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Measuring Competitive Dynamics in the Banking Industry

Abstract

This paper addresses the applicability of evolution metrics as brought forward by Andersen (2004) to

the analysis of the banking industry and the usefulness of the method as an instrument in competitive

strategies. Following Andersen (2006), we apply the concept of evolution metrics to the financial

service industry and tested its robustness and implications based on a sub-market of the banking

industry. We did so with a sample from the Swiss Fund industry. We found that especially the dynamic

properties of the market are essential when applying evolution metrics to economic problem sets.

The paper sets the measurement approach from Andersen (2004) in contrast to a descriptive

approach of descriptive approach from competitive strategies (Jacobides, Billinger 2006) and raises

the question, whether the chosen measurement framework could also support analysis and cases in

banking with quantitative arguments. After mapping the two concepts against each other and

complementing the measurement framework for additional information, the paper concludes with

dynamic measurement results for changes in one banking submarket.

Keywords: Evolution metrics, Competitive Dynamics, Financial Services Industry, Firm Boundaries

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Introduction

Industry evolution has long been a central concern, both dealt with in management literature as well

as in economics. Management literature has made several attempts to describe evolutionary

processes and to derive strategic options to handle industrial or competitive dynamics. At the

same time, economic scholars have broadly extended the way we can model and estimate how new

markets evolve and how the industrial structures are affected. Firm decisions and reactions on these

changes are of minor interest. Both approaches focused in their analysis on erecting or eroding

barriers to entry in existing markets, on shifts in demand structures or on technological changes

(Abernathy, Utterback 1985). Lately, the scope has been extended by Jacobides (2005) who observed

not only geographical and horizontal diversification in barriers to entry world to exploit rents, but

also a process of vertical diversification that fostered the creation of new intermediation markets.

This third dimension of structural change formed a major difficulty for measurement approaches.

Whereas traditionally, industry structures have been measured by concentration indices (Herfindahl

Index, Boons Index), and dynamics was defined as observed change in those index values, the

problem grew more complex, leaving room for new ideas. Competitive dynamics is addressed most

dominantly by economic modeling of sustainability or fragility of an industrys barriers to entry.

Thereby, barriers to entry can be approached as absolute cost advantages, as differentiation or as

exogenous variables, most prominently represented by regulatory issues. However, empirical work

on the erosion of barriers to entry is difficult to provide and attempts exhaust generally in detailed

case studies of past observed market disruptions or active repositioning strategies. These studies are

case specific and cannot be directly compared to the economic models of market evolution. Thus,

there is a significant gap in literature, first, with respect to measure actual trends and model future

scenarios, and second by aligning economic theory to strategic literature based evidence.

The paper introduces an inductive theoretical framework that shows how competitive dynamics can

be measured in the banking industry and how these measurements can bind strategic frameworks to

economic models. The paper is characterized inductive, because we apply existing theory (i.e. the

herein called Andersen framework) on a problem set from the financial service industry. In a

subsequent step, we focus on one particular sub-market in the financial service industry, namely

asset management or more detailed the fund industry. With this example, we apply the theoretical

framework and contrast the results against a strategic literature concept by Jacobides-Billinger

(2006). We do so, in order to gain - beside the empirical validity - also an idea about the conceptual

problems when trying to use the Andersen (2004) framework as an instrument for measuring

dynamics in the financial service industry. We have chosen the Andersen framework, since this is so

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far the only concept that emerged as a means to deal with measurement problems within an

adaptational or evolutionary theory world. We feel there is a need to test this frameworks

applicability first, since although the concept is logically stringent, it has not yet found its way into

the approved methodology set. Thus, the main objective of the paper is to apply evolution metrics as

a mean to measure competitive dynamics and make predictions or evaluations of strategic options in

the asset management market. We therefore apply the methodology of Price (1970) and Fisher

(1999), respectively the adoption of these biological concepts by Andersen (2004) and use it as a

measurement tool to describe evolutionary processes. As it is a key feature of the methodology to

split transformational business processes into selection and innovation effects, the study tracks these

splits based on the Swiss Fund Industry, respectively the effects on capital attraction by these funds.

We define capital attraction (net annual inflows) as the key fitness factor that is described as a

function of the previous year cost adjusted performance statistics of the funds. We expect that this

fitness criterion has sufficient explanatory power for the dynamics of the attached competitive asset

management market structure. The main argument is derived from this analysis is that dynamic

change effects measured with 'evometrics' can directly be applied within a competitive strategy

framework. We show this by matching measured effects to observed behavior for the case of the

definition of firm specific boundaries to vertical integration - again in the same financial service

example.

Background

The paper is grounded in literature on firm boundaries and competition in the financial service

industry. Within these strands of research, our study both complements and differs from existing

theories about vertical scope and firm boundaries. Beside the different form of analysis, it uses also

different units of analysis. Transaction cost Economics (TCE) is mainly focused on conditions that

leads firms to "make" rather than "buy" (Coase 1937; Williamson 1985) or to "ally" (Dyer 1996;

Williamson 1999), taking a micro analytical approach, looking at one transaction at a time. Thus,

research to date looks at the governance of transactions rather than the overall boundaries of any

specific organization. This emphasis on transaction neglects factors that operate that the level of the

firm, and may underpin vertical scope and affect productivity, systemic adaptation, innovative

potential and performance. The second challenge addresses the contrast between firms and

markets. When dealing with this institutional decision the traditional approach of analysis often have

to deal with the problem of juxtaposing firms and markets or "hybrids" (Foss 2003; Williamson 1996).

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There are so far no approaches describing and modeling firms and markets in an integrative

framework fully. Jacobides, Billinger (2006) present a first approach to do so, by stressing the

linkages a firm has with final and intermediate markets at various level of analysis: Thereby, they

focus on the Strategic Business Unit (SBU), on the different steps in the value -adding process and on

the corporation as a whole. They observe that rather than just "make" or "buy, firms interface with

final and intermediate markets in several different ways, leaving few room for general conclusions

apart from detailed case studies. Quantitative approaches are missing so far due to a lack of

measurement instruments.

