A European banking business models analysis: the investment services case

Paola Musile Tanzi (Department of Economics, University of Perugia and SDA Bocconi, Milan, Italy)
Elena Aruanno (Valeur SA, Lugano, Switzerland)
Mattia Suardi (ANASF, Milan, Italy)

Journal of Financial Regulation and Compliance

ISSN: 1358-1988

Publication date: 12 February 2018

Abstract

Purpose

Business Model Analysis is acquiring increasing visibility in the European banking regulatory framework, following the European Banking Authority guidelines on common procedures and methodologies for the supervisory review and evaluation process (SREP), developed to assess business and strategic risks (EBA, 2014, 2015a, 2015b, 2015c). Starting from a selected literature review, in the paper, the authors analyse business models set up by financial intermediaries, bank and non-banks, for the distribution of investment services, first by comparing European niche players with European banking global players, and second, comparing European niche players among themselves to understand the evolution of business models for the distribution of investment services at European level. The research is supported by the Baffi–Carefin Research Centre at the Bocconi University (Italy), in collaboration with ANASF, the Italian Association of Financial Advisors (Italy).

Design/methodology/approach

The authors consider a sample of European financial players from 2009 to 2014. The authors’ focus is on France, Germany, Italy, The Netherlands, Spain and the UK; overall the authors’ handmade data set is based on 162 annual reports. The authors follow two main questions: Do the niche players, as they are focused on the distribution of investment services, have an upper limit to profitability, compared to the global players, as risk-takers in many financial areas? How is the business model of niche players changing, facing increasing competition and regulatory pressures?

Findings

Answering the first research question, the highest net profitability is found in the niche players group; the global players, as risk-takers, achieve lower remuneration, in contrast with the risk premium theory. The results were assessed over a limited period, however, deemed in line with the company’s strategic planning horizon. Answering the second research question, the authors focus on the case of niche players, using a cluster analysis. The authors identify three different business models: most dynamic niche players, which combine investment services, insurance and welfare services, achieving the highest margins and stability; players mainly focused on asset management, whose key vulnerability is the degree of open architecture, especially in light of future MiFID 2 implementation; and players mainly focused on the creation of well-structured on-line platforms, which offer also brokerage services, thereby reducing their marginality and potentially increasing their business risk.

Research limitations/implications

Despite the limited time series, the authors’ research gives some inputs for those interested in deepening the business model analysis focus on the distribution of investment services and the business and strategic risk assessment, both for the global banks and the niche players (banks and non-banks).

Practical implications

The authors’ results could be of some interest during the strategic assessment of global banks and niche players, both adopting an internal perspective or an external one, as regulator.

Social implications

By giving some specific insights into the assessment and comparison of business and strategic risks among global and niche players, the authors’ research provides the basis for further research in the field of the distribution of investment services.

Originality/value

The originality mainly regards the business model risk perspective and the focus of the authors’ analysis: the distribution of investment services. This sector, unlike the asset management, does not have an easily recognisable group of comparables at European level, all the European countries analysed have very different business models. This research avails of an original database, that is unique to Europe.

Keywords

Citation

Musile Tanzi, P., Aruanno, E. and Suardi, M. (2018), "A European banking business models analysis: the investment services case", Journal of Financial Regulation and Compliance, Vol. 26 No. 1, pp. 35-57. https://doi.org/10.1108/JFRC-04-2016-0028

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Publisher

:

Emerald Publishing Limited

Copyright © 2018, Emerald Publishing Limited


1. Introduction

Business model analysis is acquiring increasing visibility not only in the literature on business strategy (Casadeus-Masanell and Ricart, 2007; Casadeus-Masanell and Ricart, 2010; Zott et al., 2010; Teece, 2010), but also in the European financial regulatory framework, following the European Banking Authority (EBA) guidelines and impact analysis (EBA, 2014, 2015a, 2015b, 2015c) and the priorities set by the European Central Bank in its 2016 supervisory plan (ECB, 2016).

In literature, the “business model” concept is defined as “the logic of the firm, the way the company operates and how it creates value for its stakeholders” (Casadeus-Masanell and Ricart, 2010).

The logic and the way in which the company operates are the reflection of the strategic choices, and in our view, these results are ultimately visible in the company balance sheet. This information acquires importance, if based on cross-temporal, cross-sector and cross-country analyses, as we try to show in this work.

Some academic studies are already focused on the evolution of the banking business models and their impact on bank stability (Altunbas et al., 2011; van Ewik and Arnold, 2013; van Oordt and Zhou, 2014; Ayadi and De Groen, 2014; Roengpitya et al., 2014; Curi et al., 2015; Kohler, 2015; Mergaerts and Vander Vennet, 2016); we try to contribute to this literature, adopting another very specific perspective, zooming in on a single business area, i.e. the distribution of investment services, where banks and non-banks are competitors.

The purpose of this paper is to analyse the evolution of business models for the provision of investment services in Europe, focusing our attention on the distribution side. The distribution of investment services is a very heterogeneous, fee-based business area, where banks of all size and non-banks are competing, in a changing regulatory framework for both banks and investment firms. To understand the real evolution and the business sustainability, we must be able to overcome the traditional contraposition between banks and non-banks, adopting a new perspective, based on the comparison between global and niche players.

Our paper is structured as follows: Section 2 is a selected review of previous researches, related to the object of our analysis; Section 3 describes the empirical research objective, methodology and survey sample; Section 4 presents the results of the comparative analysis between global and niche players; and Section 5 analyses the clusters of niche players and concludes the paper.

2. A selected literature review

Our research is based on two main fields of literature: the analysis of business models, on the one hand, and bank performance and diversification, on the other hand. From an institutional point of view, both these fields are related to regulation, as a “boost” for financial innovation.

First of all, the research is found on the literature on business models (Casadeus-Masanell and Ricart, 2007; Casadeus-Masanell and Ricart, 2010; Zott et al., 2010, Teece, 2010) and the banking business models empirical analysis (Altunbas et al., 2011; van Ewik and Arnold, 2013; van Oordt and Zhou, 2014; Ayadi and De Groen, 2014; Roengpitya et al., 2014; Curi et al., 2015; Kohler, 2015; Mergaerts and Vander Vennet, 2016). This literature is focused on a broad analysis of banking business models, both retail and investment banking-oriented, trying to understand “the set of key ratios that differentiate bank’s business profile” and “how bank changed their business models before and after the recent crisis” (Roengpitya et al., 2014). In some studies, the “business models analysis contributes to a better understanding of financial and economic performance, risk behaviour and governance at a system level” (Ayadi and De Groen, 2014). The analysis mainly shows the contribution of interest and non-interest activity to bank profitability and stability; the results are different for retail banks and investment banks (Kohler, 2015).

The banking business model literature interacts with the literature on bank performances and diversification (see among others the review and studies in Carretta et al., 2009). The increasing market pressure on bank performance and the ongoing developments in the regulatory framework are still feeding studies in this topic. According to Curi et al. (2015):

The existing literature on bank performance using diversification measures to identify the business model is characterized by two important limitations: It has used a wide variety of bank performance measures, and it has used many measures of bank diversification. Perhaps as a result, there is still no consensus despite the volume of literature.

