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Commercial Bank Profitability

in a Negative Interest Rate

Environment

A study on the relationship between negative interest rates and

commercial bank profitability in Denmark

BACHELOR DEGREE PROJECT THESIS WITHIN: Business Administration NUMBER OF CREDITS: 15 ECTS

PROGRAMME OF STUDY: International Management AUTHOR: Albarbari, Mohammed Imad & Kipper, Lukas JÖNKÖPING May 2020

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Acknowledgements

We would like to take this opportunity to extend our sincere gratitude to everyone who contributed to the making of this paper.

Our first acknowledgement is directed towards Oskar Eng, our brilliant tutor, who provided us with great expertise, support, and words of advice in the process of writing the thesis.

Our second acknowledgement is directed towards Felix Stamm and Nicolas Rix for taking the time to contribute with valuable feedback and constructive criticism.

We would further like to give a special thank you to our families and friends for all the support.

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Bachelor Thesis in Business Administration

Title: Commercial Bank Profitability In A Negative Interest Rate Environment Authors: Albarbari, Mohammed Imad and Kipper, Lukas

Tutor: Oskar Eng Date: 2020-05-18

Key terms: “Bank profitability”, “Negative interest rates”, “Danish Commercial Banks”, “Monetary Policy”, “Return on Average Assets”, “Net Interest Margin”.

Abstract

Background: Denmark, along with other European countries, has decided to cut its policy

interest rate into negative territory to meet macroeconomic objectives. This has historically been thought of as impossible and impacts commercial banks significantly. As a consequence, concerns have been raised about commercial bank profitability, which is a primary indicator of the banking industry’s soundness.

Purpose: The purpose of this thesis is to investigate the relationship between persistently

negative interest rates and commercial bank profitability in Denmark, covering an extended timeframe (2011 – 2018, 165 bank years, 21 commercial banks).

Method: Bank profitability is measured using the Return on Average Assets (ROAA) and the

Net Interest Margin (NIM). The thesis follows a simple form of mixed-methods approach – quantitatively focused, followed by a supplementary qualitative study. For the quantitative part, data is collected through the Orbis database, which provides global company data. We utilized a Fixed Effects Model with strongly balanced panel data, covering 59% of the Danish banking industry’s assets. Semi-structured interviews were then conducted with professionals working in the industry to interpret the quantitative findings.

Conclusion: The findings of this study show that in the time period observed:

1. Interest rates are not correlated with the NIM;

2. The duration of consecutive negative interest rates (in years) is negatively correlated with the NIM;

3. Interest rates are not correlated with the ROAA;

4. The duration of consecutive negative interest rates (in years) is not correlated with the ROAA;

The duration of consecutive negative interest rates seems to be more significant since it takes time for the profitability-reducing effect of negative interest rates to materialize. The ROAA is not impacted by the (years in negative) interest rates, as it is mainly determined by factors under management control.

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Table of Contents

Definitions ... viii

1

Introduction ... 1

1.1 Background ... 1 1.2 Problem ... 2

1.3 Purpose & Research Questions ... 3

2

Literature review ... 4

2.1 Literature search ... 4

2.2 Frame of reference ... 4

2.2.1 The banking industry... 4

2.2.2 Commercial banks and investment banks ... 5

2.2.3 General structure of banking profitability studies ... 5

2.2.4 Measures of commercial bank profitability ... 6

2.2.4.1 Overall Profitability: ROA and ROE ... 7

2.2.4.2 Net Interest Margin (NIM) ... 8

2.2.5 Evolution of banking profitability determinants in the academic literature ... 8

2.2.6 Bank profitability determinants... 9

2.2.6.1 Internal determinants of bank profitability ... 9

2.2.6.1.1 Size ... 9 2.2.6.1.2 Capitalization ... 10 2.2.6.1.3 Credit risk ... 10 2.2.6.1.4 Liquidity ... 11 2.2.6.1.5 Diversification ... 11 2.2.6.1.6 Operational Efficiency ... 12

2.2.6.2 External Determinates of bank profitability ... 12

2.2.6.2.1 GDP Growth/Business Cycle ... 12

2.2.6.2.2 Market concentration ... 12

2.2.6.2.3 Inflation ... 13

2.2.6.2.4 Interest Rates ... 13

2.2.6.2.5 Term Structure ... 14

2.3 Hypothesis development & independent variables ... 16

2.3.1 The profitability of core banking operations ... 16

2.3.2 Overall profitability ... 17

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3.1 Research philosophy ... 19

3.2 Research design ... 20

3.3 Mixed methods ... 21

3.4 Data collection ... 22

3.4.1 Process and sampling for the quantitative data ... 22

3.4.2 Process and sampling for the supplementary (qualitative) data ... 24

3.5 Variable selection ... 25 3.5.1 Dependent variables ... 25 3.5.2 Independent variables... 26 3.5.2.1 InterestRate ... 27 3.5.2.2 ThreeM_IBOR ... 27 3.5.2.3 ThreeM_Yield ... 28 3.5.2.4 YearsInNegInterestRate/YNeg_IBOR ... 29 3.5.3 Control variables ... 29

3.6 Possible outcomes for the independent variables... 31

3.7 Method of quantitative data analysis ... 32

3.7.1 Motivating the statistical model ... 32

3.7.2 Hausman test ... 33

3.7.3 Employed model ... 33

3.8 Method of supplementary (qualitative) data analysis ... 34

3.9 Ethical considerations ... 35

4

Results & Analysis ... 36

4.1 Quantitative results... 36

4.2 Results overview for NIM regressions ... 36

4.3 Results overview for ROAA regressions ... 37

4.4 Robustness tests ... 37

4.5 Analysis of the results ... 40

4.5.1 Interest rates and the NIM ... 40

4.5.2 Other factors influencing the NIM ... 44

4.5.3 Interest rates and the ROAA ... 45

4.5.4 Other factors influencing the ROAA ... 46

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6

Discussion ... 50

6.1 Implications for bank management ... 50

6.2 Limitations and further research ... 51

7

Reference list ... 53

8

Appendices ... 65

8.1 Definitions ... 65

8.2 Calculation of sample coverage ... 66

8.3 Description of supplementary sampling procedure... 66

8.4 Appendix interview questions ... 67

8.5 Appendix statistical models ... 68

8.5.1 Generalized Method of Moments Estimators (GMM) ... 68

8.5.2 Population-Averaged Ordinary Least Squares models (PA-OLS)... 68

8.6 Hausman tests ... 69

8.7 Multicollinearity tests... 71

8.8 Descriptive statistics... 75

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Figures

Figure 1: Denmark’s certificate of deposit rate over time. ... 1

Figure 2: The nine mixed methods designs. ... 22

Figure 3: Denmark's certificate of deposit rate over time. ... 23

Figure 4: Visualization of interest rates over time. ... 28

Figure 5: Possible outcomes for profitability of core banking operations. ... 31

Figure 6: Possible outcomes for overall bank profitability. ... 31

Figure 7: Development of the NIM in a prolonged negative rate environment. ... 43

Figure 8: NIM over time. ... 44

Figure 9: Credit Risk over time ... 47

Figure 10: Liquidity over time. ... 47

Figure 11: Efficiency over time. ... 48

Figure 12: Diversification over time ... 51

Tables

Table 1: A summary of variables commonly affecting bank profitability. ... 15

Table 2: Dependent & Independent Variables and corresponding hypotheses ... 18

Table 3: Dependent variables and hypotheses... 26

Table 4: The six models and their corresponding interest rate proxy and profitability metrics. ... 27

Table 5: A summary of the variables selected in the thesis. ... 30

Table 6: Model Specifications. ... 34

Table 7: Regression Results ... 39

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Definitions

___________________________________________________________________________ This section is a supplementary to the main section of the thesis and is taken from Appendix 8.1. We recommend having it ready while reading the thesis since it gives an overview of the most important concepts defined throughout the paper.