The final objective in analyzing markets, firms, hybrids or in proposing integrative frameworks

therefore, however, is the motivation to detect boundaries of the firm with respect to integrative or

diversification scope and ultimately, for supporting buy, sell or alliance decisions on grounded

theory. With respect to boundaries of the firm, research has considered recently how industry

boundaries evolve and how intermediate markets emerge (Jacobides 2005; Langlois 2003). It has also

considered how capabilities, at the industry level, are affected by vertical scope (Cacciatori, Jacobides

2005; Jacobides, Winter 2005). However, the observations are often case study based and not

dynamically replicable, thus there is seldom a process focus. But, as Santos and Eisenhardt (2005, p.

504) put it: "Process research can more readily uncover the causal mechanisms shaping boundary

formation []. This may allow the field to move way from simple contingencies to deeper

understanding of the complex and evolutionary role of boundaries in organizations;" thereby

influencing theory building for industry structures designs as well. In order to do so, there is so far

no established measurement technique that gives credit to these complex and evolutionary

properties. The method to do so has to nest in the evolutionary metric designs. In this respect Fisher

(1999) and Price (1970) provided early work by showing that change based upon variation properties

and that it can be measured accordingly. Especially Price's partitioning therein included not only the

effect of selection but also the effect of causes that increase variation (Knudsen 2004; Andersen

2004). Price demonstrates that this equation is an identity that may be used for the decomposition of

any kind of evolutionary change. With respect to these metrics, we see a research gap in the

applicability of the measurement of evolution and the decomposition of evolutionary aspects into

positive theory. Selection and Innovation effects can be counted percentage wise of total

evolutionary aspects, however, theory stops at this point. It is not possible to make estimates on

future industry trends given the measured effects nor is it possible to link the observed effects to

economic theory where it comes to both the traditional industry measurements (such as a Herfindahl

Index) or to competitive strategy literature.

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Multi-Segment Competition in the Banking Industry

Competition in the financial service industry has in recent literature been seen renewed as a major

concern, since in most countries, notably the U.S. and Europe banking supervision is being

increasingly homogenized (e.g. by the way of the Basel Accords) and local policy has been

deregulated (e.g. the end of the Class-Steagel Act in the U.S.). Therefore, scholars started to agree

that the pace of consolidation and market dynamics in the industry will primarily be determined by

changes in economic environments that alter the constraints faced by financial service firms on a

macro level (Raff 2001; Rime, Stiroh 2003). But moreover, with respect to competition on an

intermediary or micro level, there is a source of deep uncertainty in financial services product

markets as well. Several decades of regulatory changes have erased merely legal constraints upon

strategy and success from competitive landscapes. A burst of new product innovation going back for

approximately twenty years now, has significantly altered the product space for the industry itself.

The common acceptance of new technology and the radical changes this suggests as to the size and

location of potential customer bases also casts into doubt established ways of making money and the

attractiveness of established strategic plans. In this environment, the optimal definition of the firms

boundaries becomes a key factor for success (Jacobides, Billinger 2006).

Illustration 1: Extended Jacobides-Billinger Framework 2006

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When observing banks and bank strategies in particular, literature often differs between a high level

bank holding perspective (Berger, Demsetz Strahan 1999; Rime, Stiroh) and the analysis of different

submarkets such as the credit and lending business (Rime, Stiroh 2003; Claessens, Laeven 2004)).

From an industry dynamics point of view, it must be clear that now - as regulatory barriers eroded -

niches will evolve. Once a given set of niches comes into being, the way to earn profits lies in

occupying and dominating them (at a reasonable cost, of course). The great question of strategy in

the context of industrial dynamics therefore is how to be able to anticipate niches with future growth

potential or more simply your competitors strategy. The simple answer to this is pre-adaptation,

that is, being present in the niche before it is a niche. The old Bank of America serves as a good

example of being in the right place at the right time, from A.P. Giannini at his wood plank table

amidst the wreckage of the great San Francisco earthquake of 1906 through Californias t

tremendous growth in population (Winchester 2005). As strategic coaching goes, however, Be in the

right place at the right time may seem to be an awkward advice, given that dynamics creating

growth for niches cannot be anticipated. But there is more to the idea of pre-adaptation than this.

And banks have indeed started to live up to the notion of pre-adaption. From any given configuration

of activities, it is possible for the firm to conduct experiments to make forays into unoccupied

territory. So in general the way to fake pre - adaptation is to conduct intelligently planned forays.

(The source of the intelligent planning is usually current operations and interactions with current

customers. In the financial service industry this is generally done by issuing multiple and partially oreven complete substitutes within one group, and sometimes even under one brand. This pre-

adaptational behavior leads to multi-sub-market dynamics as depicted in illustration 2.

Illustration 2: Multi-Sub-Market Dynamics in Bank Competition

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Generally, we see competitive dynamics as shifts in industrial structure on the macro layer of these

multiple sub-markets. These shifts are driven by exogenous or endogenous motivation factors

(Claessens, Laeven 2004). When focusing on these two driving forces, we find that economic

literature has primarily focused on the first one, by describing how new markets evolve, how existing

barriers to entry emerge and existing barriers are eroded over time, thereby influencing the overall

structure of an observed industry as measurable by market concentration, competitiveness (erosion

of margins) and new entry dynamics (Sengupta 2007). Management literature on the other hand

takes a more firm centrist position, effectively combing two strands of research: First, literature that

focuses similarly to the economic literature on industry patterns, building and extending theory

within Porters famous five-force framework. Second, management literature observes reactions of

the single firms, following structure-conduct paradigms and arguing on this ground on the extent or

the need for diversification or integration, respectively on specialization and disintegration on an

aggregate level (Chen, 2005; Langlois, Robertson 1995). An exemption hereby is the work of Stigler

(1951), who suggested that the size of a market limits the extent of specialization (or disintegration

on an industry level).