For example, the research carried out by Kohler (2015) on the impact of business models on bank stability in the EU banking sector for the period between 2002 and 2011, using a large sample of banks, including non-listed banks, typically retail-oriented, shows “that banks will be significantly more stable and profitable if they increase their share of non-interest income, indicating that substantial benefits are to be gained from income diversification. Such benefits are particularly large for savings and cooperative banks. Investment banks, in contrast, become significantly more risky. This recent result contradicts the conclusion of an empirical analysis, based on a set of European banks for the period 1996-2002 on bank income structure and risk (following that study: “banks expanding into non-interest income activities present higher risk and higher insolvency risk than banks which mainly supply loans” (Lepetit et al., 2008).

Mergaerts and Vander Vennet (2016) try to combine the perspectives of business model analysis, banking business sustainability and related strategies. Analysing an extensive sample of European banks from 1998 to 2013, they found that in the long term “business models that are characterized by a more diversified income structure are on average more profitable, without being less stable”. The authors also emphasize that the acknowledgement of bank business model heterogeneity should have implications on the application of prudential regulation, specifically in light of the Basel III framework. They find also that “the European banking sector is characterized by a continuum of possible business models, rendering classification difficult”.

Without covering all banking services, in our research, we focus on the heterogeneity of business models, with regard to the investment services distribution. Adopting this approach, we compare global players and niche players, and within the group of niche players, we see as much as banks and non-banks are changing their business models, driven by market forces, new technologies and regulatory developments.

The basic idea is that the consequences of the financial crisis and the changes in the European regulatory framework led to a review of business models at European level. The MiFID 2 implementation (Directive 2014/65/EU, 2014) will further stimulate this process. As the EBA’s analysis shows that (EBA, 2014) “there are significant potential implications of the new regulatory measures for certain components of banks’ business models” (EBA, 2015a, 2015b, 2015c). Furthermore, the European Central Bank set business model and profitability risk as the first supervisory priority in 2016 (ECB, 2016).

3. Objectives, methodology and survey sample

This study aims to answer the following research questions:

RQ1.

Do the niche players – i.e. financial intermediaries, which focus on distribution of investment services, have an upper limit to profitability compared to the global players as risk-takers in many financial areas?

RQ2.

How is the business model of niche players changing, facing increasing competition and regulatory pressures?

Unlike the previous business model analysis, based on large samples of banks, we analyse the balance sheet data of a small sample of 27 European financial intermediaries, banks and non-banks, from 2009 to 2014 (Table I). In total, we examine 162 Annual Reports, to preserve data granularity[1].

The research is supported by the Baffi–Carefin Research Centre at Bocconi University in Milan, in collaboration with ANASF (the Association representing financial advisors in Italy). The survey sample consists of two groups of operators: 15 niche players, both bank and non-banks, leaders in the distribution of investment services in their own country and 12 global banks, selected considering the two major banks for each European country in our study[2]. Our research concentrates on countries of so-called old Europe: France, Germany, Italy, The Netherlands, Spain and the UK. These countries are chosen to try to foresee the impact of the implementation of MiFID 2 Directive on the sector.

The initial difficulty was the selection of the niche players. Indeed, the problem mainly pertained to the great variety of business models across Europe. While in the asset management sector the comparable group of competitors is easily recognizable, the same does not hold true for the focus of our analysis, i.e. the distribution of investment services. Hence, there is the necessity to develop an original hand-made database, which is unique to Europe.

The European sample of niche players was selected following these two steps:

  1. For each country, we tried to identify market leaders in the distribution of investment services; various sources of information were gathered and compared, namely, country-specific ranking (e.g. in Italy, the ranking, focused on the distribution side, is made by Assoreti, the National Association of Banks and Investment Firms), press information[3], insights from professionals and academic experts from different European countries.

  2. We restricted our analysis to listed companies or companies which belong to a listed group, in light of greater availability of public information.

The research is divided into two parts, which mirror the research questions.

Part 1 compares the performances of the niche and global players, according to four financial indicators: net commission (or margin on services) on gross income, gross income on total inflows, gross and net cost-to-income ratio and return on equity (RoE). This analysis is based on our exclusive data set; some trends were further verified using the Bankscope database. A statistical correlation analysis of the indicators was also carried out to complete the Part 1.

Part 2 of our work focuses on the case of niche players to answer the research question relating to the evolution of business models in the distribution of investment services, in light of increasing market competition and recent regulatory changes. Three groups, encompassing European niche players deemed to be comparable in terms of strategic positioning, were created using a cluster analysis, which were further supported by a qualitative assessment.

4. Comparative analysis of investment services business models in Europe: niche players vs global players

The logic and the way in which a company operates (Casadeus-Masanell and Ricart, 2007; Casadeus-Masanell and Ricart, 2010; Zott et al., 2010; Teece, 2010) and reacts to regulatory evolution is the reflection of strategic choices and the results are visible in the company balance sheets (Roengpitya et al., 2014; Mergaerts and Vander Vennet, 2016; EBA, 2015a, 2015b, 2015c; ECB, 2016).

In our analysis, we manually collected data on balance sheets from 2009 to 2014, and we compared niche players versus global players in France, Germany, Italy, Spain, The Netherlands and the UK.

The following indicators, focused on profitability, are considered in the comparison of business models:

  1. net commissions on gross income ratio;

  2. gross income on total inflows ratio;

  3. gross and net cost-to-income ratio (paying particular attention to the impact of depreciation and impairments of tangible and intangible assets); and

  4. RoE trend.

In our view, the profitability of the investment services business area can be captured by these four indicators. As in the real world, we consider, in this specific business area, the competition between global players and niche players able to overcome the traditional legal boundaries between banks and non-banks, that is why, unlike previous banking business models analysis, we used a mixed sample. The choice of the four key indicators is a related option, only using this kind of ratio, we could manage the objective to compare different players, global and niche players, banks and non-banks.

  1. The net commissions on gross income ratio measures the share of the total gross income, in percentage terms, determined by the revenues from fee-based activities on behalf of third parties. This indicator analyses the income structure and has a dual purpose. On the one hand, it helps to understand each player’s core business and, on the other hand, grasps its risk appetite profile, as the remaining 100 per cent share represents the share of revenue resulting from risk taking activities (either credit or market risk). As regards this first indicator, European niche players confirm their vocation to serve third parties, compared to global players (Tables AI and AII). The case of global players is completely different, as they have opted for a diversified business model, thereby operating in different business areas (retail, corporate and investment banking […]). From 2009 to 2014, this group recorded, on average, higher revenues from activities “at risk”. Global players thus bear a significant level of risk, reflected, as shown below, in a contrasting way both on gross income and net profitability.

  2. The ratio between gross income and total inflows may be considered as a proxy for gross profit margin. This is calculated comparing total revenues to total inflows. Both direct and indirect inflows (asset under management and under custody) are considered, to measure the overall gross profitability of all the assets managed or gathered by operators. The data actually show a sort of “cap” on the gross profitability of niche players, compared to global players (Tables AIII and AIV). However, the limited margin shown by niche players is in line with their risk profile highlighted by the analysis of the net commissions on gross income ratio. Global players assume a substantial share of risk, the greatest of which is credit risk, reflected in the higher gross margin.

