A trade-off between sustainability and profitability : does a stricter policy on bonus regulations in the Dutch financial sector affect company performances?

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A trade-off between sustainability and profitability

Does a stricter policy on bonus regulations in the Dutch financial

sector affect company performances?

Bachelor’s Thesis by Vasco van Poppel 10893997

BSc Economics and Business June 2018

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Statement of Originality

This document is written by Student Vasco van Poppel who declares to take full responsibility for the contents of this document.

I declare that the text and the work presented in this document are original and that no sources other than those mentioned in the text and its references have been used in creating it.

The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.

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3 1. Introduction

“The heart of the 2008 financial crisis was a coterie of reckless financial executives, working for too-big-too-fail financial companies, who were handsomely compensated for taking risks that almost ruined the economy when they failed” - Gary Weiss

In 2008 the American and European financial markets were hit by a crisis with an economic recession as a result of the collapse of the American housing market. Together with the large scale issuing of collateralized debt obligations (CDOs) without sufficient asset-backing, remuneration policies in the financial sector were also identified by Turner (2009) as being a major cause for the unstable situation on the financial markets. In a research on the global banking crisis conducted by the London Financial Services Authority, Turner (2009) states that short-term focused remuneration policies (e.g. high levels of short term variable pay) contributed to the financial crisis. Executives achieved high levels of short-term profits, therewith realizing higher compensation, through excessive risk taking even though on the long-term this could lead to potential losses to the companies as well as unstable situations on the financial markets. To prevent this from happening again, financial institutions and markets have to be stabilized (Turner, 2009).

On February the 7th_{2015, the Dutch government implemented a new legislation on}

remuneration policy in the financial sector according the former Dutch minister of finance who wrote a letter to the Dutch house of representatives (Dijsselbloem, 2016). The renewed law is called ‘Wet beloningsbeleid financiële ondernemingen’, or the Wbfo. With this new legislation the Dutch government introduces a variety of rules that financial institutions have to comply to, aiming for more controlled remuneration policies and a restriction on excessive variable pay. The Wbfo includes obligatory transparency in remuneration policies, a maximum on variable pay of 20% of the fixed salary, stricter terms on retention fees and severance pay and finally a prohibition on either guaranteed variable pay or variable pay during a situation of financial support provided by the state. All of which are applicable to companies in the Dutch financial sector. According to Dijsselbloem (2016), with these new rules, of which the financial cap on variable pay is the most prominent, the Netherlands has Europe’s strictest

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legislation on bonuses. In a letter to the Dutch House of Representatives, former minister of finance Jeroen Dijsselbloem (2016) also states that the goal of the Wbfo is to prevent perverse incentives that could lead to unnecessary risk-taking and/or the disregard of client interest, eventually leading to a more sustainable and serviceable financial sector.

The principal-agent theory is a theory in organizational economics and is linked to executive remuneration policy. Specifically, agency theory is directed at the ubiquitous agency relationship, in which one party (the principal) delegates work to another party (the agent), who performs that work (Eisenhardt, 1989). This is applicable to listed companies where the shareholders act as the principal and the CEO acts as the agent, where the remuneration package for the CEO is used to align his or her interest with that of the company. In order to stimulate the agent to exert effort, the principal offers him a wage contract, w = s + by, where (w) represents the wage, (s) represents the fixed salary, (b) represents the bonus rate as coefficient between 0 and 1, and (y) again represents the generated output. The key idea in this model is that higher bonus rates create stronger incentives for the agent to exert effort, therewith increasing company performances when the bonuses are properly aligned to the used performance measures (Gibbons, 1998). However, with a restraint on the possible levels for b in the Netherlands, due to the implementation of the Wbfo, companies are not able to supply optimal levels of b for executives who, based on their risk averseness, possibly demand a variable pay bigger than 20%. According to the agency theory the optimal fixed wage for a risk-neutral agents is negative with a high bonus rate. This is however not in line with the Wbfo, and the arguments that come with it, stating that a stricter policy on variable pay will increase the overall performance of the financial market through increased stability, sustainability and serviceability.

What we often see in firms, and which is a small variation from the standard model, is that a bonus is only paid after a certain target/output threshold is met. Despite a theoretical high optimal bonus rate, Gibbons (1998) states that efficient bonus rates are consequently often small. With these contrary statements, the question arises as to what levels of variable pay can actually be considered optimal under given circumstances and henceforth what the result will be of the Wbfo, with its 20% financial cap on variable

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pay, on the company results of Dutch financial institutions. Therefore this research will focus on a quantitative assessment of the company results of Dutch listed companies from the financial sector before and after the implementation of the Wbfo. Resulting in the following research question:

“Does a stricter policy on bonus regulations for Dutch listed financial companies have an effect on company performance?”

To test this we compared four performance measures of companies in three different peer groups between the periods 2012-2014 and 2015-2017. The peer groups consisted of Dutch financial companies, Dutch non-financial companies, and non-Dutch financial companies respectively. By comparing the performance measures TSR, ROA, ROE and EPS between the two periods, and between the respective peer groups, a conclusion can be made on the effectiveness of the implementation of the Wbfo. In our results we found that stricter bonus regulations is only positively related to the ROA for Dutch financial companies. For the other performance measures we did not find a significant effect. Therefor we conclude that a stricter policy on bonus regulations for Dutch listed financial companies does not have an effect on company performances. This conclusion can be used as an argument to impose stricter bonus regulations throughout the entire European Union because it has no negative effect on company performances but can contribute to a more sustainable and serviceable financial sector. Since the company results from 2012-2014 were compared to company results from 2015-2017 one can argue that effect of the implementation of the Wbfo cannot already be measured properly, and that the Wbfo needs a longer effective period to translate into changed company performances within the Dutch financial sector. Also because the Wbfo encompasses all the Dutch financial companies, and not only the ones that are listed on the Amsterdam Stock Exchange, the question arises as to how representative peer group 1 is to the entire Dutch financial sector.

In the next chapter the literature review will be provided. After that the methodology of this research is explained. Thereafter the empirical results will be analysed on which a conclusion will be based. Finally the discussion provides a link to previous literature as well as suggestions for future research.

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6 2. Literature Review

Jensen and Meckling (1976) define an agency relationship as a contract under which one or more persons (the principal(s)) engage another person (the agent) to perform some service on their behalf, which involves delegating some decision making authority to the agent. The agent exerts effort to produce a certain output. In order to stimulate the agent to exert effort, the principal offers him a wage that consists of a fixed salary and a bonus payment. The key idea in this model is that the agent is risk-averse. A higher bonus rate thus creates stronger incentives for the agent, but also imposes more risk on the agent (Gibbons, 1998). Therefor we can speak of a trade-off between full insurance at (b = 0) and full incentives at (b = 1). For the computation of the optimal bonus rate the incentive-intensity principle is used:

𝑏

∗

₌

𝑓

2

𝑓

2

_{∗ 𝑟 ∗ 𝑘 ∗ 𝜎}

2

The optimal incentive intensity depends on the incremental firm value added by additional effort, the agent’s responsiveness to incentives, the agent’s risk aversion and the precision of the performance measure.

Gibbons (1998) finds the classic agency model distant from real attempts to tie pay to performance. He also highlights some examples (H.J. Heinz Company, Dun & Bradstreet and Sears) that suggest according to Baker, Gibbons and Murphy (1994) that the findings of Kerr (1975) still remain true today. Those findings are that modern organization theory requires recognition that the members of organizations and society possess divergent goals and motives. And that it is therefore unlikely that principals and their agents will seek the same outcomes. This vision is shared by Jensen and Meckling (1976) stating that if both parties of the agency relationship are utility maximizing entities, there is good reason to believe that the agent will not always act in line with the best interests of the principal. This introduces the problem of alignment, or rather the lack of it.

However, Gibbons (1998) also describes three static models in which firms do get what they pay for: Baker (1992), Lazear (1989), and Hölmström and Milgrom (1991). In these articles, a strong but unremarked assumption about agency theory is

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being rejected, namely the variable y being easily measurable output. Instead of using output, Gibbons states that y represents everything the principal (in this example the company) cares about, except for wages. Henceforth calling it “total contribution to firm value”, encompassing all of the agent’s actions. Therewith, the variable y that is being used, together with the bonus factor, to compute the actual paid bonuses, aligns much better with the company goals. This shows that one of the major challenges in agency theory is to correctly define the output variables or company results that are being used as performance measures on which variable pay is based. This implies that under correct alignment between bonus payments and company results, a decrease in bonuses, as a result of the Wbfo, would also mean a decrease in company performances.

Despite the positive examples provided by Gibbons, the general findings are in line with the findings of Kerr (1975) that were previously mentioned. Three remedies are posited by Kerr: selection, training and altering the reward system. The last option, altering the reward system, is exactly what the implementation of the Wbfo aims for. This problem with altering the reward system, concerning the agency theory, is the discrepancy between managerial responsibility and the computation of remuneration policies. Bebchuk & Fried (2003) state that there are good theoretical and empirical reasons for concluding that managerial power substantially affects the design of executive compensation in companies with a separation of ownership and control, being listed companies with shareholders acting as the principals. This is a difficult problem to solve since company executives will not, from a standpoint of economic rationality, lower their own compensation packages to increase company benefits. In solving this problem, government policies play an important role. Eisenhardt (1989) states that positivist researchers have focused on identifying situations in which the principal and agent are likely to have conflicting goals and then describing the governance mechanisms that limit the agent’s self-serving behaviour. The Wbfo is implemented to do just that, namely limiting the presence of perverse incentives through a financial cap on bonus payment. This should theoretically increase company performances.

