supervision. The extent of the weaknesses and challenges are beyond the capabilities or willingness of the Board of Directors and management to manage or to correct. External forms of assistance are urgent and ongoing supervision is required. Ultimately, failure of these financial institutions is highly probable.

Once the analysis of each component is complete, the composite rating is the average of the 6 components. Table 3.1 shows the composite rating in accordance with its individual rating range, rating analysis and interpretation.

Table 3.1: The CAMELS Composite Rating Interpretation Rating Rating Rating analysis Interpretation range 1 1.0 – 1.49 Strong The financial institution is strong in every aspect. 2 1.5 – 2.49 Adequate The financial institution is primarily adequate but has some identified weaknesses. 3 2.5 – 3.49 Fair, with The financial institution has some improvement financial, operational or compliance needed weaknesses that will raise concern for supervision. 4 3.5 - 4.49 Marginal, with some The financial institution has serious level of exposure to weaknesses that will raise concern for risk of failure supervision. 5 4.5 – 5 Inadequate, with The financial institution has critical high level of weaknesses that will exhibit a high exposure to risk of probability of failure in the near future. failure

Source: Adapted from Desta (2016), Ahsan (2016)

3.2.2 Capital adequacy Dang (2011) stated that capital adequacy is the ability of a financial institution to maintain a balance between the level of capital and risk exposure, for the financial

institution to absorb potential losses and protect its debt holder. According to Baral (2005), capital adequacy is ultimately the measure of how well a financial institution can react to shocks in its balance sheet. Dang (2011) and Baral (2005) postulated that capital adequacy ratios consider the most critical financial risks, namely: foreign exchange risk, credit risk, market risk, operational risk and interest rate risk. This is done by allocating risk weightings to the financial institution’s assets (Baral 2005). Ensuring an adequate level of capital is in the best interest of the bank’s depositors (Ahsan 2016).

Baral (2005) maintained that in order to measure capital adequacy, according to the CAMELS framework, bank capital is divided into two tiers, Tier I and Tier II. Tier I is described as primary capital and Tier II is supplementary capital. Tier I mainly comprises common stock, preferred stock, convertible bonds and retained earnings and Tier II includes the amount that is derived from issued bonds by the bank (Babar & Zeb 2011). The capital adequacy ratio measures the bank’s ability to sufficiently cover losses (Aspal & Dhawan 2016).

The following ratios provide an indication of a financial institution’s capital adequacy and are measured in accordance with the applicable criteria (Dang 2011).

Table 3.2: Capital adequacy ratios

Ratio Formula Criteria

Capital adequacy ratio (Tier I capital + Tier II capital)/ ≥ 8% Risk-weighted assets Source: Adapted from Dang (2011)

Each component of the CAMELS rating system is evaluated and assigned a rating (Dang 2011). The FDIC (2014) stated that the ratings of capital adequacy are as follows:

• Component rating 1: The financial institution exhibits a strong level of capital relative to its risk profile.

• Component rating 2: The financial institution exhibits an adequate level of capital relative to its risk profile.

• Component rating 3: The financial institution exhibits a less than adequate level of capital, which fails to completely support the institution’s risk profile. This rating provides an indication that there is a need for improvement, regardless whether or not the level of capital exceeds minimum authoritative requirements.

• Component rating 4: The financial institution exhibits an inadequate level of capital, which exposes the viability of the financial institution to risk. External parties or shareholders may need to provide financial support or assistance.

• Component rating 5: The financial institution exhibits a severely low level of capital and the viability of the financial institution is at risk. External parties or shareholders will need to provide urgent financial support or assistance.

3.2.3 Asset quality The quality of a financial institution’s assets is significant in assessing its strength and level of performance (Ifeacho & Ngalawa 2014). According to Desta (2016), asset quality indicates the quality of the financial institution’s loans, which form part of the major asset that generates the major portion of its income. The assessment of the quality of assets is important to determine the component of non-performing assets as a percentage of total assets.

Dang (2011) explained that a bank should make a concerted effort to diversify its lending to various business sectors and/or business entities as this will lessen its exposure to vulnerability. According to Aspal and Dhawan (2016) the aim of a bank is to achieve the lowest possible amount of non-performing loans. Should this be achieved, profit can therefore be maximised. The number of non-performing loans has a negative impact on the profitability of a bank. Loans are subject to the highest risk of default, and therefore, should banks experience any increase in the number of non- performing loans, it will indicate a worsening quality of the assets (Rostami 2015).

Table 3.3: Asset quality ratios

Ratio Formula Criteria

Non-performing loans to Non-performing loans/ Total ≤ 1% total loans loans

Source: Adapted from Dang (2011)

The FDIC (2014) explained the rating of asset quality as follows: • Component rating 1: The financial institution exhibits a strong asset quality and sound credit administration practices. Weaknesses are minor and exposure to risk is modest relative to capital protection and the abilities of the Board of Directors and management. The asset quality of the financial institutions in this rating category is of the least concern for supervision.

• Component rating 2: The financial institution exhibits an adequate asset quality and credit administration practices. The level of weaknesses in these financial institutions requires limited concern for supervision.

• Component rating 3: The financial institution exhibits a less than adequate level of asset quality or credit administration practices. The level of weaknesses and risk exposure requires a higher level of concern for supervision than that of component rating 2.

• Component rating 4: The financial institution exhibits inadequate asset quality and credit administration practices. The level of risk exposure and problem assets are critical, inadequately controlled and could result in potential losses. The viability of these financial institutions is under threat.

• Component rating 5: The financial institution exhibits severely critical asset quality or credit administration practices, which presents an imminent threat to the financial institution’s viability.

3.2.4 Management quality The role of the Board of Directors and senior management is to conduct the financial institution in a smooth and decent manner (Ahsan 2016). For the Board of Directors and senior management to be successful, costs need to be well controlled and productivity needs to be at an increasingly high level to generate higher levels of profit. This explains the reason the cost-to-income ratio is incorporated into the management quality component of the CAMELS model. The CAMELS rating system remains important in the supervisory process and is used more readily to emphasize one subjective component - bank management quality (Effinger 2017). Effinger (2017) stressed that the quality of management ensures that financial institutions follow regulatory requirements and have substantial risk management practices in place. The quality of the Board of Directors and senior management in any financial institution is critical to its long-term success and survival (Barr & Siems 1996). The Board of Directors and senior management need to be adaptable and resilient to challenges and changes in market conditions for the institution to be profitable and remain competitive (Barr & Siems 1996).

Table 3.4: Management quality ratios

Ratio Formula Criteria

Cost/Income ratio Operating cost/Operating income ≤ 70%

Source: Adapted from Ahsan (2016)

The FDIC (2014) explained the rating of asset quality as follows: • Component rating 1: The Board of Directors and management exhibit strong performance and strong risk management practices relative to its institution’s size, sophistication and risk profile. All levels of risk are effectively and consistently identified, measured, monitored and controlled.

• Component rating 2: The Board of Directors and management exhibit relatively adequate performance and risk management practices. Insignificant risks may be present; however, no sufficiently significant risks exist that would jeopardize the soundness of the financial institution. Ultimately, all levels of risk are effectively identified, measured, monitored and controlled.

• Component rating 3: The Board of Directors and management exhibit performance that requires improvement or risk management practices that are inadequate relative to the conditions and risk profile of the financial institution. The Board of Directors and management may be incapable of identifying, measuring, monitoring or controlling significant risks.

• Component rating 4: The Board of Directors and management exhibit inadequate performance or lack risk management capabilities. The financial institution is exposed to significant risk. The Board of Directors and management face significant risks and require immediate action to preserve the soundness of the financial institution. Replacement of the Board of Directors or refining management quality may be required.

• Component rating 5: The Board of Directors and management significantly lack in performance or risk management practices. The Board of Directors and management have been incapable or unwilling to rectify problems and implement sound risk management practices. There is a significant level of problems and risks, which threaten the viability of the financial institution. Replacement of the Board of Directors or management is required.

3.2.5 Earning ability The earning ability of a banking institution indicates the quantity and trend in earnings, along with factors that may affect the sustainability of earnings (Dang 2011). Ultimately, a financial institution’s earning ability is a measure of its financial performance (Ahsan 2016). The institution depends on its earning ability to meet its responsibilities, such as funding dividends, maintaining adequate levels of capital, catering for growth and expansion through investment and competitiveness in the market (Ahsan 2016).

Higher income generally reflects a healthy financial condition and fewer financial difficulties for a financial institution; however, this is not always the case (Ifeacho & Ngalawa 2014). Higher income may also be associated with higher risk, which exposes the financial institution to a higher probability of bank failure (Ifeacho & Ngalawa 2014). For this reason, the other components of the CAMELS framework

must be considered for a holistic view in the assessment of a financial institution’s stability.

Table 3.5: Earning ability ratios

Ratio Formula Criteria Return on assets Net interest income/ Asset growth rate ≥ 1%

Return on equity Net interest income/ Shareholder’s ≥ 15% equity growth rate Source: Adapted from Dang (2011)

The FDIC (2014) explained the rating of earning ability as follows: • Component rating 1: The financial institution exhibits strong earnings. The earnings are more than adequate to support operational activities and allow for the reserve of an adequate level of capital after asset quality and growth have both been considered.

• Component rating 2: The financial institution exhibits adequate earnings that are sufficient to support operational activities and allow for the reserve of an adequate level of capital after asset quality and growth have both been considered. Earnings, which are relatively fixed or experience a slight decline, may be allocated a rating of 2 provided the financial institution’s level of earnings is adequate taking all necessary factors into account.

• Component rating 3: The financial institution exhibits a level of earnings that requires improvement. The level of earnings may not be adequate to support operational activities and may not be sufficient to maintain capital adequacy in relation to the financial institution’s overall condition.

• Component rating 4: The financial institution exhibits weak earnings, which are inadequate to support operational activities and insufficient to reserve the necessary level of capital. Financial institutions in this rating category may be pressurised by volatile fluctuations in net income or net interest margins, nominal or unsustainable earnings, or notable declines in earnings from previous years.

• Component rating 5: The financial institution exhibits seriously poor earnings and generally experience losses that expose the financial institution to a threat of non- viability, through depleted capital.

3.2.6 Liquidity Desta (2016) stated that effective liquidity management is indicative of a financial institution’s ability to meet its required obligations, mainly to depositors. For a financial institution to maintain an adequate liquidity position, it needs to have readily available access to sufficient funds, either by increasing liabilities or converting its assets at a reasonable cost (Ahsan 2016). Ahsan (2016) posited that if a financial institution experiences a liquidity crisis, it would be unable to meet its short-term obligations, therefore liquidity management is of the utmost importance.

Table 3.6: Liquidity ratios

Ratio Formula Criteria Total loan to Total loans/ Total customer deposits ≤ 81% customer deposits

Source: Adapted from Dang (2011)

The Federal Deposit Insurance Corporation (2014) explained the rating of liquidity as follows: • Component rating 1: The financial institution exhibits a strong level of liquidity and efficiently-developed funds management practices. The financial institution has adequate access to funds on favourable terms to meet present and future liquidity requirements.

• Component rating 2: The financial institution exhibits adequate levels of liquidity and adequate funds management practices. The financial institution has access to funds on acceptable terms to meet present and future liquidity requirements. Modest weaknesses may be present in the financial institution’s funds management practices.

• Component rating 3: The financial institution exhibits levels of capital and funds management practices that require improvement. Institutions in this rating category may lack immediate access to funds on reasonable terms or may exhibit notable weaknesses in funds management practices.

• Component rating 4: The financial institution exhibits inadequate levels of liquidity or funds management practices. Financial institutions in this rating category may not have or are unable to access funds on reasonable terms to meet liquidity requirements.

• Component rating 5: The financial institution exhibits levels of liquidity and funds management practices that are critically poor, and the viability of the financial institution is under threat. Immediate financial assistance is required.