Both economic and management literature see scale as a primary driving force for industry shifts. It is

irrelevant, whether the need for scale arises through technological or exogenous absolute cost

changes given constant and inelastic demand curves, whether the need for scales arises by a single

firms advantageous cost structures, the adaption of such best practices or finally the alignment to

emerging industry standards under the structure-conduct paradigm. However, taking an economist

stance, it is observable that despite ample room for specialization and scale building in economics it

doesnt always occur (Jacobides 2005). Thus, scale is generally not considered to be a good

explanation of disintegration or other industry level effects (see Langlois, Robertson 1995). There are

almost no systematic studies of the emergence of integration or disintegration, despite substantial

research on the social institutions of market exchange in general (Fligstein 2001), new research on

vertical scope (Jacobides, Winter 2005) or economic models for market evolution (Sangupta 2007).

The main reason for the relative dearth of knowledge is seen in the fact that the literature,

particularly transaction economics (Williamson, 1985; 1999) has largely focused on firm decisions to

make versus buy in given transactions. Such analysis is conducted at a sub-firm level in that the

units of observations are particular choices made on the margin by individual firms (Jacobides 2005)

and not full-cost based, by industry groups. It therefore does not look at entire industries by

examining, for example, how markets emerge to create new markets, vertical disintegration to foster

scale through specialization or horizontal integration, to increase scale in executing functions and

scope through improved allocation mechanisms or more efficient internal markets for illiquid

products.

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When observing the industry landscape, banking is clearly a multi-submarket population within a

strongly regulated environment. Nevertheless, as in other industries, the degree of competition in

the financial sector can matter for the efficiency of the production of financial services, the quality of

financial products, and the degree of innovation in the sector. Specific to the financial sector,

however is the link between competition and stability, long recognized in theoretical and empirical

research (Vives 2001). This means that the financial service environment is primarily designed by

regulators as a sufficiently stable one. While some relationships between competition and banking

system performance and stability have been analyzed in the theoretical literature, empirical research

on the issue of competition, particularly cross-country research, is still in an early stage. Theory also

suggests that performance measures, such as the size of the banking margins or profitability, do not

necessarily indicate the competitiveness of a banking system. These measures are influenced by a

number of factors, such as a countrys macro performance and stability, the form and degree of

taxation of financial intermediation, the quality of the countrys information and judicial systems,

and bank-specific factors, such as scale of operations and risk preferences (Claessens, Laeven 2004).

A key constraint that explains the current literature focus, is the difficulties when measuring shifts

and testing hypotheses with competitive dynamic backgrounds. Traditionally, the strong reliance

upon transaction cost theory led to a relatively static observation standard. Existing studies measure

industry effects by changes in concentration rates (Boon or Herfindahl Indices), by Gini-Coefficient

influenced scales comparing market share and margins of market leaders to followers (Berger,

Demsetz, Strahan 1999) or by calculating relative entry dynamics proxying new entrants relative

strength (and thus the shape of market barriers) by their ability to win - volume adjusted - faster new

market share than incumbents (King, Levine 1993). All these factors are statistic and can only be

measured ex-post, but hardly me modeled or simulated for ex-ante decision making. The long-

existing theory of industrial organization has shown that the competitiveness of an industry cannot

be measured by market structure indicators alone, such as numbers of institutions, or Herfindahl and

other concentration indexes (Baumol, Panzar, Willing 1982).

Dynamic measurements in bank strategy processes

In the intersection of measurement of dynamics we can distinguish between two main perspectives:

First, the micro-perspective of the single firm that shall be enabled not to observe only past

developments and to measure current concentration rates, but also to estimate future trends by

detangling the drivers of change experienced previously and by weighting these driving effects

according to their impact on market structure.

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Competitive Strategy does traditionally build upon economic concepts to describe and measure

markets, but with the clear objective to build a conceptual framework for the firms within the

observed industry, to adjust optimally. One way of adjustment to new or changing competitive

environments is to re-position a firms geographic or horizontal diversification patterns, another one

to rebuild its vertical architecture, defining the boundaries of the firm with respect to the value chain

of its products that are served autonomously. Research on vertical integration and vertically

differentiation at the product range is extensive, especially with respect to the technology industry.

Classical examples are disk drives' quality - to price or light bulbs longevity, octane rating of a gallon

of gasoline, and a physicians success rate (Ruebeck 2002). Of key in each of these cases is a buyers

willingness to pay for improved capacity; in the technical examples this improved capacity can be

measured in dollars per megabyte or per year of lifetime. The distinguishing feature of vertical

product differentiation is that all buyers agree that higher capacity drives are better; they only differ

in their willingness to pay for another megabyte. The main problem with financial service products

with respect to vertical integration, however, is that they apparently cannot be measured. Although

they have a price, the benefits that is gained for this price compared with other comparable products

cannot be distinguished ex-ante as markets are assumed to be rational and outperformance - e.g. of

fund products - only to be achieved at higher risks (and thus at higher prices). This has various

impacts on market structure in the financial service industry:

First, Intrafirm versus interfirm effects are affecting the competition comparable to findings in other

industries (Ruebeck 2002; Baum, Korn 1996). While the number of a bank's own products that

compete with product i at time t, has a consistently positive effect on exit, the number of rivals

competing products does not significantly affect product is likelihood of failure. Intrafirm effects

appear to be more important when considering local competition. Revenue shares also illustrate the

importance of intrafirm over interfirm effects. Investment strategy is share of its firms revenues at

time t, has a negative effect on the likelihood of its exit. Although not significant when including the

age of the bank, it is highly significant when firm dummies are included. Note, too, that controlling in

this manner for bank is importance to firm revenues does not diminish the significance of the

cannibalistic effect. The relationship between products is exit and its firms percentage share of the

local markets revenues at t, also is negatively related to exit. It would appear that if a product is

issued by a bank with local market power - e.g. characterized by an extensive branch network, that

product is less likely to exit the market. In a parallel case for the technology industry, Greenstein and

Wade find that the introduction of a new mainframe computer near model i by the same firm that

produces i increases the probability of exit, but the use of similar variables to predict disk drive exit

shows no consistent relationship. Unreported results included indicators of intra- and inter-firm

introductions nearby. Thus it does not appear that firms in technology markets are systematically

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introducing new products and then retiring older ones. Firms are introducing better drives, perhaps

to keep pace with demand and technological change, rather than in anticipation of dropping older

drives.