The question however, arising with setting a maximum variable pay of 20%, is whether Dutch financial institutions will still be able to acquire managers with comparable skill levels as in the prior situation of 20%+ bonuses. Starting the debate on skill acquisition means that you shift from incentives for various kinds of effort towards incentives for skill acquisition. This shift makes performance evaluation trickier,

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because the firm must now evaluate a worker’s potential contribution to the firm value, rather that the worker’s realized contribution (Gibbons 1998). This potential contribution evaluation is very difficult, if not impossible, and therefore makes the argument posited by many who urge caution regarding lowering salaries, in fear of losing in terms of competitiveness on the managers market, to be impossible to verify.

When implementing new legislation, one of the major factors is the situation prior to the implementation. In other words, where the remuneration policies in the financial sector prior to the financial crisis providing a reason to change them through new legislation? Ferrarini & Ungureanu (2011) analysed executive pay at European banks. According to Ferrarini & Ungureanu (2011) critics have claimed, in particular, that executive compensation was not properly related to long-term performance, while regulators have sought ways to change practices to better align pay with long-term performance. Their analysis finds that, according to their disclosure before the crisis, most large European banks adopted remuneration policies that were fairly balanced between fixed and variable pay and included long-term incentives. Henceforth stating that the compensation structures at banks before the financial crisis were not necessarily flawed. Also Ferrarini & Ungureanu (2011) submit that regulators should not replace boards in setting pay structures and that regulatory intervention concerning executive compensation at banks should be limited in scope, so as to maintain the flexibility of executive pay arrangements.

Following the debate about whether or not executive compensation policies were flawed, the question about correlation arises. Did ‘bad bank governance’ contribute to the financial crisis? If bank governance contributed to the financial crisis, and that is the case according to Turner (2009), this provides an extra argument in favour of governmental intervention in order to realize higher degrees of sustainability, exactly what the Wbfo aims for. Beltratti & Stulz (2009) investigated whether banks’ poor performance in the recent crisis was an unpreventable effect of external shocks or the result of some banks being more inclined to experience large losses. They found no evidence for the thesis advanced in a report by the Organization for Economic Co-operation and Development (“OECD”) that the financial crisis can be, to an important extent, attributed to failures and weaknesses in corporate governance arrangements. In particular, they find no evidence that banks with better governance performed better during the crisis. This is a complete opposite conclusion to the one that is made in the

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previously mentioned research on the global banking crisis conducted by the London Financial Services Authority, in which Turner (2009) states that short-term focused remuneration policies (e.g. high levels of short term variable pay) contributed to the financial crisis.

It has become clear by now that there are multiple standpoints on whether the crisis was created by flawed governance, and if so, to what extent and in what way that governance has to be altered. The evaluation of the Wbfo’s results on company performances from the Dutch financial sector that will be provided in this research could help shine a brighter and more focused light on the subject.

According to Gibbons (1998) the central lesson for incentive contracting from the organizational economics literature is the old but important notion of fit (or complementarity, as it is now called). When certain distractions are reduced through job restrictions or reduction of agent’s outside interests, the optimal incentive contract may well have a low bonus rate (Gibbons 1998). Hence, situations can be created where the optimal bonus rate will be below 20% and therefore the implementation of the Wbfo might not necessarily have a negative impact on the agent’s exerted effort or the company results. Hölmström and Milgrom (1994) articulated this idea through an important conclusion stating that the use of low-powered incentives within the firm, although sometimes lamented as one of the major disadvantages of internal organization, is also an important vehicle for inspiring cooperation and coordination. Also Duffhues & Kabir (2008) examined the pay-performance sensitivity in the Netherland by analysing a hand-collected data set of compensation paid to executives of Dutch listed companies. They failed to detect a positive pay-performance relationship.

Stricter bonus regulations have both advantages and disadvantages, which are provided in the literature review. These effects of stricter bonus regulations are expected to level out each other. Based on this the hypothesis of this research states that a stricter policy on bonus regulations for Dutch listed financial companies does not have an effect on company performance.

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10 3. Methodology

Data

The aim of the research is to assess the effect of the implementation of stricter bonus regulations on company performances. To do so we compare the company performances of three different peer groups. The first peer group consists of Dutch companies from the financial sector. This is relevant to the research because the Dutch financial sector has been subjected to the Wbfo from early 2015. The financial sector includes banks and insurance companies and the peer groups consist of companies that have been listed to the Amsterdam Stock Exchange for the entirety of the researched period. This is necessary in order to make solid comparisons as well as having full transparent datasets through the yearly financial accounts. This means that for example the large Dutch banks ABN-AMRO and Rabobank are no viable candidates because they have been state-owned, as a result of the financial crisis, during at least a part of the researched period. Peer group 1 could not be any bigger because of the lack of Dutch listed financial companies that met the requirements. Peer group 1 consists of eight companies that vary in market capitalization between 150 million (Kas Bank N.V.) and 60 billion euros (ING Groep N.V.) in 2017. All the used data is subtracted from Datastream.

The second peer group consists of Dutch companies from the non-financial sector. Dutch non-financial companies are not subjected to the Wbfo but are affected by other factors that influence the Dutch market. Therefor it provides good comparison material for peer group 1 in terms of company performances. Peer group 2 consists of thirteen companies that cover sectors as transportation, infrastructure, food and beverages, chemicals, fossil fuels, technology and publishing. This variety in different sectors for peer group 2 increases its representation of the Dutch economy. The market capitalizations vary between 150 million euros (Heijmans N.V.) and 106 billion euros (Royal Dutch Shell) in 2017.

The third peer group consists of financial companies from the UK, Germany and France. These companies are not Dutch, and are henceforth not subjected to the Wbfo. However, being from the European financial sector, these companies form a peer group that is sufficiently correlated to the Dutch financial market according to Syllignakis &

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Kouretas (2011), whose research provides a substantial evidence in favour of contagion effects in the European financial markets, particular around times of financial turmoil. This means that the peer group provides good comparison material for peer group 1 in terms of company performances. Peer group 3 consists of fourteen banks and insurance companies with market capitalizations ranging from 12 billion euros (CNP Assurances) to 150 billion (HSBC Holdings) in 2017.

A list of the companies from the respective peer groups as well as their statistics can be found in the appendix. This includes the firm size, firm age and all four of the performance measures used in this research.

Company results over the periods 2012-2014 and 2015-2017 will be analysed, and to be able to compare them to each other in a just way the companies have to be listed on the stock exchange markets at the entirety of those periods. The periods 2012-2014 and 2015-2017 where chosen because the Wbfo was implemented in February 2015, distinguishing both groups as before and after the implementation. The used company measures are available up and until 2017 and to create a comparable period the three years prior to the implementation of the Wbfo where chosen.

For the collection of data, the software program DataStream is used. This program will provide all the company measures over the years of 2012 up and until 2017. The first performance measure that will be used in the regression analysis is the Total Shareholder Return (TSR)

𝑇𝑆𝑅 =

𝑃(𝑡) − 𝑃(𝑡 − 1) + 𝐷𝑖𝑣

𝑃(𝑡 − 1)

Total shareholder return calculates the returns for shareholders, who are the companies’ owners (principals) and whose interest is represented by this measure of performance of stocks over time. Also total shareholder return is used by Doucouliagos et al (2007) and McKnight & Tomkins (1999) who both stated that total shareholder return is positively related to bonus payments.

The second performance measure that will be used is the Return On Assets (ROA). Return on assets shows returns relative to total assets, which can be used to describe a

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company’s size. Return on assets is being used as a performance measure by Doucouliagos et al (2007) who state that it is positively related to bonus payments.

𝑅𝑂𝐴 =

𝑁𝑒𝑡 𝑖𝑛𝑐𝑜𝑚𝑒

𝑇𝑜𝑡𝑎𝑙 𝑎𝑠𝑠𝑒𝑡𝑠

The third performance measure that will be used is the Return On Equity (ROE). Return on equity forms a solid performance measure because, just like the ROA, it measures a company’s return relative to another measure, in this case the shareholders equity. The shareholders equity is equal to Total Assets - Total Liabilities. ROE is also used by Doucouliagos et al (2007) who state that it is positively related to bonus payments.

𝑅𝑂𝐸 =

𝑁𝑒𝑡 𝑖𝑛𝑐𝑜𝑚𝑒

𝑆ℎ𝑎𝑟𝑒ℎ𝑜𝑙𝑑𝑒𝑟𝑠 𝑒𝑞𝑢𝑖𝑡𝑦

The final performance measure that will be used is the Earnings Per Share (EPS). Earnings per share gives the net income (corrected for dividends) and divides it by the number of shares. This way also EPS provides a measurement relative to a component that indicates a company’s size (in numbers of shares). It is used by McKnight & Tomkins (1999) and is expected to be positively related to bonus payments.