3.2.7 Sensitivity to market risk Desta (2016) stated that sensitivity to market risk indicates the degree of change in foreign exchange rates, interest rates, commodity prices or equity prices that can negatively impact on a financial institution’s earnings or economic capital. Aspal and Dhawan (2016) concurred that while these items are important, the most significant issue in banking is interest rates risk. Identifying risk is of high importance in understanding the bank’s performance model (Rostami 2015). Baral (2005) pointed out that the higher the sensitivity to market risk, the more unpredictable is the financial health of a financial institution.

Table 3.7: Sensitivity to market risk ratios

Ratio Formula

Total securities to total assets Total securities/Total assets

Source: Adapted from Babar and Zeb (2011)

The FDIC (2014) explained the rating of sensitivity to market risk as follows: • Component rating 1: The financial institution exhibits that its market risk sensitivity is highly controlled and there is minimal risk that earning performance or capital

levels will be negatively affected. The earning performance and capital levels are highly efficient for the financial institution’s exposure to market risk.

• Component rating 2: The financial institution exhibits that its market risk sensitivity is adequately controlled and there is only a moderate risk that earning performance or capital levels will be negatively affected. The earning performance or capital levels are adequate for the financial institution’s exposure to market risk.

• Component rating 3: The financial institution exhibits that its control of market risk sensitivity requires improvement or there is a serious risk that earning performance or capital levels will be negatively affected. The earning performance and capital levels may not be adequate in terms of the financial institution’s exposure to market risk.

• Component rating 4: The financial institution exhibits that its control of market risk sensitivity is unacceptable or there is a high risk that earning performance or capital levels may be negatively affected. The earning performance or capital levels are inadequate for the financial institution’s exposure to market risk.

• Component rating 5: The financial institution exhibits that its control of market risk sensitivity is unacceptable or that the level of market risk poses an imminent threat to its viability.

3.2.8 Summary of the CAMELS component ratings system approach Table 3.8 presents a summary of all the financial ratios, its formulae and applicable criteria in accordance with the CAMELS rating system.

Table 3.8: Summary of the CAMELS component rating system approach

Ratio Formula Criteria

(Tier I capital + Tier II capital)/ Risk- Capital adequacy ratio ≥ 8% weighted assets Non-performing loans to Non-performing loans/ Total loans ≤ 1% total loans

Cost/income ratio Operating cost/Operating income ≤ 70%

Net interest income/ Asset growth Return on assets ≥ 1% rate Net interest income/ Shareholder’s Return on equity ≥ 15% equity growth rate Total loan to customer Total loans/ Total customer deposits ≤ 81% deposits Total securities to total Total securities/Total assets - assets Source: Adapted from Ahsan (2016), Dang (2011), Babar and Zeb (2011)

3.3 Stress Testing Stress testing can be broadly defined as a method used to assess the vulnerability of a financial system to exceptional but plausible events (Čihák 2004). The stresses generally take the form of sensitivities such as substantial declines in price and rises in volatilities (Schuermann 2014). The stresses may also result from events such as black Monday 1987, post Lehman bankruptcy, and severe recession (Schuermann 2014). Furthermore, stress testing focuses on critical risks such as credit risk, market risk, and liquidity risk (Patel 2017). Stress tests analyses a bank’s sensitivity under pressurising economic conditions or changes (Patel 2017).

Patel (2017) emphasised the following benefits associated with stress testing of banks:

1 Since the Dodd-Frank Act Stress Test was introduced, the Federal Reserve can simulate complex scenarios under which to test the sensitivity of banks.

2 Banks are required to release the test results to the public, which provides a higher level of transparency as customers will be able assess bank performance under circumstances of major crises or economic shocks.

3 Stress testing provides the opportunity for banks to identify any weaknesses and make the necessary amendments before a crisis.

4 Banks that fail the stress tests will need to reduce their share buybacks and dividends paid to shareholders, which allows banks to improve their level of capital and overall performance.

While stress testing has its benefits, there are several downsides or challenges to consider. The primary challenge includes the ability to generate severe but plausible events and translate them accurately into risk measures (Thun 2013). The lack of data and/or the difficulty in accessing relevant data speedily is a major obstacle. Another major challenge is the ability to convey the stress scenario to enable business managers to effectively use the results in business decision-making (Thun 2013).

3.4 The significance of the CAMELS rating system approach As Raiyani (2010) pointed out, the CAMELS rating system is a widely adopted model across the world. It is used to evaluate performance and risk in the banking sector. The CAMELS model reflects the conditions and performances of banks and contributes to the efficiency of both on-site and off-site inspections (Dang 2011). The purpose of the CAMEL model is to accurately and consistently evaluate a bank’s financial soundness and operations according to the areas of capital adequacy, asset quality, management quality, earnings ability, liquidity and sensitivity to market risk (Dang 2011). Rozzani and Rahman (2013) stated that the CAMELS rating methodology is closely related to the indicators of a bank’s financial health. Furthermore, the CAMELS model is flexible to a reasonable extent and considered, with international regulatory rating systems, to evaluate the suitability of its adoption of the ratings system approach to a country’s local banking sector (Rozzani & Rahman 2013).

According to Dang (2011), the CAMELS analysis serves the purpose of summarising relevant information required for the regulators. The analysis also assists when investigating the level of supervisory concern and would assist in detecting early warning signs of failure in banks (Dang 2011). Hyz and Gikas (2015) concurred that the application of the CAMELS model will be able to assist regulators in designing and improving early warning systems in the banking sector. In response to the financial crisis of 2008-2009, the CAMELS rating system aided the American government in deciding which banks needed special assistance or attention (Dang, 2011). Finally, the study done by Desta (2016) is evidence that the CAMELS rating system can be applied to South African banks and provides scope for further research in the rating system approach to banks in South Africa. This study adds to the limited research available using the CAMELS model on banks in South Africa.

3.5 Empirical evidence of previous studies The empirical evidence of previous studies performed is presented in a structure that first reviews studies done with the application or influence of the CAMEL(S) model. Thereafter the empirical evidence that displays the application of the CAMEL(S) model in banks or banking sectors is reviewed; firstly, in the United States of America and then, internationally. Lastly, a review on the empirical evidence regarding the application of the CAMEL(S) model to banks in South Africa is discussed.

Dang (2011) assessed the usefulness of the CAMEL assessment framework in banking supervision and examined the advantages and disadvantages of the CAMELS rating system. The study found that the CAMEL rating system is important to banking supervision and is being used by regulators worldwide. The advantage of the CAMEL model is that it is an internationally standardised rating system that provides flexibility between on-site and off-site inspections. The disadvantages are that it does not follow the Vietnamese banks closely enough, ignores the interaction with a bank’s upper management, and overlooks the provisions and allowances for loan loss ratios.

Barr and Siems (1996) investigated the prediction of bank failure using data envelopment analysis (DEA) to measure management quality. It was stressed that the

quality of a bank’s management is key to its long-run success and needs to be sound, especially during times of uncertainty and risk. Barr and Siems (1996) developed two new bank-failure models, which both used proxy variables for each component in the CAMEL rating system and an added variable to consider local economic conditions. The study found that when the management variable was removed from the newly- developed models, the results were worse in terms of the model’s fit to the data and its classification accuracy.

Barr, Killgo & Zimmel (1999) applied a constrained multiplier, input-oriented, data envelopment analysis (DEA) model to assess the productive efficiency and performance of U.S. commercial banks from 1984 to 1998. The study found that the relationship between efficiency and interest income and interest expenditure is not as persuasive, which is possibly due to market competition. However, there is still evidence for efficiency to be positively correlated with interest income and negatively related to interest expenditure. Furthermore, the study found that the relationship between CAMELS ratings and efficiency scores are significant. The study further suggested that DEA may be a strong off-site tool to monitor banks between on-site inspections.

Christopoulos, Mylonakis & Diktapanidis (2011) used the CAMELS rating system to assess whether the collapse of Lehman Brothers could have been anticipated. For this reason, the authors used financial data from the period 2003 to 2007 (prior to the financial crisis). The study revealed that in accordance with the CAMELS analysis, the U.S. Federal Reserve should have been able to anticipate the collapse of Lehman Brothers and further recommended that credit rating agencies should review their way of operation to ensure transparency of assessments.

Ibrahim (2014) compared the performance of two banks (Commercial Bank of Dubai and National Bank of Abu Dhabi) in United Arab Emirates, between the period 2004 and 2009. Five groups of parameters were used to assess performance, namely: liquidity level, profitability level, management capacity, and capital structure and share performance. The study found that both banks were financially viable as the appropriate financial tools and policies were used. Furthermore, both banks were able

to adapt to their environment to become more competitive in the market and maximise profits. The study also found that the Abu Dhabi bank had a high profitability level, however, subject to a high level of instability. The analysis determined that the commercial bank of Dubai offered their customers more loans than its competitor bank, while the national bank of Abu Dhabi allocated more funds in respect of investments rather than giving loans. Using the CAMEL analysis, Ibrahim (2014) found that the commercial bank of Dubai had a strong financial structure, which was stronger than its competitor bank. Lastly, the national bank of Abu Dhabi was deemed better off, in relation to market value and earnings per share than that of its competitor bank.

Kumar, Sri Harsha, Anand & Dhruva (2012) analysed the soundness of the banks in India using the CAMEL rating system approach. Twelve commercial banks were selected for the CAMEL analysis from the period 2000 to 2011. Kumar et al. (2012) established that the private sector banks are the best performers in terms of soundness. Public sector banks, such as Union Bank and SBI, displayed low economic soundness in comparison. It was deduced that Government needed to pay closer attention to the public sector banks. The study found that the banks’ assets and other variables could not be judged purely on the absolute values of the CAMEL ratios.

Aspal and Nazeem (2014) stressed the importance of capital adequacy in the private sector banks in India. The study investigated the impact of some risks, such as credit, liquidity and sensitivity on the capital adequacy of private sector banks in India. The study found that the capital adequacy ratio is negatively correlated with proxy variables of loans, asset quality, and management efficiency. It was also found that liquidity and sensitivity to market risk are positively correlated. The regression results from the study showed that loans, management efficiency, liquidity and sensitivity to market risk have statistically significant influence on the capital adequacy of private banks in India. The regression results also showed that the independent variable, asset quality, has a negligible influence on capital adequacy of private sector banks in India. The study concluded that the private sector banks in India could maintain a strong level of capital adequacy and generate more advances to the public.

Roman and Sargu (2013) performed an analysis of the financial soundness of the commercial banks in Romania. The study applied the CAMELS rating system approach to fifteen banks to assess the financial soundness of each. Based on the set of indicators of the CAMELS framework, the study reflects a heterogeneous distribution of the sample of banks used in the study. The largest bank from the sample ranked among the best five performing banks through the indicators regarding management quality and those regarding earnings and profitability (Roman & Sargu 2013). The study established that all fifteen banks were well capitalised and could absorb potential losses. The study concluded that there is scope for further research to assess the impact of major factors (microeconomic and macroeconomic) on the financial soundness of the banks operating in Romania and other European Union countries.

Nimalathasan (2008) conducted a comparative study of the financial performance of the Bangladesh banking sector by means of the CAMELS rating system. The study found that 3 banks were rated 1 or strong, 31 banks were rated 2 or satisfactory, 7 banks were rated 3 or fair, 5 banks were rated 4 or marginal and 2 banks were rated 5 or unsatisfactory.

Hyz and Gikas (2015), applying the CAMELS model based on the four biggest commercial banks in Greece, examined the performance of the Greek banking sector during their financial crisis. The study signified the importance of early detection of weak banks and stated that the commercial banks’ risks result from uncertainty of the banking business. Weaknesses of the banks will have a major negative impact on the national economy, if not controlled timeously and efficiently. It was stressed that banking authorities have more detailed indicators than what is available to the public and this could be applied to design and improve the early warning system. Hyz and Gikas (2015) explained that the decreasing rate of the Greek GDP, during the crisis, impacted negatively on the CAMELS indicators despite efforts of government to strengthen the banking sector. Ultimately, the study concluded that the improvement of the Greek banking sector depended heavily on political, economic stability and the efforts made to strengthen the real economy.

