outsourcing strategies in asset management industry

OUTSOURCING STRATEGIES IN ASSET MANAGEMENT INDUSTRY By Ignazio Basile and Roberto Savona

Working Paper N. 90/03

March 2003

OUTSOURCING STRATEGIES IN ASSET MANAGEMENT INDUSTRY

by

Ignazio Basile and Roberto Savona

Working Paper N. 90/03

March 2003

SDA Bocconi – Research Division 1

Outsourcing strategies in asset management industry

Ignazio Basile and Roberto Savona∗

Abstract: This paper looks at asset management strategies used by banks and investment companies in light of the important changes in competitiveness of this sector. Given a European angle, we have considered both the business models used and the theoretic and actual problems connected to the management of outsourcing. An empirical analysis on a sample of 11,101 European mutual funds over the period January 1996 – December 2000 shows that portfolio managers differed greatly in terms of investment styles, management strategies and performances and points out the critical initial role of choosing the ideal partner for investment management. The delegated asset managers’ selection must be carried out with the aid of qualitative analysis and quantitative techniques useful to evaluate every single aspect of manager’s performances over time. JEL codes: G10, G21, L11, G11. Keywords : asset management – outsourcing – fund manager - performance

1. Introduction During the last decade the asset management industry has experienced a comprehensive change, which has stimulated growing integration of domestic markets and the globalization of managers’ strategies. In this radically changed competitive scenario, Italian asset managers have also had to ask themselves about the effectiveness of their strategic approaches. Italian managers have been obliged to make their analytical plans international, given the restrictions of the domestic market. A natural starting point can only be a rapid examination of the distinctive features of the managed assets market in Italy, compared to those of the other main European countries. We have therefore highlighted some particularly significant phenomena. On the demand side, the following should be noted:

• a substantial increase in the willingness of investors to delegate their investment decisions to

professional operators. This has contributed to push household assets under management, ∗ Ignazio Basile is Full Professor of Financial Markets and Institutions at University of Brescia, Department of Business Studies; Roberto Savona is Researcher of Financial Markets and Institutions at University of Brescia, Department of Business Studies.


scaled to the total financial assets, to those levels which are typical of more financially evolved countries. There has been particularly high growth over the last decade (see table 1);

• in line with international trends, there has been a gradual shift away from individual management contracts, which are more and more reserved for customers of higher standing Instead the trend has been toward forms of collective management, which are no longer looked at as merely key access to the retail market;

• the “all-in” cost levels, sustained by the Italian final investor, are decreasing and are considerably lower than the European average. This is a clear symptom of very strong competitive pressure1.

• there has been a slow move towards higher risk asset classes and towards forms of investment which require more sophisticated asset management techniques2.

On the supply side, the following can be noted:

• a lower degree of product differentiation, confirmed by the limited number of funds per management company and the particularly large average size of the funds themselves, typical of a market that is far from mature3.

• a natural inclination of asset managers towards the bonds asset class, which both reflects the prevailing preference among investors and the specific configuration of the Italian securities market, which is still heavily dominated by public sector issuers.

• the low presence of Italian asset managers in the European and world-wide rankings, due to the size of the management companies. This could leave them particularly exposed to foreign competition in the future (see table 2).

Table 1: Asset under Management as a percentage of total financial assets

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 France 23.1% 25.6% 28.1% 27.5% 26.7% 27.1% 26.7% 27.3% 29.5% 27.3% Germany 26.0% 26.7% 27.9% 26.7% 28.5% 28.7% 29.5% 30.5% 32.2% 35.1% Italy 10.9% 12.0% 12.6% 14.0% 17.2% 17.3% 20.7% 27.2% 31.4% 34.1% Japan 32.2% 32.3% 34.1% 34.6% 34.6% 34.3% 34.3% 33.8% 33.6% 31.9% UK 46.0% 47.1% 48.4% 52.9% 51.5% 53.6% 54.0% 55.5% 57.3% 58.8% USA 35.7% 36.9% 36.8% 38.7% 39.3% 40.1% 41.6% 43.6% 45.2% 46.1% Mean ex Italy 32.6% 33.7% 35.1% 36.1% 36.1% 36.8% 37.2% 38.1% 39.6% 39.8% ∆ Italy* -21.7% -21.7% -22.5% -22.1% -18.9% -19.5% -16.5% -10.9% -8.2% -5.7% * “Italy less Mean excluding Italy”. Sources: Banca d’Italia, Banque de France, Deutsche Bundesbank, Tokyo Stock Exchange, Ocse, U.S. Federal Reserve.

1 Ref. McKinsey & Company (2001). 2 Ref. Eurisko & Prometeia (2001). 3 Ref. Fefsi (2001).


Table 2: Geographical concentration of supply (31.12.2000)

Total assets in mill. Euro N° of groups in top 100 Average size in mill. Euro UK 4,587,453 35 131,070 Switzerland 3,697,059 13 284,389 Germany 2,451,134 11 222,830 France 1,951,385 9 216,821 Netherlands 1,573,604 9 174,845 Italy 449,934 4 112,484 Sweden 243,910 5 48,782 Denmark 174,013 4 43,503 Spain 160,597 2 80,299 Belgium 129,427 2 64,714 Ireland 68,530 2 34,265 Norway 18,232 1 18,232

Average 119,353 Source: Our processing of Institutional Investor data. Also considering the high unit concentration in Italy, it is easy to see the reason for the high level of interest shown by the leading international asset managers in our market. In such a competitive context, each sector involved must workout a strategy able to reconcile its own characteristics with the critical factors for success of the different market sectors in which they are working. Therefore, we would like to look at the possible business models which could be adopted by banks in a competitive market with particular reference to the choice of outsourcing or not. We have concentrated on this latter point to verify the compatibility study which each operator must make between target business and investment style of the out-sourced company. After having looked at the theoretical aspect of the problem, we will look at the empirical side with an analysis of 11,101 samples of European mutual funds with particular emphasis on their investment strategies. The results of this analysis have shown that both management styles and performances differ greatly from country to country. Outsourcing must therefore be coherent with the model of business chosen, given that an inappropriate choice of partner could expose an institution to high risks which may be difficult to manage. The paper is structured as follows: In Section 2 there is a brief description of the changes which are effecting the competitiveness of the asset management sector. In section 3, we have made some reflections on the business models which could be adopted by banks which operate in this sector. In section 4, after having presented the analytical methodology used, we have shown the results of the empirical study of our selected sample. Finally in Section 5 we present our conclusions.


2. The ongoing changes in the market competitive structure It is a physiological factor that the competitive supply structure is much more divided than in the past. Indeed, competition does not only grow within the Italian banking system itself, but also involves foreign private banks, investment banks and commercial banks, specialised domestic intermediaries and on-line operators. Each of these categories of operators enjoys some specific competitive advantages, in relation to the others, which are measurable in terms of:

- service customisation; - width of product range; - customer access to the service; - confidentiality and exclusiveness of the relationship with the customer; - price policy.