𝐸𝑃𝑆 =

𝑁𝑒𝑡 𝑖𝑛𝑐𝑜𝑚𝑒 − 𝐷𝑖𝑣𝑖𝑑𝑒𝑛𝑑𝑠 𝑝𝑎𝑦𝑒𝑑

#𝑆ℎ𝑎𝑟𝑒𝑠 𝑜𝑢𝑡𝑠𝑡𝑎𝑛𝑑𝑖𝑛𝑔

In order to improve the quality of the regression and avoid internal validity problems control variables are added. The first one is the companies’ size. This is expressed with market capitalization, being the price per share times the number of shares outstanding. The second control variable is the age of the companies in number of years since their respective dates of foundation. It is arguable that both the firm size and the firm age affect the companies’ performance measures. By adding them into the regression we control their effects and find a more accurate coefficient for the dummy variable

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13 Regression

To compare the company performance measures for the three respective peer groups between the two respective periods, an OLS regression is run by use of the software program Stata. The formula of the particular OLS regression from this research is given as follows:

(𝑃𝑒𝑟𝑓. 𝑀𝑒𝑎𝑠. ) = 𝛼 + 𝛽(𝑓𝑖𝑟𝑚𝑠𝑖𝑧𝑒) + 𝛽(𝑓𝑖𝑟𝑚𝑎𝑔𝑒) + 𝛽(𝑎𝑓𝑡𝑒𝑟2015) + 𝜀

This regression is going to be run a total of twelve times. Four for every peer group, each consisting of a different company measure as the dependent variable. This will provide twelve coefficients for the variable after2015 that show the difference of the particular performance measure over the two used periods. We use the p-value to determine the significance of each individual coefficient.

After analysing these coefficients and determining their significance, we must compare the particular coefficients for each respective performance measure over the three different peer groups. When the β1(after2015) is significantly larger (or smaller in

case of a negative effect) than both the β2(after2015) and the β3(after2015), we can assign the changes in the performance measures to an independent variable that applies only to Dutch listed companies from the financial sector, and not to Dutch listed companies from the non-financial sector nor to non-Dutch companies from the financial sector. Hereby we exclude a lot of possible effects that influence the computed coefficients of

β1(after2015).

To test equality of coefficients from two different regressions we use the Z-test. A Z-test follows a standard normal distribution with a mean of 0 and a standard deviation of 1 and the formula is as follows:

𝑍 =

𝛽1 − 𝛽2

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𝐻0: 𝛽1 = 𝛽2

𝐻1: 𝛽1 ≠ 𝛽2

The zero hypothesis for the Z-test states that the tested coefficients are considered to be equal. The alternative hypothesis for the Z-test states an inequality between the two tested coefficients. Based on whether the coefficients of after2015 for a particular performance measure differ significantly between the peer groups Dutchfinancial and

Dutchnonfinancial, and between Dutchfinancial and Nondutchfinancial we can make a

statement on the effect of the implementation of the Wbfo on that particular performance measure as well as about its significance. Conclusions about the significance of the Z-values will be made based on their p-values.

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15 4. Regression analysis and Empirical results

The total of twelve regressions that were run resulted in twelve coefficients that are provided in table 4.1, 4.2, and 4.3. These consist of the coefficients for peer group 1

[β1(after2015)], peer group 2 [β2(after2015)] and peer group 3 [β3(after2015)], for all four of the performance measures (TSR, ROA, ROE and EPS). The analysis will cover the three peer groups, Dutch financial companies, Dutch non-financial companies and non-Dutch financial companies, individually and in a chronological order.

β1(after2015) represents the change in the dependant variable (i.e. performance measure) between the periods 2012-2014 and 2015-2017 for the Dutch companies from the financial sector, provided in table 4.1. The computed coefficient shows that within the analysed dataset the period of 2015-2017, which includes the stricter bonus regulations through the Wbfo, has a ROE that is on average 8.99 percentage points higher than the period 2012-2014 (at α=0.1). Also the EPS increases with €0.94 on average between the two periods (at α=0.05). The effects of the dummy variable

after2015 on TSR and ROA are insignificant for this peer group with a two-sided test.

However at a one-sided test, the effect of after2015 on ROA is significant (at α=0.1).

Table 4.1 - OLS regression with dependent variables 'TSR', 'ROA', 'ROE' and 'EPS' respectively for Dutch

financial companies (β1)

Dependent variable TSR ROA ROE EPS

(1) (2) (3) (4)

Firmsize 7.77e-6 ** -0.0001 -0.00003 2.89e-6 (0.053) (0.510) (0.871) (0.794) Firmage 0.0004 0.0047 -0.0044 0.0012 (0.464) (0.716) (0.869) (0.462) after2015 -0.0974 3.9033 8.9885 ** 0.9397 *** (0.345) (0.127) (0.061) (0.002) Std. Err. 4.5469 2.4894 4.6533 0.2847 _cons -0.0037 0.0079 2.5886 0.2775 (0.974) (0.998) (0.625) (0.404)

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β2(after2015) represents the change in the dependant variable (i.e. performance measure) between the periods 2012-2014 and 2015-2017 for the peer group containing Dutch listed companies that are not from the financial sector, provided in table 4.2. The computed coefficients show that within the analysed dataset the effect of the dummy variable after2015 on the dependent variable is insignificant for all four of the performance measures. This means that within the Dutch non-financial sector, none of the four performance measures has changed significantly between the periods 2012-2014 and 2015-2017.

Table 4.2 - OLS regression with dependent variables 'TSR', 'ROA', 'ROE' and 'EPS' respectively for

Dutch non-financial companies (β2)

(1) (2) (3) (4) Firmsize 5.18e-7 0.00004 0.00015 0.00001 *** (0.712) (0.065) (0.591) (0.050) Firmage -0.0002 0.0014 -0.1829 -0.0003 (0.806) (0.925) (0.326) (0.935) after2015 -0.0349 0.0473 15.5547 0.3163 (0.622) (0.966) (0.265) (0.257) Std. Err. 0.0703 1.1033 13.8492 0.2770 _cons 0.1498 *** 3.3498 *** 15.8083 1.0750 *** (0.039) (0.004) (0.262) (0.000)

Note: ***(**) significant at 5% (10%) according to two-sided test.

β3(after2015) represents the change in the dependant variable (i.e. performance measure) between the periods 2012-2014 and 2015-2017 for the peer group that contains non-Dutch listed companies from the financial sector, being banks and insurance companies from France, Germany and the UK, provided in table 4.3. The computed coefficients show that within the analysed dataset the period of 2015-2017, which does not include the stricter bonus regulations through the Wbfo since that is only applicable to Dutch financial companies, has a TSR that is on average 14.47 percentage points lower that the

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TSR over the period 2012-2014 (at α=0.05). The effect of the dummy variable after2015 on the other three performance measures is insignificant.

non-Dutch financial companies (β3)

(1) (2) (3) (4)

Firmsize 1.40e-6 2.04e-6 0.00004 ** -0.0001 (0.176) (0.155) (0.083) (0.470) Firmage -0.0018 -0.00023 -0.0348 *** 0.0256 (0.685) (0.742) (0.001) (0.631) after2015 -0.1447 *** -0.0982 -0.8909 -5.8547 (0.028) (0.273) (0.572) (0.467) Std. Err. 0.0652 0.0888 1.5702 0.8.0160 _cons 0.1206 0.4757 *** 8.3827 26.9009 ** (0.128) (0.000) (0.000) (0.007)

The Z-values that are computed by inserting the coefficients and their standard errors in the formula for Z are shown in table 4.4. The Dutch financial companies vs. Dutch non-financial companies column provides the Z-values and differences for β1(after2015) and β2(after2015) for all four of the respective performance measures. The Dutch financial companies vs. non-Dutch financial companies column provides the Z-value and differences for β1(after2015) and β3(after2015) for all four of the respective performance

measures. In order to determine an inequality between coefficients we must reject the null hypothesis. This happens when the computed Z-value has a p-value lower than α. The Z-test provides relevance to the research since it can be used to show a difference in the effect of the different time periods on the companies’ performance measures for the respective peer groups, therewith possibly describing the effect of the Wbfo on company performance.

As shown in table 4.4, the Z-values for both inequality tests with ROA as the dependent variable are significant (P < 0.1). Also the t-value of the coefficient

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β1(after2015) is significant (P < 0.1) at a one-sided test with alternative hypothesis (β1 >

0), meaning that there is a significant rise in the actual value of the dependent variable

ROA between the periods 2012-2014 and 2015-2017 in the Dutch financial sector. If we combine the significant Z-values with the effect of after2015 on ROA in the Dutch financial sector we can state that the rise in ROA between the two periods is significant and that it is caused by an effect that is only applicable to the Dutch financial sector, likely to be the Wbfo. This means that there is a negative relation between bonus payments and ROA. This conclusion is contradictory with the findings of both Doucouliagos et al (2007) and McKnight & Tomkins (1999) who stated that this relation is positive.