  3. The cost-to-income ratio assesses the efficiency of each player, measuring the portion of the overall revenue, absorbed by structural operating costs. This indicator not only assesses the structural efficiency profile, it is also widely used as a business risk proxy. The higher is the cost-to-income ratio, the more rigid is the operational structure and, therefore, the higher is the exposition to volatile markets and economic trends. This analysis is carried out taking into account impairments and the impact of the depreciation of tangible and intangible assets (see also ECB, 2014a). Therefore, the indicator is calculated both with (gross cost to income) and without (net cost to income) the aforementioned values. The sample shows a situation where the data relating to the two groups are not clearly distinguishable (Tables AV-AVIII). The cost-to-income ratio value for both groups is generally high, thus confirming the existence of substantial operational entry barriers to the sector, but also a rigid cost structure, making operators very vulnerable to market situation (see also ECB, 2014b). In particular, the group of niche players, despite the attempts to regain efficiency, faces business sustainability problems. The comments would be very similar even if we consider the “cleansed” cost-to-income ratio. The percentage incidence of impairments on total operating costs is used to calculate the impact of tangible and intangible asset depreciation. From this point of view, the two groups show similar figures. However, the maximum and minimum standard deviation values show that the situation within each group is not uniform, confirming that the strategic decisions reached by each operator weigh heavily on their present and future efficiency and profitability profiles. However, it should be noted that neither the gross cost-to-income ratio, nor the impact of tangible and intangible asset depreciation, justifies a lower net profitability for global players (European Central Bank, 2014), penalized by credit portfolio adjustments.

  4. The net profitability of capital used or RoE analysis entails interesting observations (Tables AIX and AX). In recent literature, RoE has been considered an unreliable measure for the profitability of a company’s resources, unable to include information on the risks and prone to adjustments in preparing balance sheets (Reimann, 1989; Jensen and Meckling, 1999; De Wet and Du Toit, 2007; ECB, 2010). RoE’s failure to measure the “capacity to generate sustainable profitability” (ECB, 2010) was clearly evident following the financial crisis of 2007 (ECB, 2010). However, in our opinion, the recent development of prudential regulation in Europe, particularly with the CRDIV (Capital Requirements Directive), may reinstate RoE’s meaningfulness. Indeed, the European prudential regulation is reinforcing the capital requirements for banks and the risk impacts on bank profitability through the impairment process, acting on both the ratio sides. At the European level, the net profitability of the niche players appears far better than that of the global players throughout all the period considered. The significant variations in the group of the global players is owing to the presence of risky assets, impacting on their net profitability and forcing them to write off credits and impair fixed assets (Tables AXI-AXII). In particular, the group of the global players owes its variable results to the significant credit value impairments, resulting from the economic and financial crisis and, to a limited degree, from an incorrect assessment of the strategic risk, as mentioned in Point 3. On looking into it, we analysed, only for the global players group, two more indicators, i.e. non-performing loans (npl) ratio and impaired loans on equity. The NPL ratio, measuring the percentage incidence of impairments on total loans to customers, and the impaired loans on equity, which measure the extent of impaired loans compared to the common equity, were observed using the Bankscope database, to understand which elements actually contributed to the drastic reduction in net profitability of major European banking groups (Tables AXIII-AXIV). The trend of both these indicators confirm that global players suffer the consequences of bad loans, weighing heavily on their profitability level and increasing their vulnerability.

Based on these results, we tried to understand if there is a systematic relationship between the risk profile of the players and their net profitability. By means of a linear regression, we find the results shown in Figure 1 and the parameters obtained in Table AXV. The inputs are “net commissions on gross income ratio” and the RoE values for each player from 2009 to 2014.

The cloud of points shows a clear positive correlation between the percentage of revenue from third party services and the net profitability. The correlation value is equal to 0.407. The index r2 assumes a value of 0.166, whilst the Fisher test equals to 30.37 with a p-value of 1.48e-07, definitely under the significance level α of 0.05. The value assumed by the F-test demonstrates that the null hypothesis, according to which “the model is non-explanatory” should be eliminated even if the model explains only 16 per cent of the total deviance, as indicated by the measure of goodness-of-fit (r2). Therefore the model is statistically significant, although characterized by only one independent variable χ. The model is of limited statistical value, as it only considers one explanatory variable, anyway it shows clear high correlation between the two variables: the lower is the risk taken by the players, the greater is their net profitability.

From January 2009 to December 2014, the group of global players was penalized by the risk assumption, while the niche players had a more efficient risk-profit profile, i.e. there is no risk premium for the risky activities of global players. This result seems to contrast with the risk premium theory on which modern finance is based (Sharpe, 1964; Lintner, 1965; Mossin, 1966; Black et al., 1972). This statement merits further clarification, as the result changes according to whether the gross profit margin or the net RoE perspective is adopted.

Specifically, if the first perspective is considered, the global players have a higher gross profit margin than the niche players, which seem to exhibit a kind of cap on their gross profitability. This finding is in line with the theory that the greater is the risk, the greater is the return.

However, if we adopt the second perspective, the result turns around and the niche players are in the best conditions. Indeed, the RoE does not show a risk premium for the global players, their net profitability is clearly affected by provisions and impairments, resulting from their risky businesses. Particularly, in the observed period (2009-2014), the expected excess return on assets at risk fails to materialise in the final balances.

This time horizon may not be appropriate to analyse the existence of risk premium; in fact in literature, the equity risk premium studies (including amongst others Dimson et al., 2003; Cappiello et al., 2008; Zeng et al., 2014) are based on long-term historical series. Considering our findings, we just give evidence, in such a difficult period as the one from 2009 to 2014, for the failure to achieve a risk premium associated with risky activities and we suppose that this six-year horizon might be long enough for the operators to redesign business models.

Owing to the granularity of our data set, we show the linkage between the profitability of financial intermediaries and their ability to deliver services, whereas the previous literature, focused exclusively on the banking sector, underline the overall non-interest contribution, where the non-interest component is including many different kinds of income, some of them risk-based (Altunbas et al., 2011; Kohler, 2015).

5. The distribution of investment services: the evolution of business models

In this section, we focus on the group of niche players, to understand the main characteristics of their different business models and highlight cross-border similarities, according to the empirical evidence that no business model fits all (Curi et al., 2015). Using the key indicators, already presented in the previous section of this work, we make a cluster analysis based on:

  • net commissions on gross income ratio;

  • gross income on total inflows ratio;

  • gross cost-to-income ratio; and

  • RoE.

Two qualitative dummy variables are added to understand the distinctive features of the business models adopted by the various players:

  • The first dummy variable assumes a value equal to one for all intermediaries that collect more than 10 per cent of their revenue from insurance and welfare management. Some niche players have clear business models based on insurance and welfare services (the percentage of insurance and welfare management revenues on total revenues is over 40 per cent); while for others it seems more appropriate to talk about a diversification policy. Anyway, the contribution of these services to their revenues is relevant.

  • The second dummy variable identifies those operators that have made significant investments in technology. This variable assumes a value equal to one for those operators who have a well-structured online investment platform, i.e. operators that focus on their investment platform as one of their main competitive advantages (as Binckbank, Comdirect Group, FinecoBank, Hargreaves Lansdown Plc).

The cluster analysis is conducted by using the Manhattan distance as variable distance measure (owing to the presence of dummy variables) and a hierarchical aggregation algorithm with the average link method. Two niche players, OVB Holding (Germany) and Hargreaves Lansdown (UK) were first eliminated from the statistical analysis, because of missing observations; they were subsequently included in the clusters, following a qualitative assessment and a specific analysis of their annual reports.

The result of this statistical and qualitative process entails the identification of three clusters corresponding to the three macro types of business models adopted by the financial intermediaries operating in the distribution of investment services in Europe. These groups include financial intermediaries that are “comparable” in terms of strategic positioning. Although the three clusters overlap to some degree, each of them has its own distinctive features.