Desta (2016) applied the CAMEL model to analyse the financial performance of seven of the best banks in Africa. The study found that the banks are rated as strong and satisfactory in terms of capital adequacy ratio and earning ability. However, all banks showed supervisory concern when rated in terms of asset quality, management quality and liquidity. Moreover, the banks were compositely rated 3 or fair. However, differences were observed when each component was considered individually. Only six financial ratios were used in the analysis and therefore, the study recommended further research to yield a more complete model.

Ifeacho and Ngalwa (2014) applied the CAMEL model to investigate the impact of bank-specific variables and selected macroeconomic variables on the banking sector in South Africa for the period 1994 to 2011. The largest four banks, ABSA, First National Bank, Nedbank, and Standard Bank were analysed. The study used return on assets (ROA) and the return of equity (ROE) as a measure of bank performance and found that all bank-specific variables are statistically significant determinants of bank performance.

3.6 Summary The CAMELS rating system has been widely used around the world and has shown accurate, measurable quantitative results to test the financial soundness of banks. The CAMELS rating system assesses six components individually, followed by an overall rating assigned to the bank in question in accordance with its performance and soundness. The six components of the CAMELS rating system are: (i) capital adequacy; (ii) asset quality; (iii) management quality; (iv) earnings; (v) liquidity, and (vi) sensitivity to market risks. Each component is allocated a rating from 1 to 5 (1 being the strongest and 5 being the weakest).

As the CAMELS rating system has been used for almost 40 years, critics have claimed that the model is outdated and needs improvement or even replacement. However, it is evident that the rating system proved to be a reliable model in the assessment of bank soundness. The following chapter explains the research methodology in the application of the CAMELS rating system to the selected banks.

CHAPTER FOUR RESEARCH METHODOLOGY 4.1 Introduction Chapter Three provided an in-depth explanation of the CAMELS model and presented the empirical evidence. Chapter Four elaborates on the research methodology and the analysis technique applied in the study. Section 4.2 discusses the research design of the study, followed by the research method in section 4.3. In sections 4.4 and 4.5 this chapter explains how data was collected and analysed, followed by a summary in section 4.6.

4.2 Research Design The research design specifies the arrangement of conditions for collection and analysis of data that is applicable to the purpose of the research (Kothari 2004). In addition, the research design is the conceptual structure that provides the direction of the approach taken to conduct research, which incorporates the plan for the collection, measurement and analysis of data (Kothari 2004).

The research design is descriptive and quantifiable in the approach to measure, analyse, and interpret the results of the applied CAMELS rating system model. As per the empirical evidence discussed in Chapter Two, the CAMELS model has been widely used around the world and in various banking industries to determine bank soundness and performance. The CAMELS rating system approach possesses characteristics of reliability and accuracy. In addition, the CAMELS rating system approach has a strong relationship with the BASEL III framework. There are three pillars of BASEL III: Minimum Regulatory Capital, Supervisory Review, and Market Disclosure (Sarwar & Asif 2011). Figure 4.1 illustrates the three pillars of the BASEL III framework.

Figure 4.1: BASEL III framework

Source: Anderhuber (2016)

4.3 Research Method Based on the Bank Supervision Department Annual Report (2018), Table 4.1 below was compiled to provide an overview of the number of banks in each identified category:

Table 4.1: Number banks in South Africa

Category Number of banks Registered banks 19 Mutual banks 3 Co-operative banks 3

Local branches of foreign banks 15

Foreign banks with approved local representative offices 31

Source: Adapted from Bank Supervision Department (2018)

The study selected seven banks, including the four major banks with the largest total assets recorded, namely: Standard Bank, FirstRand, ABSA and Nedbank. Owing to recent negative attention in the media and serious events that have taken place, the following banks have also been included in the study: Capitec, African Bank, and VBS Mutual Bank. The study focuses on data from 2015 and 2016.

The data for each bank is analysed with the application of the long-standing CAMELS rating system model, which has been used around the world to assess financial soundness of a bank or financial institution. The data of the selected banks are then analysed and interpreted individually by way of the CAMELS model, which examines six components of each bank. A composite rating is then allocated to each bank to assess and derive a status of financial soundness. The CAMELS model displays value in its simplicity and ease of understanding (Ifeacho & Ngalawa 2014). Figure 4.2 illustrates the system of banking soundness measured applying the CAMELS rating system approach.

Figure 4.2: Research method to evaluate bank soundness

Source: Bastan, Mazrai & Ahmadvand (2016)

4.4 Data Collection This study adopted a quantitative approach and applied data from audited annual financial reports, income statements and balance sheets published by the selected banks. In addition, quantitative data was obtained from The Banker Database, which is a service of the Financial Times that provides comprehensive financial data and insights involving 5 000 of the world’s leading banks in over 160 countries (The Banker 2018). The Banker Database provided key financial data that enabled analysis of the selected banks according to the indictors of the CAMELS model.

4.5 Data Analysis 4.5.1 The CAMELS model The sets of quantitative data collected are used to evaluate the financial soundness of each bank according to the CAMELS rating system approach. Table 4.2 shows the indicators used to assess each component of the CAMELS model. Each indicator is then evaluated according to a set of criteria, whereby each ratio is given a rating, between 1 and 5. This is shown in Table 4.3.

Table 4.2: Breakdown of the CAMELS model

Component Indicator Formula

(Tier I + Tier II)/Total risk weighted Capital adequacy Capital adequacy ratio assets

Asset quality Asset quality ratio Non-performing loans/Total loans

Management quality Cost/income ratio Operating cost/Operating income

Return on assets Net profit/Total assets Earning ability Return on equity Net profit/Own capital

Liquidity Total loans to total deposits Total loans/Total deposits

Sensitivity to market Total securities to total Total securities/Total assets risk assets

Source: Adapted from Babar and Zeb (2011), Desta (2016)

Table 4.3: Criteria and ratios ratings

Ratios rating Ratio 1 2 3 4 5 Capital adequacy ratio >15% 12 - 14.99% 8 - 11.99% 7 - 7.99% <6.99% Asset quality ratio <1.25% 2.5 - 1.26% 3.5 - 2.6% 5.5 - 3.6% >5.6% Cost/income ratio <25% 30 - 26% 38 - 31% 45 - 39% >46% Return on assets >1% 0.9 - 0.8% 0.35 - 0.7% 0.25 - 0.34% <0.24% Return on equity >22% 17- 21.99% 10 - 16.99% 7 - 9.99% <6.99% Total loans to total ≤55% 62 - 56% 68 - 63% 80 - 69% ≥81% deposits Total securities to total ≤25% 30 - 26% 37 - 31% 42 - 38% ≥43% assets Source: Adapted from Babar and Zeb (2011) and Desta (2016)

The data is assessed according to each ratio within the six components (capital adequacy, asset quality, management quality, earnings, liquidity and sensitivity to market risk. Each component is subjected to a rating, which is then combined to establish the overall composite rating. The findings and results are quantifiable and displayed in graphical representations to better explain the findings.

4.5.2 One-way ANOVA This study uses Arithmetic Mean, Standard Deviation and one-way variance (ANOVA) for analysis and interpretation of the data. Hypotheses have been tested at a 5% level of significance.

The analysis of variance (ANOVA) is a statistical method used to analyse multi-group experiments and is often used when one or more variables are investigated in the same experiment (Pagano 2009). The null hypothesis applied to this study states that there are no significant differences observed, which implies that each group shares the same mean value (Pagano 2009). Therefore, if there are k groups, the null hypothesis is described as follows (Pagano, 2009): µ1 = µ2 = µ3 = …. = µk as shown in Figure 4.3 below.

Figure 4.3: Analysis of variance technique

Source: Pagano (2009)

Figure 4.3 shows the analysis of variance technique used to find Fobt. Fobt is assessed by comparing it to Fcrit (Pagano 2009). Pagano (2009) stated that if Fobt is equal to or greater than Fcrit, H0 is rejected. The decision rule is described as follows (Pagano

2009):

If Fobt ≥ Fcrit, reject H0.

If Fobt ≤ Fcrit, accept H0.

4.5.3 Hypotheses of the study To critically evaluate the performance and financial soundness of the selected banks by way of the CAMELS model, the following hypotheses are stated:

Hypothesis II (II) H0 : There is no significant difference in the capital adequacy ratio of the selected banks under the CAMELS model.

(II) Ha : There is a significant difference in the capital adequacy ratio of the selected banks under the CAMELS model.

4.6 Summary This chapter discussed the research design and the method of the study. The CAMELS rating system model was chosen due to its simplicity and reliability in evaluating bank soundness and performance. The study focused on seven selected banks of South Africa over the period 2015 to 2016. The seven banks assessed are: Standard Bank, ABSA, FirstRand, Nedbank, Capitec, African Bank, and VBS Mutual Bank.

Secondary data was obtained for this study, which brought about limitations to the sample size and time-period evaluated. Each component of the CAMELS model was subjected to component ratios used to allocate a component rating. Each ratio was analysed and interpreted by way of Arithmetic Mean, Standard Deviation and one-way analysis of variance, ANOVA. Hypotheses have been tested at 5% level of significance. The research findings are discussed in Chapter Five.

CHAPTER FIVE EMPIRICAL FINDINGS 5.1 Introduction Chapter Four provided the research design and research method that was identified to analyse the financial soundness of the selected banks. Chapter Five incorporates the application of the model to the data and presents findings of the analyses. The main objective of this chapter is to present the findings of the financial data analysed, as defined in the research methodology chapter. The Banker Database (2018) and ABSA’s annual consolidated and separate financial statements were used to collect secondary data, which was then applied to the CAMELS rating system model. The results of the financial ratios and the CAMELS composite rating were analysed and interpreted using the statistical tool, one-way ANOVA. Microsoft Excel ANOVA was used to analyse the results of the CAMELS model. Section 5.2 shows the results of the CAMELS model followed by hypotheses testing by means of one-way ANOVA in section 5.3. Section 5.4 presents a summary of the hypotheses testing. Importantly, this chapter serves to fulfil the primary objective of this study as well as the secondary objectives number 1 and 2 in section 1.4 of Chapter One.

5.2 The Camels Rating System Model The financial data obtained was applied to the CAMELS model to assess the financial soundness of the selected banks in South Africa for 2015 and 2016. The results are shown in Table 5.1 and 5.2. As displayed in Table 5.3, the results for the years 2015 and 2016 were then aggregated to derive average component scores for the selected banks. Table 5.4 exhibits the aggregate CAMELS component ratings of each selected bank. The component ratings were assessed to derive the CAMELS composite ratings expressed in Table 5.5. Each bank was awarded a composite rating from 1 to 5. A rating of 1 being the strongest and a rating of 5 the weakest. The banks were then ranked according to their CAMELS composite rating score, as shown in Table 5.6. The lowest composite score for a bank indicates the best results and the highest composite score indicates the worst results of all banks evaluated.

Figure 5.1: The CAMELS composite rating

Managament Earning Quality Ability

Asset Quality Liquidity

CAMELS Capital Sensitivity to Adequacy COMPOSITE Market Risk RATING

Source: Researcher’s own construct

As shown in Figure 5.1, each component is assessed individually to award the CAMELS composite rating for each selected bank. Table 5.1 shows the banks’ ratios for year 2015 according to the CAMELS model. Each component was assessed by means of its relevant ratio, which was applied to each selected bank. African Bank was placed under curatorship with effect from 10 August 2014 and was subjected to limited availability of relevant data (Batra 2014). Hence, there was no financial data input for African Bank in 2015. Capitec and VBS Mutual Bank also posed limitations on available data for the year 2015. Table 5.2 shows each bank’s ratio for 2016 according to the CAMELS model. Table 5.3 shows the aggregate results of the banks’ ratios for the years 2015 and 2016 according to the CAMELS model.