The t-values of the coefficient β1(after2015) for both the ROE and the EPS as

dependent variables are significant (P < 0.05) with for a one-sided test with alternative hypothesis (β1 > 0). However, ROE only has a significant Z-value for the difference between the Dutch financial sector and the non-Dutch financial sector. This means that we can state that the difference in ROE for the Dutch financial sector between the two periods is significant but that it is caused by an effect that is applicable to both the financial and the non-financial sector in the Netherlands, therewith excluding the Wbfo as the explanatory factor since that is only applicable to the Dutch financial sector. This means that bonus payments are not positively correlated to ROE. This conclusion differs from the research conducted by Doucouliagos et al (2007) who state that there is a positive relation between ROE and bonus payments.

Also, EPS only has a significant Z-value for the difference between the Dutch financial sector and the Dutch non-financial sector. This means that we can state that the difference in EPS for the Dutch financial sector between the two periods is significant but that it is caused by an effect that is applicable to both the Dutch and the non-Dutch financial sector, therewith excluding the Wbfo as the explanatory factor since that is only applicable to the Dutch financial sector. This means that bonus payments are not positively correlated to EPS. This conclusion differs from the research conducted by McKnight & Tomkins (1999) who state that there is a positive relation between EPS and bonus payments.

For the TSR as the dependent variable we can state that there is no significant difference in its value between the two periods and that there is no significant difference between the coefficients of the respective peer groups.

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19

Table 4.4 - Z-test for equality of coefficients, for β1=β2 and β1=β3 respectively. Dutch Financial Companies vs.

Dutch Non-Financial Companies

Dutch Financial Companies vs. Non-Dutch Financial Companies Dependent Variable Z-value Significant difference? Z-value Significant difference? TSR -0.5049 P > 0.1 0.391 P > 0.1 ROA 1.416 P < 0.1 1.6064 P < 0.1 ROE -0.4494 P > 0.1 2.0117 P < 0.05 EPS 1.5644 P < 0.1 0.8469 P > 0.1

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20 5. Discussion

This research showed that the respective performance measures of ROA, ROE and EPS were significantly higher, at a one-sided test, in the period 2015-2017 than in 2012-2014 for Dutch companies from the financial sector. However, regarding ROE, this was also the case for Dutch companies from the non-financial sector, and regarding EPS, this was also the case for non-Dutch financial companies. This means that any changes in the two performance measures ROE and EPS for the Dutch financial sector are highly unlikely to be caused by the implementation of the Wbfo, because that legislation is only applicable to the Dutch financial sector. In theory it is possible that both the peer group with the Dutch non-financial companies and the peer group with the non-Dutch financial companies have been subjected to external legislation implementations in early 2015 themselves, which are not applicable to the Dutch financial sector, that compensate for the effect of the Wbfo. This is however very unlikely. For the performance measure ROA this research showed that the increase between the periods 2015-2017 and 2012-2014 for Dutch financial companies was bigger than for Dutch non-financial companies and for non-Dutch financial companies. This means that according to this research the Wbfo has had a positive effect on the ROA.

Based on the results posited in this research we can conclude that a stricter policy on bonus regulations for Dutch listed financial companies has not had an effect on company performances. This is in line with the findings of Hölmström and Milgrom (1994) and the findings of Duffhues & Kabir (2008) who both state that lowering bonus payments does not necessarily have a negative effect on company performances. This research provides a strong argument in favour or stricter bonus regulations in for example the European Union. Both Beltratti & Stulz (2009) and Turner (2009) state that bad governance within financial companies has contributed to the financial crisis. By implementing legislation comparable to the Wbfo across the European Union, a more sustainable and serviceable financial market can be realized without negatively affecting the financial companies’ performances.

One limitation of this research is the timeframe. Data from the periods 2012-2014 and 2015-2017 provides for a short-term focused comparison. Because the intention of the Wbfo is to create a sustainable, durable and serviceable financial sector, that is more likely to withstand a financial crisis, perhaps it takes more time to realize

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21

this than just three years. Also the question arises whether a sustainable and more serviceable financial sector expresses itself through higher values of the performance measures that were used in this research. Therefore possible future research could focus on both longer periods that are also further apart to test the results of a similar implementation of new legislation (or even the Wbfo itself), and difference performance measures. Also in case of an economic downturn it might be interesting to see to what extent the new financial market can withstand a possible financial recession. Another limitation is the size of the peer groups, in particular peer group 1. The amount of Dutch financial companies that are listed on the Amsterdam Stock Exchange is limited. Future research could focus on more extensive peer groups, providing for peer groups that are more representative to the sectors that are being researched.

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22 7. References

Bebchuk, L.A., and Fried, J.M., 2003. Executive Compensation as an Agency Problem.

Journal of Economic Perspective, 17(3), pp. 71-92.

Beltratti, A., and Stulz, R.M., 2009. Why did Some Banks Perform Better During the Credit Crisis?: A Cross-Country Study of the Impact of Governance and Regulation.

National Bureau of Economic Research. Working Paper 15180.

Dijsselbloem, J., (2016, 5 September). Wijziging van de Wet op het financieel toezicht houdende regels met betrekking tot het beloningsbeleid van financiële ondernemingen (Wet beloningsbeleid financiële ondernemingen) [Kamerbrief]. Geraadpleegd van https://zoek.officielebekendmakingen.nl/kst-33964-43.html?zoekcriteria=%3fzkt%3dEenvoudig%26pst%3d%26vrt%3dwet%2bbelonings beleid%2bfinanciele%2bondernemingen%26zkd%3dInDeGeheleText%26dpr%3dAfgel openDag%26spd%3d20180624%26epd%3d20180625%26sdt%3dDatumBrief%26ap %3d%26pnr%3d22%26rpp%3d10%26_page%3d23%26sorttype%3d1%26sortorder %3d4&resultIndex=223&sorttype=1&sortorder=4

Doucouliagos, H., Haman, J., and Askary, S., 2007. Directors Remuneration and Performance in Australian Banking. Corporate Governance: An International Review. 15(6), pp. 1363-1383.

Duffhues, P. and Kabir, R., 2008. Is the Pay-Performance Relationship Always Positive? Evidence from the Netherlands. Journal of Multinational Financial Management, 18(1), pp. 45-60.

Eisenhardt, K.M., 1989. Agency Theory: An Assessment and Review. The Academy of

Management Review, 14(1), pp. 57-74.

Ferrarini, G. and Ungureanu, M.C., 2011. Economics, Politics and The International Principles for Sound Compensation Practices: An Analysis of Executive Pay at European Banks. Vanderbilt Law Review, 64(2), pp. 429.

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23

Gibbons, R., 1998. Incentives in Organizations. Journal of Economic Perspective, 12(4), pp. 115-132.

Hölström, B., and Milgrom, P., 1994. The Firm as an Incentive System. The American

Economic Review. 84(4), pp. 972-991.

Jensen, M.C., and Meckling, W.H., 1976. Theory of the Firm: Managerial Behavior, Agency Costs and Ownership Structure. Journal of Financial Economics, 3(4), pp. 305-360.

Kerr, S., 1975. On the Folly of Rewarding A, While Hoping for B. Academy of Management

Journal, 18(4), pp. 769-783.

McKnight, P.J., and Tomkins, C., 1999. Top Executive Pay in the United Kingdom: A Corporate Governance Dilemma. International Journal of the Economics of Business, 6(2), pp. 223-243.

Syllignakis, M.N., and Kouretas, G.P., 2011. Dynamic correlation analysis of financial contagion: Evidence from the Central and Eastern European Markets. International

Review of Economics and Finance. 20(4), pp. 717-732.

Turner, A. 2009. The Turner Review: A Regulatory Response to the Global Banking Crisis. London, England: Financial Services Authority.