Denmark’s Policy Interest Rate

The interest rate banks receive on deposits and pay for borrowing at the central bank of Denmark (Nationalbanken). Denmark’s central bank can set this rate higher or lower to meet its goals. This rate moves the interest paid and received by Commercial Banks. A lower policy interest rate will usually result in cheaper loans (e.g. mortgages) and lower interest rates on saving accounts. The policy interest rate is a powerful tool to stimulate (or cool down) a country’s economy.

Core Banking Operations

In the context of this thesis, core banking operations are defined as accepting deposits (on which interest is traditionally paid) and lending out a multiple of these deposits for receiving interest.

Net Interest Margin (NIM)

Usually, the interest commercial banks pay on deposits is lower than the interest they receive on loans. The difference between interest paid and interest received is the Net Interest Margin. In other words, it is the profit margin of a commercial banks’ Core Banking Operations. Commercial

Banks

”A bank with branches in many different places that offers services to people and businesses, for example, keeping money in accounts and lending money” (Cambridge University Press, 2020b)

Put another way, commercial banks generally focus on Core Banking

Operations. However, commercial banks may also engage in other

business activities, like financial guarantees and derivative sales/trading (A. N. Berger & Bouwman, 2015). Commercial banks should be differentiated from investment banks, which generally focus on helping companies to generate funding (e.g. issuing stocks and bonds) and company mergers & acquisitions (Stowell, 2010).

”Passing on Negative Rates”

Means commercial banks pass on the negative policy rate to their customers to retain profitability. One would then likely have to pay to deposit money at a bank.

Proxy Variable „A variable used instead of the variable of interest when that variable of interest cannot be measured directly“ (Oxford Reference, 2020).

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1 Introduction

______________________________________________________________________

In this chapter, the reader is firstly introduced to the topic of negative interest rates and its possible effects on bank profitability. The importance of investigating this topic from a business administration perspective is also highlighted. Secondly, a gap in the literature is identified regarding the factors affecting bank profitability. Lastly, the purpose and research questions of this paper are presented.

1.1 Background

The last decade has spawned a unique climate for European banks. The shifts in monetary policy we have seen from various central banks can be described as unusual at best and as extreme at worst. The central banks of countries such as Denmark, Sweden, Switzerland and even the European Central Bank all decided to cut their interest rates to near-zero levels and, eventually, into the negative territory (see Figure 1 for Denmark). Reasons for this measure include meeting growth, inflation or exchange rate targets.

The policy interest rate is a part of a central bank’s monetary policy tools. It represents the cost of borrowing as a proportion of the amount of money that is lent, deposited, or borrowed (Faure, 2014).

Simplified, a negative interests rate means, that if an individual or institution borrows money, they pay back less than what they borrowed. Negative policy interest rates have been thought of as impossible by researchers for a long time – economists believed in the existence of the so-called ‘zero lower bound’ which refers to the lowest possible level (zero) that interest rate can drop to (Buiter, 2009).

A group of firms primarily affected by central bank policy is commercial banks. They make most of their money by lending out money at a higher rate than they pay on deposits. Due to

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the banks’ potential unwillingness to pass on negative rates, such monetary policy has caused concerns about its impact on commercial bank profitability, which is a primary indicator of the banking industry’s soundness (Bikker & Vervliet, 2018). Negative rates are unseen in history, and since they break through the zero-lower bound, the impacts are unknown.

Currently, it seems like the negative interest rate climate is rather permanent, making the concerns outlined even more valid. Due to the Coronavirus pandemic, many experts now further believe we are headed for a strong recession (Goodman, 2020). The usual monetary policy move in a recession is to lower interest rates to stimulate the economy. This is already happening in part, as the United States Federal Reserve bank dropped their policy rate (”Target federal funds rate”) to 0.00 on the 16th of March 2020 (Board of Governors of the Federal Reserve System, 2020). Currently (May 2020), futures contracts even seem to price in negative US policy rates (Bolingbroke, 2020). The current negative economic outlook makes a long-term, global negative interest rate environment therefore more likely than ever. Should that be the case, decision makers in banks need to understand what their situation and future look like. If, for example, continued exposure to negative interest rates were to erode core banking operations’ profitability, it is important for leaders of the financial industry to start diversifying their business into other areas or even fundamentally change their business model.

Studying the banking industry has tremendous implications for both the economic- as well as the business administration- field of research. In economics, a profitable banking industry has the ability to resist negative shocks and shows stability of the financial system (Athanasoglou et al., 2008). In the business administration field, profitability is a must for banks to survive and grow as businesses, especially since stakeholders generally view bank performance as profits made despite risks taken (Bikker, 2010). Studying bank profitability has therefore attracted the attention of company management, policymakers and researchers. It has also captured our interests, and hence, we decided to examine the topic of negative interest rates in relation to the Danish banking industry. No other country has had negative interest rates for such a long time. Furthermore, the effects of the low interest rates on the banks’ business seem to materialize substantially in Denmark, more than anywhere else in the world. For example, if one deposits money with the Danish Jyske Bank, one now has to pay interest for providing the bank funding. This is highly unusual, since banks are usually reluctant to lower deposit rates below zero due to the potential loss of customers (Claessens et al., 2018). According to the Financial Times, Jyske Bank is even offering the first negative-interest mortgage since 2015 – which essentially means that one gets paid for borrowing money (Milne, 2019).