Second, the uncertainty about the product's properties ex-ante force the banks to be present with

multiple product options in a way of the above described pre-adaptation, thereby directly accepting

rivalry effects and (marginal) increased cost burdens on their own product portfolios.

When observing the development in one financial service product markets - the fund industry - this

behavior is mirrored in high growth and high dynamics (meaning entering and existing of the

market). For instance, in Switzerland 1997, there were 1631 funds with asset under management of

8 billion Swiss Francs (SNB 2003). A decade later, the number of accredited funds has grown to 4620,

managing approx. 220 billion Swiss francs (SNB 2006), whereas the actual funds managing these

sums have experienced average lifetimes of only approx. 3 years before being replaced, merged or

absorbed by substituting products from the same issuer (SFA 2006). However, not only the market

size has increased and market structure has changed over the observed period, but also the diversity

in terms of product diversity and firm positioning has spread. The three generic strategic options for

market positions can in short be described as (product) specialization, cost consciousness as reported

almost in idealistic forms, but also service specialization for third parties (white labeling) and broad

integration approaches can be observed. To track these changes empirically and estimate scenarios

strategically, an according measurement framework, giving credit to the complexity of the

relationships and the evaluative industry dynamics is needed.

Theory

Evolutionary thinking in economics roots in biological theory building and was successfully

introduced into theory building, but less so into empirical work. Theory building with evolutionary

elements started in describing relative simple game theoretic constellations and gradually moved on

to shape the discussion in industrial dynamics literature with more complex multiple interactions.

The underlying principles for this transfer are the principle of variation, the principle of heredity and

the principle of selection (Lewotin, 1974; Brandon 1990). Thereby, the variation principle means that

all entities out of any given population are multicharacteristical and varies at least with respect to

one potentially selecting characteristic that is observable and can be copied with fewer or more

efforts. Accordingly, heredity as the second principle in evolutionary models guarantees that in any

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interacting system, there exists a mechanism that enables copying, e.g. through learning effects.

Thus, given changes in the environment, any system has components that adopt more easily and

therefore grow faster than the industry average. Over time however, these superior growth rates of

the lucky entities are eroded through copying of the competitors, brining the system back to

stability of more or less equally distributed growth rates. However, in any adaption process there are

both among the lucky entities with the right characteristics, as well as within the copying and

adapting competitors, some entities that have the ability to go faster through such a disruptive

process. The principle that explains this inequality in adaption rates is selection; in an economic

setting thus selection is independent of the ex-ante firm specific characteristics the reason for the

pace of a markets reaction to disruption and also the principle that sets independent of the ex-

ante characteristics winner and losers of change (Metcalfe 2001). This means that not all markets

react equally fast to disruptive shocks i.e. technology oriented markets are generally assumed to be

faster than agriculture or notably banking. And within a market some firms are again more able or

willing to adopt i.e. entrepreneurial organized firms are perceived to embrace change better than

hierarchical organization (Chen 1996). From a methodological stance it is therefore important to

analyze different markets different and to give credit to selection enabling resources (financial

means, learning capability and learning opportunity (Teece, Pisano, Shen 1997). Thus, evolutionary

models focus on analyzing populations within markets and describe transformation patterns, entity

characteristics and learning effects within these populations, selection and innovation thereby aregenerally the most important independent variables. When for instance describing changes in an

industry, total change can be into selection dominated transformation, where institutions with

different characteristics enjoy different growth rates and variation, where open or hidden

characteristics of the population entities allow for the formation of entirely new products or new

sub-markets. Given these relationships, Andersen (2004) proposes a framework called Evometrics

that builds upon these evolutionary mechanisms and allows to measure change effects, respectively

selection or variation based components of change. Methodologically, this measurement rests upon

two mathematical concepts, the Price equation and the Fisher Theorem.

The Fisher Theorem states that mean change (w) of a once identified fitness factor of any given

population equals the variance of all items within this population.

( )iwVarw =

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Fisher describes thereby the observation that between two time periods, entities with above-average

fitness manage to increase their relative weight within a population. This approach is complemented

by the Price equation.

( ) ( )iiii zwEzwCovzw += ,,

Selektionseffekt Innovationseffekt

Methodology

As we are dealing with a multi-submarket environment that uses partly the same resources

(synergies), that is partly in competition with same-firm units or competitors for these very

resources (rivalry) or that gain momentum as a consequence of prior settings or prior fitness,

it is a necessity to disentangle the relationships and to build a consistent measurement

framework; especially, when applying measurement results to strategic decisions. In order to

reduce complexity, we are analyzing the financial service submarket asset management.

Within the asset management, we deal with the fund industry as one product market. This

observation base is sufficiently large and sufficiently divers to gain consistent results for all

other possible asset management products. Asset management products are all forms of

financial service solution that meet customer demands with respect to individually defined

risk-return patterns. By using exclusively funds as product proxy, we can control for net risk-

return characteristics, as here in contrast to equity or structured products we can build

upon a relatively transparent cost base (measured as TER).

The methodological concepts applied vary along the three objectives in this paper: First, the

theoretical framework by Andersen is tested by building the required equations for the sub-

case of the fund industry. The goodness of fit of the theory is tested by testing the

equilibrium of the resulting equations for fund strategies and for fund issuers. Given the

applicability and validity of the results, we use the model to build a causal relationship,

linking interpopulation selection and innovation, intrapopulation selection and innovation as

well as supporting proxy variables for the relative influence of difference in sub-market

growth, K-innovation and (branch-) network externalities. We use a linear OLS regression

model to measure the influence of these variables on the reported industry dynamics.