For example, the traditional strengths of private banking organisations, i.e. a customised service, confidentiality and exclusiveness of the relationship with the customer, are the major weaknesses of retail banks and on-line operators who, in their turn, often base their own competitive capability on their widespread distribution network and price leadership. Also demand does not seem to be any longer insensitive to supply conditions offered by various competitors. The different segments into which it can be split, have strong priorities, which are anything but uniform. High Net Worth customers appear, without doubt, to be less elastic on price than affluent or retail customers. However they give priority in choosing their asset/wealth manager, firstly, to the level of customisation of the service and the methods of accessing it and, only afterwards, to the size and exclusiveness of the product range offered4. On the other hand, retail customers continue to regard asset management services as a sort of commodity, being attentive, when choosing, above all to the explicit pricing of the service and to ease of access. Therefore, in defining his business model, an intermediary must make a prior check, relative to the compatibility of his own skill and the characteristics of his target customers, and estimating the width of any gap which may be found to exist between them. It is, indeed not realistic that a bank can compete on all fronts, adopting an autarchic model and leveraging only on its own production and distribution capability. Obviously this excludes the small number of banking groups who can operate as global players. For all the other asset managers, the choice of a fairly high level of specialisation and greater focus of their distribution and production activities would therefore appear to be necessary conditions for growth and even for eventual survival. As shown in table 3, the critical success factors, in order to excel in the field of distribution, are different from those required by a pure asset manager. It would be difficult for small sized intermediaries to be able to excel in the field of production and therefore they must try to exploit,

4 On this specific theme, there are highlighted, among others, some recent studies by consultancy companies which show, on an empirical basis also, the criticalities to be faced when one tries to set up a winning strategy in the field of private banking intended as global wealth management. Ref. Boston Consulting Group (2001); Booz Allen & Hamilton (2001); PriceWaterhouseCoopers (2001); Merrill Lynch-CapGemini Ernst & Young (2001).


distributively, the network of relationships they have built up within their own range of action. In the same way someone who excels in the production area does not necessarily have full control of their end markets. This is the case of the largest US asset management companies, who do not have their own distribution networks abroad, and whose market penetration capability is therefore conditioned by the effectiveness of the distribution agreements they negotiate with other intermediaries. Table 3: Critical success factors

CRITICAL SUCCESS FACTORS Distributor Integrated Player Producer 1. Brand 2. Distribution capability 3. Customer relation strength 4. Innovation product/service 5. Networking 6. Range size products/services 7. Leadership price/cost 8. Know how 9. Ownership search 10. Timing 3. Definition of the business model For most intermediaries, it would appear to be preferable to adopt an open architectural model, which is built up on the one hand, to safeguard the competitive advantage they derive from their own distinctive skills. On the other hand, it can integrate these flexibly, which can go from simple alliances with third party distributors to outsourcing, or even on a total basis, of front- and back-office activities. In designing the optimal architecture it is necessary to bear firmly in mind such things as:

• the competitive structure of the market; • the propensity of the intermediaries to resort to outsourcing5 policies; • the value chain which is typical of this sector.

Above all, limiting our attention to Europe, all the main producers have an evident “vocation” for their own domestic market. However individual national market leaders have not been able to reproduce their own business model outside their own countries. To increase their market share, they have had to make distribution agreements with local intermediaries or undertake acquisition policies, which are particularly costly6. Indeed local intermediaries have a firm control over their final domestic markets and, even in the most internationalised marketplaces, the market share held by local asset managers is by far the largest, going from a minimum of 75% for the UK to a maximum of 95% for Italy, according to a recent study by Invesco (2000).

5 Some interesting empirical evidence on the theme are supplied by Djielic B., Koopmans R., Leroy S. (2000), which shows how it is possible to achieve economies of scale both at front office and back office level. 6 Ref. Invesco (2000).


In Italy, production is concentrated among a few bank-owned institutions, whose growth in size has been accompanied by increasingly aggressive policies of innovation and product differentiation. As often happens in systems which are not very market oriented, however, it is the banks themselves who are, far and away, the most important distribution channel, through their branches and the networks of controlled financial promoters. At the same time, we need to consider that outsourcing is generally still not widespread in Europe and in particular in Italy. Furthermore it is also relative to only some specific stages of the total production process such as asset allocation, managers selection and performance evaluation. Also relations with outsourcer, once they have been set up, tend to remain “frozen” for a long time and they are subject to ineffective monitoring systems by delegating party. Finally, on the basis of a recent analysis by McKinsey7, the charge born by who, in Italy, uses asset management services is less than the European average at 2.16% versus 2.55%. Analysing the split of total revenues between the various units which participate in the value chain, it can be seen that the cost of production of collective asset management services to retail/affluent customers in Italy appears to be in line with that of other European countries. However, efficiency levels are higher than the European average. The cost/income percentage of Italian asset managers is, in fact, 28%, versus an European average of 36% (see table 4). In all the countries, the most highly added-value activity is distribution, both if one considers the relative cost on the volumes managed and if one looks at the percentage weight of the costs, relative to the total. In figure 1 below one can see how in Europe, the distribution channels on average, account for 70% of the cost sustained by the investor. In this case, Italy is slightly under the average at 68%. Table 4: Analysis of the value chain of mutual funds in Europe (31.12.2000)

Distribution of revenues between the subjects involved in the whole process France Germany Italy Spain UK Average Total Revenues 2.21% 3.05% 2.16% 1.82% 3.52% 2.55% Fees to channels 1.66% 2.27% 1.46% 1.10% 2.49% 1.80% Fees to other operators 0.14% 0.24% 0.17% 0.22% 0.27% 0.21% Asset Manager’s net revenues 0.41% 0.54% 0.53% 0.50% 0.76% 0.55% Asset Manager’s operating cost 0.19% 0.22% 0.15% 0.12% 0.30% 0.20% Asset Manager’s operating profit 0.22% 0.32% 0.38% 0.38% 0.46% 0.35% Fees to channels/total revenues 75% 74% 68% 60% 71% 70% Asset manager Cost/Income 46% 41% 28% 24% 39% 36% Source: McKinsey (2001).

7 Ref. McKinsey&Company (2001).


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Figure 1: Split of total costs sustained by affluent/retail customers in percentage values (31.12.2000) Therefore, it is fundamental for the Italian banks to move forward, from the de facto monopolistic position they hold versus demand, to correctly and effectively segment the market. Thus they would concentrate the distribution effort towards those customers whose financial and asset/wealth needs they can properly satisfy and adopt a selective strategy in the direct production of asset management services. The extension of the range of their offer can only be achieved via mechanisms of management delegation in order to be able to exploit the skills of specialised intermediaries. It must also be taken into consideration that the most innovative products require extremely specialised skills, which are not readily available in the domestic market even from large operators (see table 5), This is so even if they have high autonomous market potential and also if they are found to be indispensable for improving the combination of return/risk even for the most traditional products. Table 5: Product innovation and specialised skills