1.2 Problem

There are very few studies that specifically investigate the relationship between interest rates and banking profitability, making it an under-researched area (Borio et al., 2017). There exist even fewer studies that examine the effect of persistently low (yet does not mean negative) interest rates on bank profitability – a notable example being Claessens et al. (2018), who suggests, that a persistently low interest rate environment continually reduces profitability. However, we could not find a single peer-reviewed study examining the effects of a persistently

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negative interest rate environment, as currently seen in Denmark. Indeed, negative interest rates were only introduced in the past decade, yet several years have passed without comprehensive academic research conducted to investigate the topic. Some institutions such as the International Monetary Fund (Jobst et al., 2016) and the European Central Bank (Altavilla et al., 2017) have published working papers to debate over the subject. Still, these are not peer-reviewed articles, and one can argue their objectivity since central banks and international institutions usually have their own agenda. Therefore, there is a gap in the literature that requires further investigation, and the aforementioned working papers confirm the actuality and relevancy of the topic. The lack of knowledge in this area may give rise to business and, potentially, survival problems for banks in the future, if the negative interest rate environment keeps on for a prolonged period of time as it is currently expected. Consequently, clarifying the neglect in the literature of this bank profitability is imperative for a functioning industry.

1.3 Purpose & Research Questions

To address the gap identified within the literature, this paper aims to investigate the relationship between persistently negative interest rates and bank profitability in the Danish banking industry. The study will examine an extended timeframe that represents the ultra-low interest rate environment of Denmark (2011-2018, 21 banks, 165 bank years). Exploring the tension between these two variables is vital since interest rates have been historically low in the past decade, and the negative territory could become the norm in future.

The above discussion leads to the following two main questions:

• Do interest rates affect Danish commercial bank profitability in a (near-) negative interest rate environment? If yes: Is the effect positive or negative and how strong is it? • How does it affect Danish commercial bank profitability when interest rates stay in the

negative territory for a prolonged period of time?

The remainder of this paper is structured as follows: Section 2 provides an overview of the literature on the determinants of bank profitability. Section 3 describes the method and presents the data of the Danish banks. Section 4 reports the empirical results and analyzes their implications. Finally, Section 5 concludes the paper and Section 6 discusses additional findings and suggests further research.

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2 Literature review

______________________________________________________________________

The purpose of this chapter is to provide a theoretical base and gather previous research on banking profitability. The chapter begins with an overview of the banking industry and explains different types of banks. Secondly, several banking profitability metrics will be discussed and compared. Thirdly, factors determining bank profitability will be categorized and compared. Finally, and based on the previous parts, the hypotheses tested in the paper will be developed.

2.1 Literature search

To obtain a comprehensive overview of prior research on the determinants of bank profitability, the procedure for a systematic literature search provided by Collis & Hussey (2014) was followed. The purpose was to create a conceptual framework and identify a particular research gap accordingly. Google Scholar, Web of Science, and Primo Search were initially used to search for journals and articles of highest relevance. A combination of keywords was searched for before being able to identify the most related keywords such as: macroeconomic*, interest rate*, financial performance of bank*, and bank profitability. Because this combination of keywords provided us with hundreds of articles, we decided to narrow down our topic towards interest rates only and their link to banking profitability. Using Primo Search and taking advantage of its advanced search option, we specified the terms as follows:

Title – Contains – Interest rate*

AND – Title – Contains – Bank* profit*

OR – Title – Contains – Financial performance bank*

Only peer-reviewed articles were selected, and no specific time-period was taken into consideration. The motivation of this was to get a comprehensive understanding of the subject from credible and trustworthy sources. However, because financial institutions engage in research to keep their knowledge updated as well as forecast future business conditions, they often publish working papers about current topics. We found that some of these working papers are useful and, hence, are used (with caution) in our literature review.

2.2 Frame of reference

2.2.1 The banking industry

Banks are financial institutions that provide banking and other financial services. They have an intermediation function based on raising funds, either from private depositors or wholesale funding sources, to provide loans to borrowers or to finance other investments (Idiab et al., 2011). One of the most prominent theories explaining how banks work is the Financial Intermediation Theory (Werner, 2016). In imperfect markets, savers and investors are unable to trade directly with each other in an optimal way, mainly because of informational asymmetries. Therefore, financial intermediaries, specifically banks, act as agents and delegate

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monitors to identify investors of behalf of savers because of their comparative informational advantage over the other parties. Consequently, the theory suggests that financial intermediaries are there to reduce transactions costs and information asymmetries (Scholtens & van Wensveen, 2003) by collecting deposits and then lending these out (Werner, 2016). They also have other functions such as “bridging the maturity mismatch between savers and investors and facilitate payments between economic parties by providing a payment, settlement and clearing system”. Banks come in several different forms, depending on their activities. The most relevant types are the following two:

2.2.2 Commercial banks and investment banks

Commercial banks typically act as a classical intermediary institution that accepts deposits from savers and extends credit to borrowers. They have three primary functions: 1) holding time deposits (saving accounts), 2) holding demand deposits (checking accounts), and 3) issuing credits mainly to corporates, but also to individuals (Mizruchi, 2001). The importance of commercial banks, through their intermediation role, lies with benefiting “the financial markets by reducing transaction costs, spreading risk and realizing economies of scale and specialization” (Clews, 2016). Commercial banks are highly regulated because savers, when depositing money, place significant trust in these commercial banks. Failure in managing credit exposure and ensuring the safety of the depositors’ funds represents a real risk (A. N. Berger & Bouwman, 2015; Clews, 2016).

Investments banks, on the other hand, focus mainly on corporate-, investment- and government-related clients. Typical investment banking services are security underwriting (e.g. Initial Public Offerings, Debt Issuances) and strategic advisory services (Stowell, 2010). Therefore, the most important differentiation between commercial and investment banks is that commercial banks mainly focus on lending activities, which is peripheral for investment banks.

Given these widespread activities as well as the multiple stakeholders involved – such as individuals, investors, economists, policymakers, managers, employees – the significance of bank performance becomes visible and tangible. Bank performance can be measured in various ways. Examples include efficiency, reliability (Bikker, 2010), cost structure, size and loan portfolio composition (Arshadi & Lawrence, 1987). Generally however, stakeholders perceive performance in terms of profitability regardless of risk taken (Bikker, 2010), which makes this one of the most important performance measures.

2.2.3 General structure of banking profitability studies

When assessing the profitability of a banking industry, there is a quite specific structure most studies follow. Generally, data points of several banks are gathered over a multi-year period. These data points are then put into a model, which can be (very generally) described as follows:

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The dependent variable is therefore a profitability metric, which is determined by several factors. β is the coefficient for each factor (the amount by which a one-unit increase in the factor influences the profitability metric) and 𝜀is the error term.

For now, the following section will describe the left side of this equation (most relevant profitability metrics) and the section after that will describe the right side (most relevant factors influencing bank profitability). Near the end of the literature review, a summary table will give a concluding overview over which studies found which factors to be influencing which profitability metrics. All the studies referenced in the table use the abovementioned structure.

2.2.4 Measures of commercial bank profitability

There are two main issues with widespread business profitability measures, like net income and EBITDA. Firstly, they are absolute and do not take into account bank size, asset base or deposits. Secondly, they are inflation variant, meaning the same amount of net income has a different real value in 2011 than it has in 2018. Both attributes reduce comparability and skew results.