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24 8. Appendix

List of Companies

Peer group 1: Peer group 3:

AEGON Bank N.V. Allianz (German)

BinckBank N.V. AXA (French)

Delta Lloyd N.V. Barclays (English)

Euronext N.V. BNP Paribas (French)

ING Groep N.V. CNP Assurances (French)

Kas Bank N.V. Crédit Agricole (French)

NN Group N.V. Deutsche Bank (German)

Van Lanschot Kempen N.V. HSBC Bank Plc (English)

Lloyds Bank Plc (English)

Peer group 2: Münchener Rück (German)

Prudential plc (English)

Air France-KLM RBS (English)

AkzoNobel Société Générale (French)

Heijmans N.V. Standard Chartered (English) Heineken

Koninklijke Philips Randstad N.V. Royal Dutch Shell SBM Offshore Sligro Food Group TomTom

Unilever

Koninklijke Vopak Wolters Kluwer

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25

Table 4.1 - OLS regression with dependent variables 'TSR', 'ROA', 'ROE' and 'EPS' respectively for Dutch

financial companies (β1)

(1) (2) (3) (4)

Firmsize 7.77e-6 ** -0.0001 -0.00003 2.89e-6 (0.053) (0.510) (0.871) (0.794) Firmage 0.0004 0.0047 -0.0044 0.0012 (0.464) (0.716) (0.869) (0.462) after2015 -0.0974 3.9033 8.9885 ** 0.9397 *** (0.345) (0.127) (0.061) (0.002) Std. Err. 4.5469 2.4894 4.6533 0.2847 _cons -0.0037 0.0079 2.5886 0.2775 (0.974) (0.998) (0.625) (0.404) 0.1138 0.0847 0.0941 0.2350 0.0399 -0.0012 0.0186 0.1746 F(k, n-k) 1.54 0.99 1.25 3.89 No. Observations, n 40 36 40 42

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26

Dutch non-financial companies (β2)

(1) (2) (3) (4) Firmsize 5.18e-7 0.00004 0.00015 0.00001 *** (0.712) (0.065) (0.591) (0.050) Firmage -0.0002 0.0014 -0.1829 -0.0003 (0.806) (0.925) (0.326) (0.935) after2015 -0.0349 0.0473 15.5547 0.3163 (0.622) (0.966) (0.265) (0.257) Std. Err. 0.0703 1.1033 13.8492 0.2770 _cons 0.1498 *** 3.3498 *** 15.8083 1.0750 *** (0.039) (0.004) (0.262) (0.000) 0.0049 0.0646 0.0299 0.0865 -0.0355 0.0266 -0.0099 0.0494 F(k, n-k) 0.12 1.70 0.75 2.33 No. Observations, n 78 78 77 78

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27

non-Dutch financial companies (β3)

(1) (2) (3) (4)

Firmsize 1.40e-6 2.04e-6 0.00004 ** -0.0001 (0.176) (0.155) (0.083) (0.470) Firmage -0.0018 -0.00023 -0.0348 *** 0.0256 (0.685) (0.742) (0.001) (0.631) after2015 -0.1447 *** -0.0982 -0.8909 -5.8547 (0.028) (0.273) (0.572) (0.467) Std. Err. 0.0652 0.0888 1.5702 0.8.0160 _cons 0.1206 0.4757 *** 8.3827 26.9009 ** (0.128) (0.000) (0.000) (0.007) 0.0698 0.0405 0.1406 0.0174 0.0349 -0.0012 0.1084 -0.0194 F(k, n-k) 2.00 0.97 4.36 0.47 No. Observations, n 84 73 84 84

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28

Dutch non-financial market statistics

Market Value (in millions Euro) controle variable 1

Start 2011

End 2017

Frequency Y

Name 2011 2012 2013 2014 2015 2016 2017

AIR FRANCE-KLM - MARKET VALUE 3240.87 1021.35 2167.28 3517.07 2167.28 2056.8 3294.9 AKZO NOBEL - MARKET VALUE 11125.66 8887.64 11076.68 13381.23 16234.07 15260.48 19102.35

HEIJMANS - MARKET VALUE 347.69 101.72 130.04 252.89 229.7 174.1 151.88

HEINEKEN - MARKET VALUE 23414.5 21902.49 30453.25 29197.56 38707.37 47998.28 50296.54 PHILIPS ELTN.KONINKLIJKE - MARKET VALUE 18262.44 14999.95 21097.87 22634.5 22496.43 21876.9 29678.91 RANDSTAD - MARKET VALUE 5484.61 3887.98 5640.19 7918.52 9420.69 8671.44 9511.08 ROYAL DUTCH SHELL A - MARKET VALUE 85778.88 92974.06 94208.63 114153.5 101356.5 99166.75 106410.9 SBM OFFSHORE - MARKET VALUE 2872.41 1810.41 2891.92 2478.17 2393.58 2475.24 2896.8 SLIGRO FOOD GROUP - MARKET VALUE 1115.23 944.84 1137.35 1414.83 1529.01 1498.03 1841.67 TOM TOM - MARKET VALUE 1215.76 746.01 878.26 1204.76 2193.88 1954.37 2139 UNILEVER DR - MARKET VALUE 38126.92 42876.72 52830.7 54159.61 63384.86 69780.75 86096.44 VOPAK - MARKET VALUE 4316.36 6083.68 5930.92 4825.79 5759.62 6240.92 5237.41 WOLTERS KLUWER - MARKET VALUE 4550.07 3541.25 4936 6566.25 8232.72 10747.52 11601.88

Max 2017 (Royal Dutch Shell) 106,410.90

Min 2017 (Heijmans) 151.88 TSRt = (Pt-(Pt-1)+Div)/(Pt-1) Start 2011 End 2017 Frequency Y Name 2011 2012 2013 2014 2015 2016 2017 AIR FRANCE-KLM 10.795 3.402 7.219 11.715 7.219 6.851 10.975

AIR FRANCE-KLM - DIV.PER SHR. 0 0 0 0 0 0 0

TSR -0.684854099 1.121987066 0.622800942 -0.383781477 -0.05097659 0.601955919

AKZO NOBEL 48.025 37.87 46.19 55.8 66.91 61.63 75.75

AKZO NOBEL - DIV.PER SHR. 1.35 1.45 1.45 1.45 1.45 1.55 1.65

TSR -0.181259761 0.257987853 0.239445767 0.225089606 -0.055746525 0.255881876 HEIJMANS 18.5354 5.6009 7.306 12.995 10.73 8.133 7.094 HEIJMANS - DIV.PER SHR. 0 0.32 0.24 0.15 0 0 0 TSR -0.680562599 0.347283472 0.799206132 -0.174297807 -0.242031687 -0.127751137 HEINEKEN 40.65 38.025 52.87 50.69 67.2 83.33 87.32 HEINEKEN - DIV.PER SHR. 0.65 0.83 0.89 0.89 1.1 1.3 1.34 TSR -0.044157442 0.413806706 -0.02439947 0.3474058 0.259375 0.063962559 PHILIPS ELTN.KONINKLIJKE 15.5151 13.0202 20.2187 22.915 24.065 23.495 31.925 PHILIPS ELTN.KONINKLIJKE - DIV.PER SHR. 0.62 0.64 0.68 0.77 0.8 0.8 0.8 TSR -0.119554499 0.605098232 0.1714403 0.085097098 0.009557449 0.392849542

RANDSTAD 32.095 22.595 31.835 43.965 51.49 47.38 51.9

RANDSTAD - DIV.PER SHR. 1.18 1.25 1.25 0.95 1.29 1.68 1.89

TSR -0.257049385 0.464262005 0.410868541 0.200500398 -0.04719363 0.135289152

ROYAL DUTCH SHELL A 23.86 25.155 24.805 29.045 26.025 23.195 23.77

ROYAL DUTCH SHELL A - DIV.PER SHR. 1.25 1.27 1.32 1.35 1.66 1.78 1.67

TSR 0.107502096 0.038560922 0.225357791 -0.046823894 -0.040345821 0.096788101

SBM OFFSHORE 16.7094 10.3612 13.895 11.855 11.4 11.655 13.57

SBM OFFSHORE - DIV.PER SHR. 0.47 0 0 0 0 0.18 0.21

TSR -0.379917891 0.341060881 -0.146815401 -0.03838043 0.038157895 0.182325182

SLIGRO FOOD GROUP 25.2 21.35 25.7 31.97 34.55 33.85 41.615

SLIGRO FOOD GROUP - DIV.PER SHR. 1 1.05 1.05 1.05 1.1 1.2 1.3

TSR -0.111111111 0.2529274 0.284824903 0.115107914 0.01447178 0.267799114

TOM TOM 5.479 3.362 3.958 5.421 9.725 8.479 9.206

TOM TOM - DIV.PER SHR. 0 0 0 0 0 0 0

TSR -0.386384377 0.177275431 0.369631127 0.793949456 -0.128123393 0.085741243 UNILEVER DR 22.235 25.005 30.81 31.585 36.965 40.695 50.21 UNILEVER DR - DIV.PER SHR. 0.88 0.92 1 1.09 1.16 1.23 1.32 TSR 0.165954576 0.272145571 0.060532295 0.207060313 0.134180982 0.266248925 VOPAK 33.765 47.59 46.395 37.75 45.055 48.82 40.97 VOPAK - DIV.PER SHR. 0.8 0.8 0.88 0.9 0.9 1 1.05 TSR 0.433140826 -0.006619038 -0.166936092 0.217350993 0.105759627 -0.139287177 WOLTERS KLUWER 15.235 11.73 16.35 21.75 27.27 35.6 38.43

WOLTERS KLUWER - DIV.PER SHR. 0.67 0.68 0.69 0.7 0.71 0.76 0.8

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29

ROA = Net income / Total Assets (%)