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Industry dynamics is defined at the level of the retail customer interface. This is the shift in

demand for the different products. We group the products within the sample market along

the characteristics firm and type. Intrapopulation analysis thereby focuses on the

interaction of different (potentially rivalrious) fund types within one firm, whilstinterpopulation analysis focuses on the competition between multiple firms with

substitutive or complementary product bases. In both analysis frameworks, we assume a

constant market. We do so, because it is likely to argue that every imaginable asset solution

can be structured in an equal property shaped synthetic fund solution. This approach clearly

builds on rational decision making and ignores marketing issues or behavioral explanations

for customer diversion. Based upon an evolutionary framework, we assume that every firm

will win additional market share through above-average fitness, which is the ability to

generate the best cost-based performance per risk class. Based on the Andersen or Fisher-

Price setting, we would expect to find only fitness as explaining variable. However, we

assume that also size matters due to information cost advantages for the customer, be it

either due to improved market visibility or accessibility (large branch network). Third, we

compare the results and the measurement framework with the strategic concept of

Jacobides-Billinger (2006) to design the boundaries of the firm.

In order to assess the validity of the Andersen framework, a respective fitness criterion had to be

chosen and fund volumes, as well as organic growth and growth of the (end-) customer demand

measured. We start by running the Fisher-Price equations for selecting intrapopulation effects based

on the Andersen framework. Thereby, the Fisher theorem is given by

( )iwVarw =

which means that changes in performance over the entire sample hast o equal the variation

of the single funds or submarkets performance of the total sample.

The price equation on thereby states that

( ) ( )iiii zwEzwCovzw += ,,

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thereby showing that a shift in the distinct characteristic can be traced back to the sum of

the individual funds or submarkets selection and innovation effects. The selection

coefficient is given by regressing the sample for the fitness factor as independent variable

and corresponds to the net new money inflow to funds as shown in equation (4a) andthereby enabled volume based above average growth, respectively economies of scale.

Innovation effects are estimated based on past absolute (out-) performance and the

inherent expectations of investors. Innovation thus can be attributed to distinct capabilities

of the fund manager or as technical innovation reducing the relative cost base for a

comparable transaction, thereby bringing down absolute (after-cost) performance even

given average market yields only.

Similar to the application on the intrapopulation level, change can also be a measured on an

interpopulation level. Methodologically, the subscript i in this context do not any stand for

single funds (entities), but rather for subgroups of funds (sub-markets), whilst the general

focus is on the overall population. On the interpopulation lever, competition is focusing on

innovation again, but not any longer on selection. However, there is a competition for scarce

and non-dividable resources. Thus, selection is replaced by a density function, describing the

ability to use the scarce good efficiently, denoted K-Factor. Firms that do not correctly

allocate resources or are not able to manage operations above average in terms of

operations' efficiency and effectiveness are disappearing. Thus, interpopulation competition

is highest in markets were the growth of the critical factor is low (or even negative) or where

drop-out rate of incumbents is low (e.g. as a consequence of high barriers to exit).

We than calculated the fitness criterion w as (Sharpe-Ratio Performance TER). Thereby,

Sharpe Ratio is an established concept to express risk adjusted returns of financial products

and TER is the legally binding measure for all Swiss funds to express total cost directly

related with the purchase of a fund-product. In order to distinguish between 'organic fund

growth', i.e. the growth of the assets under management as stock market valuation

increases and customer demand for fund products, i.e. the net new money in- or outflow of

the products, we calculate the annual change in the reported fund volume and subtract the

yield of the reported fund performance; we thus have change of the volume split into

'organic' - say market induced growth and net new inflows, representing the end-customerdemand for a specific fund. We assume that end customers form their decision to buy fund

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primarily on the past reported sharpe adjusted performance, meaning that c.p. those funds

should attract most new money that reported highest 'organic' growth within their peer

group. As we observe the funds after cost, bank specific transaction costs should not matter

and thus the issuer should not be of relevance for the decision. We use this assumption totest the measure for competition among banks at the corporate level under the label

'interpopulation effects'.

Hypothesis: New money attraction per asset management strategy type is independent of

the name of the issuing bank. We test Hypothesis 1 with a Chi-Square independence test.

The strategic Jacobides-Billinger (2006) framework suggests, that pure (demand induced)

market growth specific capabilities of the bank do matter as well. Besides, we saw that there

are interdependencies between the different products market growth on the

interpopulation level and the growth of the sub-market's products (K-Innovation). We

therefore calculate measures for specific capabilities, and for externalities. These proxies for

K-Innovation and distribution network externalities are depicted in tables 5, 6.

Proposition: Competitive dynamics in the banking industry can be measured by identifying

the determinants for end-customer demand for a specific product. These determinants are

interpopulation and intrapopulation characteristics, capabilities and market adjustments for

externalities. The variables significance and the validity of the proposition are shown with a

linear OLS regression model.

Data

For the test frame, the paper focused on one sub-market in the financial service industry,

the asset management for retail customers. This segment was chosen, as complexity can be

reduced without losing critical information. The underlying assumption is that every retail

customer invests is long-term savings according to his personal risk taking behavior. Banks

have responded to this with a broad set of product options. However, there are all

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substitutive to some degree. We therefore focus on one product market that we observe

and test in more detail, which is the fund market that - in contrast to other products - is

relatively transparent in terms of descriptive market statistics.

We used a data set from the fund industry, consisting of asset-under-managements or fundvolume, fund type, fund management firm, fund performance and commissions and other

costs associated with the funds. We collected these data for all funds registered under Swiss

law to implicitly control for regulatory dispersion. The basis for these dataset was formed by

the list of accredited funds of the Swiss Banking Supervisory Authority (EBK). In a second

step, the data corresponding to the accredited funds were collected from their annual

statements. We excluded special funds and fund-like entities for qualified investors only and

derived at a regulatory homogenous sample of funds with Swiss domicile and Swiss

accreditation for retail customers. Due to the only recently changed law on fund

transparency, we have comparable performance data for the past four years (but not yet

beyond). The sample size counts 171 homogenous entities.

Results

As all banking sub-market, the fund market is shaped by high barriers to entry on the issuer

side, as new competitors must comply with regulatory conditions and capital requirements.