These considerations caused researchers examining bank profitability to use inflation invariant and size-dependent metrics – which can further be grouped into two categories: overall profitability metrics and margins on core banking operations.

Overall profitability measures how profitable a bank is as a whole. This category of metrics considers income from core banking operations and other diversified sources of income (like trading, fee-based services, etc.). Overall profitability is usually measured by Return on Assets (ROA) or Return on Equity (ROE). The second category of profitability metrics is the margin on core banking operations - excluding all other sources of income. This metric is usually measured through the Net Interest Margin (NIM).

The literature distinguishes between these categories for two main reasons. Firstly, a bank can have a slim NIM, yet be highly profitable overall (e.g. when a bank shifts away from possibly unprofitable core banking operations and diversifies into more profitable fields) or vice versa. Furthermore, a certain factor (e.g. GDP growth) can influence NIM and overall profitability differently. It is therefore common practice to observe the effect of the factor of interest (like GDP growth) on both NIM and overall profitability and then compare the results (e.g. Dietrich & Wanzenried, 2011).

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2.2.4.1 Overall Profitability: ROA and ROE

The Return on Assets (ROA), expressed in percent, generally describes how much net income a company generates for each Danish Crown (DKK) of assets a company had in a specific year. Return on Equity, also expressed in percent, describes how much net income a company generates for each DKK of shareholders’ equity a company had in a specific year. Both are standard metrics to measure overall business profitability and are used in the corporate finance literature. There has been some discussion on which metric reflects the overall bank profitability more accurately:

Athanasoglou et al., (2008) state, that ROA should be preferred over ROE since it considers the risk taken on through leverage.1 They argue that the risk taken on through leverage is one of the central considerations of the core banking business; therefore, the overall profitability metric should reflect this factor. Several scholars argue that ROA is further favourable since it describes the ability of bank management to generate profit based on a bank’s asset base and cannot be influenced by high equity stakes (Menicucci & Paolucci, 2016; Rivard & Thomas, 1997). This is relevant since ROE can be influenced by shareholders increasing/decreasing their investment in a bank - such fluctuations could skew ROE. These considerations made ROA widely recognised as the key overall profitability indicator for banks (Athanasoglou et al., 2008; Dietrich & Wanzenried, 2011; Golin, 2001).

Although Return on Assets (ROA) and Return on Average Assets (ROAA) are used almost interchangeably in the banking profitability literature, they are not the same. ROA measures the company’s assets year-end, while ROAA takes the average of a company’s asset base in a specific year. Taking into consideration the fluctuations in asset balances throughout the year is generally seen as more accurate than measuring assets year-end (Ayadi & Boujelbene, 2012; Dietrich & Wanzenried, 2011). Jewell & Mankin (2011) further write, that ROAA has two main

1 Definition of leverage: “The relationship between the amount of money that a company or

organization owes and the value of the company or organization“ (Cambridge University Press, 2020c). In the case of commercial banking a high leverage means, that a bank gives out a large sum of loans for each DKK in deposits they hold – therefore increasing the banks‘ risk.

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benefits over ROA: Firstly, ROAA preserves the basic matching principle of accounting. Secondly, ROAA is less affected by random changes in total assets.

2.2.4.2 Net Interest Margin (NIM)

Traditionally, banks make money by lending out money at a higher interest rate than the interest rate they pay a depositor (which we refer to as the “core banking operations”). The net interest margin (NIM) describes the difference between the interest income of loans and the interest paid to depositors, divided by a bank’s asset base. It is a metric specific to banks and measures the profitability of core banking operations. Generally, the interest customers pay on loans and receive on deposits is strongly influenced by the policy interest rate. The policy interest rate dictates, for which interest rate the bank can make short-term loans from the central bank. This rate then influences the rate for which the bank’s customers can borrow and deposit. Simplified, the interest rate a customer receives on deposits will be somewhat lower than the policy interest rate. In contrast, the interest paid on loans will be somewhat higher than the policy interest rate – the policy rates are therefore generally passed on to customers. The spread between these two rates is the profit of the bank (Choudhry, 2017).

The above section describes the essential metrics one needs to understand how banking profitability is determined. Now a focus will be on the variables that determine the banking profitability metrics according to the literature (the right side of the equation introduced under 2.2.3 General Structure of banking profitability studies).

2.2.5 Evolution of banking profitability determinants in the academic literature

Early researchers started by grouping possible determinants into internal and external factors (Short, 1979). As the name implies, internal determinants are micro or bank-specific factors that are associated with bank management. External determinants are industry-specific and macroeconomics factors that can affect the performance of financial institutions, even though they are not related to bank management. In his paper, Short (1979) considered banks’ profit rates as the appropriate measure of their performance. After Short’s pioneering study, Bourke (1989) conducted one of the most impactful studies in the research field. He also differentiates between internal and external determinants of overall bank profitability and settles for several independent variables, including capitalization and market concentration.

While the determinants have been widely discussed, Bourke’s approach of comparing a large sample of banks over an extended timeframe using both internal and external determinants is one of the most critical research pieces. His methodology has been replicated multiple times (e.g. Molyneux & Thornton, 1992). The general idea of explaining banking profitability in such a way has been applied under various circumstances, for example spanning different business cycles (Dovern et al., 2010) or examining the 2008 crisis (Athanasoglou et al., 2008).

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2.2.6 Bank profitability determinants

The interest in identifying the determinants of commercial bank profitability has led researchers to test an abundance of possible variables. Still, there are only a few variables that can be reasonably expected to influence bank profitability. Since there is no standard of which variables to include in a banking profitability model, it is up to every researcher to examine which variables could impact profitability. To analyze which variables impact profitability, a table was created with possible determinants on the left and its effect on bank profitability in the middle. Studies supporting this finding are found on the right side. Determinants that have weak empirical support were omitted from the table. A summary of this analysis can be found in Table 1. Based on the overarching theme in the literature, we will now discuss the most important profitability determinants, grouped into internal- and external factors.2

2.2.6.1 Internal determinants of bank profitability

Studies that focus on internal determinants examine variables such as bank size, capital and liquidity, credit risk and business diversification (see e.g. Alper & Anbar, 2011; Athanasoglou et al., 2008; Dietrich & Wanzenried, 2011). This variable group usually determines overall profitability (ROA, ROAA, ROE, ROAE) more than NIM. These same variables will also be used as control variables in our study.

2.2.6.1.1 Size

Bank size is used to account for the potential of economies, or diseconomies, of scale in the banking sector, and is usually measured through the logarithm of total assets (see e.g. Al-Jafari & Alchami, 2014; Athanasoglou et al., 2008; Tan & Floros, 2012).