Start 2011

End 2017

Frequency Y

Name 2011 2012 2013 2014 2015 2016 2017

AIR FRANCE - KLM - RETURN ON ASSETS - TOTAL (%) -0.72 -3.24 -5.85 0.56 1.44 4.14 -0.38 AKZO NOBEL N.V. - RETURN ON ASSETS - TOTAL (%) 3.39 -10.31 5.81 4.51 7.19 7.12 5.87 HEIJMANS NV - RETURN ON ASSETS - TOTAL (%) -1.74 -5.31 0.3 -3.23 -1.31 -9.08 3.56 HEINEKEN N.V. - RETURN ON ASSETS - TOTAL (%) 6.8 11.01 5.27 5.48 6.21 4.76 5.79 KONINKLIJKE PHILIPS - RETURN ON ASSETS - TOTAL (%) -3.6 1.44 5.52 2.95 3.02 5.84 6.85 RANDSTAD NV - RETURN ON ASSETS - TOTAL (%) 2.98 0.68 3.86 5.52 7.93 7.62 7.08 ROYAL DUTCH SHELL - RETURN ON ASSETS - TOTAL (%) 8.95 7.84 4.83 4.27 0.9 1.68 3.76 SBM OFFSHORE NV - RETURN ON ASSETS - TOTAL (%) -7.52 0.21 2.74 6.46 1.26 2.94 0.67 SLIGRO FOOD GROUP NV - RETURN ON ASSETS - TOTAL (%) 8.94 7.77 7.27 7.16 8.07 6.67 6.32 TOMTOM N.V. - RETURN ON ASSETS - TOTAL (%) -18.83 8.12 1.36 1.65 1.21 0.83 -13.59 UNILEVER N.V. - RETURN ON ASSETS - TOTAL (%) 10.54 10.49 11.56 12.05 10.73 10.36 11.16 KONINKLIJKE VOPAK NV - RETURN ON ASSETS - TOTAL (%) 11.4 8.44 8.16 6.27 6.53 11.16 6.1 WOLTERS KLUWER N.V. - RETURN ON ASSETS - TOTAL (%) 3.28 6.34 6.58 8.02 6.55 6.72 8.9

ROE = Net income / Shareholders equity

Start 2011

End 2017

Frequency Y

Name 2011 2012 2013 2014 2015 2016 2017

AIR FRANCE - KLM - RETURN ON EQUITY - TOTAL (%) -9.82 -21.74 -62.67 -25.16 NA 512.62 -17.76 AKZO NOBEL N.V. - RETURN ON EQUITY - TOTAL (%) 5.24 -26.94 12.75 9.59 15.95 14.88 13.4 HEIJMANS NV - RETURN ON EQUITY - TOTAL (%) -8.64 -24.15 0.59 -16.53 -10.38 -54.18 12.85 HEINEKEN N.V. - RETURN ON EQUITY - TOTAL (%) 14.51 27.48 11.79 12.73 14.59 11.5 14.57 KONINKLIJKE PHILIPS - RETURN ON EQUITY - TOTAL (%) -9.45 1.93 10.45 3.76 5.73 11.94 13.5 RANDSTAD NV - RETURN ON EQUITY - TOTAL (%) 6.34 1.13 8 10.53 14.11 14.38 14.75 ROYAL DUTCH SHELL - RETURN ON EQUITY - TOTAL (%) 18.32 15.05 9.34 8.24 1.2 2.54 6.72 SBM OFFSHORE NV - RETURN ON EQUITY - TOTAL (%) -26.63 -5.85 6.4 24.99 1.22 7.04 -6.09 SLIGRO FOOD GROUP NV - RETURN ON EQUITY - TOTAL (%) 15.03 12.7 12.08 12.1 13.78 11.84 12.68 TOMTOM N.V. - RETURN ON EQUITY - TOTAL (%) -47.54 16.7 2.32 2.58 1.94 1.22 -23.35 UNILEVER N.V. - RETURN ON EQUITY - TOTAL (%) 29.55 30.42 32.57 36.94 33.75 32.61 40.38 KONINKLIJKE VOPAK NV - RETURN ON EQUITY - TOTAL (%) 24.98 18.02 18.22 13.85 14.98 24.22 9.65 WOLTERS KLUWER N.V. - RETURN ON EQUITY - TOTAL (%) 7.61 20.93 22.24 25.78 18.48 19.2 27.11

Trailing EPS = (Net income - Dividens) / Outstanding shares

Start 2011

End 2017

Frequency Y

Name 2011 2012 2013 2014 2015 2016 2017

AIR FRANCE-KLM - EARNINGS PER SHR 2.08 0 0 0 0 1.7 2.46

AKZO NOBEL - EARNINGS PER SHR 3.21 1.8 0 3.16 2.35 4.26 3.86

HEIJMANS - EARNINGS PER SHR 0.84 0 0 0.1 0 0 0

HEINEKEN - EARNINGS PER SHR 2.55 2.44 5.13 2.37 2.64 3.31 2.7

PHILIPS ELTN.KONINKLIJKE - EARNINGS PER SHR 1.25 0 0 1.22 0.41 0.63 1.8

RANDSTAD - EARNINGS PER SHR 1.75 0.94 0.17 1.38 1.85 3.01 3.22

ROYAL DUTCH SHELL A - EARNINGS PER SHR 2.84 3.6 2.94 1.8 1.88 0 0.86

SBM OFFSHORE - EARNINGS PER SHR 1.07 0 0 0.41 2.08 0.12 0.78

SLIGRO FOOD GROUP - EARNINGS PER SHR 1.59 1.78 1.58 1.55 1.58 1.85 1.66

TOM TOM - EARNINGS PER SHR 0.52 0 0.58 0.13 0.03 0.13 0.01

UNILEVER DR - EARNINGS PER SHR 1.51 1.51 1.71 1.71 1.82 1.73 1.83

VOPAK - EARNINGS PER SHR 2.06 3.08 2.52 2.45 1.94 2.21 4.19

WOLTERS KLUWER - EARNINGS PER SHR 0.97 0.44 1.08 1.22 1.22 1.44 1.68

Firm age (in years at 2017) controle variable 2

AIR FRANCE-KLM 13 AKZO NOBEL 23 HEIJMANS 94 HEINEKEN 153 PHILIPS ELTN.KONINKLIJKE 126 RANDSTAD 57

ROYAL DUTCH SHELL 110

SBM OFFSHORE 52

SLIGRO FOOD GROUP 82

TOM TOM 26

UNILEVER 88

VOPAK 18

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30

Dutch financial market statistics Market Value (in millions Euro) controle variable 1

Start 2011

End 2017

Frequency Y

Name 2011 2012 2013 2014 2015 2016 2017

AEGON - MARKET VALUE (~E ) 8807.32 6462.27 10821.57 13967.44 14609.6 9500.64 8962.05

BINCKBANK - MARKET VALUE (~E ) 806.46 484.47 503.17 634.89 590.72 397.46 307.93

DELTA LLOYD (OTC) - MARKET VALUE (~E ) 2670.32 1724.36 2698.17 3431.24 3434.93 1988.75 2420.85

EURONEXT - MARKET VALUE (~E ) NA NA NA NA 2737 2569.7 3256.4

ING GROEP - MARKET VALUE (~E ) 31193.35 18982.85 26740.93 41699.04 56528.69 42591.11 58433.86

KAS BANK - MARKET VALUE (~E ) 177.4 112.25 136.9 168.76 174.26 138.94 156.83

NN GROUP - MARKET VALUE (~E ) NA NA NA NA 8949.03 9759.75 10876.78

V LANSCHOT KEMPEN - MARKET VALUE (~E ) 941.73 674.3 676.41 762.91 812.74 661.16 1043.47

Max 2017 (ING GROEP) 58433.86

Min 2017 (KAS BANK) 156.83

TSRt = (Pt-(Pt-1)+Div)/(Pt-1) Start 2011 End 2017 Frequency Y Name 2011 2012 2013 2014 2015 2016 2017 AEGON - Pt 4.1944 3.1724 5.0747 6.553 6.808 4.425 4.32 AEGON - DIV.PER SHR. 0.09 0.09 0.2 0.22 0.23 0.26 0.26 TSR -0.222201 0.6626844 0.33466018 0.0740119 -0.311839 0.03502825 BINCKBANK - Pt 10.825 6.503 6.754 8.522 8.32 5.598 4.337 BINCKBANK - DIV.PER SHR. 0.47 0.41 0.29 0.39 0.41 0.39 0.22 TSR -0.3613857 0.08319237 0.31951436 0.02440742 -0.2802885 -0.1859593