Table 5.1: Banks’ ratios for year 2015 (%)

Standard Coefficient Component Ratio ABSA ABL CB FRB NED SB VBS Mean Deviation of Variance

Capital CAR 13.60 - 35.70 15.71 14.10 15.70 22.00 19.47 8.50 43.67% adequacy

Asset quality NPL/TL 2.90 - 5.40 2.29 2.53 3.20 - 3.26 1.24 38.10%

Management C/I 54.08 - 31.56 48.81 54.10 44.13 93.58 54.38 20.95 38.52% quality

ROA 1.58 - 6.60 2.90 1.59 1.86 0.25 2.46 2.20 89.14% Earning ability

ROE 24.38 - 30.78 31.17 18.64 20.28 2.61 21.31 10.53 49.40%

Liquidity TL/TD 108.19 - 105.80 88.11 95.48 92.67 62.93 92.20 16.28 17.66%

Sensitivity to TS/TA 3.71 - - 8.77 10.00 10.74 - 8.31 3.17 38.16% market risk

Source: Researcher’s compilation based on data from The Banker Database (2018) and ABSA (2017)

Table 5.2: Banks’ ratios for year 2016 (%)

Standard Coefficient Component Ratio ABSA ABL CB FRB NED SB VBS Mean Deviation of Variance Capital CAR 14.00 38.52 34.90 16.90 15.30 16.60 14.30 21.50 10.50 48.81% adequacy

Asset quality NPL/TL 3.40 19.53 5.60 2.59 2.70 3.10 1.50 5.49 6.32 115.07%

Management C/I 52.93 61.20 31.10 48.67 57.37 51.13 79.16 54.51 14.47 26.55% quality

ROA 1.46 -4.27 7.10 2.75 1.51 1.90 0.46 1.56 3.35 215.10% Earning ability ROE 19.35 -19.37 32.74 29.26 17.88 20.71 3.27 14.83 17.78 119.85%

Liquidity TL/TD 109.47 482.76 93.84 89.32 94.44 88.57 112.18 152.94 145.74 95.29%

Sensitivity to TS/TA 6.14 2.28 5.77 16.13 13.48 12.60 1.45 8.26 5.79 70.01% market risk

Source: Researcher’s compilation based on data from The Banker Database (2018) and ABSA (2017)

Table 5.3: Aggregate banks’ ratios for years 2015 and 2016 (%)

Standard Coefficient Component Ratio ABSA ABL CB FRB NED SB VBS Mean Deviation of Variance

Capital CAR 13.80 38.52 35.30 16.31 14.70 16.15 18.15 21.85 10.42 47.70% adequacy

Asset quality NPL/TL 3.15 19.53 5.50 2.44 2.62 3.15 1.50 5.41 6.34 117.23%

Management C/I 53.51 61.20 31.33 48.74 55.74 47.63 86.37 54.93 16.73 30.45% quality

ROA 1.52 -4.27 6.85 2.83 1.55 1.88 0.36 1.53 3.30 215.33%

Earning ability

ROE 21.87 -19.37 31.76 30.22 18.26 20.50 2.94 15.17 17.92 118.16%

Liquidity TL/TD 108.83 482.76 99.82 88.72 94.96 90.62 87.56 150.47 146.71 97.51%

Sensitivity to TS/TA 4.93 2.28 5.77 12.45 11.74 11.67 1.45 7.18 4.70 65.45% market risk

Source: Researcher’s compilation based on data from The Banker Database (2018) and ABSA (2017)

The selected banks generally performed well in the capital adequacy component. All banks achieved the minimum requirements for the capital adequacy ratio of 8%. The results in this component show that the selected banks have performed more than satisfactorily to meet the international standards set under Basel III. ABSA and Nedbank were the only two banks to show some identified weaknesses, however they still revealed adequate results.

Under the asset quality component, Capitec performed marginally and was rated 4. Standard and Poor’s (2017) stated that Capitec have been under pressure in this component primarily owing to South Africa’s weak economic growth and the continuous credit risk of over-leveraged households. Capitec exhibited some level of exposure to the risk of failure and called for increased concern for supervision. African Bank was awarded a rating 5 or inadequate, exhibiting a high level of exposure to the risk of failure and needs urgent formal supervision.

Under the management quality component, the selected banks generally performed inadequately, with Capitec being the exception. It begs the question as to why management quality is so weak in the South African banking sector. A weak result based on the cost-to-income ratio implies management inefficiencies, and according to the results, this is one component that needs urgent formal supervision and attention. If management quality is not addressed, this could potentially have serious implications within a given bank and ultimately affect the financial system and national economy. As discussed in Chapter Two, VBS Mutual Bank is a prime example of a banks downfall as a result of inefficient or inadequate management quality. Furthermore, banks will need to re-evaluate management policies and devise schemes to improve on productivity and efficiency.

All banks, including Capitec, received a rating 5 for liquidity, which raises a red flag regarding liquidity management practices among the selected banks. The study conducted by Mashamba and Kwenda (2017), aimed to determine the current liquidity management practices of banks in South Africa. This was done by examining whether South African banks have target liquidity levels which are successfully pursued, and by assessing the variables that drive bank liquidity ratios. The study found that South

African commercial banks passively manage their liquidity. Owing to high adjustment costs, these banks gradually revert to their target liquidity levels. Effective and efficient liquidity management is crucial to the ongoing viability of banks and equally important to the financial system as liquidity shortfalls can have systemic implications (Mashamba & Kwenda 2017).

All selected banks achieved a rating of 1 or strong for the sensitivity to market risk component. This implies that the selected banks have effective management practices in place to address market risk. Table 5.4 shows the awarded component rating for each bank according to the CAMELS model.

Table 5.4: Aggregated CAMELS component ratings for years 2015 and 2016

Standard Coefficient of Component ABSA ABL CB FRB NED SB VBS Mean Deviation Variance

Capital Adequacy 2 1 1 1 2 1 1 1.29 0.49 37.95%

Asset quality 3 5 4 3 3 3 2 3.29 0.95 28.95%

Management quality 5 5 3 5 5 5 5 4.71 0.76 16.03%

Earning ability 2 5 1 1 2 2 4 2.43 1.51 62.25%

Liquidity 5 5 5 5 5 5 5 5.00 0.00 0.00%

Sensitivity to market 1 1 1 1 1 1 1 1.00 0.00 0.00% risk

Total 18 22 15 16 18 17 18

Source: Researcher’s compilation

Table 5.5 shows a summary of the best and worst performing banks for years 2015, 2016 and the aggregated results of 2015 and 2016. In the aggregated results for 2015 and 2016, Capitec Bank and VBS Mutual Bank achieved “best” the highest number of times and African Banks appeared in the “worst” category the highest number of times.

Table 5.5: Summary of best and worst performing banks for each ratio

2015 2016 Aggregated (2015 and 2016)

Component Ratio Best Worst Best Worst Best Worst

Capital adequacy CAR Capitec ABSA African Bank ABSA African Bank ABSA

VBS Mutual VBS Mutual Asset quality NPL/TL FirstRand Capitec African Bank African Bank Bank Bank Management VBS Mutual VBS Mutual VBS Mutual C/I Capitec Capitec Capitec quality Bank Bank Bank VBS Mutual ROA Capitec Capitec African Bank Capitec African Bank Bank Earning ability VBS Mutual ROE FirstRand Capitec African Bank Capitec African Bank Bank VBS Mutual Standard VBS Mutual Liquidity TL/TD ABSA African Bank African Bank Bank Bank Bank Sensitivity to Standard VBS Mutual VBS Mutual TS/TA ABSA FirstRand FirstRand market risk Bank Bank Bank

Source: Researcher’s compilation

As presented in Figure 5.2, the results found that all selected banks were rated 5, or inadequate in these two components, except for Capitec Bank, which achieved a rating of 3 (fair) for management quality. This was one of the secondary objectives in Section 1.4.

Figure 5.2: Banks’ aggregate component ratings for years 2015 and 2016

2 Component rating

0 Capital Adequacy Asset quality Management Earning ability Liquidity Sensitivity to quality market risk Component

ABSA ABL CB FRB NED SB VBS

Source: Researcher’s own construct

Owing to strict banking regulations, the selected banks performed the best in the components: capital adequacy and sensitivity to market risk. The findings show that all selected banks met the minimum requirement of the capital adequacy ratio, above 8%. This further shows that regulators and internal management have performed well to meet the international standards.

With a mean component rating value of just under 2.5 for earning ability, the selected banks exhibited a satisfactory performance. The exceptions in respect of this component are African Bank and VBS Bank. Within the last five years, both banks have been subjected to curatorship, investigations and restructuring.

Table 5.6: Banks’ CAMELS composite rating Total Total CAMELS CAMELS Bank components component composite rating score score/6 rating analysis CB 15 2.50 3 Fair

FRB 16 2.67 3 Fair

SB 17 2.83 3 Fair

NED 18 3.00 3 Fair

VBS 18 3.00 3 Fair

ABSA 18 3.00 3 Fair

ABL 22 3.67 4 Marginal

Source: Researcher’s interpretation

The banks’ performance in each component was individually summed to calculate the total composite scores as shown in Table 5.6. The CAMELS composite rating was derived from the data. As shown in Table 5.6, all banks received a CAMELS composite rating of 3 or fair. However, African Bank performed the weakest and was subsequently awarded a CAMELS composite rating of 4 or marginal.

According to the CAMELS model, the primary reason African Bank performed the worst was owing to the bank’s negative performance in return on assets and return on equity. The results also showed that African Bank has an exceptionally high total loans to total deposits ratio. The data analysed was after the curatorship and the bank was still regaining its customer base and market confidence. The bank has since restructured its business model focusing on higher quality credit risk and secured lending (Momoniat, Havemann & National Treasury 2014).

The Financial Stability Board (FSB) considers both private interest and public interest but stresses the focus on public interest during times of bank failure (Momoniat et al. 2014). The FSB developed a set of international standards, known as Key Attributes, which outlines the essential features that resolution regimes should contain (The Banking Policy Department 2013). The Banking Policy Department (2013) further

explained that the framework described by the Key Attributes considers whether a financial institution is non-viable as its failure could be systemically significant or critical. To avoid liquidation when possible, the framework of the Key Attributes, firstly, considers a financial institution that is failing. The second step is then to determine whether the failure is systemically significant or critical. If so, the resolution is enforced to secure continuity for some or all its business functions. If failure is not systemically significant or critical, the financial institution will then be considered for liquidation, which is the closure of the company. African Bank clearly exhibited high levels of systemic risk and to avoid collapse of the bank, restructuring and business rescue was the best alternative.

Capitec, on the other hand, displayed strength in respect of the following components: return on assets and return on equity ratios. It ultimately performed better than the other selected banks. Since the inception of Capitec, the company has strategically placed itself in the marketplace, providing affordable products and targeting a lower income market. Moreover, the company’s image has remained strong and this has been reflected in its growing client and asset base. While Capitec remains a dominant bank in the low-income segment, it has gradually been attracting an increased consumer base in the middle and upper-middle income segments (Manson 2012). This adds further pressure on the other major players in South Africa’s banking sector.

Table 5.7: Banks ranking according to the CAMELS rating system

Bank name Total Components score Ranking

CB 15 1

FRB 16 2

SB 17 3

NED/ VBS/ ABSA 18 4

ABL 22 5

Source: Researcher’s interpretation

As displayed in Table 5.7, all selected banks were ranked according to the total component score with Capitec Bank performing the best amongst the selected banks, with a total component score of 15 and, therefore, ranked first. Nedbank, VBS Mutual Bank and ABSA all achieved the same total components score and were subsequently ranked forth. African Bank’s performance was the weakest when assessing all the CAMELS component scores for the bank and therefore, was ranked fifth.

As all selected banks exhibited at least two components with a rating of 5, with the exception being Capitec. In addressing the first secondary objective in section 1.4 of this study, it is difficult to use the CAMELS model as an early warning system. However, the CAMELS model provides supervisors with a good indication for needed supervision when considering each component individually. In this way, supervisors will be able to urgently supervise a specific component of a bank and mitigate risks of failure. As previously stated in Chapter 2, VBS Mutual Bank went under curatorship in 2018. The results show little indication, amongst its peers, of bank failure. Especially when considering Table 5.7, as it achieved the same total component score as Nedbank and ABSA. Moreover, VBS Mutual Bank was rated a composite rating 3.