Specifically, the three clusters differ according to their focus:

  1. insurance and welfare management services (Banca Fideuram, Banca Generali, Banca Mediolanum, MLP AG, OVB Holding, St. James’s Place, Union Financière de France Banque);

  2. asset management (Azimut Holding, Brewin Dolphin, Renta 4 Banco, Van Lanschot); and

  3. online platforms (BinckBank, Comdirect Group, FinecoBank, Hargreaves Lansdown).

The first group consists of operators with a more distinct focus on insurance and welfare management services. The range of insurance services varies between the different operators: from unit-linked insurance products to distinctly welfare-related products or real estate insurance products. The gross profitability profile achieved by firms in this sub-group is the highest of the three clusters (in 2014, the gross income on the total inflows was on average 117 bps, with a minimum of 84 bps and a maximum of 178 bps). In turn, this evidence poses an important question as to the future development of this business model that, in recent years, has attracted considerable interest amongst Italian niche players. The adoption of MiFID 2 leads to the application of the provisions governing investment services, relating to financial instruments, also to the distribution of insurance-based investment products[4]. By creating a framework of harmonized rules, this development will make it possible to assess whether the higher profitability profile of the operators focused on insurance and welfare management services was either determined by their ability to meet market needs or by a “regulatory advantage”, as these services did not fall within the scope of MiFID.

The second cluster consists of operators that are more focused on asset management activities, even though they also distribute investment services. The operators included in this cluster have different degrees of open architecture, reflected in the gross profit margins achieved. On average, the ratio between gross income and total inflows is more limited than that of the previous cluster, as it is equal to 94 bps in 2014, with a maximum of 108 bps and a minimum of 77 bps recorded by the operator with the most significant open architecture profile. Analysing the operators of this cluster, it can be said that the lower the open architecture, the greater the margin of profit. Therefore, the choice of the degree of open architecture becomes a strategic decision, in that opening the business structure entails the reduction of the margin in the short term and the increase of the assumption of operational risks[5]. This observation raises certain points for discussion, especially in light of the recent regulatory developments in Europe. In accordance with the new regulatory developments, distribution systems based on rebates, qualified as inducements according to the MiFID directive, can be kept, if the inducement is capable of enhancing the quality of the service provided to the client (ESMA, 2015; European Commission, 2016). The European regulator considers the provision of investment advice on a wide range of financial instruments as an example that would presumably meet the aforementioned quality enhancement test. In this connection, the preference shown within MiFID for open architecture models and, more in general, for investment advice based on a wide market analysis does not appear to be completely justified as, according to our analysis, the greater the degree of open architecture, the lower the profitability: this insight may be interpreted in light of the increase of business risk and the potential reduction of financial stability for those intermediaries which are more oriented towards open architecture solutions.

The third cluster includes those operators that consider their online platforms as their strength. In some cases, well-structured online platforms are the main distribution channel. This cluster has a more limited average margin. The ratio between gross income and total inflows equals to 75 bps in 2014, with a maximum of 92 bps and a minimum of 58 bps for the operator with the greatest focus on Web-based distribution. The lower margins for the operators of this group may be the consequence of execution only services, which are not very remunerative given their low intrinsic added value, despite the significant investment in technology that these services require. Nowadays, technology cannot be conceived as the exclusive source of competitive advantage; better results may be achieved if technology is combined with other distribution methods: this comment is in line with the European legislator’s approach that, as already mentioned, aims to strengthen investor protection by standardizing the rules for different distribution channels.

The rationale of limited margin seems to apply specifically to asset management operators and those mainly using online platforms. Therefore, the European legislator’s attention to product governance is not by chance. Indeed, one of the most significant developments from MiFID 1 to MiFID 2 is the introduction of specific organisational requirements as regards the relationships between manufacturers and distributors of financial instruments: in particular, manufacturers will be required to define and apply an approval process for each financial instrument prior to its distribution to end clients, whilst distributors must help to implement distribution strategies that are appropriate to the target market[6].

The results of the cluster analysis represent an opportunity to assess the most recent developments in the European regulation of the distribution of financial services from a totally unique perspective. It should be noted that the actions of the European institutions are increasingly guided by a precise project to level the playing field, to promote greater harmonization in the regulation of financial services. This path is moving along two very closely linked directions:

  1. the application of rules to achieve as much consistency as possible among different distribution channels (traditional, online and mobile), to enhance investor protection; and

  2. the equal treatment of all operators in the market, by aligning the standards which govern the various sectors of financial services.

The first of the two aforementioned directions leads to the extension of the requirements and measures to protect investors, already foreseen for “traditional” distribution models (that is, distribution based on a direct contact between clients and intermediaries) to the new distribution channel represented by online platforms[7]. This option does not fit all, both consumers and business models. Regarding this point, it could be useful to consider the recent debate in the UK, with specific regard to the investment advice service and the RDR[8] implementation:

Whilst it seems clear that consumers need to be able to rely on the expertise of a more professionalised advice sector, the effect of the RDR and its associated regulatory costs has had the opposite effect, creating an advice gap. (Ring, 2016).

The second direction to achieve greater harmonisation mainly entails the transposition of the provisions regulating investment services from MiFID to the distribution of insurance-based investment products. This choice is justified as insurance-based investment products are often proposed as potential alternatives to investments in financial instruments, resulting in the need to provide an appropriate protection for retail customers and guarantee the same conditions for similar products. This approach is also attributable to the European Regulation No 1286/2014 setting out common provisions on the key information document for packaged retail and insurance-based investment products (PRIIPs[9]), as the regulation applies to all distribution channels.

The need to level the playing field among the various sectors and channels for the distribution of financial services makes the outcome of the comparison between the three clusters of niche players even more important. In fact, this need can be interpreted as the necessary condition to create a competitive market, where each operator can define its own strategic positioning within a clear and precise regulatory framework, which avoids legislative or administrative obstacles and prevents regulatory arbitrage.

Conclusions

We analyse the evolution of business models for the distribution of investment services at the European level, in light of the recent regulatory changes, especially those related to the MiFID 2 framework.

In the Phase 1 of the research, we compare various business models, according to balance sheet indicators, deemed significant in the supervisory review and evaluation process (SREP). The comparison shows how, despite the higher gross margin on total inflows, in terms of overall net RoE the global players, as risk takers, achieve lower remuneration than the niche players, which are focused on the provision of investment services on behalf of third parties, in fact, there is no risk premium for the risky activities of global players. In particular, for the period from 2009 to 2014, the assumption of credit risk appears to penalize the global players. The best net profitability is noted for the group of niche players, even if they face business sustainability issues, owing to their increasing operating costs, their key vulnerability. These results are assessed over a limited period of time, however deemed to be in line with the company’s strategic planning horizon.

In the Part 2 of the analysis, we focus on the niche players to understand the evolution of their business models. This evaluation is based on a cluster analysis, conducted according to a statistical method and further supported by qualitative assessments. The analysis resulted in the identification of three different types of business models, which, although partially overlapping, correspond to three different clusters. The particular features of these clusters offer an opportunity to assess, from a totally original perspective, the most recent developments in the European regulation governing the distribution of financial services and increase the awareness of the strategic decisions adopted by financial intermediaries in terms of business risk and sustainability of business models. The cluster corresponding to the niche players that are more dynamic (in that they also offer insurance and welfare-management services) achieves the highest gross profit margin. Conversely, those niche players, which are mainly focused on well-structured online platforms and provide execution only services exhibit a reduction of their gross profit margin and increase their business risk. Finally, for those niche players, which are mainly focalized on asset management, the critical issue is the definition of the proper degree of open architecture, as the greater is this degree, the lower is the gross profit margin and, as a consequence, the greater is the business risk.