MGMNT. TYPES FINAL PRODUCTS SKILLS

STRUCTURED PRODUCTS

1. Mutual Funds 2. Funds of Funds

1. Technology 2. Pricing experience 3. Trading experience 4. Legal and fiscal skills

HEDGE 1. Individual Management Account 2. Funds of Funds

1. Trading focused approach 2. Quantitative mentality 3. Risk management

PRIVATE EQUITY

1. Individual Management Account 2. Funds of Funds

1. Entrepreneurial or analytical/management experience

2. Qualitative approach 3. Link with investment bank and access

to final market

QUANTITATIVE MANAGEMENT

1. Mutual Funds 2. Funds of Funds 3. Individual Management Account

1. Analytical skills 2. IT and technological skills 3. Relative value approach


Regarding distribution, however, it is reasonable to expect that a small bank would aim at retail customers and in particular at the most toward the affluent segment. It maytry as best as it can, to satisfy the needs of HNW customers through agreements with specialised private banks, which are not very interested in products with a small service content and low added value. In this latter case, the traditional channels (bank branches network) can turn out to be ineffective even if it they are side by side with Hi-tech support channels. Specialisation appears to be obligatory also for those organisations, such as asset management companies which, even if they are focused exclusively on producing asset management services, do achieve the critical mass required to be able to follow a globalization strategy. They must therefore opt for the direct management of some specific asset classes and delegate the management of the others. A foreign asset manager, who wishes to successfully enter into a “protected” market like the Italian one, must necessarily seek to gain leverage relative to the weak points of smaller banks and Investment Companies who, although they have strong customer relationships, are not as competitive as far as their products are concerned. 4. Asset management outsourcing: an empirical analysis of European asset manager strategies The choice of optimal partner, in terms of investment style, asset allocation policies and, obviously, of risk-return trade-off, presupposes a preliminary mapping of operators in the European asset management market. The differences in various countries, regarding the social/economic context, the skill sets acquired over time, and the established operational practices are, in fact, reflected in market policies and managerial approaches. It should be noted that these are not homogeneous and can significantly influence performances. Only when such an analysis is completed, is it possible to select the best performing managers, in relation to the objectives stated by the firm. This is a very complex evaluation procedure, which must take into account both qualitative and quantitative items. To give an empirical answer at least to the quantitative profile of the problem, we have concentrated on the mutual funds sector, where the practice of delegating managerial choices is well established and for which databases enable a performance comparison at an international level. Specifically, the sample comprises returns of 11,101 mutual funds managed in eight European countries over the period from January 1996 to December 2000. Using various statistical techniques we examined certain aspects related to investment style, performances and strategies followed by managers. More in detail we tried to understand if, in the individual domestic markets, there exists a dominant philosophy and whether managers operate with a common management style. The results obtained by empirical analyses were extremely interesting in various aspects. First of all, it was evident that asset managers differ in terms of management approaches. Consequently, the geographic origin of companies is one of the main explanatory factors of return behaviour. Secondly, the analysis of the performances achieved by the various operators showed a very dynamic scenario in which none of the European countries seemed to be absolutely dominant. Lastly, the inspection of the


strategies followed by the managers, showed a major prevalence of indexed management policies, even if there were some managers who exhibited some market timing ability. 4.1. The database and the analytical methodology The data used in this research derive from a database of monthly fund valuations provided by Datastream and Bloomberg for 11,101 mutual funds, was split geographically as follows: Austria 1,163, Belgium 1,007, France 4,143, Germany 900, Italy 891, Holland 264, Switzerland 982, of which 323 domestic and 659 offshore, and UK 1,751. From January 1996 to December 2000 we selected all the funds with at least 12 monthly observations. Returns were computed using NAV at the end of each month per unit of investment trust contract. Pre-tax NAV obtained by foreign funds were netted8 in order to have a homogeneous comparison between the data relative to the performances of Italian funds. These latter had share values published net of tax charge, which is 12.5% on the matured return. This type of solution enables a comparative judgement on the results achieved by the funds, eliminating the distortion caused by the tax variable9. The data processing was carried out using a series of statistical techniques well established and widely used in the literature, relative to both realized performances achieved and management strategies10. Firstly, the management style was interpreted using Sharpe’s model (1992), on the basis of which the return of the funds is made comparable and explained, via a series of ratios, which are expressions of the performances of the different asset classes11.

8 Alternatively, the Italian fund shares could have been grossed up. This procedure would require an exact knowledge of the amounts of the taxes matured, of the subscriptions and of the withdrawals. However, this alternative is not applicable in practice, given that the necessary information is only available from the asset management companies. 9 The asymmetry existing between Italian and foreign funds is mainly due to the time at which the tax charge is paid. This is on a daily basis for Italian funds and at the time of receipt of the proceeds for foreign funds. Current legislation lays down that on returns, derived from the possession of shares in foreign funds, the tax rate applicable is12.5%, which is the same as for Italian funds. Consequently, the netting procedure, made necessary for comparing Italian and foreign funds, can be held to be acceptable for the valuation of performances achieved. The different tax treatment of the funds is only relative to the timing of the payment and does not extend to other elements as, for example, the composition of the fund’s portfolio. Therefore, the analysis of the policies of asset allocation, carried out on the net returns, can be held to be correct, given that the current legislation does not permit a fiscal arbitrage. 10 We recall, among others, the contributions of Brown et al. (2001), regarding the analyses of the Japanese mutual investment funds market and of Fung, Hsieh (1997), relative to the investigation of hedge funds. The former, in particular, was the first empirical application of the GSC procedure, which will be used in this paper, outside of the US market. The results obtained are very interesting for having shown evidence on the true causes of the negative performances trend of Japanese funds in the last decade, mainly due to fiscal reasons. The fiscal legislation in Japan has, in fact, pushed asset managers to make investment choices, which are heavily conditioned by the possibility of profiting from a fiscal arbitrage. The second study is important both because it, usefully, modified the original set up of Sharpe’s analysis, in particular, allowing that the coefficients could be negative, relative to the possibility of short selling by hedge funds, and also for having introduced an analytical procedure, for the analysis of management strategies, by the examination of the management performance, using arguments which belong to option theory, i.e. option like pay-off. 11 Sharpe (1992).


tjt

J

jjt PR εβα ++= ∑

=1

∑ =∀≥j jj j 1;0 ββ

Very synthetically, Sharpe’s model enables the inference, from the joint analysis of the return achieved by the manager and the standard performances of the different markets referred to, of the asset class towards which the fund management has been mainly exposed. In other words, the model shows a linear-type relationship between the performance and a series of “passive portfolios”, using market indexes, which are representative of the different asset classes. Furthermore, the possibility of short selling is excluded, which is perfectly in line with the contract governing the management mandate12. In analytical terms, Sharpe’s model is expressed as follows: (1) with where: Rt is the return of the portfolio at time t; α is a constant; βj is the jth style factor, expressed as a share of the portfolio, which can have a value between 0 and

1; Pjt is the return of the jth asset class at time t; εt is the error term. After having analysed the investment style, we focused on the management strategies adopted in various countries. For this purpose, we used some particular cluster analyses techniques which enabled the aggregation of the funds on the basis of the performance trends over time, no matter what the conventions or official classifications were. This method gave us information regarding the dynamic strategies adopted by the funds, since it admitted the variations of the portfolio loadings through time. As a result, we obtained more information than can be derived merely from simplistic information relative to the category to which the funds belong. This analytical technique, proposed by Brown, Goetzmann (1997) and known as Generalized Style Classification (GSC), allows the derivation of investment strategies from historical performances, by using multivariate analysis algorithms and allows the grouping of funds which display homogeneous characteristics, with reference to the dynamics of the returns13. This methodology can also be applied to performance measurements, as long as the groups of funds obtained permit the identification of true style benchmarks appropriate to the measurement and 12 Mutual funds are not allowed to sell short, whereas hedge funds can. From a methodological point of view this is equivalent to a non-negativity constraint on the regression coefficients, which express the weight of the portfolio of the relative investment class. 13 Brown and Goetzmann (1997).