Fund market being a sub market of the financial service industry indicates also therefore, the

relative cost of entry for an existing registered bank into this sub-market is quite low,

whereas for new potential issuers there are restrictions to the minimal capital requirement

and the firm's reputation. Once, however an issuer is accredited, the barriers to launch new

fond products are almost extinguished on the regulatory side. Thus, every launch of a new

product-fund by an issuer is marginally less expensive than the last, due learning effects and

synergies on the back-office side (administration of the funds). As a consequence, we

observe that often even the same firm has multiple and likely substitutive products in the

market. Products of one firm that do not succeed can easily be merged into a more

successful venture of the same issuer. The market thus is shaped distinct evolutionary

properties in terms of a survival of the fittest concept.

The Andersen model does allow to run basic analysis of the intrapopulation and

interpopulation factors thus of markets and submarkets, e.g. in the financial service

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industry (Tables 1, 2). However, forming evometric equations alone does not add yet

significantly to the understanding of the market. The results from testing hypothesis 1

indicate that there is not only the one-dimensional aspect of observed fitness that forms

demand and the basis for success for the future years. The attraction of new money clearly isnot independent from the issuing bank. In other words, there must be some kind of

capability in a bank that allows attracting new funds, although the expected cost and risk

adjusted performance is lower than for peers.

One possible explanation for what these capabilities could be might be seen in on the

customer side, another one on the bank side. On the bank side, it could be argued that when

a bank has a significantly broad product portfolio that is managed in a pre-adaptive way,

below average performance of one product will be neglectible for future decisions as the

money will be re-allocated automatically to a better performing in-house product.

From the customer's point of view, it is mainly information and search cost argument. Either

the absolute search cost prevent him from choosing the optimal product, or the trust in the

above described re-allocation mechanism does give sufficient reason for bearing the burden

of the marginal increased information cost when comparing competitive products.

In both cases, the interaction can be modeled with externalities. First, with the relative

pressure for the bank to re-allocate, measured by the density function of the market growth;

second with the information cost of the customer, measured by the externalities, e.g. of a

large bank's broad branch network.

We assumed therefore that competitive dynamics in banking can be measured by combining

the Andersen variables interpopulation and intrapopulation selection, respectively

innovation, the Jacobides-Billinger capabilities and externalities from K-Innovation and

branch network. The results from the regression model indicate that this measurement

approach has a high power in explaining end-customer demand variation (R-Square of 0.842

in model 1, respectively 0.905 in model 2). However, with respect to the observed

submarkets, only a small set of coefficients were shown to be significant. These are

Innovation on the Intrapopulation level, being in model 1 the sole variable and explaining

alone 0.84% of the variation in customer demand. Second, also interpopulation Selection

and the intermediately capability variable on intrapopulation effects proved to be

significant. However, nor K-Innovation, nor network externalities were able to improve themodel. The key problem with the other variables was very high collinearity.

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With respect to the applicability of the Andersen framework as a measurement tool for

dynamics in the banking sector, the results can be seen as positive. The explanatory power is

high and the argumentation fits also into more strategy oriented descriptions of the market,

such as Jacobides-Billinger 2006. Strategically, this enables us to link the measurementresults to both capability and learning effects or to scale and scope arguments as key drivers

for winning market share. However, we see two distinct problems: First, the system

measures only the fraction of the change and not the change itself (does it does not tell

whether the market is growing, stable or even shrinking); second, the degree of total market

growth does have an impact on the segmentation as acknowledged by Andersen by

introducing K-Innovation factors. In order to take account of these problems, it might be

necessary to extent the research framework on other banking submarkets. Additionally,

there has to be research towards finding less collinear measures for external effects as well.

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Measuring Competion on the Interpopulation level

AV Sharpe after cost wiwi Cov(wi,wi) E(wi,wi) Var(wi) E(wiwi) Holding Equation 1 Holding Equation 2

wi wi wiwi Cov(wi,wi) E(wi,wi) Selection Var(wi) E(wiwi) Innovation Residual Residual

BSI 3.20% -12.27% -0.39% 0.10% -0.39% -0.29% 0.80% -0.39% 0.40% -0.10% -0.70%

Clariden -5.51% 15.21% -0.84% 0.10% -0.84% -0.74% 1.43% 0.00% 0.59% -0.10% -1.33%

Credit Suisse 1.41% 21.70% 0.31% 0.10% 0.31% 0.41% 1.37% 0.00% 1.68% -0.10% -1.27%

Gottardo 3.28% 36.31% 1.19% 0.10% 1.19% 1.29% 3.64% 0.00% 4.83% -0.10% -3.54%

Gutzwiller 5.06% 4.35% 0.22% 0.10% 0.22% 0.32% 0.00% 0.00% 0.22% -0.10% 0.10%

LODH 3.74% 40.61% 1.52% 0.10% 1.52% 1.62% 4.53% 0.00% 6.05% -0.10% -4.43%

MI -3.00% -19.58% 0.59% 0.10% 0.59% 0.69% 0.92% 0.00% 1.50% -0.10% -0.82%

Pictet 1.12% -22.14% -0.25% 0.10% -0.25% -0.15% 1.80% 0.00% 1.56% -0.10% -1.70%

Raiffeisen 6.00% -48.64% -2.92% 0.10% -2.92% -2.82% 9.95% 0.00% 7.03% -0.10% -9.85%

Reichmuth 5.08% 45.18% 2.30% 0.10% 2.30% 2.40% 5.36% 0.00% 7.65% -0.10% -5.26%

Swiss Life 7.73% 16.51% 1.28% 0.10% 1.28% 1.38% 0.26% 0.00% 1.53% -0.10% -0.16%

Swisscanto 0.85% 42.72% 0.36% 0.10% 0.36% 0.46% 5.84% 0.00% 6.21% -0.10% -5.74%

UBS 1.68% 36.99% 0.62% 0.10% 0.62% 0.72% 4.16% 0.00% 4.78% -0.10% -4.06%

Vontobel 29.56% 17.78% 5.26% 0.10% 5.26% 5.36% 0.46% 0.01% 5.72% -0.10% -0.36%

Wegelin 12.42% 15.39% 1.91% 0.10% 1.91% 2.01% 0.03% 0.00% 1.94% -0.10% 0.07%

XMTCH 20.36% 31.80% 6.47% 0.10% 6.47% 6.57% 0.44% 0.01% 6.91% -0.10% -0.34%

ZKB 2.51% 40.61% 1.02% 0.10% 1.02% 1.12% 4.84% 0.00% 5.86% -0.10% -4.74%

InterpopulationDynamic 5.62% 15.44% 0.87% 0.10% 0.97% 0.32% 0.78% 1.19% -0.10% -0.22%