DELTA LLOYD (OTC) - Pt 15.3505 8.3313 13.2382 16.56 12.5432 4.95 5.91

DELTA LLOYD (OTC) - DIV.PER SHR. 0.59 0.68 0.65 0.7 0.72 0.31 0.1

TSR -0.4129637 0.66699075 0.30380263 -0.1990821 -0.5806493 0.21414141

EURONEXT - Pt NA NA NA NA 39.1 36.71 46.52

EURONEXT - DIV.PER SHR. NA NA NA NA 0.84 1.24 1.42

TSR -0.0294118 0.3059112

ING GROEP - Pt 8.144 4.956 6.967 10.81 14.615 10.985 15.04

ING GROEP - DIV.PER SHR. 0 0 0 0 0.12 0.65 0.66

TSR -0.3914538 0.40577078 0.5516004 0.36308973 -0.2039001 0.42922167

KAS BANK - Pt 11.3 7.15 8.72 10.75 11.1 8.85 9.99

KAS BANK - DIV.PER SHR. 0.73 0.5 0.64 0.64 0.64 0.64 0.64

TSR -0.3230088 0.30909091 0.30619266 0.09209302 -0.145045 0.20112994

NN GROUP - Pt NA NA NA NA 25.525 29.26 31.195

NN GROUP - DIV.PER SHR. NA NA NA NA 0 1.51 1.55

TSR 0.20548482 0.11910458

V LANSCHOT KEMPEN - Pt 29.5 19 16.895 18.6 19.815 16.09 25.36

V LANSCHOT KEMPEN - DIV.PER SHR. 0.7 0.4 0 0.2 0.4 0.45 1.2

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31

ROA = Net income / Total Assets

Start 2011

End 2017

Frequency Y

Name 2011 2012 2013 2014 2015 2016 2017

AEGON N.V. - RETURN ON ASSETS - TOTAL (%) 0.12 0.51 0.28 0.35 0.2 0.17 0.67

BINCKBANK NV - RETURN ON ASSETS - TOTAL (%) 1.04 0.76 0.79 1.06 0.97 0.2 0.39

DELTA LLOYD - RETURN ON ASSETS - TOTAL (%) 0.58 -1.06 0.62 0.85 0.52 0.58 NA

EURONEXT NV - RETURN ON ASSETS - TOTAL (%) 15 10.77 10.52 13.92 22.06 26.66 26.87

ING GROEP N.V. - RETURN ON ASSETS - TOTAL (%) NA NA NA NA 0.74 0.77 0.84

KAS BANK NV - RETURN ON ASSETS - TOTAL (%) 0.18 NA 0.27 0.59 0.36 0.35 0.35

NN GROUP NV - RETURN ON ASSETS - TOTAL (%) NA 0.5 0.25 0.61 1.3 1 1.19

VAN LANS - RETURN ON ASSETS - TOTAL (%) 0.58 -0.44 NA 0.94 0.52 0.69 0.82

Start 2011

End 2017

Frequency Y

Name 2011 2012 2013 2014 2015 2016 2017

AEGON N.V. - RETURN ON EQUITY - TOTAL (%) -0.66 6.9 2.97 6.27 2.59 2.54 12.88

BINCKBANK NV - RETURN ON EQUITY - TOTAL (%) 7.29 5.21 4.34 7.24 6.76 1.09 2.27

DELTA LLOYD - RETURN ON EQUITY - TOTAL (%) -8.52 -58.31 6.83 14.19 5.09 8.03 NA

EURONEXT NV - RETURN ON EQUITY - TOTAL (%) 33.47 38 50.14 41.07 43.77 39.59 38.2

ING GROEP N.V. - RETURN ON EQUITY - TOTAL (%) 9.56 6.31 5.33 1.03 8.09 9.53 9.79

KAS BANK NV - RETURN ON EQUITY - TOTAL (%) 5.76 8.7 6.06 11.91 7.56 6.84 6.59

NN GROUP NV - RETURN ON EQUITY - TOTAL (%) NA 4.17 0.05 3.42 7.49 5.35 9.06

VAN LANS - RETURN ON EQUITY - TOTAL (%) 2.32 -11.06 NA 7.69 2.64 4.98 6.7

Start 2011

End 2017

Frequency Y

Name 2011 2012 2013 2014 2015 2016 2017

AEGON - EARNINGS PER SHR 0.47 0.22 0.5 0.36 0.46 0 0.34

BINCKBANK - EARNINGS PER SHR 0.6 0.41 0.26 0.3 0.52 0.42 0.06

DELTA LLOYD (OTC) - EARNINGS PER SHR 2.46 0 0 0.62 1.33 0.44 0.57

EURONEXT - EARNINGS PER SHR NA NA NA NA 2.27 2.47 2.77

ING GROEP - EARNINGS PER SHR 0.75 0.72 0.72 0 1.42 1.08 1.11

KAS BANK - EARNINGS PER SHR 1.27 0.7 1.06 0.83 1.65 1.03 1.01

NN GROUP - EARNINGS PER SHR NA NA NA NA 1.75 4.04 4.15

V LANSCHOT KEMPEN - EARNINGS PER SHR 1.47 0.84 0 0.71 2.42 0.47 1.61

AEGON 34 BINCKBANK 17 DELTA LLOYD 210 EURONEXT 166 ING GROEP 26 KAS BANK 211 NN GROUP 54 V LANSCHOT KEMPEN 280

(32)

32

Non-Dutch financial market statistics

Market Value (in millions Euro)

Start 2011

End 2017

Frequency Y

Name 2011 2012 2013 2014 2015 2016 2017

ALLIANZ - MARKET VALUE (~E ) 40905 34784.91 47683.21 59523.03 63000.61 74982.69 71890.63 AXA - MARKET VALUE (~E ) 29766.94 24585.27 31883.65 48813.18 46898.54 61171.55 58497.72 BARCLAYS - MARKET VALUE (~E ) 36893.39 25711.92 39606.29 52668.43 51766.1 49908.76 44397.98 BNP PARIBAS - MARKET VALUE (~E ) 59110.85 37790.14 52881.04 70498.75 61368.93 65088.3 75220 CNP ASSURANCES - MARKET VALUE (~E ) 8175.52 5941.51 7467.82 10230.61 10110.45 8541.53 12163.44 CREDIT AGRICOLE - MARKET VALUE (~E ) 23629.94 11073.72 15197.96 23277.28 27721.68 28715.88 33754.78 DEUTSCHE BANK - MARKET VALUE (~E ) 36947.61 27989.09 30684.63 35356.24 34688.7 31150.89 24243.47 HSBC HOLDINGS - MARKET VALUE (~E ) 133288.39 105041.08 147355.53 149914.45 150699.54 143206.36 152879.58 LLOYDS BANKING GROUP - MARKET VALUE (~E ) 51767.68 21314.04 41554.47 67662.75 69732.02 70758.83 52267.71 MUENCHENER RUCK. - MARKET VALUE (~E ) 21687.05 17440.92 24404.02 28829.26 28795.28 30870.28 28988.09 PRUDENTIAL - MARKET VALUE (~E ) 19682.32 19475.71 27284.44 41236.89 49366.99 53435 49211.26 ROYAL BANK OF SCTL.GP. - MARKET VALUE (~E ) 26439.32 14308.97 24287.53 25207.21 32352.41 47630.66 31109.2 SOCIETE GEN. (XET) - MARKET VALUE (~E ) 31133.23 13581.39 22229.92 33767.18 28306.51 34698.57 37943.43 STANDARD CHARTERED - MARKET VALUE (~E ) 46836.3 40214.57 46772.47 39669.3 30680.89 25071.53 25530.3

TSRt = (Pt-(Pt-1)+Div)/(Pt-1) Start 2011 End 2017 Frequency Y Name 2011 2012 2013 2014 2015 2016 2017 ALLIANZ (~E ) 90 76.4 104.58 130.39 137.857 164.076 157.31