valuation of the returns achieved. In other words, the funds which belong to each cluster, have to show homogeneous characteristics with reference to the return dynamics, and the relative style benchmark (GSC) is obtained by calculating the average of the equally weighted returns of the funds belonging to the same group. In particular, if one specifies K possible groups, corresponding to different management styles, the ex-post return of the jth fund belonging to the style J at time t, where ∑J = K, can be expressed as follows: (2) Rjt = µJt + εjt where µJt is the expected return from the style J at time t, while εjt is the idiosyncratic component of the return (with zero mean ex-ante and uncorrelated across funds). It is interesting to note that this type of representation, which is also in line with the models produced relative to asset pricing models14, ensures greater explanatory power relative to the expected return of the funds. The empirical analysis was carried out firstly, on each single domestic market and, secondly, on an overall European level, without limiting the examination to the asset allocation policies used in the individual countries. However we sought to also understand whether European managers were similar to one another, in terms of their managerial approach, independently of their localization. 4.2. The investment styles of European managers The style analysis showed evidence of significant differences between managers in various European countries, who are clearly focused either on equity or on bond market. By examining the empirical results, it is possible to note that each of the countries examined has its own particular management style. Specifically, we can highlight the typical orientation of European managers and trace a fairly clear outline regarding asset allocation, “decoding” the various investment styles in Europe regarding the following asset classes: Liquidity, Bond Europe, Bond US, Bond Japan, Emerging Markets, Equity Europe, Equity US, Equity Japan, Equity Pacific. In particular, the individual asset classes were represented by the following indexes:

- Liquidity: SB 3M US$ Euro Dep. TR - Bond Europe: ML Euro-ECU TR, - Bond US: JPM US 1+ Yr. Gvt. TR, - Bond Japan: JPM Japan 1+ Yr. Gvt. TR, - Emerging Markets: JPM – EMBI Composite TR, - Equity Europe: MSCI Europe TR, - Equity US: MSCI US TR, - Equity Japan: MSCI Japan TR, - Equity Pacific ex Japan: MSCI Pacific ex Japan TR.

14 See Brown, Goetzmann (1997) for a more in depth discussion of the subject and, in which, it is possible to find the formal demonstration of the link between multiple factor models and the cross-sectional equation presented here.


From table 6 we report the average values of the breakdown of the portfolio estimated using the Sharpe equation. Here we can infer the prevalent investment strategy of European asset managers. At first sight, Austrian managers seem mainly focused on the bond asset class, the weight of which is decidedly higher than the European average. Italy and Germany, on the other hand, have an almost perfect half and half split between Equity and Bonds, while the UK leans much more towards Equity investment. Finally, Switzerland is distinguished by a significant number of managers who are specialised in Emerging Markets. Table 6: Average portfolio breakdown, 1996-2000 – percentage values

Bond Emerging Equity

Liquidity (Europe-US-Jap.) Markets (Europe-US-Jap-Pac)

Austria 13.6 60.4 0 26,0 Belgium 6.1 26.5 1.1 66.4 France 10.3 29.9 0 59.8 Germany 9.1 44.1 0 46.8 Italy 13.9 41.9 0 44.3 Holland 9.6 33.4 0 57,0 Switzerland 9.5 25.5 6.3 58.7 UK 12.1 13.2 0.8 73.9 In order to be able to reach definitive conclusions, it is necessary to pass from a static dimension of the analysis to a dynamic one, looking in parallel at how the management style has changed over time. For this purpose, Sharpe’s model was used again, in which the weights of the asset classes were derived by estimating the basic regression equation (see the preceding paragraph) on 12-months rolling windows (see table 7). In this case, the empirical evidence also shows modes of conduct and management approaches which are not homogeneous, given that certain managers are notably dynamic in choosing their main asset class and the diversification of their portfolio. Swiss and UK managers, indeed, are much more directed towards Equity investments than other European operators. This shows that there is more willingness towards taking risks in those places where a culture of asset management is more deeply rooted and the market has more sophisticated needs. However, a progressive movement of portfolios towards Equity investments can be found all over Europe. This is a natural reflection of the change of investors’ attitude towards forms of asset management, which can notably change the risk exposure of their kind of portfolio. Specifically, asset managers have shown an increasing interest in the European Equity market, which has risen from 17.27% in 1996 to 40.57% in 2000. This is especially evident in Germany, Austria and Italy.


Table 7: Average values and standard deviations of Sharpe’s loadings Bond Europe Liquidity EM Bond Bond Japan Bond US Equity Europe Equity US Equity Japan Equity Pac ex Jap Austria Mean 49.015 5.421 0.742 5.435 13.702 19.958 2.212 1.545 1.971 Std. dev. 14.375 5.847 2.316 6.240 14.690 11.679 3.718 1.628 2.131 First 12-months 55.773 3.343 0.000 17.791 0.000 3.375 16.691 0.148 2.881 Last 12-months 43.259 3.735 10.865 1.800 0.000 38.163 0.000 2.179 0.000 Belgium Mean 23.643 6.606 2.333 5.169 4.951 36.657 8.338 5.266 7.037 Std. dev. 7.730 4.974 2.681 4.458 5.037 9.015 4.094 3.735 5.130 First 12-months 24.723 15.727 2.433 2.742 6.812 23.967 12.782 4.110 6.702 Last 12-months 23.005 3.715 4.545 9.206 0.000 32.244 1.491 13.254 12.539 France Mean 29.484 9.934 0.943 4.512 4.526 36.293 5.386 4.170 4.753 Std. dev. 8.736 7.257 1.845 3.454 7.665 10.171 3.431 1.945 2.398 First 12-months 35.894 24.507 0.000 6.050 0.000 16.593 14.906 0.000 2.050 Last 12-months 25.208 2.809 5.124 1.580 0.000 43.998 2.936 7.140 11.203 Germany Mean 33.923 6.093 1.199 6.060 9.801 34.242 6.351 1.860 0.472 Std. dev. 15.696 7.858 3.094 6.579 11.282 14.875 6.197 2.137 0.812 First 12-months 54.541 24.180 0.000 0.643 0.000 3.499 16.314 0.121 0.700 Last 12-months 32.210 1.505 14.043 0.000 0.000 52.240 0.000 0.000 0.000 Italy Mean 25.353 11.186 1.499 6.733 13.943 29.829 9.134 1.999 0.326 Std. dev. 15.756 12.846 3.530 6.778 14.646 13.127 8.193 2.060 0.669 First 12-months 54.568 10.328 0.000 3.924 0.696 11.948 13.645 4.891 0.000 Last 12-months 29.060 15.231 12.875 0.000 0.000 41.575 0.000 1.261 0.000 Netherlands Mean 26.647 7.539 2.226 8.518 3.161 30.883 9.300 4.705 7.021 Std. dev. 7.747 6.962 3.579 6.974 4.032 12.495 7.581 2.142 4.605 First 12-months 36.445 22.471 0.293 1.298 0.000 20.901 8.356 4.004 6.231 Last 12-months 27.955 0.890 13.670 0.000 0.000 47.561 1.435 5.290 3.196 Switzerland Mean 19.107 8.589 4.654 5.342 8.245 29.690 5.792 6.688 11.894 Std. dev. 6.639 6.398 3.956 3.364 5.810 6.733 5.993 3.886 5.928 First 12-months 21.440 19.267 2.235 5.455 4.800 21.222 5.430 1.968 18.183 Last 12-months 19.238 9.880 4.655 4.471 0.236 31.323 0.209 11.838 18.149 UK Mean 8.329 10.406 2.496 6.511 6.264 36.045 9.528 9.740 10.682 Std. dev. 6.018 6.131 3.348 4.993 4.829 12.055 6.028 2.717 9.032 First 12-months 10.420 17.091 0.000 3.743 0.000 36.654 10.159 9.983 11.953 Last 12-months 6.506 7.343 9.660 6.656 0.000 37.425 0.518 9.865 22.028 The table shows the average value and the standard deviation of Sharpe’s loadings estimated on rolling intervals of 12 months of all the funds analysed, divided by country of origin. The market indexes used to estimate the factor loadings are: ML Euro-ECU TR; SB 3 Mo US$ EuroDep TR; JPM-EMBI (Emerging) Composite TR; JPM Japan 1+ Yr. Gvt TR; JPM U.S. 1+ Yr. Gvt TR; MSCI Europe TR; MSCI U.S. TR; MSCI Japan TR; MSCI Pacific ex Japan TR.


