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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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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