ALLIANZ - DIV.PER SHR. (~E ) 4.1 4.5 4.5 4.5 5.3 6.85 7.3

TSR -0.101111111 0.427748691 0.289825971 0.09791395 0.239879005 0.003254589

AXA (~E ) 12.83 10.43 13.35 20.21 19.205 25.23 24.14

AXA - DIV.PER SHR. (~E ) 0.55 0.69 0.69 0.72 0.81 0.95 1.1

TSR -0.133281372 0.34611697 0.567790262 -0.009648689 0.36318667 0.000396354

BARCLAYS (~E ) 2.8 1.95 2.99 3.27 3.14 2.97 2.62

BARCLAYS - DIV.PER SHR. (~E ) 0.05 0.06 0.07 0.07 0.08 0.09 0.06

TSR -0.282142857 0.569230769 0.117056856 -0.01529052 -0.025477707 -0.097643098

BNP PARIBAS (~E ) 49.335 31.29 42.585 56.65 49.26 52.23 60.35

BNP PARIBAS - DIV.PER SHR. (~E ) 1.5 2.1 1.2 1.5 1.5 1.5 2.31

TSR -0.323198541 0.399328859 0.365504286 -0.103971756 0.090742996 0.199693663

CNP ASSURANCES (~E ) 13.76 10 11.605 14.9 14.725 12.44 17.715

CNP ASSURANCES - DIV.PER SHR. (~E ) 0.75 0.77 0.77 0.77 0.77 0.77 0.77

TSR -0.217296512 0.2375 0.350280052 0.039932886 -0.102886248 0.485932476

CREDIT AGRICOLE (~E ) 9.839 4.433 6.084 9.305 10.76 10.88 11.86

CREDIT AGRICOLE - DIV.PER SHR. (~E ) 0.45 0 0 0 0.35 0.35 0.6

TSR -0.549446082 0.372434018 0.529421433 0.19398173 0.043680297 0.145220588

DEUTSCHE BANK (~E ) 33.817 25.618 28.085 29.504 22.462 20.171 15.698

DEUTSCHE BANK - DIV.PER SHR. (~E ) 0.64 0.64 0.64 0.64 0.67 0.67 0

TSR -0.223526629 0.121281911 0.073313156 -0.215970716 -0.072166325 -0.221754003

HSBC HOLDINGS (~E ) 7.54 5.88 7.98 7.96 7.84 7.27 7.7

HSBC HOLDINGS - DIV.PER SHR. (~E ) 0.25 0.3 0.32 0.31 0.42 0.46 0.44

TSR -0.180371353 0.411564626 0.036340852 0.037688442 -0.014030612 0.119669876

LLOYDS BANKING GROUP (~E ) 0.76 0.31 0.59 0.95 0.98 0.99 0.73

LLOYDS BANKING GROUP - DIV.PER SHR. (~E ) 0 0 0 0 0 0.02 0.03

TSR -0.592105263 0.903225806 0.610169492 0.031578947 0.030612245 -0.232323232

MUENCHENER RUCK. (~E ) 115.07 97.25 136.076 160.751 166.502 185.025 179.99

MUENCHENER RUCK. - DIV.PER SHR. (~E ) 5.75 6.25 6.25 7 7.25 7.75 8.25

TSR -0.100547493 0.463506427 0.232774332 0.080876635 0.1577939 0.01737603

PRUDENTIAL (~E ) 7.73 7.64 10.67 16.11 19.23 20.77 19.07

PRUDENTIAL - DIV.PER SHR. (~E ) 0.23 0.3 0.32 0.37 0.45 0.52 0.46

TSR 0.027166882 0.438481675 0.544517338 0.22160149 0.107124285 -0.059701493

ROYAL BANK OF SCTL.GP. (~E ) 4.52 2.42 4 4.06 5.08 4.1 2.63

ROYAL BANK OF SCTL.GP. - DIV.PER SHR. (~E ) 0 0 0 0 0 0 0

TSR -0.46460177 0.652892562 0.015 0.251231527 -0.192913386 -0.358536585

SOCIETE GEN. (XET) (~E ) 41.71 17.5 28.49 42.28 35.15 43.04 46.98

SOCIETE GEN. (XET) - DIV.PER SHR. (~E ) 0.25 0 0 0.45 1 1.2 2

TSR -0.580436346 0.628 0.4998245 -0.144985809 0.258605974 0.138011152

STANDARD CHARTERED (~E ) 19.01 16.06 18.46 15.56 11.81 7.65 7.77

STANDARD CHARTERED - DIV.PER SHR. (~E ) 0.47 0.5 0.59 0.64 0.62 0.13 0

(33)

33

ROA = Net income / Total assets

Start 2011

End 2017

Frequency Y

Name 2011 2012 2013 2014 2015 2016 2017

ALLIANZ SE - RETURN ON ASSETS - TOTAL (%) 0.55 0.94 1 0.97 0.92 0.91 0.87 AXA SA - RETURN ON ASSETS - TOTAL (%) 0.64 0.63 0.63 0.66 0.67 0.66 0.72 BARCLAYS PLC - RETURN ON ASSETS - TOTAL (%) 0.43 0.23 0.04 -0.01 -0.02 0.16 -0.14 BNP PARIBAS SA - RETURN ON ASSETS - TOTAL (%) 0.46 0.48 0.35 0.03 0.4 0.45 0.46 CNP ASSURANCES - RETURN ON ASSETS - TOTAL (%) 0.29 0.3 0.31 0.31 0.31 0.33 0.35 CREDIT AGRICOLE SA - RETURN ON ASSETS - TOTAL (%) 0.33 -0.07 0.51 0.45 0.51 0.47 0.4 DEUTSCHE BANK AG - RETURN ON ASSETS - TOTAL (%) 0.32 0.07 0.09 0.19 NA 0.06 0.01 HSBC HOLDINGS PLC - RETURN ON ASSETS - TOTAL (%) 0.83 0.69 0.78 0.65 0.72 0.21 NA LLOYDS BANKING GROUP - RETURN ON ASSETS - TOTAL (%) 0.45 0.47 0.4 0.45 NA NA NA MUNCHENER RUCKVER - RETURN ON ASSETS - TOTAL (%) 0.57 1.5 1.47 1.35 1.26 1.03 0.19 PRUDENTIAL PLC - RETURN ON ASSETS - TOTAL (%) 0.65 0.84 0.51 0.72 0.75 0.53 0.59 ROYAL BANK - RETURN ON ASSETS - TOTAL (%) 0.14 -0.2 NA NA NA NA NA STE. GENL. DE FRANCE - RETURN ON ASSETS - TOTAL (%) 0.39 0.24 NA 0.33 0.41 0.38 0.31 STANDARD CHARTERED - RETURN ON ASSETS - TOTAL (%) 1.02 1 0.84 0.51 -0.12 0.2 0.33

Start 2011

End 2018

Frequency Y

Name 2011 2012 2013 2014 2015 2016 2017

ALLIANZ SE - RETURN ON EQUITY - TOTAL (%) 6.35 12.28 14.26 13.72 13.29 12.71 11.27 AXA SA - RETURN ON EQUITY - TOTAL (%) 9.9 9.84 7.88 10.01 10.06 9.96 10.92 BARCLAYS PLC - RETURN ON EQUITY - TOTAL (%) 5.65 -1.91 0.99 -0.21 -0.54 2.81 -2.71 BNP PARIBAS SA - RETURN ON EQUITY - TOTAL (%) 7.69 7.78 5.29 -0.1 7.46 8.27 8.1 CNP ASSURANCES - RETURN ON EQUITY - TOTAL (%) 9.19 9.57 8.79 7.69 7.41 7.44 7.66 CREDIT AGRICOLE SA - RETURN ON EQUITY - TOTAL (%) -3.32 -15.68 6.08 4.59 6.29 5.85 5.87 DEUTSCHE BANK AG - RETURN ON EQUITY - TOTAL (%) 8.09 0.44 1.23 2.6 -10.35 -2.74 -1.71 HSBC HOLDINGS PLC - RETURN ON EQUITY - TOTAL (%) 10.73 8.45 9.56 7.26 7.15 0.79 5.89 LLOYDS BANKING GROUP - RETURN ON EQUITY - TOTAL (%) -6.06 -3.17 -2.07 2.71 1.15 4.41 6.46 MUNCHENER RUCKVER - RETURN ON EQUITY - TOTAL (%) 3.56 15.7 13.3 13.54 11 9.63 1.49 PRUDENTIAL PLC - RETURN ON EQUITY - TOTAL (%) 17.38 23.22 13.72 22.03 21.96 14.26 16.17 ROYAL BANK - RETURN ON EQUITY - TOTAL (%) -2.66 -8.35 -14.12 -5.98 -3.62 -13.63 1.55 STE. GENL. DE FRANCE - RETURN ON EQUITY - TOTAL (%) 5.06 1 3.67 4.3 6.23 5.62 3.86 STANDARD CHARTERED - RETURN ON EQUITY - TOTAL (%) 11.64 11.11 9.12 5.29 -4.95 -0.99 1.54

Start 2011

End 2018

Frequency Y

Name 2011 2012 2013 2014 2015 2016 2017

ALLIANZ - EARNINGS PER SHR 11.06 7.07 9.82 13.02 13.72 14.12 14.28

AXA - EARNINGS PER SHR 1.45 2.53 0.86 1.65 1.97 1.97 2.26

BARCLAYS - EARNINGS PER SHR 26.42 20.32 25.22 26.14 13.8 16.9 16.6

BNP PARIBAS - EARNINGS PER SHR 6.59 5.71 5.47 4.04 0.06 5.69 5.43

CNP ASSURANCES - EARNINGS PER SHR 1.74 1.26 1.76 1.4 1.51 1.51 1.55

CREDIT AGRICOLE - EARNINGS PER SHR 0.87 0.52 0 0 0.9 1.11 1.37

DEUTSCHE BANK - EARNINGS PER SHR 3.69 4.32 2.67 0 0 0 0

HSBC HOLDINGS - EARNINGS PER SHR 32.57 56.49 44.27 54.64 50 49.91 36.8

LLOYDS BANKING GROUP - EARNINGS PER SHR 0 0 0 2.1 0.1 8.4 7.9

MUENCHENER RUCK. - EARNINGS PER SHR 14.35 2.94 18.82 14.62 20.65 18.48 17.46

PRUDENTIAL - EARNINGS PER SHR 84.8 73.2 73.2 84.4 55.8 108.4 130.6

ROYAL BANK OF SCTL.GP. - EARNINGS PER SHR 0 31 0 20.6 0 0.8 27.5

SOCIETE GEN. (XET) - EARNINGS PER SHR 3.62 3.88 1.82 1.1 2.43 4.34 4.63 STANDARD CHARTERED - EARNINGS PER SHR 109.04 117.45 126.96 142.61 100.95 59.86 0

ALLIANZ SE 127 AXA SA 35 BARCLAYS PLC 121 BNP PARIBAS SA 17 CNP ASSURANCES 58 CREDIT AGRICOLE SA 123 DEUTSCHE BANK AG 147 HSBC HOLDINGS PLC 181 LLOYD BANKING GROUP 252 MUNCHENER RUCKVER 137

PRUDNTIAL PLC 169

ROYAL BANK 290

STE. GENL. DE FRANCE 153 STANDARD CHARTERED 48