4.3. Asset managers’ strategies In Europe, geographical location of the asset manager is undoubtedly one of the main factors in explaining performance patterns. Indeed, if we look at the whole European market it is possible to identify certain management styles, which are differentiated by their risk/return profile, and the portfolio policies followed. The groups identified in this way are mainly made up of managers coming from specific countries. This confirms the fact that in each domestic market local operators have a common management philosophy, reflecting the dominant culture within the environment in which they operate. In detail, the analysis, using the Brown-Goetzmann methodology, has identified 8 GSC groups, corresponding to 8 different management styles. These style benchmarks can provide significant information regarding the degree of differentiation of strategies followed by European asset managers. In parallel, in order to test the hypotheses of geographical differentiation in management styles, the Fung-Hsieh methodology (1997) was used (see table 8). Table 8: Breakdown of the GSC Austria Belgium France Germany Italy Holland Switzerland UK

GSC 1 4.43 2.29 4.89 52.59 1.48 19.48 13.50 1.34

GSC 2 0.20 3.36 6.93 12.50 36.98 28.38 10.51 1.14

GSC 3 0.67 13.64 31.24 1.03 0.90 1.15 19.16 32.22

GSC 4 46.02 28.39 1.77 1.31 1.27 2.72 15.63 2.90

GSC 5 0.00 29.57 65.73 0.00 0.00 0.00 3.14 1.56

GSC 6 0.61 0.22 4.25 0.00 0.00 0.00 45.52 49.41

GSC 7 0.00 0.62 0.07 0.00 0.00 0.34 0.18 98.79

GSC 8 0.10 0.00 0.11 0.00 0.00 0.00 0.00 99.79 This table shows the breakdown of each GSC, split as a percentage of the managers belonging to the group. The portfolios of the funds mainly linked to the GSC were built up following the procedure of Fung-Hsieh (1997) to get these results. These enables the decoding of the style benchmarks, according to a kind of geographical attribution of them. With regard to the asset allocation policies, the results obtained show that British and Swiss managers are able to modify the portfolio composition according to market expectations. The dynamics of their investment choices appear to be more than that shown by other asset managers, who limit their risk exposure. In synthesis, the style benchmarks of the different groups are the following: GSC 1 This group consists almost totally Germany managers who show a balanced style almost total directed towards the European area. The split between the Equity Europe and Bond Europe areas is practically constant. This style has shown a low level of dynamism during the time period examined.


GSC 2 This group, mainly consisting of Italian managers, and most Dutch ones, is characterised by an orientation towards the Equity Europe sector which is higher than all others. The Bond Europe asset class tends to progressively diminish in favour of Equity Europe and Emerging Markets. GSC 3 In this group there are some managers who are active in France, UK, Belgium and Switzerland. This group is highly diversified, which does not enable the identification of a precise link with the geographical location of the managers. The portfolios have an international focus with a gradual reallocation between the US and European markets both in the Bond and Equity segments. At the end of the period, the prevailing investment is in Europe for both asset classes. GSC 4 This group consists mainly of Austrian and Belgian managers. The investment style shows a prevailing orientation towards the Bond sector, alternating between the European and Japanese/US markets. Asset allocation is not very dynamic. GSC 5 This group consists mainly of French based managers. Examination of the investment style shows high international diversification. It should also be added that the share of the portfolio invested in bonds is progressively reducing until it practically disappears. GSC 6 The managers belonging to this group are mainly based in Switzerland and the UK. It shows a strong bias to the Equity area. There is also significant dynamism with a notable switching between the European and US/Pacific areas. GSC 7 The group consists of UK based managers whose style is aimed mainly at the Equity Europe area; it is the first case in which Emerging Markets has a significant weight. GSC 8 This group consists of managers based in the UK, who show a flexible investment style. The asset managers in this GSC are extremely dynamic and this is shown by the alternating in the investment choices between the Bond area, which was the main one at the start of the period, and the Equity one, which has gradually replaced the Bond area. The weights of the Liquidity and Emerging Markets are also significant, especially at the end of the period. The evolution of the asset allocation of the differing clusters is summarised in table 9.


Table 9: Average values and standard deviations of Sharpe’s loadings estimated at intervals of 12 months Bond Europe Liquidity EM Bond Bond Japan Bond US Equity Europe Equity US Equity Japan Equity Pac ex Jap GSC 1 Mean 31.334 5.282 0.997 6.799 7.385 39.456 6.336 2.072 0.341 Std. dev. 13.941 7.740 3.071 7.190 11.760 11.546 5.824 2.448 0.885 First 12-months 47.160 22.100 0.000 0.000 0.000 14.140 16.600 0.000 0.000 Last 12-months 33.710 0.000 14.900 0.000 0.000 51.380 0.000 0.000 0.000 GSC 2 Mean 24.701 9.620 2.946 7.799 7.932 38.046 7.317 1.076 0.565 Std. dev. 14.734 12.478 7.047 7.307 13.100 15.353 7.287 1.639 1.785 First 12-months 42.890 28.020 0.000 0.000 0.000 14.280 14.430 0.380 0.000 Last 12-months 21.040 0.000 29.720 0.000 0.000 49.240 0.000 0.000 0.000 GSC 3 Mean 27.756 11.190 0.459 5.726 14.774 24.613 3.212 8.973 3.297 Std. dev. 19.440 12.705 1.478 6.666 16.748 5.904 4.272 3.840 4.719 First 12-months 41.290 19.670 0.000 5.480 0.000 20.800 8.580 2.460 1.720 Last 12-months 39.190 12.610 8.340 0.000 0.000 30.640 0.000 9.210 0.000 GSC 4 Mean 29.035 13.823 0.571 6.730 4.102 30.806 6.038 1.298 7.596 Std. dev. 14.294 10.834 2.233 7.876 8.557 11.354 6.196 1.895 4.505 First 12-months 28.850 36.350 0.000 5.850 0.000 0.000 19.630 0.000 9.330 Last 12-months 39.820 0.000 13.030 0.000 0.000 41.010 0.000 6.150 0.000 GSC 5 Mean 17.955 10.933 0.716 4.064 8.828 38.767 3.065 5.083 10.588 Std. dev. 17.548 12.564 3.832 6.662 15.968 12.150 4.995 7.470 11.830 First 12-months 47.780 0.000 0.000 0.000 0.000 49.000 3.220 0.000 0.000 Last 12-months 1.610 0.260 25.520 0.000 0.000 32.620 0.000 27.870 12.120 GSC 6 Mean 9.228 9.321 2.318 4.601 3.414 47.638 7.431 5.721 10.328 Std. dev. 11.388 11.625 5.157 8.330 10.025 22.607 10.812 7.897 11.281 First 12-months 0.000 41.090 0.000 2.140 0.000 31.150 16.580 0.000 9.050 Last 12-months 0.000 0.000 22.100 0.000 0.000 51.640 0.000 11.670 14.590 GSC 7 Mean 2.091 7.363 4.582 7.434 2.243 60.804 7.995 0.384 7.106 Std. dev. 4.826 10.010 9.575 7.720 7.245 19.083 8.691 1.099 7.274 First 12-months 0.000 29.100 0.000 0.000 0.000 40.100 13.660 0.000 17.140 Last 12-months 0.000 0.000 35.140 0.000 0.000 64.860 0.000 0.000 0.000 GSC 8 Mean 13.437 10.222 7.169 6.476 11.128 24.069 15.938 0.624 10.938 Std. dev. 16.355 15.303 13.282 10.763 16.722 18.298 12.682 1.569 16.554 First 12-months 0.000 0.000 0.000 29.100 0.000 47.240 23.660 0.000 0.000 Last 12-months 0.000 0.000 47.210 0.000 0.000 38.510 0.000 7.730 6.550 The table shows the average value and the standard deviation of Sharpe’s loadings estimated on rolling intervals of 12 months on the GSC obtained via the Brown-Goetzmann procedure. The market indexes used for estimating the factor loadings are: ML Euro-ECU TR; SB 3 Mo US$ EuroDep TR; JPM-EMBI (Emerging) Composite TR; JPM Japan 1+ Yr. Gvt TR; JPM U.S. 1+ Yr. Gvt TR; MSCI Europe TR; MSCI U.S. TR; MSCI Japan TR; MSCI Pacific ex Japan TR.