In light of these results, we hope that our analysis may not only provide the basis for further academic research, but also contribute to a better understanding of business strategic planning and spur the regulatory debate. Particularly, the activity of niche players (i.e. financial intermediaries specialized in the distribution of financial services) deserves further analysis: from this point of view, the identification of the different business models, adopted by niche players, may open the way for the establishment of a new specific research field.

Figures

Linear regression between net commissions/gross income ratio and RoE (2009-2014)

Figure 1.

Linear regression between net commissions/gross income ratio and RoE (2009-2014)

European sample

Name Type of players Type of financial intermediaries Country
Azimut Holding Spa Niche player Non-bank Italy
Banca Generali Spa Niche player Bank Italy
Banca Mediolanum Spa Niche player Bank Italy
Banco Santander SA Global player Bank Spain
Barclays Plc Global player Bank UK
BBVA Group Global player Bank Spain
Binckbank NV Niche player Bank The Netherlands
BNP Paribas Group Global player Bank France
Brewin Dolphin Holdings Plc Niche player Non-bank UK
Comdirect Group Niche player Bank Germany
Commerzbank Group Global player Bank Germany
Crédit Agricole Group Global player Bank France
Deutsche Bank Group Global player Bank Germany
FinecoBank Niche player Bank Italy
Gruppo Banca Fideuram Niche player Bank Italy
Gruppo Intesa Sanpaolo Global player Bank Italy
Hargreaves Lansdown Plc Niche player Non-bank UK
HSBC Holdings Plc Global player Bank UK
ING Group Global player Bank The Netherlands
MLP AG Niche player Non-bank Germany
OVB Holding AG Niche player Non-bank Germany
Rabobank Group Global player Bank The Netherlands
Renta 4 Banco Niche player Bank Spain
St. James’s Place Plc Niche player Non-bank UK
UniCredit Group Global player Bank Italy
Union Financière de France Banque Niche player Bank France
Van Lanschot Niche player Bank The Netherlands

Net commissions/gross income, niche players, 2009-2014

Niche players 2014 (%) 2013 (%) 2012 (%) 2011 (%) 2010 (%) 2009 (%)
Azimut Holding Spa 92.12 90.69 92.99 95.11 97.52 97.69
Gruppo Banca Fideuram 82.34 81.01 81.50 77.72 77.96 69.43
Banca Generali Spa 61.37 62.46 63.34 77.13 77.83 72.44
Banca Mediolanum Spa n.a. 58.16 56.58 69.69 76.49 65.24
FinecoBank 43.14 44.43 34.59 51.20 55.90 54.13
Union Financière de France Banque 99.51 98.58 99.38 99.76 99.21 102.40
Comdirect Group 56.16 55.97 51.70 55.95 60.68 53.29
OVB Holding AG 99.09 98.60 97.95 98.23 97.58 97.76
MLP AG 85.95 86.22 86.88 88.08 87.27 85.92
Renta 4 Banco 78.78 76.58 86.21 86.90 85.91 94.50
Binckbank N.V. 81.45 82.50 77.35 75.44 74.14 73.20
Van Lanschot 43.69 44.11 41.41 42.96 37.98 33.42
St. James’s Place Plc 86.55 44.88 53.84 73.33 37.35 −81.18
Brewin Dolphin Holdings Plc 99.65 99.60 99.66 99.79 99.64 96.82
Hargreaves Lansdown Plc n.a. n.a. n.a. n.a. n.a. n.a.
Annual average 77.68 73.13 73.10 77.95 76.11 65.36
Variance 7.49 4.10 4.67 3.12 4.17 20.44
Standard deviation (SD) 19.39 20.24 21.62 17.65 20.42 45.22
Median 82.34 78.80 79.43 77.42 77.89 72.82
Minimum 43.14 44.11 34.59 42.96 37.35 −81.18
Maximum 99.65 99.60 99.66 99.79 99.64 102.40

Net commissions/gross income, global players, 2009-2014

Global players 2014 (%) 2013 (%) 2012 (%) 2011 (%) 2010 (%) 2009 (%)
Gruppo Intesa Sanpaolo 34.56 32.83 25.52 29.05 30.89 27.71
UniCredit Group 35.11 31.12 31.39 32.24 33.02 28.28
BNP Paribas Group 21.53 21.10 22.02 23.12 22.27 21.33
Crédit Agricole Group 23.60 24.64 23.48 35.44 27.82 28.96
Santander Group 22.67 24.38 23.46 23.76 23.21 23.14
BBVA Group 19.17 19.66 18.32 19.56 21.66 21.62
Deutsche Bank Group 38.97 38.80 34.88 37.23 38.37 31.67
Commerzbank Group 34.35 34.36 32.70 40.47 28.49 45.40
HSBC Holdings Plc 26.54 26.50 27.75 24.34 26.42 27.86
Barclays Plc 32.56 31.42 34.59 26.73 28.32 29.04
Rabobank Group 14.61 15.36 16.36 22.28 22.26 20.71
ING Group 14.99 16.35 15.41 16.52 21.62 29.07
Annual average 26.56 26.38 25.49 27.56 27.03 27.90
Variance 0.64 0.51 0.43 0.50 0.25 0.40
SD 8.00 7.13 6.58 7.10 5.02 6.33
Median 25.07 25.57 24.50 25.53 27.12 28.07
Minimum 14.61 15.36 15.41 16.52 21.62 20.71
Maximum 38.97 38.80 34.88 40.47 38.37 45.40

Gross income/total inflows, niche players, 2009-2014

Niche players 2014 (%) 2013 (%) 2012 (%) 2011 (%) 2010 (%) 2009 (%)
Azimut Holding Spa 1.08 1.20 1.38 0.96 1.10 1.17
Gruppo Banca Fideuram 1.04 0.98 0.89 1.00 0.97 0.94
Banca Generali Spa 1.15 1.29 1.29 1.05 1.08 1.15
Banca Mediolanum Spa n.a. 1.87 1.98 1.53 1.57 1.58
FinecoBank 0.92 0.86 1.04 0.86 0.82 0.81
Union Financière de France Banque 1.78 1.97 1.98 2.41 2.49 2.17
Comdirect Group 0.58 0.61 0.66 0.78 0.67 0.78
OVB Holding AG n.a. n.a. n.a. n.a. n.a. n.a.
MLP AG 1.07 1.15 1.53 1.60 1.64 1.98
Renta 4 Banco n.a. 0.51 0.63 0.74 0.76 0.72
Binckbank N.V. 0.75 0.87 0.98 1.24 1.21 1.61
Van Lanschot 0.96 0.99 1.00 1.08 1.27 1.56
St. James’s Place Plc 0.84 1.40 1.25 0.74 1.34 0.99
Brewin Dolphin Holdings Plc 0.77 0.76 0.98 1.04 1.02 0.94
Hargreaves Lansdown Plc 0.76 0.80 0.91 0.85 0.91 1.12
Annual average 0.97 1.09 1.18 1.13 1.20 1.25
Variance 0.002 0.002 0.002 0.002 0.002 0.002
SD 0.29 0.42 0.41 0.44 0.45 0.44
Median 0.94 0.98 1.02 1.02 1.09 1.13
Minimum 0.58 0.51 0.63 0.74 0.67 0.72
Maximum 1.78 1.97 1.98 2.41 2.49 2.17