Table 1

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Fisher Price Intrapopulationen und Interpopulation mit K-Innovation

AV Sharpe after cost wiwi Cov(wi,wi) E(wi,wi) Var(wi) E(wiwi) Holding Equation 1 Holding Equation 2

wi wi wiwi Cov(wi,wi) E(wi,wi) Selection Var(wi) E(wiwi) Innovation Residual

BSI 3.20% -2.21% -0.07% 0.10% -0.07% 0.03% 0.10% -0.07% 0.03% -0.10% 0.00% -0.10%

Clariden -5.51% -1.95% 0.11% 0.10% 0.11% 0.21% 0.04% 0.00% 0.15% -0.10% 0.06% -0.04%

Credit Suisse 1.41% -1.43% -0.02% 0.10% -0.02% 0.08% 0.03% 0.00% 0.01% -0.10% 0.07% -0.03%

Gottardo 3.28% -2.10% -0.07% 0.10% -0.07% 0.03% 0.10% 0.00% 0.03% -0.10% 0.00% -0.10%

Gutzwiller 5.06% -2.24% -0.11% 0.10% -0.11% -0.01% 0.18% 0.00% 0.06% -0.10% -0.08% -0.18%

LODH 3.74% -1.83% -0.07% 0.10% -0.07% 0.03% 0.10% 0.00% 0.03% -0.10% 0.00% -0.10%

MI -3.00% -2.53% 0.08% 0.10% 0.08% 0.18% 0.00% 0.00% 0.08% -0.10% 0.10% 0.00%

Pictet 1.12% -3.31% -0.04% 0.10% -0.04% 0.06% 0.07% 0.00% 0.03% -0.10% 0.03% -0.07%

Raiffeisen 6.00% -3.95% -0.24% 0.10% -0.24% -0.14% 0.33% 0.00% 0.09% -0.10% -0.23% -0.33%

Reichmuth 5.08% -4.22% -0.21% 0.10% -0.21% -0.11% 0.29% 0.00% 0.07% -0.10% -0.19% -0.29%

Swiss Life 7.73% 1.44% 0.11% 0.10% 0.11% 0.21% 0.13% 0.00% 0.24% -0.10% -0.03% -0.13%

Swisscanto 0.85% 1.94% 0.02% 0.10% 0.02% 0.12% 0.00% 0.00% 0.02% -0.10% 0.10% 0.00%

UBS 1.68% 1.62% 0.03% 0.10% 0.03% 0.13% 0.00% 0.00% 0.03% -0.10% 0.10% 0.00%

Vontobel 29.56% 1.98% 0.58% 0.10% 0.58% 0.68% 2.54% 0.00% 3.12% -0.10% -2.44% -2.54%

Wegelin 12.42% 1.80% 0.22% 0.10% 0.22% 0.32% 0.38% 0.00% 0.60% -0.10% -0.28% -0.38%

XMTCH 20.36% 1.62% 0.33% 0.10% 0.33% 0.43% 1.17% 0.00% 1.50% -0.10% -1.07% -1.17%

ZKB 2.51% 2.00% 0.05% 0.10% 0.05% 0.15% 0.00% 0.00% 0.05% -0.10% 0.10% 0.00%

-5.45%

Intrapopulation Dynamic 5.62% -0.79% 0.04% 0.10% 0.04% 0.14% 0.32% 0.00% 0.36% -0.10% -0.22% -0.32%

Population Dynamic(Measured at Customer Interface)

5.62% 2.89% 0.61% 0.10% 0.26% 0.02% 0.03% 0.19% -0.10% 0.08% -0.02%

Table 2

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Issuer and Strategies (Number of Substituting Products)

Geldmarkt Bond Strategy Equity Index

BSI - 4.00 1.00 4.00

Clariden 4.00 - - -

CS - 7.00 2.00 12.00 1.

Gottardo - 4.00 2.00 3.00

Gutzwiller - - 2.00 -

LODH 1.00 - 1.00 5.00

MI - - - 1.00

Pictet 4.00 - - 3.00 1.

Raiffeisen 1.00 2.00 - 2.00

Reichmuth - - - -

Swisslife - 3.00 - 1.00

Swisscanto - 3.00 4.00 11.00

UBS - 8.00 6.00 16.00 2.

Vontobel - - - 2.00

Wegelin - 1.00 - 3.00

XMTCH - - - - 3.

ZKB 2.00 2.00 3.00 2.00

Fonds in Strategie 12.00 34.00 21.00 65.00 7.

ChiSquare Independence:

Hypothesis 2 Test-Values

H0: There is no causal relationhip between the issuer of a fund and the fund's strategy Critical Chi-Square

H1: There is a causal relatinship between fund issuer and strategy Freiheitsgrade: df = (r-1)*(k-1)

Confidence

Statistic

r = rows = 17 (Issuer) The Chi-Square Value of 328.44 exceeds the critical value of 135.8

k = columns = 8 (Product Types) Hypotesis H0 can be seen on a 99% confidence level as not valid

Issuer and Strategies (Volume)

Table 3

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Issuer and Strategies (Volume)