4.4. Asset managers’ performances The analysis of performances obtained by European asset managers were carried out by firstly comparing the average returns of the managers, grouped by origin and then by performance attribution. This enabled us to discover the active/passive nature of the management strategies. The empirical results showed a very unstable picture, given that the managers of no one country were able to maintain a clearly extra-performance over the long term compared to competitors. However, the empirical findings would appear to underline, in some cases, the widespread presence of a tendency to use dynamic management strategies. This allows for extremely positive returns, when the markets are going up, and to limit negative performances, when markets are going down. To measure performances, we calculated the Jensen’s alpha for each of the 11,101 funds, by using the 8 style benchmarks (GSC) as explanatory variables. The model we applied is technologically specified according to the Sharpe equations, allowing for a constant term. The results of the analyses are summarised in table 10, in which the average values of the alpha measure are shown, obtained from the funds offered in each country.

In order to express a relative judgement regarding the ability of the managers in each country to generate extra-performances, we compared the average alphas of the various countries with the grand mean alpha of all the funds included in the sample. As we can see in table 10, the value of this overall mean was positive in all years, with the exception of 1999.

By inspecting performances, one can observe an evolving picture, in which the results obtained by Belgium managers in 1997 and 1998, Swiss managers in 1996 and 2000 and British ones in 1999 all stand out.

With reference to Italian managers there was a very positive tendency in 1997 and 1998, while the years 1996 and 1999 were negative. On the other hand, in 2000 Italian managers were placed in an intermediate position, compared to other asset managers (see figure 2).


Table 10: The average performances of asset managers in the period 1996-2000

Average alphas of all funds 1996 1997 1998 1999 2000 Average t-value Mean 0.78% 1.96% 2.75% -2.85% 0.77% 0.68% 0.79 t-value 3.8829 5.9191 5.2823 -3.1902 5.9624

Average alphas of the funds within each country 1996 1997 1998 1999 2000 Average t-value Austria -3.31% -1.46% -0.21% -8.94% 1.56% -2.47% -1.54 Belgium 1.49% 7.06% 9.10% 0.78% 0.70% 3.82% 2.41 France 2.38% 0.19% 3.37% -1.43% 1.15% 1.13% 1.51 Germany -2.32% 3.12% 5.88% -4.87% -0.93% 0.18% 0.10 Italy -3.24% 6.24% 7.35% -10.25% 0.95% 0.21% 0.07 Netherlands -0.06% -2.64% 6.29% -5.78% 0.36% -0.36% -0.20 Switz domestic 1.70% 4.92% 5.51% -1.84% 3.42% 2.74% 2.32 Switz offshore 2.90% 2.51% 0.25% -3.56% 3.82% 1.18% 1.00 UK 1.41% 2.98% -4.24% 1.08% -0.87% 0.07% 0.07

Differences (average alpha of the country minus grand mean alpha) 1996 1997 1998 1999 2000 Average t-value Austria -4.09% -3.42% -2.96% -6.09% 0.78% -3.16% -3.15 Belgium 0.71% 5.10% 6.35% 3.63% -0.08% 3.14% 2.84 France 1.60% -1.77% 0.62% 1.42% 0.37% 0.45% 0.84 Germany -3.09% 1.16% 3.13% -2.02% -1.71% -0.50% -0.49 Italy -4.01% 4.28% 4.60% -7.39% 0.17% -0.47% -0.22 Netherlands -0.83% -4.60% 3.54% -2.92% -0.41% -1.05% -0.85 Switz domestic 0.92% 2.96% 2.76% 1.01% 2.65% 2.06% 5.12 Switz offshore 2.12% 0.55% -2.50% -0.70% 3.05% 0.50% 0.57 UK 0.64% 1.02% -6.99% 3.94% -1.64% -0.61% -0.37 This table shows the annualised average value of the regression alpha, obtained by comparing each fund included in the sample with the 8 style benchmarks, estimated using the Brown-Goetzmann procedure. Analytically, the equation Rjt = αj + ∑JβJjµJt +εjt was estimated, in which Rjt is the return of the jth fund at time t, the coefficients βJj are the loadings estimated in accordance with Sharpe’s procedure, and the µJt factors refer to the style benchmarks.


Figure 2: The performances of European managers

To summarize, this dynamic picture does not show any clear superiority of managers in any specific market. This is confirmed by looking at the alpha differentials, which exhibit that only Belgian and Swiss managers succeeded in achieving results, which were, on average, higher than those of the total sample.

However, these conclusions do not give enough information to enable a bank to direct its policies of asset management outsourcing. In fact, it is also necessary to correctly evaluate the persistence level of the returns and the consistency of the investment strategy, compared to its benchmark.