5.3 Analysis of Variance - Anova The hypotheses defined in Chapter One have been tested in this section of the study.

As previously stated, Fobt is assessed by comparing it to Fcrit (Pagano 2009). Pagano

(2009) stated that if Fobt is equal to or greater than Fcrit, H0 is rejected. The decision rule is applied as follows (Pagano 2009):

1 If Fobt ≥ Fcrit, reject H0.

2 If Fobt ≤ Fcrit, accept H0.

5.3.1 Capital adequacy ratio As previously defined in Chapter 3, the implication of a strong level of capital adequacy indicates that a bank will be able to maintain a balance between the level of capital and risk exposure. Furthermore, a bank would be able to meet statutory requirements and absorb any potential losses that could negatively impact on the bank. ABSA performed the worst among the banks and was allocated a component rating of 2, which is considered adequate.

African Bank, Capitec Bank, FirstRand Bank and Standard Bank performed strongly and were given a rating of 1. ABSA and Nedbank performed adequately. This implies that all selected banks are meeting statutory requirements in this category. The Basel III requirement for capital adequacy is a minimum of 8%.

Table 5.8: One-way ANOVA – analysis of capital adequacy ratio Source of SS df MS F P-value F crit Variation Between Groups 1003.89 6 167.32 31.49 0.00 4.28 Within Groups 31.88 6 5.31 Total 1035.77 12

Source: Researcher’s own computation using Microsoft Excel software

As shown in Table 5.8, the F value (31.49) is greater than the critical value (4.28) at the 5% level of significance. The results show a significant difference in the capital (II) adequacy ratio amongst the selected banks. Therefore, the null hypothesis (H0 ) was (II) rejected and the alternate hypothesis (Ha ) was accepted.

5.3.2 Asset quality ratio VBS Mutual Bank exhibited a mean of 1,5 which achieved a component rating of 2 and performed best amongst the banks. FirstRand Bank, Standard Bank and Nedbank performed “fair” but require improvement. African Bank was placed under curatorship in 2015 and went through a restructuring process. However, the results showed that the bank still achieved an inadequate level of asset quality and continued to exhibit a high level of risk exposure. According to Standard and Poor’s (2017), Capitec continued to show profitability, despite the early signs of concern for asset quality deterioration. Moreover, Capitec has been under pressure in this component, primarily owing to South Africa’s weak economic growth and the continuous credit risk of over- leveraged households (Standard & Poor's 2017).

Table 5.9: One-way ANOVA – analysis of asset quality ratio

Source of SS df MS F P-value F crit Variation Between Groups 259.74 6 43.29 1033.42 0.00 4.95 Within Groups 0.21 5 0.04 Total 259.95 11

Source: Researcher’s computation using Microsoft Excel software

Table 5.9 shows the F value (1033.42) to be greater than the critical value (4.95) at the 5% level of significance. The results show a significant difference in the asset (III) quality ratio of the selected banks. Therefore, the null hypothesis (H0 ) was rejected (III) and the alternate hypothesis (Ha ) accepted.

5.3.3 Cost to income ratio All banks performed inadequately in the cost to income component and were therefore awarded a rating of 5. The results imply that all selected banks pose critical asset quality or credit administration practices and present a threat to the viability of the banks.

Table 5.10: One-way ANOVA – analysis of cost to income ratio

Source of SS df MS F P-value F crit Variation Between 3315.73 6 552.62 24.64 0.00 4.28 Groups Within Groups 134.59 6 22.43 Total 3450.32 12

Source: Researcher’s computation using Microsoft Excel software

As shown in Table 5.10, the F value (24.63547) is greater than the critical value (4.28387) at the 5% level of significance. The results showed a significant difference (IV) in the cost to income ratio of the selected banks. Therefore, the null hypothesis (H0 ) (IV) was rejected and the alternate hypothesis (Ha ) was accepted.

5.3.4 Return on assets ratio Capitec outperformed its competitor banks in this component with a mean ratio of 6.85%. The good performance of Capitec is owing to its rapid growth in the number of clients as it targets the low-income market and attracts middle-market earners with low banking fees. This has resulted in strong growth in its return on assets ratio for the years 2015 and 2016. The second-best bank in respect of the return on assets component was FirstRand Bank, achieving a mean ratio of 2.825%. African Bank performed the worst in this category and suffered a loss resulting in the return to assets ratio of -4.27%. The new African Bank started business in April 2016 and continued to show signs of difficulty for the duration of the year (SARB 2016).

Table 5.11: One-way ANOVA – analysis of return on assets ratio

Source of SS df MS F P-value F crit Variation Between Groups 94.02 6 15.67 554.68 0.00 4.28 Within Groups 0.17 6 0.03 Total 94.19 12 Source: Researcher’s computation using Microsoft Excel software

Table 5.11 shows that the F value (554.68) is greater than the critical value (4.28) at the 5% level of significance. The results show a significant difference in the return on (V) assets for the selected banks. Therefore, the Null Hypothesis (H0 ) was rejected and (V) the alternate hypothesis (Ha ) accepted.

5.3.5 Return on equity ratio Capitec and FirstRand performed best and were subsequently awarded a rating of 1. African Bank performed the worst, exhibiting a loss resulting in the return on equity ratio of -19.37%. VBS Mutual Bank also performed inadequately and was rated 5 as a result. VBS Mutual Bank exhibited critically poor earnings, which exposes the bank to the threat of non-viability. This is a good example of the benefits of the CAMELS model as the model could have been used as an early-warning mechanism to call for supervision and financial assistance. This would have prevented the rapid decline of the bank’s health resulting in it being placed under curatorship in March 2018.

Table 5.12: One-way ANOVA – analysis of return on equity ratio

Source of SS df MS F P-value F crit Variation Between 2569.27 6 428.21 151.18 0.00 4.28 Groups Within Groups 16.99 6 2.83 Total 2586.26 12

Source: Researcher’s computation using Microsoft Excel software

Table 5.12 shows that the F value (151.18) is greater than the critical value (4.28) at the 5% level of significance. The results show a significant difference in the return on (VI) equity ratio for the banks. Therefore, the Null Hypothesis (H0 ) was rejected and the (VI) alternate hypothesis (Ha ) was accepted.

5.3.6 Total loans to total deposits ratio All banks performed inadequately in this component and were subsequently awarded a rating of 5. The banks therefore, exhibit severely poor levels of liquidity and funds practice management, and the viability of the banks is under threat. African Bank performed the worst with total loans to total deposits ratio of 482.76%.

Table 5.13: One-way ANOVA – analysis of total loans to total deposits ratio

Source of SS df MS F P-value F crit Variation Between 139389.33 6 23231.56 107.65 0.00 4.28 Groups Within Groups 1294.80 6 215.80 Total 140684.13 12 Source: Researcher’s computation using Microsoft Excel software

As shown in Table 5.13, the F value (107.65) is greater than the critical value (4.28) at the 5% level of significance. The results show a significant difference in the total loans to total deposits ratio of the selected banks. Therefore, the null hypothesis (VII) (VII) (H0 ) was rejected and the alternate hypothesis (Ha ) accepted.

5.3.7 Total securities to total assets ratio This component may not be accurate due to the limited data available. However, based on the data collected all banks performed strongly and were awarded a rating of 1 as a result. This implies that the banks’ market risk sensitivity is highly controlled and there is minimal risk that earnings or capital levels will be negatively affected. The banks’ earnings performance and capital levels are highly efficient and well managed.

Table 5.14: One-way ANOVA – analysis of total securities to total assets ratio

Source of SS df MS F P-value F crit Variation Between Groups 193.17 6 32.19 3.40 0.13 6.16 Within Groups 37.82 4 9.46 Total 230.99 10

Source: Researcher’s computation using Microsoft Excel software

Table 5.14 shows that the F value (3.40) is smaller than the critical value (6.16) at the 5% level of significance. The results show no significant difference in the total securities to total assets ratio for the selected banks. Therefore, the null hypothesis (VIII) (VIII) (H0 ) was accepted and the alternate hypothesis (Ha ) rejected.

5.3.8 Financial soundness of selected banks Table 5.15 shows each component rating according to the CAMELS rating system model for all the selected banks. Table 5.15: Banks’ aggregated component ratings (2015-2016)

CAMELS component ABSA ABL CB FRB NED SB VBS Capital Adequacy 2 1 1 1 2 1 1 Asset quality 3 5 4 3 3 3 2 Management quality 5 5 3 5 5 5 5 Earning ability 2 5 1 1 2 2 4 Liquidity 5 5 5 5 5 5 5 Sensitivity to market risk 1 1 1 1 1 1 1 Total 18 22 15 16 18 17 18 Source: Researcher’s compilation with data from The Banker Database (2018)

Table 5.16: Summary of banks’ component ratings

Groups Count Sum Average Variance ABSA 6 18 3.00 2.80 ABL 6 22 3.67 4.27 CB 6 15 2.50 3.10 FRB 6 16 2.67 3.87 NED 6 18 3.00 2.80 SB 6 17 2.83 3.37 VBS 6 18 3.00 3.60 Source: Researcher’s computation using Microsoft Excel software

Figure 5.3 shows each bank’s aggregated component ratings for years 2015 and 2016. Six of the seven selected banks achieved a rating of 3, which implies that the banks exhibit some level of concern for supervision in one or more component area. The areas of concern range from moderate to severe and may exert pressure on the bank’s health. Banks with this CAMELS composite rating may be less capable of enduring business shocks and more vulnerable to external influences than those banks rated 1 or 2. African Bank was the only bank to be rated 4. Although it was already placed under curatorship and went through structural changes, there were still areas of concern identified in this study. This shows that there may be unsound and risky practices in the business that must still be addressed and fully resolved. There may also be severe financial or managerial weaknesses that have exhibited negative return on assets and equity.

Figure 5.3: Banks’ aggregated component ratings (2015-2016)

2 Component Component rating 1

0 ABSA ABL CB FRB NED SB VBS Bank

Capital Adequacy Asset quality Management quality Earning ability Liquidity Sensitivity to market risk

Source: Researcher’s own construct

Table 5.17: One-way ANOVA – analysis of banks’ financial soundness

Source of SS df MS F P-value F crit Variation Between 4.90 6.00 0.82 0.24 0.96 2.37 Groups Within Groups 119.00 35.00 3.40 Total 123.90 41.00

Source: Researcher’s computation using Microsoft Excel software

Table 5.17 shows the F value (0.24) to be less than the critical value (2.37) at the 5% level of significance. The results show no significant difference in the financial soundness of the selected banks, which is a secondary objective of the study. (I) (I) Therefore, the null hypothesis (H0 ) is accepted and the alternate hypothesis (Ha ) rejected.

5.4 Summary of hypotheses testing 5.4.1 Null hypotheses decisions Table 5.18 stipulates the decisions made to reject or accept each of the null hypotheses stated in Chapter One. According to the study to test for no significant difference in the financial soundness of the selected banks as a measure of the

(I) CAMELS model, the null hypothesis (H0 ) was accepted. Furthermore, the null (VIII) (II) (III) (IV) (V) hypotheses (H0 ) was accepted and the null hypotheses (H0 , H0 , H0 , H0 , (VI) (VII) H0 and H0 ) were rejected.

Table 5.18: Null hypotheses decisions

Null hypotheses Decision

(I) There is no significant difference in the financial soundness of (I) H0 Accept H0 the selected banks as a measure of the CAMELS model.

(II) There is no significant difference in the capital adequacy ratio (II) H0 Reject H0 of the selected banks under the CAMELS model.

(III) There is no significant difference in the asset quality ratio of (III) H0 Reject H0 the selected banks under the CAMELS model.

(IV) There is no significant difference in the cost to income ratio of (IV) H0 Reject H0 the selected banks under the CAMELS model.

(V) There is no significant difference in the return on assets ratio (V) H0 Reject H0 of the selected banks under the CAMELS model.