Gross income/total inflows, global players, 2009-2014

Global players 2014 (%) 2013 (%) 2012 (%) 2011 (%) 2010 (%) 2009 (%)
Gruppo Intesa Sanpaolo 2.27 2.21 2.53 2.26 1.96 2.10
UniCredit Group 3.80 4.27 4.25 4.45 4.39 4.54
BNP Paribas Group n.a. 2.36 2.40 2.62 2.57 2.93
Crédit Agricole Group n.a. n.a. n.a. n.a. n.a. n.a.
Santander Group 3.00 3.23 3.15 3.09 3.08 3.15
BBVA Group 5.56 5.28 5.02 4.67 4.96 5.26
Deutsche Bank Group n.a. n.a. n.a. n.a. n.a. n.a.
Commerzbank Group n.a. n.a. n.a. n.a. n.a. n.a.
HSBC Holdings Plc n.a. n.a. n.a. n.a. n.a. n.a.
Barclays Plc n.a. n.a. n.a. n.a. n.a. n.a.
Rabobank Group 3.94 3.95 4.07 4.06 4.26 4.34
ING Group 3.16 3.69 3.83 4.06 4.14 3.38
Annual average 3.62 3.57 3.61 3.60 3.62 3.67
Variance 0.01 0.01 0.01 0.01 0.01 0.01
SD 1.03 1.00 0.89 0.87 1.02 1.00
Median 3.48 3.69 3.83 4.06 4.14 3.38
Minimum 2.27 2.21 2.40 2.26 1.96 2.10
Maximum 5.56 5.28 5.02 4.67 4.96 5.26

Gross cost-to-income ratio, niche players, 2009-2014

Niche players 2014 (%) 2013 (%) 2012 (%) 2011 (%) 2010 (%) 2009 (%)
Azimut Holding Spa 41.61 36.70 32.08 48.36 39.51 33.38
Gruppo Banca Fideuram 42.91 46.71 60.12 50.99 59.03 69.43
Banca Generali Spa 49.15 46.42 48.46 61.07 61.02 64.83
Banca Mediolanum Spa n.a. 41.94 44.66 59.69 57.34 63.48
FinecoBank 48.35 55.97 50.63 63.94 68.67 69.77
Union Financière de France Banque 82.70 82.14 84.42 75.67 70.01 78.79
Comdirect Group 78.75 77.23 73.53 71.11 73.76 71.26
OVB Holding AG 80.55 81.88 80.18 87.29 87.68 82.41
MLP AG 87.22 88.29 77.04 94.24 85.52 88.24
Renta 4 Banco 68.73 71.23 80.13 78.50 73.91 78.78
Binckbank N.V. 91.43 74.57 86.73 76.43 72.70 69.39
Van Lanschot 61.29 77.26 87.71 79.48 71.96 79.23
St. James’s Place Plc 70.57 39.87 41.49 83.98 48.01 75.94
Brewin Dolphin Holdings Plc 99.81 96.35 94.67 97.49 93.27 99.20
Hargreaves Lansdown Plc 23.17 34.35 34.90 40.13 46.28 47.42
Annual average 66.16 63.39 65.12 71.22 67.24 71.44
Variance 7.06 4.11 4.25 2.66 2.24 2.34
SD 21.55 20.27 20.63 16.32 14.97 15.29
Median 69.65 71.23 73.53 75.67 70.01 71.26
Minimum 23.17 34.35 32.08 40.13 39.51 33.38
Maximum 99.81 96.35 94.67 97.49 93.27 99.20

Gross cost-to-income ratio, global players, 2009-2014

Global players 2014 (%) 2013 (%) 2012 (%) 2011 (%) 2010 (%) 2009 (%)
Gruppo Intesa Sanpaolo 51.51 64.21 49.93 61.75 61.81 59.33
UniCredit Group 66.25 74.54 62.59 69.68 64.26 59.26
BNP Paribas Group 77.32 76.83 77.60 71.72 69.59 66.67
Crédit Agricole Group 48.60 51.64 50.92 67.76 50.83 50.04
Santander Group 52.13 55.53 50.69 50.34 48.01 45.92
BBVA Group 48.50 49.94 45.74 47.43 42.82 40.80
Deutsche Bank Group 85.67 86.80 89.93 80.81 82.12 69.59
Commerzbank Group 74.23 72.63 70.74 92.54 68.63 109.82
HSBC Holdings Plc 68.61 62.17 72.50 58.92 57.38 54.26
Barclays Plc 81.38 79.07 85.15 64.76 63.76 57.67
Rabobank Group 62.65 75.00 66.12 65.18 64.45 64.65
ING Group 67.06 61.96 68.67 65.65 71.31 92.10
Annual average 65.33 67.53 65.88 66.38 62.08 64.17
Variance 1.54 1.25 1.91 1.36 1.10 3.47
SD 12.40 11.18 13.81 11.66 10.48 18.63
Median 66.66 68.42 67.39 65.42 64.01 59.30
Minimum 48.50 49.94 45.74 47.43 42.82 40.80
Maximum 85.67 86.80 89.93 92.54 82.12 109.82

Net cost-to-income ratio without depreciation and amortization of tangible and intangible assets, niche players, 2009-2014

Niche players 2014 (%) 2013 (%) 2012 (%) 2011 (%) 2010 (%) 2009 (%)
Azimut Holding Spa 39.70 35.21 31.32 47.67 38.65 32.44
Gruppo Banca Fideuram 41.45 45.06 58.19 48.99 56.67 66.62
Banca Generali Spa 48.10 45.07 47.13 59.39 59.42 62.57
Banca Mediolanum Spa n.a. 40.48 43.32 56.99 54.61 60.00
FinecoBank 46.41 53.82 48.64 61.11 65.55 66.60
Union Financière de France Banque 81.89 81.03 83.07 74.37 69.01 77.24
Comdirect Group 73.04 71.94 68.72 65.98 68.82 66.83
OVB Holding AG 76.04 77.58 75.43 81.38 82.81 77.21
MLP AG 82.67 84.11 73.17 88.58 80.64 83.54
Renta 4 Banco 62.71 65.19 73.63 72.70 68.06 72.27
Binckbank N.V. 73.54 56.38 61.96 55.60 52.38 49.03
Van Lanschot 57.20 74.26 81.84 72.58 65.97 73.46
St. James’s Place Plc 44.49 25.34 26.84 54.71 31.16 52.41
Brewin Dolphin Holdings Plc 83.83 91.66 90.00 93.28 90.58 95.80
Hargreaves Lansdown Plc 23.17 34.35 34.90 40.13 46.28 47.42
Annual average 59.59 58.77 59.88 64.90 62.04 65.56
Variance 5.45 4.02 3.81 2.21 2.46 2.36
SD 18.64 20.06 19.52 14.85 15.67 15.35
Median 59.96 56.38 61.96 61.11 65.55 66.62
Minimum 23.17 25.34 26.84 40.13 31.16 32.44
Maximum 83.83 91.66 90.00 93.28 90.58 95.80

Net cost-to-income ratio without depreciation and amortization of tangible and intangible assets, global players, 2009-2014