To analyse the persistence of performances, we performed cross-sectional regressions on the alphas achieved by the managers over time, by comparing the coefficient of a year with that of the previous year. This auto-regressive process of first order (AR(1)) permits the estimation of the correlation degree between two cross-sectional series of alphas. It is obvious, in fact, that a positive mathematical sign would show that there existed a persistence in the returns, just as it is clear that a negative sign would indicate that there existed a reversal type phenomenon. This methodology, already used in

1996

It Au

BeFr

Ge

NeUKSdom

Soffshore

-4.5%

-3.0%

-1.5%

0.0%

1.5%

3.0%

4.5%

alph

a

1997

It

Au

Be

Fr

Ge

Ne

UKSoffshore

Sdom

-5.0%

-2.5%

0.0%

2.5%

5.0%

7.5%

10.0%

alph

a

1998

It

Au

Be

Fr

Ge Ne

UK

Sdom

Soffshore

-5.0%

-2.5%

0.0%

2.5%

5.0%

7.5%

10.0%

alph

a

1999

ItAu

BeFr

Ge Ne

UK

Soffshore

Sdom

-12.5%

-10.0%

-7.5%

-5.0%

-2.5%

0.0%

2.5%

alph

a

2000

ItAu

BeFr

Ge

Ne

UK

SdomSoffshore

-2.0%-1.0%0.0%1.0%2.0%3.0%4.0%5.0%

alph

a


Goetzmann, Ibbotson (1994), was applied, both on the total sample of the European funds, and at the level of individual domestic markets. The results are summarised in table 11. The conclusions of these analyses were coherent with the widespread view of this subject and highlight the fact that the phenomenon was strictly dependent upon the time interval observed. The table showed, in fact, that only in the first two-year period, the performances achieved in one year, 1996, were repeated in the next year, 1997. More precisely, this conclusion is valid for both those who have shown a positive alpha value, i.e., repeat winners, and those who have shown a negative alpha, i.e., repeat losers. Breaking down the data to individual countries, we have evidence of persistence between 1996 and 1997, which is particularly evident in the UK. For the following pairs of years, the level of persistence of performances, on average, reduces notably. Also, at a country breakdown level, there were some exceptions both with phenomena of performance persistence (Netherlands, Italy and Switzerland in 1997-1998) and of reversal (Switzerland in 1999-2000 and UK in 1998-1999).


Table 11: Cross-sectional regressions on the alphas 1997 on 1996 1998 on 1997 1999 on 1998 2000 on 1999 Average t-Statistic Prob.

Intercept 0.000812 0.001311 -0.002068 0.000656 0.000178 0.269530 0.804997 Slope 0.356107 0.072701 -0.012675 -0.00264 0.103373 1.381991 0.260900 T-Stat. 8.731704 3.172334 -2.87991 -1.864407 O

vera

ll

R2 0.084912 0.00036 0.00036 0.000321 Intercept -0.001059 0.000466 -0.005582 0.001216 -0.001240 -0.940053 0.416540 Slope 0.225364 0.125305 0.004306 0.002703 0.089420 1.924761 0.149935 T-Stat. 1.77731 1.625156 0.747079 0.964461 A

ustr

ia

R2 0.041895 0.000284 0.000284 0.000041 Intercept 0.003673 0.006719 -0.000282 0.000625 0.002684 1.950336 0.146226

Slope 0.403135 0.032225 0.010541 -0.000824 0.111269 1.317369 0.279293 T-Stat. 3.305059 1.64868 0.533825 -0.445095 B

elgi

um

R2 0.015095 0.000027 0.000027 0.000046 Intercept -0.000354 0.002741 -0.001602 0.001015 0.000450 0.557486 0.616090

Slope 0.132913 0.078349 -0.012532 0.004296 0.050757 1.736150 0.180939 T-Stat. 2.582647 2.117704 -2.686602 0.45035 F

ranc

e

R2 0.020167 0.006632 0.006632 0.000298 Intercept 0.001711 0.001407 -0.005609 -0.000791 -0.000821 -0.560385 0.614342 Slope 0.541773 0.018654 -0.012682 0.000248 0.136998 1.171116 0.326108 T-Stat. 3.406795 1.132258 -1.05017 0.155464 G

erm

any

R2 0.280827 0.001732 0.001732 0.000027 Intercept -0.002609 0.001214 -0.003973 0.000205 -0.001291 -1.236198 0.304349 Slope 0.602315 0.24966 -0.042264 -0.040279 0.192358 1.452753 0.242236 T-Stat. 4.293717 3.458313 -2.302661 -0.911239

Net

herl

ands

R2 0.366754 0.03477 0.03477 0.008903 Intercept 0.005635 0.001093 -0.00456 0.000781 0.000737 0.408257 0.710476 Slope 0.229045 0.546047 -0.0034 -0.005789 0.191476 1.695646 0.188523 T-Stat. 2.698921 15.856 -0.62175 -1.27753 It

aly

R2 0.031266 0.000907 0.000907 0.00353 Intercept 0.003436 -0.000287 -0.007121 0.002673 -0.000325 -0.156020 0.885925 Slope -0.035602 0.583783 0.099046 -0.150543 0.124171 0.887970 0.439965 T-Stat. -0.216303 3.834275 2.116238 -3.786608

Switz

erla

nd

R2 0.001453 0.024315 0.024315 0.03833 Intercept 0.000595 -0.004199 0.000528 -0.00067 -0.000937 -0.960665 0.407589 Slope 1.120711 0.079683 -0.162305 -0.02997 0.252030 0.990684 0.394872 T-Stat. 25.241 2.178669 -5.292278 -3.584303

UK

R2 0.59729 0.023963 0.023963 0.017494 The table shows the results of cross-sectional regression, carried out on the alpha coefficients of the funds, estimated using Sharpe’s equation on 8 GSC. Analytically, the alphas of one year, the dependent variable, were regressed on the alphas of the previous year, the independent variable.


4.5. Market timing

The final point of our analysis was about the evaluation of market timing capability of European managers, i.e. their ability to amplify bullish tendencies and limit bearish ones.

The approach used to analyse the strategic choices made by European managers was based both on the traditional models of performance measurement and on non-parametric based statistical techniques. We used the Treynor, Mazuy (1966) model and the methodology proposed by Fung, Hsieh (1997). Treynor, Mazuy enables us to establish if there exists a non-linear relationship between the results achieved and a reference benchmark. This is done by verifying whether some investors can forecast future market trend by selecting an investment alternative which enables them to maximize the expected return by an arbitrage between the market portfolio and the risk-free asset. To estimate this ability the following equation is used: (3) in which: rit is the return on portfolio i (GSC) at time t; rft is the return on the risk-free asset f at time t (IMF Euro Area 3 Mo Interbank Rate TR); rMt is the return on the market portfolio M at time t, obtained as the equally-weighted average of the

returns of all the funds in the sample; α is a constant, interpretable as the Jensen’s alpha; β is the beta of the CAPM; δ is the parameter which represents the contribution given by market timing; εit is the regression error. Once the Treynor-Mazuy model has been estimated, the methodology proposed by Fung, Hsieh allows a more accurate interpretation of the strategies used by managers. This is achieved by examining, in particular, their behaviour in the various stages of bull and bear markets. More precisely, the performances were compared with the reference market going from the worst to the best scenario. The returns of the GSC, grouped in performance deciles, were compared with the trend shown by the market benchmark, where this latter is the average returns of all the funds in the sample. Because the objective is to establish whether the managers were skilled in profiting from market timing choices, the data used refer to the excess return of the GSC compared to the benchmark. This allows us to quantify the profits and losses coming from the market timing associated with each market situation and, at the same time, to be able to evaluate how the strategy adopted has evolved. By inspecting the results obtained in the analysis, it was evident that the strategies implemented by the European managers were mainly indexed (see table 12). The Treynor-Mazuy equation points to a low contribution of the timing component for almost all the GSC. However, GSC 7 and GSC 8 showed a fairly high coefficient, with a statistical significance which is almost acceptable. The p-values are, respectively, 0.1641 and 0.1056. Initially, it would appear that the conservative attitude of the majority of managers has not allowed for excess returns compared to the general market average. However this ability did appear in some cases.