(VI) There is no significant difference in the return on equity ratio (VI) H0 Reject H0 of the selected banks under the CAMELS model.

There is no significant difference in the total loans to total (VII) (VII) H0 deposits ratio of the selected banks under the CAMELS Reject H0 model.

(VIII) There is no significant difference in the total securities to total (VIII) H0 Accept H0 assets ratio of the selected banks under the CAMELS model.

Source: Researcher’s own compilation

5.4.2 Alternate hypotheses decisions Table 5.19 stipulates the decisions made to reject or accept the alternative hypotheses stated in Chapter One. According to the study to test if there was a significant difference in the financial soundness of the selected banks as a measure of the (I) CAMELS model, the alternate hypothesis (Ha ) was rejected. Furthermore, the (II) (III) (IV) (V) (VI) (VII) alternate hypotheses (Ha , Ha , Ha , Ha , Ha and Ha ) were accepted and the (VIII) alternate hypothesis (Ha ) was rejected.

Table 5.19: Alternate hypotheses decisions

Alternate hypotheses Decision

(I) There is a significant difference in the financial soundness of (I) Ha Reject Ha the selected banks as a measure of the CAMELS model.

(II) There is a significant difference in the capital adequacy ratio of (II) Ha Accept Ha the selected banks under the CAMELS model.

(III) There is a significant difference in the asset quality ratio of the (III) Ha Accept Ha selected banks under the CAMELS model.

(IV) There is a significant difference in the cost to income ratio of (IV) Ha Accept Ha the selected banks under the CAMELS model.

(V) There is a significant difference in the return on assets ratio of (V) Ha Accept Ha the selected banks under the CAMELS model.

(VI) There is a significant difference in the return of equity ratio of (VI) Ha Accept Ha the selected banks under the CAMELS model.

(VII) There is a significant difference in the total loans to total (VII) Ha Accept Ha deposits ratio of the selected banks under the CAMELS model.

(VIII) There is a significant difference in the total securities to total (VIII) Ha Reject Ha assets ratio of the selected banks under the CAMELS model.

Source: Researcher’s own compilation

5.5 Summary Chapter Five provided empirical findings of the research conducted. The data collected was first applied to the CAMELS rating system model and then analysed using the statistical tool, analysis of variance (one-way ANOVA). Section 5.2.1 described the results of the application of the CAMELS model to the data for 2015 and 2016. The two years were then aggregated to derive the six component ratings for all the selected banks. The primary objective of the research was to assess the financial soundness of the selected banks in South Africa according to the CAMELS rating system. This was achieved by summing the component ratings of each bank to establish a CAMELS composite rating. The study showed that Capitec was the most financially sound bank amongst the selected banks and was rated 3 or fair, according

to the CAMELS model. African Bank was identified as the least financially sound bank and was rated 4 or marginal, according to the CAMELS model. Due to limitations of the availability of data, there were no relevant financial data for African Bank for the year 2015. This posed its own limitations on the results of the study.

According to the results of the study, it was found that all banks, except Capitec, performed inadequately in respect of the management quality component. Capitec was the only bank to be given a rating of 3 or fair for this component. Sensitivity to market risk also posed a challenge to the study as the study was due to limited available data. However, it was found that all banks achieved a rating of 1 or strong in this component. Table 5.20 shows a summary of the selected banks and the CAMELS composite ratings awarded.

Table 5.20: The CAMELS composite ratings awarded to each selected bank

Bank CAMELS composite rating CAMELS ratings analysis

CB 3 Fair FRB 3 Fair SB 3 Fair NED 3 Fair VBS 3 Fair ABSA 3 Fair ABL 4 Marginal

Source: Researcher’s compilation

The results of the CAMELS model were then analysed and interpreted using the statistical tool, analysis of variance (one-way ANOVA). The application of this tool was used to test the hypotheses stated in Chapter One. It was found that there was no significant difference in the financial soundness of the selected banks as a measure of the CAMELS model, which was a secondary objective of the study. Therefore, the (I) null hypothesis (H0 ) was accepted. This means each financial ratio was subject to hypotheses testing to test the significant differences in the ratios of the selected banks.

The decisions made to reject or accept each hypothesis is defined in section 5.4. This chapter fulfils the primary objective of the study, namely, to assess the financial soundness of selected banks in South Africa by applying the CAMELS rating system model. Secondary objectives number 1, 2 and 3 were also achieved in this Chapter. Chapter Six will provide recommendations and comprises conclusion to the study.

CHAPTER SIX CONCLUSION AND RECOMMENDATIONS 6.1 Introduction As stated in Chapter One, the primary objective of this study was to investigate the financial soundness of selected banks in South Africa using the CAMELS rating system approach. The cost of bank failure can be high and has the potential to cause severe instabilities in a country’s financial system (Basu 2003). This study considered seven banks in South Africa with an aim to fulfil the primary and secondary objectives stated in Section 1.4 of Chapter One. It must be noted that for the CAMELS rating system to provide accurate and conclusive findings, the model requires the most recent and complete set of financial data. With the recent and proper data available, all the financial ratios of the model could be assessed and provide a more comprehensive view of the financial soundness of a bank. A supervisor would then be able to focus attention on the areas of the bank that require formal or informal supervision. Furthermore, supervisors and senior management would be able to be pro-active in avoiding bank failure or substantial losses. South Africa has experienced several bank failures since 1994, which have had significant systematic impact on the domestic economy.

The study evaluated the financial soundness of selected banks in South Africa and found almost all banks to be rated 3 or fair. African Bank was the only bank to be awarded a rating 4 or marginal. Although most of the selected banks were rated 3, the results showed that almost all banks displayed critical weaknesses in the management quality and liquidity components, which was a secondary objective of the study.

6.2 Conclusion In Chapter One, the hypotheses of the study were established, and the results were presented in Chapter Five. The first hypothesis (hypothesis I) tested whether there was a significant difference in the financial soundness of the selected banks as a measure of the CAMELS model. The remaining hypotheses of the study were to examine if there was (or was not) a significant difference in each component of the selected banks under the CAMELS model. According to the findings of the study, to test whether there was no significant difference in the financial soundness of the

(I) selected banks as a measure of the CAMELS model, the null hypothesis (H0 ) was (VIII) accepted. Furthermore, the null hypotheses (H0 ) was accepted and the null (II) (III) (IV) (V) (VI) (VII) hypotheses (H0 , H0 , H0 , H0 , H0 and H0 ) were rejected. To test whether there was a significant difference in the financial soundness of the selected banks, the (I) (II) alternate hypothesis (Ha ) was rejected. Furthermore, the alternate hypotheses (Ha , (III) (IV) (V) (VI) (VII) (VIII) Ha , Ha , Ha , Ha and Ha ) were accepted and the alternate hypothesis (Ha ) was rejected.

The empirical findings in Chapter Five showed that almost all selected banks, expect African Bank, were rated a composite rating of 3 or fair, which was the primary objective of the study. A study done by Desta (2016), applied the CAMEL model to “The Best African Banks” for three consecutive years (2012, 2013, and 2014). Amongst the banks selected for this study, were included FirstRand Bank and Standard Bank. The results of the two banks showed similarities in the findings obtained from this study as FirstRand Bank and Standard Bank were also awarded a composite rating of 3 or fair.

The only bank to be rated a composite rating of 4 or marginal was African Bank. African Bank experienced significant losses at the time and was thereafter placed under curatorship (2014). The bank was subsequently restructured to avoid liquidation. The significance of the bank’s failure was that it showed a loss of R4,5 billion for the financial year 2013 and the market had lost confidence in the bank. The results (2018) still show signs of serious weaknesses and call for intensive supervision.

Although most of the banks were awarded a rating of 3, Capitec was evaluated to be the most financially sound bank amongst the selected banks. This was attributable to three very strong components, namely: capital adequacy, earning ability, and sensitivity to market risk. Furthermore, Capitec was the only bank that scored a rating 3 for management quality. All other selected banks achieved a rating 5 and display a high concern for supervision over this component of the banks. Capitec has strategically placed itself in the marketplace, providing affordable products and targeting the lower income market. The bank has also benefited from successfully growing its client base and in turn, has seen rapid growth year-on-year in total assets.

The negative attention drawn to Capitec proved to be fruitless in Viceroy’s attempt to bring about failure in the bank. It was identified that the statement made by Viceroy was subjective and false. Capitec continued to grow its client and asset base and the results have shown that the Bank was the most financially sound amongst the selected banks of the study. The results further show that Capitec Bank has a satisfactory quality of management, which implies that the bank is searching and recruiting skilled employees with correct internal policies in place to achieve a better CAMELS component and composite rating score compared to its competitors.

As the study focused on financial data for the years 2015 and 2016, the results have indicated critical weaknesses in VBS Mutual Bank. The results reflected that the bank’s capital adequacy, asset quality and sensitivity to market risk components, all showed above adequate interpretations. However, and importantly, attention must be drawn to the poor ratings of the management quality, liquidity, and earnings ability components. If these components were improved and supervised effectively and efficiently the bank could have avoided curatorship failure, which is now (2018) a reality. Investigators have identified critical weaknesses in the bank and further investigations are being conducted. The empirical findings show that the CAMELS model is not readily used as an early warning system for bank failure, on its own. However, the model adds constructive insights into a bank if used in conjunction with other early warning systems and bank evaluation models. This was revealed in a study by Hyz and Gikas (2015), namely, that the application of the CAMELS model would be able to assist regulators in designing and improving early warning systems in the banking sector. The study performed by Dang (2016) agreed that evaluating ratings assist to identify whether banks need further extensive supervisory attention before bank failure can occur.

Gondesi (2016) further argued that the strength and performance of banks cannot only be judged on the evaluation of financial statements, but rather on a more holistic assessment considering various other contributing factors. A holistic assessment will include the analysis of financial ratios, effective and regular on-site and off-site inspections, and monitoring comprehensive early warning models (Gondesi 2016).

Moreover, the CAMELS model fails to incorporate or consider key economic indicators, such as gross domestic product, income per capita, unemployment, inflation, and exchange rates.

6.3 Recommendations Banks in South Africa have experienced several failures since 1994. These bank failures can cause severe systemic risks, which negatively impact the national economy, financial system and market and business confidence in the country. This highlights the importance of mitigating bank risks, as well as bank failure, to ensures a sound and stable banking system.

One of the main concerns derived from the findings, is the weak level of management quality exhibited by almost all the selected banks, with Capitec being an exception. Although, management quality may be argued that it is a more qualitative component, the interpretation in this study was based on the CAMELS model framework. The study by Desta (2016) also raised concern for the management quality of Standard Bank and FirstRand Bank, which formed part of the research. This implies that management quality in the selected banks is a prolonged issue and calls for urgent supervisory attention.

Firstly, the selected banks will need to re-evaluate the quality of candidates being considered for management. Furthermore, they will need to change internal policies to obtain and retain high quality management. This could be done by having stricter requirements for management, including higher levels of education and experience. Once a superior quality management is in charge, it is important that the bank adequately position internal measures to ensure that highly efficient and effective management are retained.

Secondly, the banks’ internal policies need to align, not only with the company’s business targets, but also the contentment and fulfilment of employees and managers. This should reduce conditions of frictional unemployment in the banking sector. It is therefore, recommended that banks invest and grow their human resource departments with the specific aim to attract and retain managers with a supervisor

level of quality and integrity. Managers and employees should also attend routine seminars that provide up-to-date information on the latest changes in regulation, technology and innovation in the banking sector.

Thirdly, senior management need to work together with external supervisors on a routine basis regarding the analysis of financial statements and other outcomes pertaining to on-site and off-site inspections. It would be advantageous for management to be kept abreast of any information that could have a major negative or positive impact on the bank. Management can develop tools and incorporate models, such as the CAMELS model, to continuously analyse the financial soundness of the bank. Moreover, management must avoid any component being rated a 4 or worse. As the implication has the potential to threaten the viability of the bank.