Global players 2014 (%) 2013 (%) 2012 (%) 2011 (%) 2010 (%) 2009 (%)
Gruppo Intesa Sanpaolo 46.32 46.08 44.81 55.36 55.02 52.71
UniCredit Group 60.66 59.88 56.96 59.87 57.73 53.65
BNP Paribas Group 72.75 72.18 73.09 67.58 65.41 62.72
Crédit Agricole Group 45.88 48.70 48.01 64.08 47.93 47.19
Santander Group 46.85 49.55 45.70 45.47 43.38 41.85
BBVA Group 43.24 44.88 41.43 43.36 39.19 37.40
Deutsche Bank Group 85.32 86.55 84.36 80.81 82.02 70.06
Commerzbank Group 69.45 68.37 66.67 87.05 63.94 100.45
HSBC Holdings Plc 64.76 58.47 68.36 54.78 54.77 51.54
Barclays Plc 76.29 74.73 80.67 59.52 59.07 53.51
Rabobank Group 59.25 70.94 62.25 60.86 59.96 60.41
ING Group 66.49 61.13 67.10 63.74 66.06 88.52
Annual average 61.44 61.79 61.62 61.87 57.87 60.00
Variance 1.70 1.57 1.89 1.46 1.19 3.14
SD 13.04 12.53 13.74 12.07 10.90 17.72
Median 62.71 60.51 64.46 60.37 58.40 53.58
Minimum 43.24 44.88 41.43 43.36 39.19 37.40
Maximum 85.32 86.55 84.36 87.05 82.02 100.45

RoE, niche players, 2009-2014

Niche players 2014 (%) 2013 (%) 2012 (%) 2011 (%) 2010 (%) 2009 (%)
Azimut Holding Spa 14.51 22.73 27.25 18.81 24.02 35.26
Gruppo Banca Fideuram 33.20 29.30 27.80 27.00 28.00 27.90
Banca Generali Spa 46.20 48.70 33.31 28.43 29.94 23.54
Banca Mediolanum Spa n.a. 31.29 30.73 25.27 30.77 22.88
FinecoBank 36.49 23.58 32.72 19.82 16.54 14.05
Union Financière de France Banque 25.83 18.95 16.13 24.43 30.78 18.62
Comdirect Group 15.50 15.10 17.30 21.20 16.80 17.60
OVB Holding AG 10.43 9.66 9.80 5.15 4.76 10.11
MLP AG 7.68 6.60 13.60 3.10 8.00 5.80
Renta 4 Banco 16.94 15.15 9.32 6.64 9.60 9.75
Binckbank N.V. 7.15 4.38 5.14 7.26 9.41 9.82
Van Lanschot 8.05 2.50 −11.05 2.75 4.10 −2.00
St. James’s Place Plc 18.11 21.00 14.05 15.75 9.38 7.37
Brewin Dolphin Holdings Plc 3.22 9.60 11.31 9.11 15.22 13.14
Hargreaves Lansdown Plc n.a. n.a. n.a. n.a. n.a. n.a.
Annual average 18.72 18.47 16.96 15.34 16.95 15.28
Variance 1.69 1.44 1.44 0.82 0.92 0.89
SD 12.53 12.01 12.00 9.05 9.59 9.43
Median 15.50 17.05 15.09 17.28 15.88 13.60
Minimum 3.22 2.50 −11.05 2.75 4.10 −2.00
Maximum 46.20 48.70 33.31 28.43 30.78 35.26

RoE, Global players, 2009-2014

Global Player 2014 (%) 2013 (%) 2012 (%) 2011 (%) 2010 (%) 2009 (%)
Gruppo Intesa Sanpaolo 2.93 −10.24 3.33 −17.28 5.19 5.58
UniCredit Group 4.52 −27.83 1.33 −16.80 1.95 2.85
BNP Paribas Group 0.57 6.21 8.51 8.98 12.31 9.48
Crédit Agricole Group 5.76 6.73 4.79 1.47 5.28 4.11
Santander Group 6.48 5.19 6.60 8.71 10.87 12.77
BBVA Group 5.97 6.65 5.31 8.70 13.33 14.94
Deutsche Bank Group 2.31 1.24 0.59 8.10 4.77 13.53
Commerzbank Group 1.37 0.62 0.21 3.01 5.20 −17.43
HSBC Holdings Plc 7.35 9.35 8.37 10.80 9.16 4.93
Barclays Plc 1.28 2.03 0.30 6.14 7.31 17.59
Rabobank Group 4.74 5.03 4.89 5.84 6.80 5.83
ING Group 2.46 6.51 7.75 11.55 7.03 −2.65
Annual average 3.81 0.96 4.33 3.27 7.43 5.96
Variance 0.05 0.99 0.09 0.90 0.10 0.81
SD 2.18 9.93 3.02 9.50 3.24 8.98
Median 3.73 5.11 4.84 7.12 6.92 5.70
Minimum 0.57 −27.83 0.21 −17.28 1.95 −17.43
Maximum 7.35 9.35 8.51 11.55 13.33 17.59

Depreciation and amortization of tangible and intangible assets as a percentage of total operating costs, niche players, 2009-2014

Niche players 2014 (%) 2013 (%) 2012 (%) 2011 (%) 2010 (%) 2009 (%)
Azimut Holding Spa 4.58 4.04 2.37 1.42 2.17 2.82
Gruppo Banca Fideuram 3.40 3.54 3.21 3.92 3.99 4.04
Banca Generali Spa 2.14 2.91 2.74 2.75 2.62 3.50
Banca Mediolanum Spa n.a. 3.48 3.01 4.52 4.77 5.47
FinecoBank 4.02 3.85 3.92 4.43 4.55 4.53
Union Financière de France Banque 0.98 1.35 1.59 1.72 1.42 1.96
Comdirect Group 7.25 6.85 6.55 7.21 6.69 6.22
OVB Holding AG 4.88 4.47 4.96 5.71 4.52 4.99
MLP AG 5.22 4.74 5.02 6.00 5.70 5.33
Renta 4 Banco 8.76 8.48 8.11 7.39 7.92 8.26
Binckbank N.V. 19.57 24.39 28.56 27.25 27.95 29.34
Van Lanschot 6.68 3.89 6.69 8.68 8.33 7.28
St. James’s Place Plc 0.97 1.01 1.93 2.19 3.10 2.97
Brewin Dolphin Holdings Plc 16.01 4.87 4.94 4.32 2.89 3.42
Hargreaves Lansdown Plc n.a. n.a. n.a. n.a. n.a. n.a.
Annual average 6.50 5.56 5.97 6.25 6.19 6.44
Variance 0.29 0.31 0.43 0.38 0.40 0.43
SD 5.34 5.54 6.54 6.20 6.36 6.57
Median 4.88 3.97 4.43 4.48 4.54 4.76
Minimum 0.97 1.01 1.59 1.42 1.42 1.96
Maximum 19.57 24.39 28.56 27.25 27.95 29.34

Depreciation and amortization of tangible and intangible assets as a percentage of total operating costs, global players, 2009-2014

Global players 2014 (%) 2013 (%) 2012 (%) 2011 (%) 2010 (%) 2009 (%)
Gruppo Intesa Sanpaolo 10.09 28.25 10.24 10.35 10.99 11.16
UniCredit Group 8.45 19.67 9.00 14.09 10.16 9.46
BNP Paribas Group 5.90 6.05 5.81 5.77 6.01 5.92
Crédit Agricole Group 5.60 5.68 5.71 5.42 5.70 5.68
Santander Group 11.26 12.05 10.92 10.72 10.66 9.72
BBVA Group 10.84 10.14 9.43 8.58 8.49 8.34
Deutsche Bank Group 0.41 0.29 6.19 0.00 0.13 −0.68
Commerzbank Group 6.4 5.87 5.76 5.93 6.83 8.53
HSBC Holdings Plc 5.62 5.95 5.71 7.03 4.55 5.02
Barclays Plc 6.26 5.49 5.25 8.09 7.36 7.22
Rabobank Group 5.43 5.41 5.85 6.63 6.97 6.56
ING Group 0.86 1.34 2.27 2.91 7.36 3.89
Annual average 6.43 8.85 6.85 7.12 7.10 6.73
Variance 0.11 0.58 0.06 0.12 0.08 0.09
SD 3.30 7.60 2.40 3.53 2.85 3.03
Median 6.08 5.91 5.83 6.83 7.16 6.89
Minimum 0.41 0.29 2.27 0.00 0.13 −0.68
Maximum 11.26 28.25 10.92 14.09 10.99 11.16