Boyd & Runkle (1993) linked the size of banks to the concept of economies of scales in what they called the modern intermediation theory. Briefly, the theory suggests that large banks, or financial intermediaries, can benefit from their ability to access a high number of lenders and borrowers. This usually translates into more diversification – which in turn likely leads to a reduction of contracting costs as well as to a reduction of risks. As a result, the modern intermediation theory predicts that large banks are more cost-efficient and less likely to fail than small banks. This might make large banks more profitable. The positive link between size and profitability has been generally confirmed by the literature (see e.g. Demirguc-Kunt and Huizinga, 1998; Alper & Anbar, 2011; Borio et al., 2017; Pervan et al., 2015; Tan & Floros, 2012), with some studies finding an insignificant relationship (Nessibi, 2016). However, the

2 It should be noted, that some studies find a certain variable to be significant, while other ones

do not. The conflicting results between different studies on the same variables is expected as studies differ in time period and countries examined, data used, statistical methods chosen, and combination of control variables used.

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heightened profitability of large banks seems to be eroded during financial crises and times of economic uncertainty (Campmas, 2020; Dietrich & Wanzenried, 2011).

2.2.6.1.2 Capitalization

Bank capital is defined as “the funds – traditionally a mix of equity and debt – that banks have to hold in reserve to support their business” (Jenkins, 2010). The variable is measured as the ratio of equity over assets (Claessens et al., 2018; Demirguc-Kunt & Huizinga, 1999).

Research suggests that higher capitalization is associated with:

• Higher overall profitability (Abreu & Mendes, 2001; Athanasoglou et al., 2008; A. N. Berger & Bouwman, 2013; Claessens et al., 2018; Demirguc-Kunt & Huizinga, 1999; Nessibi, 2016)

• Higher NIM (Claessens et al., 2018; Demirguc-Kunt & Huizinga, 1999)

• Enhanced performance through higher survival probabilities and higher market shares (A. N. Berger & Bouwman, 2013)

The positive correlation between profitability and capitalization exists because better-capitalized banks will:

• Get access to less risky, and thus cheaper, funding (Bourke, 1989)

• Face lower expected bankruptcy cost, leading to lower funding cost and higher interest margins (Abreu & Mendes, 2001)

• Have a safety net in case of adverse developments (Bikker & Vervliet, 2018)

A notable exception is the study of Alper & Anbar (2011), who found that capitalization had no impact on bank profitability in Turkey.

2.2.6.1.3 Credit risk

Risk is defined as “an uncertain but possible event which can cause some losses. It corresponds only to negative deviations from the expected outcome, a positive one would be considered as an opportunity” (Campmas, 2020). There are several types of risks that banks are subject to and need to take into consideration, e.g. market risk, operational risk and solvency risk. However, credit risk is considered to be the most important one (Gilchrist & Mojon, 2018).

Credit risk is usually measured through the ratio of loan loss provisions over gross loans; in other words: the expenses set aside for defaulting loans per total loans. A higher ratio therefore equals a lower credit risk. According to Campmas (2020), increases in relative Loan Loss Provisions (a decrease in credit risk) decreases overall profitability because these expected loan losses are deducted from the banks’ income. By contrast, Dietrich and Wanzenried (2011) reported that Loan Loss Provisions Per Gross Loans does not have a statistically significant effect on overall bank profitability during normal economic conditions. The determinant

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seemed to increase in significance during the years of the financial crisis, and as a result, hurt profitability.

Finally, it should be noted that the effect of the ratio on the NIM is not entirely clear: Dietrich & Wanzenried (2011) find a positive correlation, while Campmas (2020) finds a negative correlation.

2.2.6.1.4 Liquidity

The liquidity of a bank refers to the banks’ ability to meet obligations (lending and investment commitments or deposit withdrawals) as they fall due (Lartey et al., 2013). Liquidity can be measured by dividing net loans by total assets, which indicates the amount of the bank’s total assets that are tied up in loans (Munteanu, 2012). The higher the ratio, the higher the liquidity of the bank.

There is a conflict between the researchers on whether liquidity positively or negatively affects banking profitability. Bourke (1989), as well as Duraj and Moci (2015), argue that there is a positive relationship between liquidity and bank profitability as banks, by holding more liquid assets, increase their ability “to absorb any possible unforeseen shock”. On the other hand, Molyneux and Thornton (1992), as well as Goddard et al. (2004), found a negative relationship between the two variables as these researchers believed that liquidity, usually, represents an expense to the bank. This insight was predicted by ‘conventional wisdom’ of Bourke (1989) who, however, failed to provide evidence of the negative relationship in his research.

2.2.6.1.5 Diversification

Diversification is the extent to which a bank diversifies its income streams away from core banking operations into other business areas. It is therefore measured as (non) interest income per operating income (e.g. in Campmas, 2020; Dietrich & Wanzenried, 2011).

The previously explained modern intermediation theory proposed by Boyd & Runkle (1993) shows that higher diversification can lead to a reduction of risks taken and a lower probability of failure. Additionally, higher diversification tends to have positive effects on business stability as it improves the allocation of resources through internal capital markets3 (Stein, 1997). Furthermore, in their working paper about the anatomy of bank diversification, Elsas et al. (2006) used panel data from nine countries over seven years to examine how revenue diversification affects bank value. The researchers found that revenue diversification increases bank value by enhancing bank profitability. The latter is enhanced by 1) higher margins from non-interest businesses, and 2) lower cost-to-income ratios. On the same line, Dietrich & Wanzenried (2011) explained that larger banks are more profitable due to their ability to enjoy higher product and loans diversification possibilities. The insights from these researchers show

3 I.e. markets where corporate headquarters allocate capital to their business units, as opposite

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that diversification is essential for banks which aims to reduce their risks and enhance both their profitability and value.

When it comes to net interest margin, Campmas (2020) argues that diversification lowers NIM since it leads to a higher relative share of non-interest income.

2.2.6.1.6 Operational Efficiency

Operational efficiency, measured as cost-to-income ratio, is defined as “the operating costs (such as the administrative costs, staff salaries, and property costs, excluding losses due to bad and non-performing loans) over total generated revenues4” (Dietrich & Wanzenried, 2011). The ratio focuses on non-interest costs because those are considered to be mostly influenced by bank management. The ratio does not include the bad and non-performing loans as they reflect the quality of a bank’s credit portfolio rather than its performance (Tripe, 1998). The empirical studies observing a positive relationship between efficiency and overall bank profitability are plenty (Athanasoglou et al., 2008; Dietrich & Wanzenried, 2011; García-Herrero et al., 2009; Tan & Floros, 2012), while some also find a positive correlation with the NIM (García-Herrero et al., 2009).

2.2.6.2 External Determinates of bank profitability

Since banks are heavily dependent on the economic climate they operate in, several authors decided to focus on important macroeconomic variables in their research. We will give an outline of the most significant external determinants of bank profitability found in the literature.

2.2.6.2.1 GDP Growth/Business Cycle

Dietrich and Wanzenried (2011) Kanas et al. (2012), Trujillo‐Ponce (2013) and many others measured the effect of the business cycle on bank profitability. The empirical results of their research suggested that bank profitability and the business cycle, approximated by real GDP growth, seems to have a positive relationship, and they concluded that profitability is pro-cyclical. One explanation of the profit pro-cyclical feature is that, during cyclical upswings, demand for lending increases which leads up to a more profitable business (Dietrich & Wanzenried, 2011).