Money Mar-

ket Bond Strategy Equity Index

BSI - 4'946.20 144.00 920.00 -

Clariden 11'806.02 - - - -

CS - 2'191.96 - 8'364.30 88.0

Gottardo - 742.73 271.08 218.00 -

Gutzwiller - - 1'086.34 - -

LODH 177.00 - 130.00 1'377.54 -

MI - - - 60.00 -

Pictet 6'253.35 - - 3'438.00 202.0

Raiffeisen 49.00 81.00 - 215.00 -

Reichmuth - - - - -

Swisslife - 746.00 - 881.00 -

Swisscanto - 2'759.00 634.00 2'387.99 -

UBS - 3'805.59 4'027.00 15'962.70 565.0

Vontobel - - - 454.00 -

Wegelin - 31.00 - 44.00 -

XMTCH - - - - 2'810.0

ZKB - 380.00 279.00 54.55 -

Fonds in Strategie 18'285.36 15'683.47 6'571.41 34'377.08 3'665.0

ChiSquare Independence: Test-Values

Hypothesis 2 Critical Chi-Square

H0: There is no causal relationhip between the issuer of a fund and the fund's strategy Freiheitsgrade: df = (r-1)*(k-1)

H1: There is a causal relatinship between fund issuer and strategy Confidence

Statistik

r = rows = 17 (Issuer) The Chi-Square Value of 328.44 exceeds the critical value of 135.

k = columns = 8 (Product Types) Hypotesis H0 can be seen on a 99% confidence level as not valid

Table 4

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Externalities (Distribution Channels)

Volume % Distributed* Weights Standardized Weigths

BSI 6010.2011 0.06 2.43 4.87

Clariden 11806.0161 0.13 4.78 9.56

CS 10757.26238 0.12 4.36 10.71

Gottardo 1276.5411 0.01 0.52 3.03

Gutzwiller 1086.338 0.01 0.44 0.88

LODH 2150.086892 0.02 0.87 1.74

MI 3498 0.04 1.42 4.83

Pictet 9928.3477 0.11 4.02 8.04

Raiffeisen 345 0.00 0.14 2.28

Reichmuth 2001.0898 0.02 0.81 1.62

Swisslife 1627 0.02 0.66 3.32

Swisscanto 6588.9876 0.07 2.67 11.34

UBS 32125.62279 0.34 13.01 28.01

Vontobel 454 0.00 0.18 0.37

Wegelin 128 0.00 0.05 0.54

XMTCH 2810 0.03 1.14 2.28

ZKB 713.5475 0.01 0.29 6.58

Total 93306 37.78 100.00

*Distributed through proprietory or partner network versus direct sales/ Issuer trading desk

Table 5

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27

K-Innovation / Interpopulation Analysis

Volume in the Swiss Fund Market

Sensitivity to Capacity-Extension

2003 2004 2005 2006 Mean Values

Distribution

%

Money Market 21.10% -14.40% -3.30% -3.10% 0.001

Bond 26.50% 3.50% 4.50% 28.70% 0.158

Equity 26.70% 17.60% 24.90% 33.10% 0.256

Strategy 20.20% 10.90% 18.80% 9.50% 0.149

Real Estate 2.60% 0.50% 0.40% 1.70% 0.013

Others 2.90% -0.70% 16.70% 15.70% 8.65%

Total New Money

Money Market -1'456.00 -5'334.00 -10'775.00 -3'629.00 -5'298.500

Bond 3'487.00 4'774.00 16'222.00 -14'332.00 2'537.750

Equity 189.00 211.00 326.00 711.00 359.250

Neutral 120.00 136.00 3'487.00 13'047.00 4'197.500

Strategy 187.00 242.00 843.00 -17.00 313.750

Others 415.00 717.00 2'564.00 13'207.00 4'225.750

Total 2'942.00 746.00 12'667.00 8'987.00 6'335.500

SFA

Density Effects

New Issuer in Market 3'000.00 3'400.00 3'700.00 3'900.00 3'500.000

Market Growth 200.00 400.00 300.00 200.00 275.000

Squared Growth Component 40'000.00 160'000.00 90'000.00 40'000.00 82'500.000

Table 6

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Model Summary

.923a .852 .842 .96432

.958b .917 .905 .74688

Model1

2

R R Square

Adjusted

R Square

Std. Error of

the Estimate

Predictors: (Constant), IntrapopInnovationa.

Predictors: (Constant), IntrapopInnovation,

CapIntermedIntrapopInn

b.

Coefficients(a)

UnstandardizedCoefficients

StandardizedCoefficients

Model B Std. Error Beta t Sig.

(Constant) -.354 .257 -1.379 .1881

IntrapopInnovation 280.147 30.191 .923 9.279 .000

(Constant) -.135 .210 -.642 .531IntrapopInnovation 388.196 40.095 1.279 9.682 .000

2

CapIntermedIntrapopInn 85159.095 25670.391 .438 3.317 .005

a Dependent Variable: GrowthCustomerDemand

Excluded Variablesc

-.324a -2.450 .028 -.548 .425

.021a .201 .843 .054 .930

-.333a -1.785 .096 -.431 .248

-.008a -.077 .939 -.021 .933

.005a .043 .966 .012 .918

.155a 1.602 .131 .394 .956

.063a .618 .547 .163 .998

.438a 3.317 .005 .663 .340

.016a .156 .878 .042 .969

-.178b -1.368 .195 -.355 .330

.042b .515 .615 .141 .924

-.211b -1.350 .200 -.351 .230

-.005b -.064 .950 -.018 .933

.011b .131 .898 .036 .917

.093b 1.160 .267 .306 .892

-.004b -.053 .959 -.015 .930

.032b .402 .695 .111 .965

InterpopSelection

InterpopInnovation

IntrapopSelection

ExternalitiesSalesOrg

KInnovation

CapIntermedInterpop

CapIntermedIntrapopSel

CapIntermedIntrapopInn

CapIntermedInterpopInn

InterpopSelection

InterpopInnovation

IntrapopSelection

ExternalitiesSalesOrg

KInnovation

CapIntermedInterpop

CapIntermedIntrapopSel

CapIntermedInterpopInn

Model

1

2

Beta In t Sig.Partial

Correlation Tolerance

Collinearity

Statistics

Predictors in the Model: (Constant), IntrapopInnovationa.

Predictors in the Model: (Constant), IntrapopInnovation, CapIntermedIntrapopInnb.

Dependent Variable: GrowthCustomerDemandc.

Tables 7 a, b, c