Impaired loans on gross loans (NPL’s ratio), 2009-2014

Global players 2014 (%) 2013 (%) 2012 (%) 2011 (%) 2010 (%) 2009 (%)
Gruppo Intesa Sanpaolo 16.54 14.70 11.12 9.13 8.03 7.54
UniCredit Group 14.95 14.34 12.54 10.66 10.08 8.54
BNP Paribas Group 6.29 7.07 6.48 6.31 6.06 5.66
Crédit Agricole Group 3.69 3.78 3.74 4.51 4.16 3.41
Santander Group 5.41 6.09 4.87 4.17 3.78 3.50
BBVA Group 6.59 7.64 5.56 4.39 4.47 4.57
Deutsche Bank Group 2.28 2.94 2.75 2.48 1.76 2.75
Commerzbank Group 5.49 6.94 7.50 7.08 7.07 6.43
HSBC Holdings Plc 2.97 3.62 3.95 4.34 4.79 3.32
Barclays Plc n.a. n.a. n.a. n.a. 7.24 5.20
Rabobank Group n.a. n.a. n.a. n.a. n.a. n.a.
ING Group 3.23 n.a. n.a. 2.20 2.23 2.05
Annual average 6.74 7.46 6.50 5.53 5.42 4.82
Variance 0.25 0.23 0.15 0.10 0.08 0.05
SD 4.72 4.09 3.17 2.61 2.41 1.97
Median 5.45 6.94 5.56 4.45 4.79 4.57
Minimum 2.28 2.94 2.75 2.20 1.76 2.05
Maximum 16.54 14.70 12.54 10.66 10.08 8.54

Source: Bankscope

Impaired loans on equity 2009-2014

Global players 2014 (%) 2013 (%) 2012 (%) 2011 (%) 2010 (%) 2009 (%)
Gruppo Intesa Sanpaolo 129.95 116.78 85.86 76.33 58.51 54.73
UniCredit Group 146.52 148.10 104.74 113.17 86.24 78.57
BNP Paribas Group 45.81 49.82 45.16 51.03 49.17 47.77
Crédit Agricole Group 29.41 33.79 36.13 45.46 39.86 33.73
Santander Group 45.00 50.63 43.43 37.76 34.49 32.56
BBVA Group 43.99 57.10 46.32 39.06 40.99 49.40
Deutsche Bank Group 12.77 20.41 20.37 18.92 14.38 18.97
Commerzbank Group 43.93 57.78 72.10 79.44 75.76 82.04
HSBC Holdings Plc 14.64 19.49 21.12 25.04 30.26 22.56
Barclays Plc n.a. n.a. n.a. n.a. 51.21 38.28
Rabobank Group 28.87 n.a. n.a. n.a. n.a. n.a.
ING Group n.a. n.a. n.a. 26.53 29.55 30.13
Annual average 54.09 61.54 52.80 51.27 46.40 44.43
Variance 20.03 19.66 10.80 10.29 5.32 5.13
SD 43.78 40.91 27.25 28.22 20.02 19.88
Median 43.96 50.63 45.16 42.26 40.99 38.28
Minimum 12.77 19.49 20.37 18.92 14.38 18.97
Maximum 146.52 148.10 104.74 113.17 86.24 82.04

Source: Bankscope

Linear regression between commissions on intermediation income and RoE (ANOVA Table)

Df Sum square Mean square F-value Pr(>F)
RoE 1 2.420 2.420 30.371 <0.0001
Residuals 154 12.189 0.080
Notes:

Significance codes: 0 “***” 0.001 “**” 0.01 “*” 0.05 “.” 0.1 “.” 1

Notes

1.

Using a small sample and creating an handmade data set, we are able to overcome the problem underlined by Mergaerts and Vander Vennet (2016) and referred to public data set: “The data concerning non-interest income subcategories are not sufficiently granular of European banks to implement such subdivisions in our analysis”.

2.

Within the global players group, 11 banks on 12 are SIFIs, see Financial Stability Board (2015), 2015 update of list of global systemically important banks (G-SIBs).

3.

See, for example, World’s Top Banks, 2014 (ranking by assets and market capitalization, www.relbanks.com/worlds-top-banks); Top 150 banks worldwide ranked by asset size (The Banker, July 2013, www.cba.ca/contents/files/statistics/stat_bankranking_en.pdf).

4.

MiFID 2 Directive amended the Insurance Mediation Directive introducing a new Chapter (IIIA) with “Additional customer protection requirements in relation to insurance-based investment products” (these requirements are generally inspired by the duty to act in the best interests of the client). MiFID 2 was adopted on 15 May 2014; Member States shall comply with its provisions from 3 January 2018.

5.

This comment is confirmed by recent developments in the offer structure of the Italian niche players which are more focused on asset management: in the last three years, these players have seen a significant return on investments in products issued by the firm’s own group, to the detriment of the return on no captive products (Assoreti, 2015).

6.

See art. 16, Directive 2014/65/UE (MiFID 2).

7.

In its final report of December 2014, ESMA informs the European Commission of the need to extend the rules on suitability assessment to profile the investor when investment advice or portfolio management services are provided via Web platforms. The firm’s responsibilities are in no way reduced as a result of the services being provided via an electronic system (ESMA, 2015).

8.

“The retail distribution review, or RDR, is the name that has been given to a new set of rules that will be enforced in the UK from the beginning of 2013. The rules are aimed at introducing more transparency and fairness in the investment industry. The most significant change is that financial advisers are no longer be permitted to earn commissions from fund companies in return for selling or recommending their investment products. Instead, investors now have to agree fees with the adviser upfront. In addition, financial advisers now have to offer either “independent” or “restricted” advice and explain the difference between the two – essentially making clear whether their recommendations are limited to certain products or product providers”, FT, www.ft.com See comment on Ring, 2016.

9.

“Packaged retail investment product” means an investment where “the amount repayable to the retail investor is subject to fluctuations because of exposure to reference values or to the performance of one or more assets which are not directly purchased by the retail investor” (EU Regulation no. 1286/2014, 2014 art. 4).

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Acknowledgements

The authors are grateful to ANASF, the Italian Association of Financial Advisors, for the precious support (its President, Maurizio Bufi and the General Manager, Germana Martano). The authors are also very grateful to the Baffi–Carefin Center of Bocconi University for the ongoing support and Professor Carlo Altomonte for the ongoing encouragement. The authors also thank the participants in the discussion held in September 2015 at the XXXVII General meeting of AIDEA, the Italian Academy for Business Administration and Management, a special thanks to Professor Luciano Munari and Professor Claudio Giannotti. The authors acknowledge the European Financial Planning Association Scientific Committee Members, Germán Guevara and Professor Wolfgang Reittinger for the useful information. The authors have benefited from helpful discussion with a CONSOB (Commissione Nazionale per le Società e la Borsa) group of researchers, thanks to Nadia Linciano, Head of Economic Studies. All errors are of the authors.

Corresponding author

Paola Musile Tanzi can be contacted at: paola.musiletanzi@sdabocconi.it