Fourthly, the other component that performed inadequately was liquidity, as all the selected banks were allocated a rating of 5. Nyandoro (2010) explained that the banks in South Africa have increased unsecured funding to a highly indebted population. Nyandoro (2010) further posited that although the Basel III Accord has set liquidity standards to guide banks in liquidity risk management, banks need to re-evaluate its position in the market place. This is one of the components of the CAMELS model that leaves several questions unanswered as it fails to incorporate imperative macroeconomic factors, such as, inflation rate, household debt and income per capita. For this reason, the CAMELS model requires supporting macro-economic models to provide a more complete depiction of this component. It is recommended that the selected banks work closely with regulators and supervisors to implement sound liquidity management practices.

Furthermore, due to the inadequate ratings of all selected banks, it is imperative that formal supervision is implemented immediately. The FDIC (2014) stated that the rating awarded for this component exposes levels of liquidity and funds practice management that are critically poor, and the viability of the financial institution is under threat.

Finally, while the CAMELS model isn’t perfect, it proves to be valuable in its simplicity and reliability. As stated previously, the model is quantifiable, which allows the results to be analysed, interpreted within its framework and conclusive. The model clearly depicts the financial soundness of a bank that can be compared to other competitive banks within a country. For this reason, the model would be easily understandable not only for supervisors and senior management, but also for investors, stakeholders, the general population and customers. It is therefore recommended that the SARB, using the CAMELS model, publish a detailed annual report including the analysis, for public scrutiny. A report of this nature would improve competition among the banks and provide useful information annually regarding the state of the financial soundness of all banks in South Africa. This can be further analysed to evaluate the financial soundness of the banking sector of South Africa.

6.4 Limitations of the research This research encountered several limitations, including:

• The data collected was based on secondary data (annual financial reports, journals, websites, and existing dissertations). Therefore, limitations of secondary data apply to the research and analysis performed;

• The data presently available (2018) for this study confined the scope of the CAMELS rating system model, which could have been more broadly adapted. The study was therefore limited to partial consideration of all financial ratios and

indicators of the model;

• Due to monetary constraints, access to specific items of financial data was limited, which imposed limitations to the sample size and time-period applied to the analysis, and

• Generalisation of this study to the banking sector of South Africa is not suitable.

6.5 Scope for further research This study was subject to its own limitations. Therefore, with a broader scope of data for a larger period, more conclusive findings could be possible. For further research, it would be more accurate and convincing to have all relevant data for all financial ratios

of the CAMELS model. Furthermore, it would be of greater significance to conduct the analysis over a longer period to evaluate growth of the banks; outcomes of supervision; and outcomes during supervision. The major problem encountered in the research was finding relevant financial data for more than two consecutive years.

This study is not a reflection of the entire banking sector of South Africa. Further research could apply the CAMELS model to all banks of South Africa to deduct the financial soundness of the country’s banking sector. The importance of a country’s banking sector has been stressed in Chapters 1 and 2 and requires further research to avoid and mitigate risks and weaknesses in the banks.

Another aspect to consider is to compare the model to other similar models, namely, the EAGLES model and Bankometer model. These various models evaluate different aspects of a bank. Further research could possibly investigate the comparisons of the models and conduct further evaluations of the banks in South Africa.

Lastly, there is a lack of studies that employ these models to analyse banks in South Africa. Therefore, the financial soundness of the country’s banking sector requires further research to stimulate ideas and solutions to contribute to severe improvement.

REFERENCES

ABSA. 2017. Absa Bank Limited. [Online] Available at: https://www.absa.co. za/cont ent/dam/south-africa/absa/pdf/reports/2016/annual-consolidated-and-separate- financial-statements-31-december-2016.pdf [Accessed 2018 August 2018].

African Bank. 2016. African Bank. [Online] Available at: https://www.africanbank.co. za/en/home/about-us-our-company [Accessed 9 July 2018].

Ahsan, M.K. 2016. Measuring financial performance based on CAMEL: A study on selected Islamic Banks in Bangaladesh. Asian Business Review, 6(13): 47-56.

Anderhuber, F. 2016. Basel III Favours some regions financing solution over others. [Online] Available at: https://globalriskinsights.com/2016/08/basel-iii-favours- some-regions-plans/ [Accessed 17 October 2018].

Aspal, P.K. & Dhawan, S. 2016. CAMELS Rating Model for evaluating financial performance of banking sector: A theoretical perspective. International Journal of System Modeling and Simulation, 1(3):10-15.

Aspal, P.K. & Nazneen, A. 2014. An empirical analysis of capital adequacy in the Indian Private Sector Banks. American Journal of Research Communication, 2(11): 28-42.

Babar, H.Z. & Zeb, G. 2011. CAMELS Rating System for the banking industry in Pakistan. [Online] Available at: https://www.diva-portal.org/smash/getdiva2: 448378/ fulltext01.pdf [Accessed 19 March 2018].

Bank for International Settlements. 2017. High-level summary of Basel III reforms. [Online] Available at: https://www.bis.org/bcbs/publ/d424_hlsummary.pdf [Ac- cessed 31 August 2018].

Bank for International Settlements 2018. Structural Changes in anking after the crisis. [Online] Available at: https://www.bis.org/publ/cgfs60.pdf [Accessed 20 October 2018].

Baral, K.J. 2005. Health check-up of commercial banks in the framework of CAMEL: A case study of joint venture banks in Nepal. The Journal of Nepalese Business Studies, 2(1): 41-55.

Barclays, 2017. Barclays Africa Corporate Strategy. [Online] Available at: https:// www. barclaysafrica.com/about-us/our-strategy/ [Accessed 10 July 2018].

Barclays. 2017. Financial Results. [Online] Available at: https://www.absa.africa /content/dam/africa/absaafrica/pdf/results/annual/2016-full-year-results-booklet. pdf [Accessed 22 August 2018].

Barr, R.S., Killgo, K.A. & Zimmel, S. 1999. Evaluating the productive efficiency and performance of U.S. Commercial Banks. [Online] Available at: https://www.re searchgate.net/publication/5030104 [Accessed 21 March 2018].

Barr, R.S. & Siems, T.F. 1996. Bank failure prediction using DEA to measure management quality. [Online] Available at: https://s3.amazonaws.com/acade mia.edu.documents/41220840/Bank_Failure_Prediction_Using_DEA_to_Mea2 0160113-22193-12mxc5g.pdf20160115-19908-17vy48g.pdf?AWSAccessKeyId =AKIAIWOWYYGZ2Y53UL3A&Expires=1521805438&Signature=DrELd5WdG WFQKf0ZLf4rHqOJ 2OI%3D&response- [Accessed 23 March 2018].

Bastan, M., Mazrae, M.B. & Ahmadvand, A.M. 2016. Dynamics of Banking Soundness Based on CAMELS Rating System. [Online] Available at: https://www.researchgate.net/publication/308400698_Dynamics_of_banking_ soundness_based_on_CAMELS_rating_system [Accessed 10 July 2018]

Basu, S. 2003. Why do Banks Fail?. International Review of Applied Economics, 17(3): 231-248.

Batra, S. 2014. Credit Research Initiative. [Online] Available at: https://rmicri.org/med ia/static/images/thumbnail-pdf/WCBAUG05AUG112014.pdf [Accessed 3 July 2018].

BIS, 2018. History of the Basel Committee. [Online] Available at: https://www.bis.org /bcbs/history.htm [Accessed 25 October 2018].

Botha, E. & Makina, D. 2011. Financial regulation and supervision: Theory and practice in South Africa. International Business & Economics Research Journal, 10(11): 27-36.

Business Report. 2018. All the South Arican Banks that have failed in the last 30 years. [Online] Available at: https://businesstech.co.za/news/banking/231009/all-the- south-african-banks-that-have-failed-in-the-past-30-years/ [Accessed 4 Septem- ber 2018].

Cameron, B. 2014. African Bank Rescue Plan. [Online] Available at: https://www. news 24.com/MyNews24/African-Bank-Rescue-Plan-20140831 [Accessed 25 Octo- ber 2018].

Capitec Bank Holdings Limited. 2013. Our Strategy and Leadership. [Online] Available at: https://www.capitecbank.co.za/downloads/08.Our_strategy_and_ leadership.pdf [Accessed 10 July 2018].

Capitec Bank Holdings Limited 2018. Capitec Bank Stands Steadfast with Strong Brand and Continued Growth. [Online] Available at: https://www.capitec bank. co.za/ media-centre/news-article/696 [Accessed 14 August 2018].

Christopoulos, A.G., Mylonakis, J. & Diktapanidis, P. 2011. Could Lehman Brothers' collapse be anticipated? An examination using CAMELS Rating System. International Business Research, 4(2): 11-19.

Čihák, M. 2004. Stress Testing: A Review of Key Concepts. [Online] Available at: http://www.cnb.cz/en/research/research_publications/irpn/download/irpn_2_200 4.pdf [Accessed 19 April 2018].

Dang, U. 2011. The CAMEL Rating System in Banking Supervision: A Case Study. [Online] Available at: https://www.theseus.fi/bitstream/handle/ 10024/38344/ Dang_Uyen.pdf [Accessed 13 March 2018].

Dash, M. & Das, A. 2010. A CAMELS Analysis of the Indian Banking Industry. [Online] Available at: https://poseidon01.ssrn.com/delivery.php?ID=50900510011809 701402711209900409410106201108407607006910510408210112011007208 900410400112606004110905609607507110001409008100202902207509306

010001200300306409507110605502601308510008306700301710701410006 90290041 [Accessed 14 March 2018].

Desta, T.S. 2016. Financial performance of "The Best African Banks": A compara-tive analysis through CAMEL rating. Journal of Accounting and Management, 6(1): 1-20.

Deutsche Bank. 2018. What is Basel IV?. [Online] Available at: https://www.db.com/ newsroom_news/2018/what-is-basel-iv-en-11456.htm [Accessed 11 September 2018].

Effinger, C. 2017. The CAMELS' Broken Back. [Online] Available at: 2018 [Accessed April 19 2018].

Federal Deposit Insurance Corporation 2014. Uniform Financial Institutions Rating System. [Online] Available at: https://www.fdic.gov/regulations/laws/rules/5000- 900. html [Accessed 15 July 2018].

FirstRand 2013. Strategy and business philosophy. [Online] Available at: https:// www. firstrand.co.za/AboutUs/Pages/strategy-and-business-philosophy.aspx [Access- ed 10 July 2018].

Foggitt, G. M., Heymans, A., van Vuuren, G. W. & Pretorius, A. 2017. Measuring the Systemic Risk in the South African Banking Sector. South African Journal of Econo-mic and Management Sciences, 20(1).

Gilbert, R.A., Meyer, A.P., Vaughan, M.D. 2002 Could a CAMELS Downgrade Model Improve Off-Site Surveillance? [Online] Available at: https://pdfs.semanticscholar.org/6d29/b134f4ddd6f3ea0fe9ae01f2ba9ccf4fba85 .pdf?_ga=2.76718929.514130907.1550842144-713682302.1550842144 [Accessed 28 April 2018]

Global Credit Rating Co. 2018. GCR Downgrades ABSA Bank Limited's Rating to AA(ZA); Outlook Stable. [Online] Available at: https://globalratings.net/news/art icle/gcr-downgrades-absa-bank-limiteds-rating-to-aaza-outlook-stable [Access- ed 7 No-vember 2018].

Gondesi, S.K. 2016. Shodhganga: A Reservoir of Indian Theses @ INFLIBNET. [Online] Available at: http://shodhganga.inflibnet.ac.in:8080/jspui/handle/10603/ 186037 [Accessed 31 August 2018].

Hafeman, M. & Randle, T. 2009. On and Offsite. [Online] Available at: http://sitere sources.worldbank.org/FINANCIALSECTOR/Resources/Primer13_On_and_Off site_Inspections.pdf [Accessed 13 March 2018].

Hyz, A. & Gikas, G. 2015. CAMELS and Greek Banking Sector Performance during the Crisis - An Analysis and Review of the Evidence. [Online] Available at: https://www.researchgate.net/publication/280598256_CAMELS_and_Greek_B anking_Sector_Performance_during_the_crisis_-_an_analysis_and_review_of_ the_evidence [Accessed 21 March 2018].