2.2.6.2.2 Market concentration

Market concentration, or market structure, is measured by the Herfindahl–Hirschman-Index, or HHI. This index is defined as “the sum of the squares of the market shares of all the banks within the industry, where the market shares are expressed as fractions” (Dietrich & Wanzenried, 2011). The effect of market concentration on bank profitability is unclear for two reasons. One the one hand, higher market concentration lowers competition and allows banks to charge higher (lower) interest rates on loans (deposits). Such collusion would lead to a

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positive relationship between market concentration and profitability. One the other hand, tougher competition might be expected to be the reason behind a higher bank concentration, suggesting a negative relationship between the two variables (Campmas, 2020; Dietrich & Wanzenried, 2011b; Goddard et al., 2004).

2.2.6.2.3 Inflation

Inflation is another critical determinant of bank profitability. Demirguc-Kunt and Huizinga (1999) findings about the positive relationship between inflation and bank profitability seem to be valid in many countries such as China (Tan & Floros, 2012a), the United States (Kanas et al., 2012) and Spain (Trujillo‐Ponce, 2013). Other researchers, on the contrary, found a negative relationship in a 47-country study (Claessens et al., 2018).

2.2.6.2.4 Interest Rates

Interest rates usually refer to interest rates the banks are subjected to. This can be measured in several ways. The most common one is the policy interest rate set by the central bank of the respective country (e.g. in Campmas, 2020; Short, 1979). Other measurements include the Interbank Overnight Rate (e.g. in Borio et al., 2017) and the yield on short-term sovereign debt (e.g. in Claessens et al., 2018). For a detailed discussion of these proxies, please refer to the 3.5.2 Independent variables.

The link between banking profitability and interest rates is sometimes seen as a “by-product” in the literature and not the specific focus of research (Borio et al., 2017). The studies that use interest rates as a profitability predictor have conflicting results. Some find a positive relationship (Europe: Molyneux & Thornton, 1992; International: Borio et al., 2017; Campmas, 2020; Demirguc-Kunt & Huizinga, 1999), while some find that lower interest rates do not lower overall bank profitability (Altavilla et al., 2017; Claessens et al., 2018).

The abovementioned conflict is especially interesting since most available research focuses on the link between interest rates and profitability in what might be considered a (relatively) high-interest rate environment today. A negative high-interest rate environment can have unique effects on banking profitability since some research suggests that the interest margin compressing effect of low interest rates gets a lot stronger the lower the interest rate (Borio et al., 2017). Additionally, Claessens et al. (2018) show in a multi-country study carried out between 2005 and 2013, that not only do low interest rates depress overall profitability, but the effect gets stronger over time.

Because negative interest rates only arrived in Europe the past decade, it is still unclear whether this likely strong compression of net interest margins can be offset through other activities/factors. A recent working paper by the European Central Bank claims that the potentially NIM compressing effect of low interest rates is offset by an increase in credit quality, amongst others (Altavilla et al., 2017). A recent study by Campmas (2020) supports this, as she finds that the impact of interest rates is more substantial on the NIM than on overall profitability. However, the study’s panel only lasts until 2015, which is relatively short for investigating

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negative interest rates, considering they have only been introduced in 2014 (ECB) and 2012 (Danish Nationalbanken), respectively. In 2015, the Danish interest rate dropped significantly into the negative territory when it was lowered to -0,75% - the lowest rate a country has seen so far (Nationalbanken, 2020).

2.2.6.2.5 Term Structure

The term structure of interest rates, also sometimes known as the yield curve, represents the “interest rates on debt securities and how these rates vary with respect to varying dates of maturity” (Knopf & Teall, 2015). In other words, the term structure describes how much more yield investors demand for lending their money for a longer period versus a shorter period. It is therefore usually calculated by subtracting the yield of short-term government bonds (e.g. 2-year bonds) from longer-term government bonds (e.g. 5-2-year bonds) – e.g. in Garcia & Guerreiro (2016). Generally, the term structure of interest rates is a frequently used indicator to determine investor sentiment and the macroeconomic outlook. A reasonably high term structure is a sign that investors have a positive outlook on the economy (Choudhry, 2017).

The literature on banking profitability has not given substantial attention to the term structure of interest rates (Borio et al., 2017), and hence there are comparatively fewer studies available. The available studies generally find a significant positive correlation between Term Structure and Profitability (Borio et al., 2017; Claessens et al., 2018; Dietrich & Wanzenried, 2011; Garcia & Guerreiro, 2016).

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2.3 Hypothesis development & independent variables

Following the research questions stated in the purpose section, two independent variables are of interest: The first independent variable will be the interest rate a bank is subjected to. The second independent variable will be the duration for which banks are consecutively

subjected to negative interest rates in years. More details and motivation follow in 3.5.2

Independent variables.

2.3.1 The profitability of core banking operations

Lower policy interest rates are generally expected to lower core banking profitability (which is essentially always measured as Net Interest Margin - Bikker & Vervliet, 2018; Claessens et al., 2018). However, due to the newness of the negative interest rate policy, it is not entirely clear if this effect holds when rates drop into below zero. It would be plausible since banks might be reluctant to lower interest paid to depositors below zero, even when the policy interest rate is negative. Reasons for this effective lower bound may be the danger of losing customers to competition or customers switching to cash savings (Claessens et al., 2018). A bank might

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choose to accept a lower interest margin (i.e. not passing on the full effect of lower policy interest rates) to keep its customers for future cross-selling opportunities (Berger et al., 1993). While there seems to be a reluctance to pass on negative rates, it can still happen if banks deem it necessary to stay afloat (see the example of Jyske Bank in Milne (2019)). Should banks decide to pass on the negative rates, this would have the potential to widen the NIM and therefore increase core banking profitability.

There is a further lack of information when the long-run effects of negative interest rates are considered. One study suggests that an enduring low interest rate environment will further compress the NIM (Claessens et al., 2018). However, no study is available for the effect of negative interest rates. It is therefore unclear whether this effect will hold in an ongoing negative interest rate environment. It is very plausible that Net Interest Margins will increase to positive policy rate levels over time, once banks take the step to lower the interest rates on customer deposits below zero. However, this would contradict the effect observed by Claessens et al. (2018). Several further questions arise through this vacuum of information: Does NIM compress further, extending the effect of Claessens et al., (2018)? If so, how strong is the compression caused by negative rates? Could core banking operations even become unprofitable? Or does NIM recover over time as soon as banks start to partly pass on negative interest rates to customers? If so, at which pace? These questions have not been addressed adequately by the literature yet, which brings us to the following set of hypotheses:

H1: The interest rate is not correlated with core banking operations’ profitability

H2: The duration of consecutive negative interest rates in years is not correlated with core banking operations’ profitability

2.3.2 Overall profitability

As outlined in 2.2.6.2.4 Interest Rates, the effect of interest rates on overall profitability is still unclear. Based on the literature, it is very difficult to predict if negative interest rates will influence overall profitability and whether this effect is positive or negative. Negative interest rates may influence overall bank profitability strongly. If negative interest rates would compress the NIM significantly, and banks are dependent on a wide Net Interest Margin, overall profitability would suffer. In this case, a NIM decline would lead to a decline in overall profitability.