Ibrahim, M. 2014. A comparative performance of two banks in United Arab Emirates. Research Journal of Finance and Accounting, 5(21): 24-29.

Ifeacho, C. & Ngalawa, H. 2014. Performance of the South African Banking Sector Since 1994. The Journal of Applied Business Research, 30(4): 1183-1196.

Klingelhofer, H.E., Erasmus, L., Teka, M.G. 2016. Improving Financial Oversight of Development Finance Institutions. African Journal of Public Affairs, 9(2): 102- 114.

Kock, S., Schneider, R., Schneider, S. & Schrock, G. 2017. Bringing Basel IV into Focus. [Online] Available at: https://www.mckinsey.com/business-functions/risk/ our-insights/bringing-basel-iv-into-focus [Accessed 25 October 2018].

Kothari, C.R. 2004. Research Methodology: Methods and Techniques. 2nd ed. New Delhi: New Age International (P) Ltd.

KPMG. 2018. Basel 4: The Way Ahead, s.l.: KPMG.

Kumar, M.A., Sri Harsha, G., Anand, S. & Dhruva, N.R. 2012. Analyzing Soundness in Indian Banking: A CAMEL Approach. Research Journal of Managament Sciences, 1(3): 9-14.

Liu, G. & Molise, T. 2018. Is Basel III Counter-Cyclical? The Case of South Africa. [Online] Available at: https://www.ekon.sun.ac.za/wpapers/2018/wp102018/ wp102018.pdf [Accessed 25 October 2018].

Makhubela, S. 2006. Causes of bank failure in the Post Democratic South Africa. [Online] Available at: https://researchspace.ukzn.ac.za/xmlui/handle/10413/ 1295 [Accessed 6 September 2018].

Manson, H. 2012. Capitec's 6 P's of Marketing. [Online] Available at: http://www.ma rklives.com/2012/04/capitecs-6-ps-of-marketing/ [Accessed 28 October 2018].

Mare, E., Sanderson, L.B. & de Jongh, D.C. 2017. Banking Regulations: An Exami- nation of the Failure of African Bank using Merton's Structural Model. South African Journal of Science, 113(7/8): 1-7.

Mashamba, T. & Kwenda, F. 2017. A Look at the liquidity management practices of banks in South Africa. Journal of Economics and Behavioural Studies, 9(3): 113- 120.

Mchunu, S., 2018. S&P Global: Outlook on all SA Banks is Stable. [Online] Available at: https://www.iol.co.za/business-report/companies/s-and-p-global-outlook-on- all-sa-banks-is-stable-13104695 [Accessed 30 August 2018].

Meiring, I. 2012. Banking Regulation. [Online] Available at: https://www.werksmans. com/wp-content/uploads/2013/04/safinal.pdf

Mishi, S. 2015. Dynamic interactions between stock markets and banks in South Africa: testing for market disciplining of banks. [Online] Available at: http://libd space.ufh.ac.za/bitstream/handle/20.500.11837/958/S%20MISHI%20DCOM%2 0ECONOMICS%20THESIS.pdf?sequence=1&isAllowed=y [Accessed 6 July 2018].

Momoniat, I., Havemann, R. & National Treasury, 2014. Update on the Curatorship of African Bank Ltd. [Online] Available at: http://pmg-assets.s3-website-eu-west-1.a mazonaws.com/150310africanbank.pdf [Accessed 29 October 2018].

Muralidhara, P. & Lingam, C. 2017. CAMEL Model as an effective measure of financial performance of Nationalised Banks. Internation Journal of Pure and Applied Mathematics, 117(7): 255-262.

Myburgh, J.F. 2016. African Bank and the Myburgh Report: SARB Briefing. [Online] Available at: https://pmg.org.za/committee-meeting/22601/ [Accessed 14 July 2018].

Myburgh, J.F. 2016. Release of Myburgh Report on African Bank Limited. [Online] Available at: https://www.resbank.co.za/Publications/Detail-Item-View/Pages/Pu blications.aspx?sarbweb=3b6aa07d-92ab-441f-b7bf-bb7dfb1bedb4&sarblist=2 1b5222e-7125-4e55-bb65-56fd3333371e&sarbitem=7288 [Accessed 14 July 2018].

National Treasury. 2018. National Treasury's Views on Viceroy's Capitec Report. [Online] Available at: https://www.capitecbank.co.za/resources/National_Treas ury_Media_Statement_-_Viceroys_Capitec_Report.pdf [Accessed 14 August 2018].

Nedbank Group 2018. Integrated report for the year ended 31 December 2017, Johannesburg: Nebank Group.

Nkosi, B. 2018. VBS Looted in R1.5bn Epic Scam. [Online] Available at: https://www. iol.co.za/news/south-africa/gauteng/vbs-looted-in-r15bn-epic-scam-15970489 [Accessed 14 August 2018].

Nyandoro, E.M. 2010. Bank Liquidity: Basel III and South Africa's Banking Industry. [Online] Available at: http://www.gaaaccounting.com/bank-liquidity-basel-iii-and- south-africas-banking-industry/ [Accessed 13 November 2018].

Okeahalam, C., 1998. The Political Economy of Bank Failure and Supervision in the Republic of South Africa. [Online] Available at: http://pdfproc.lib.msu.edu/?file= /DMC/ African%20Journals/pdfs/political%20science/volume3n2/ajps00300 2004.pdf [Ac-cessed 5 September 2018].

Pagano, R.P. 2009. Understanding statistics in the behavioral sciences. 9th ed. Wadsworth: Cengage Learning.

Patel, B. 2017. Best Practices for Stress Testing in Banks. [Online] Available at: http://www.bonova.net/best-practices-for-stress-testing-in-banks/ [Accessed 19 April 2018].

Peacock, B. 2016. African Bank The House that Debt Built. [Online] Available at: https://www.pressreader.com/south-africa/sunday-times/20160515/2822867294 88839 [Accessed 25 October 2018].

PwC. 2017. Balancing Resilience and Growth. [Online] Available at: https://www. pwc.co.za/en/assets/pdf/major-banks-analysis-sept-2017.pdf [Accessed 18 Oc- tober 2018].

PwC. 2017. 'Basel IV': Big Bang - or the Endgame of Basel III?, s.l.: PwC.

Rabana, P. 2016. The Myburgh Report regarding African Bank's Demise: Key Learn- ings in a Governance Context. [Online] Available at: http://www.perpetuaim.co. za/2016/09/28/myburgh-report-regarding-african-banks-demise-key-learnings- governance-context/ [Accessed 25 October 2018].

Raiyani, J.R. 2010. Effect of mergers on efficiency and productivity of Indian banks: A CAMELS. Asian Journal of Management Research.

Roman, A. & Sargu, A.C. 2013. Analysing the Financial Soundness of the Com- mercial Banks in Romania: An Approach Based on the CAMELS Framework. [Online] Available at: https://www.sciencedirect.com/science/article/pii/ S22125 67113001925 [Accessed 21 March 2018].

Rostami, M. 2015. CAMELS' analysis in Banking Industry. Global Journal of Engin- eering Science and Research Management, pp.10-26.

Roy, D., Kohli, B. & Khatkale, S. 2013. Basel I to Basel II to Basel III: A Risk Man- agement Journey of Indian Banks. AIMA Journal of Management & Research, 7(2/4).

Rozzani, N. & Rahman, R.A. 2013. CAMELS and Performance Evaluation of Banks in Malaysia: Conventional Versus Islamic. Journal of Islamic Finance and Business Research, September, 2(1):36-45.

Sadien, E., 2017. The Impact of the change from Basel II to Basel III on the Profit- ability of the South African Banking Sector. [Online] Available at: https://open. uct.ac.za/handle/11427/27387 [Accessed 25 October 2018].

Sahajwala, R. & Van den Bergh, P. 2000. Supervisory Risk Assessment and Early Warning System. [Online] Available at: https://www.bis.org/publ/bcbs_wp04.pdf [Accessed 24 October 2018].

SARB. 2016. Bank Supervision Department Annual Report 2016, Unknown: SARB.

SARB. 2018. Bank Supervision Department Annual Report 2017, Unknown: SARB.

SARB. 2018. SARB Commissions Forensic Investigation into VBS Mutual Bank. [On- line] Available at: https://www.resbank.co.za/Lists/News%20and%20Publication s/Attachments/84 03/SARB%20commissions%20forensic%20investigation%20 into%20VBS%20Mutual%20Bank.pdf [Accessed 14 August 2018].

Sarwar, A. & Asif, S. 2011. Safety & Soundness of the Financial Sector of Pakistan. Interdisciplinary Journal of Contemporary Research in Business, 3(2): 624-630.

Schuermann, T. 2014. Stress testing banks. International Journal of Forecasting, 30(3): 717-728.

Shakdwipee, P. & Mehta, M. 2017. From Basel I to Basel II to Basel III. International Journal of New Technology and Research, 3(1): 66-70.

Standard and Poor's. 2017. South Africa-Based Capitec Bank Ltd. 'BB+/B' and 'zaAA/zaA-1' Ratings Affirmed; Outlook Negative. [Online] Available at: https: //www.capitecbank.co.za/resources/standard_and_poors_credit_opinion_sept_ 2017.pdf [Accessed 7 November 2018].

Standard Bank, 2015. Our Strategy. [Online] Available at: http://www.standardbank. com/pages/StandardBankGroup/web/Strategy.html [Accessed 10 July 2018].

The Banker, 2018. The Banker Database. [Online] Available at: http://www.thebank er com/Information/Services/The-Banker-Database [Accessed 19 May 2018].

The Banking Policy Department. 2013. New International Standards on Effective Resolution Regimes for Financial Institutions. [Online] Available at: https://www .hkma.gov.hk/media/eng/publication-and-research/quarterly-lletin/qb201309/fa1 .pdf [Accessed 29 October 2018].

The Economist 2018. South Africa: Banking Sector Risk. [Online] Available at: http: //www.eiu.com/industry/article/116578195/south-africa-banking-sector-risk/20 18-03-13 [Accessed 18 October 2018].

Thompson, W. 2018. Explosive Report Lifts Lid on 'Great VBS Bank Heist'. [Online] Available at: https://www.heraldlive.co.za/news/2018-10-11-explosive-report- lifts-lid-on-great-vbs-bank-heist/ [Accessed 25 October 2018].

Thun, C. 2013. Common Challenges and Pitfalls in Stress Testing of Banks. [Online] Available at: https://www.moodysanalytics.com/-/media/whitepaper/2013/2013- 20-02-common-challenges-and-pitfalls-in-stress-testing-of-banks.pdf [Accessed 19 April 2018].

UCT, 2017. New Ranking shows which SA Banks Contribute Most to Systemic Risk. [Online] Available at: https://www.news.uct.ac.za/article/-2017-03-09-new-rank ing-shows-which-sa-banks-contribute-most-to-systemic-risk [Accessed October 2018]. van Wyk, R. 2016. African Bank Report no longer Private & Confidential - SARB. [On line] Available at: http://www.politicsweb.co.za/opinion/report-of-the-commission ers-investigation-into-afr [Accessed 25 October 2018].

VBS Mutual Bank, 2018. About VBS Mutual Bank. [Online] Available at: https://www. vbsmutualbank.co.za/about-vbs [Accessed 10 July 2018].

Viceroy Research Group. 2018. Capitec: A Wolf in Sheep's Clothing. [Online] Available at: https://viceroyresearch.files.wordpress.com/2018/01/capitec-30- jan-2017.pdf [Accessed 14 August 2018].

Wachira, G. D. 2010. Relationship between Financial Performance and CAMEL Rat- ing of Commercial Banks in Kenya. [Online] Available at: http://erepository.uon bi.ac.ke/bitstream/handle/11295/13016/Wachira%20David%20Githinji_Relation

ship%20between%20financial%20performance%20and%20camel%20rating%2 0of%20Commercial%20Banks%20in%20Kenya.pdf?sequence=3 [Accessed 25 October 2018].