On the other hand, a declining NIM would not influence overall bank profitability if banks diversified their business into other (profitable) fields. If negative interest rates lead to a widening of the NIM, overall profitability might increase if banks are still relying heavily on core banking operations. A widening NIM might not influence overall profitability if banks shift away from core banking operations. It is therefore essential to note that a negative interest rate environment can affect NIM and overall profitability differently. To bring clarity, we specify the following hypotheses:

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H4: The duration of consecutive negative interest rates (in years) is not correlated with overall profitability

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3 Data, Model Specification and Methodology

______________________________________________________________________

This section begins with a description and justification of our research philosophy, the research design, the mixed-methods approach, and the data collection process. Moreover, the variable selection is described, followed by a summary table. The section ends with the methods used for analysing both quantitative and qualitative data.

3.1 Research philosophy

The finance and banking literature is usually employing a positivist paradigm. Positivism sees the world as a set of observable facts, which can be interpreted through reason and logic (Ayer, 1959). Therefore, a positivist paradigm goes hand in hand with quantitative data. If one looks at the very nature of banking, the positivist preference of previous researchers can be understood. Banking, in its essence, deals with taking deposits and other assets and lending them out at a maximized profit, while minimizing the probability of defaulting loans. Due to this number-driven nature of financial institutions, the literature takes on similar tendencies and emphasises large samples and statistically derived truths about the firm and the industry. Still, we perceive pure positivism to come with limitations - the purely quantitative focus and the exclusion of qualitatively derived findings are two of them. Therefore, we will come from a similar, yet not identical philosophy. We will adopt a view that became popular with other technical fields, like information system research. Scholars in these fields had become aware of positivism’s limitations and started to argue for a ‘mixed methods’ approach to create better findings and, consequently, employed post-positivism (Miles & Huberman, 1994). Post-positivism argues for “methodological pluralism”, i.e. selecting quantitative and qualitative methods that address the research question best (Wildemuth, 1993). It also argues that quantitative findings do not reflect absolute truth, but instead have to be understood in the context of the research and the researcher (Guba, 1990).

We believe that such a paradigm is particularly relevant for our thesis due to the following reasons. Even in positivist studies about banking profitability, results differ widely at times. We pointed out some conflicting effects of variables on banking profitability in the literature review. The post-positivist paradigm allows us to see that each researcher brings his/her own biases to the table (reflecting in the choice of methods, sampling sources, control variables used, …) and therefore results differ. Studies with differing results are not necessarily “wrong” in any of these dimensions. Instead, there is a spectrum of acceptable practices which may bring different results.

The statistical methods used, for example, can differ among Ordinary Least Squares models (e.g. Bourke, 1989), Generalized Method of Moments estimators (e.g. Athanasoglou et al. 2008) or Fixed Effects Models (e.g. Garcia & Guerreiro, 2016). Every author had good motivations on why to use the specific model. However, selecting a model presents a certain bias in itself due to each model’s shortcomings – none of them is perfect. While classical positivists might argue that there would be one superior way to make sense of the data, post-positivists argue that

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there is a spectrum of acceptable practices and the unavoidable researcher bias should be taken into account. To mitigate this bias, one should triangulate and refer to other, qualitative methods to have credible results. Through adding a qualitative dimension, we as researchers are forced to look at our research questions and results from a different perspective. We believe that this additional step in thinking will reduce our researcher bias and allow us to make sense of the quantitative data better.

Post-positivism states that researchers should not limit themselves to quantitative nor qualitative data. Instead, researchers should select (a mix of) data sources/analyses that suits their purpose and research questions best (Guba, 1990). Our research questions will be primarily answered by a rigorous analysis of available financial data. While no model is perfect, it is possible to build a robust, statistical model, which is grounded in theory. According to post-positivism, however, the sole analysis of quantitative data by the authors is biased and does not answer the question adequately. While our quantitative analysis will give us an overview of the overall effects for the industry, the interpretation and analysis will be helped through interviews with industry professionals. This combination of methods and data sources further mitigates author bias and the distance between research and real-world business.

3.2 Research design

To optimally test the effect of (prolonged) negative interest rates, we will use a deductive approach. The deductive approach is “a study in which a conceptual and theoretical structure is developed and then tested by empirical observations; thus, particular instances are deduced from the general inferences” (Collis & Hussey, 2014).

We decided to study this effect using Denmark due to the following reasons. Firstly, Denmark presents the most interesting case for studying the business effects of extremely loose monetary policy, since interest rates reached a lower level, and have been in negative territory for the most prolonged period of any country. Secondly, Danish commercial banks provide richer data than many other countries.

The best way to objectively assess the interest rates’ effect on profitability is through analysing a large sample (n) over a relevant time period (t). We aim to collect as many observations of commercial banks in Denmark as possible and make them our sample. After that, different statistical methods will be reviewed, and an adequate model will be built in Stata, including variables for interest rates and control variables for other relevant profitability determinants. Stata is used since it provides a comprehensive, easy-to-use set of statistical analysis tools and is readily available in the JU Computer Lab.

To round off the study, we will talk to active or former bankers in the Kingdom of Denmark to get their view on current (interest rate) conditions and its implications on their business. Therefore, we will conduct interviews and leverage them to interpret our quantitative results. This method of supplementation would allow us to gather insights into the businesses affected by monetary policy decisions. There is support from the academic community that this practice is productive: Rossman & Wilson (1985) suggest, that linking qualitative and quantitative data

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allows researchers to confirm findings, develop analysis, provide richer detail and generate new lines of thinking.

3.3 Mixed methods

Our motivation for the use of mixed methods is that we want to obtain a fuller picture and a more in-depth understanding of the effect of negative interest rates on banking profitability. Since the phenomenon is so new, some impacts of the negative rates might not be reflected in our quantitative data, which lasts until 2018.

Johnson, Onwuegbuzie and Turner (2007) defined mixed methods research as “the type of research in which a researcher or team of researchers combines elements of qualitative and quantitative research approaches (e.g., use of qualitative and quantitative viewpoints, data collection, analysis, inference techniques) for the broad purposes of breadth and depth of understanding and corroboration.”

Johnson and Onwuegbuzie (2004) provided nine mixed methods designs from which authors can choose when designing their study (see Figure 2). To be able to choose appropriately, the researchers need to answer two questions: 1) Would they operate primarily within one dominant paradigm (quantitatively vs qualitatively dominant)?, and 2) Would they carry out the phases of the study concurrently (i.e. at the same time) or sequentially (i.e. one after another)? Johnson and Onwuegbuzie (2004) stated that the findings of both quantitative and qualitative studies must be integrated at some point.

We adopted the view of Johnson and Onwuegbuzie (2004) and decided to conduct a study that has a quantitative dominant status and that is sequential. Therefore, we ended up on the fourth-quadrant design where quantitative dominant study is conducted first, and then followed by a qualitative study (QUAN –> qual).

The goal here is not to conduct an in-depth qualitative study, but rather to take the simplest form of mixed-methods approach: Enhancing analysis and reporting of the main quantitative study with supplementary qualitative data. In line with Bazeley (2018), the purpose of supplementary data in its simplest form is to “[use] the alternative data for illustrative purposes, or to contribute to an explanation, or to contextualize information arising in the primary source.” In this case, it is justified that the supplementary qualitative part would be unable to be a study on its own, but merely supports the primary (quantitative) analysis (Bazeley, 2018).

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Figure 2: The nine mixed methods designs of Johnson and Onwuegbuzie (2014).

3.4 Data collection

3.4.1 Process and sampling for the quantitative data

We considered several high-quality data sources for our quantitative analysis and ended up trusting the Orbis database of public and private companies by Bureau van Dijk (BvD)/Moody’s Analytics as a secondary data source (Bureau Van Dijk, 2020). Since BvD’s data is generally seen as credible by researchers and industry professionals, it is used for the vast majority of banking profitability studies referenced. After checking several countries in Orbis for the availability of rich data, the Danish banking industry seemed to be well represented in BvD’s database. To assess the quality available for each country, we started a new search query where all the following conditions had to be fulfilled: (1) the company had to be active, (2) the company had to be a bank (3) it had to be a legal entity in the selected country.

The query for Denmark spawned 100 banks (all types), 83 of them including at least some financial company information. After the query, we selected 34 metrics per company per year for the timeframe of 2011 to 2018. These datapoints included information like ROAA, interest income & expense and the number of employees. This dataset was then downloaded into excel and non-commercial banks were excluded at this stage. We selected commercial banks for three reasons: 1) Commercial banks account for most of the Danish banking industry measured by assets. 2) Commercial banks are important contributors to economic growth, since they provide investors with funds to borrow and increase financial deepening in a country (Otuori, 2013). 3) Since Investment Banks make significant parts of their revenue by security underwriting rather than with core banking operations, it is less likely that they are as heavily affected by negative interest rates as commercial banks are.

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That followed an analysis for consistency and integrity: If a bank lacked a primarily relevant metric (in other words: a metric being used in this study), it was excluded from the sample. The data was then imported into Stata and specified as panel data using the xtset command and grouped by bank. The bank is therefore the unit of observation.

This study includes a strongly balanced panel on 21 Danish commercial banks, which accounts for the vast majority of the Danish commercial banking industry and for 59% of the entire Danish banking industry (measured by assets – Appendix 8.2).

Cross-sectional data (where t=1) would not allow us to answer our research question adequately, since it observes units at only one point in time. Time-series data (where n=1) would vastly decrease the reliability of our results, since it is not meaningful to infer from one bank observed to the overall population. Therefore, panel data5 (where n>1 and t>1) is most suited for our

study, since it is much “richer” compared to cross-sectional or time series data. Having multiple units observed over a long timeframe allows us to conduct relevant statistical analyses and have a large enough sample for the population.

According to Hsiao (2007), panel data further reduces the collinearity among explanatory variables and increases the efficiency of estimators. However, richer estimation methods than OLS regressions are often needed to make sense of panel data with all its pitfalls.

The primary period of interest is from 2013 onwards, because the average policy interest rates have been negative since then, as seen in Figure 3. However, we decided to include data from 2011 and 2012 to have two years of positive interest rates for comparison to the years of negative interest rates. Data before 2011 was excluded due to the strong effect of the financial

5 “Panel data are repeated measurements at different points in time on the same units” (Cameron

& Trivedi, 2009). Also known as cross-sectional time-series data.

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crisis on the banking industry in these years. Including these years could skew our results, since we believe the crisis years are neither an accurate representation of the normal banking industry’s functioning nor relevant to our research. The period studied is therefore 2011 until 2018,6 observing 8 years of ultra-low interest rates, 6 years of negative interest rates and a total of 165 bank years.7

Bank-independent variables were downloaded from the available source, we judged most trustworthy based on general reputation and closeness to official institutions. We could only examine sources that were available to us at low cost or through university access. For interest rate proxies this equates to: Denmark’s Nationalbank’s certificate of deposit rate (InterestRate) was therefore downloaded directly from the Nationalbanken Website (Nationalbanken, 2020); the three month yield on Danish government bonds (ThreeM_Yield) was downloaded from a media company providing historical financial data to traders, Fusion Media Ltd. (2020a); the three month interbank overnight rate (ThreeM_IBOR) was downloaded from the ECB statistical data warehouse (European Central Bank, 2020). The data to calculate the interest rate term structure (TermStructure) was also downloaded from the financial data provider Fusion Media Ltd. (namely the yields on 5- and 2-year Danish sovereign bonds – Fusion Media Ltd., 2020b; Fusion Media Ltd., 2020c).

A problem that arises when downloading interest rate data on a yearly basis is, that often year-end values are provided (i.e. the interest rate on Dec-31). This is an issue, since the year-year-end interest rate does not accurately represent the average interest rate throughout the year (i.e. the interest rate banks have actually been subjected to). To avoid this pitfall, each of these variables was downloaded with monthly intervals and then averaged for each year by the authors. This method takes into account intra-year changes of interest rates and quite accurately represents the interest climate a bank was operating in. We use the Annual Percentage Rates (APR) for all rates to enhance comparability in line with the literature.

Yearly GDP growth (GDP) was downloaded from the OECD website (OECD, 2020).

3.4.2 Process and sampling for the supplementary (qualitative) data

We believe that the analysis of our quantitative data would benefit from leveraging qualitative data directly from the industry. We further believe, that the people working directly with core banking operations can offer the most valid industry insights. To capture the thinking ideally, semi-structured interviews seem appropriate. They have the benefit of allowing us to aim for specific topics, while maintaining enough conversational flexibility to capture each banker’s unique insights. This allows us to ask follow-up questions if a certain point the interviewee

6 2018 was the last year we were able to obtain data for.

7 The bank years observed is 165 instead of 168 since some banks were lacking some control

variable data. This deviation is normal, even in strongly balanced panels and slightly reduces the number of bank years observed.

Figure

Figure  1: Denmark’s certificate of deposit rate over time (Nationalbanken, 2020).
Table 1: A summary of variables commonly affecting bank profitability.
Table 2:  Dependent & Independent Variables and corresponding hypotheses
Figure 2: The nine mixed methods designs of Johnson and Onwuegbuzie (2014).
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References

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