( ) ( ) itftMtftMtftit rrrrrr εδβα +−+−+=− 2


Table 12: Market timing GSC 1 GSC 2 GSC 3 Variable Coefficient t-Statistic Prob. Coefficient t-Statistic Prob. Coefficient t-Statistic Prob. α 0.0014 1.1320 0.2624 -0.0007 -0.7744 0.4420 0.0003 0.1952 0.8459 β 0.7406 26.2232 0.0000 0.8224 37.9309 0.0000 0.6530 17.8845 0.0000 δ -1.5408 -2.6871 0.0095 0.1868 0.4243 0.6729 -0.6012 -0.8109 0.4209 Adj. R2 0.9220 0.9616 0.8460 F-statistic 343.8444 727.6284 160.3334 GSC 4 GSC 5 GSC 6 Variable Coefficient t-Statistic Prob. Coefficient t-Statistic Prob. Coefficient t-Statistic Prob. α -0.0017 -1.0081 0.3177 -0.0020 -1.1064 0.2733 0.0039 2.1856 0.0330 β 0.7507 19.4596 0.0000 1.0325 24.1834 0.0000 1.3833 33.3165 0.0000 δ -0.9536 -1.2174 0.2286 -0.0219 -0.0253 0.9799 -0.5517 -0.6545 0.5155 Adj. R2 0.8667 0.9102 0.9505 F-statistic 189.5436 295.0465 558.2106 GSC 7 GSC 8 Variable Coefficient t-Statistic Prob. Coefficient t-Statistic Prob. α 0.0002 0.0928 0.9264 -0.0013 -0.4830 0.6310 β 1.4486 31.3322 0.0000 1.1689 18.0857 0.0000 δ 1.3235 1.4099 0.1641 2.1588 1.6451 0.1056 Adj. R2 0.9451 0.8530 F-statistic 500.6209 169.2795 This table shows the results of the Treynor-Mazuy regression estimated on the GSC. In analytical terms, the equation used is

( ) ( ) itftMtftMtftit rrrrrr εδβα +−+−+=− 2 , where rit is the return of the ith GSC at time t , rMt is the return of the market index at time

t, rft is the return on the risk-free asset (IMF Euro Area 3 Mo Interbank Rate TR) at time t, and finally εit is the error term. The market index used in regressions is the equally-weighted average of all the funds belonging to the sample.

In confirmation of this it seems clear how managers seek to limit losses in the case of downward moving markets and accept the fact of only partially benefiting from rising market trends. We can see this by looking at figure 3, where there are payoffs of the excess return of the GSC (GSC return minus the market portfolio return computed as equally weighted average of all the funds in the sample). If we look more carefully at individual cases, GSC 1, 2, 3, 4 showed a fairly similar profile to the typical one of a short-call position, where profits only come by the limiting the downside risk, while the upside potential never becomes an active type of management. A willingness to follow an indexed strategy can be seen in the analysis of GSC 5. The payoff shows an evolution, in relation to the differing market scenarios, which is similar to that of a reverse-strangle. This guarantees a return in line with the average market trend in periods of moderate volatility, but which is exposed to significant losses during periods of wide-ranging generalised fluctuations, either downwards or upwards. GSC 7 shows the greatest market timing skill. Its payoff, in fact, is typical of a long-call position. In a similar way GSC 6 also shows a certain amount of ability in profiting from upward trends but the size of the benefit has an upper limit. In this case the payoff is more similar to that of a short-put position. Lastly, of particular interest, there is the analysis of the payoff shown by GSC 8. This is skilled in market timing, when the market is moving both up and down. Again using the vocabulary associated with options strategies, it shows a profile which is very similar to the strangle, which guarantees excess return in periods of high market volatility. Summarising, the strategies followed by European managers are mainly indexed, even if there are some asset managers who show a significant ability to successfully follow active strategies. Indeed in certain cases, there is an ability to amplify the upward trends of the reference market emerges, just as there clearly appears to be the ability to limit losses in the case of downward movements. The managers who show such an attitude are particularly concentrated in the British and Swiss markets15. Furthermore, they show a risk/return profile which is considerably higher than that of their foreign competitors. This is a further proof that the most sophisticated managerial approaches are concentrated in those areas where asset management activities are better established and have a longer history.

15 Note the results achieved by groups 5, 6, and 7, which, as has been documented in the previous paragraph, mainly consist of managers located in Switzerland and the UK.

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Figure 3: Option-like payoff of GSC excess return

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5. Conclusion In a system which is becoming more and more competitive, those intermediaries who aim at successfully competing in the field of asset management must favour the usage of open type architectures. This conclusion is valid for both domestic operators, who often show deficiencies in the production stage, and for the foreign operators, who are interested in the Italian market and who are not able to control the final markets. Outsourcing can be relative both to back-office and front-office activities. In the latter case, the selection of the delegated manager, who is called upon to create added-value, appears to be extremely critical. This is so even while respecting the risk limits imposed by the institution who decides to externalise part of the production process. The analyses carried out on mutual funds in eight European countries 1996-2000, have highlighted significant differences between asset managers their investment strategies and relative methods of implementing them. Although our results cannot be interpreted in deterministic terms, some elements seem to be particularly useful in delegating management activity. However, we should note that any decision cannot be made without also taking into account valuation elements of a qualitative type. These latter tend to have a heavier weight the more innovative the production environment looked at is. The quantitative analysis based on historical performances must be integrated with results obtained through due diligence exercises, aimed at identifying any weaknesses of potential outsourcers, at organisational and managerial levels. This is so especially in less traditional areas, e.g. structured and alternative products.

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References Boston Consulting Group (2001): Richer Prospect in Wealth Management, Global Wealth. Booz Allen & Hamilton (2001): Wealth Management: the Challenge. Brown, S. J., Goetzmann W. N. (1997): “Mutual fund style”. Journal of Financial Economics 43, pp. 373-400. Brown, S. J., Goetzmann W. N., Hiraki, T., Otsuki, T., and Shiraishi, N. (2001): “The Japanese Open End Mutual fund Puzzle”. Journal of Business 74, pp. 59-78. Djielic B., Koopmans R., Leroy S. (2002): “Asset Management in Europe”. The McKinsey Quarterly, n.2. Eurisko & Prometeia (2001): Osservatorio sui risparmi delle famiglie. Fefsi (2001): The State Of The European Investment Funds Industry. Fung, W., Hsieh, D. (1997): “Empirical Characteristics of dynamic trading strategies: The case of hedge funds”. Review of Financial Studies 10, pp. 275-302. Goetzmann, W. N., Ibbotson R. G. (1994): “Do Winners Repeat?”. Journal of Portfolio Management, Winter, pp. 9-18. Hartigan, J. A. (1975): Clustering algorithms, Wiley, New York. Invesco (2000): European Institutional Asset Management Survey, July. Jensen, M. (1968): “The performance of mutual funds in the period 1945-1964”. Journal of Finance 23, 389-416. McKinsey & Company (2001): Analysis of the Competitiveness of Retail Mutual Funds Industry in 5 European Countries, June. Merrill Lynch-CapGemini Ernst & Young (2001): World Wealth Report. PriceWaterhouseCoopers (2001): European Private Banking, Wealth Management Survey. Sharpe, W. F. (1992): “Asset allocation: management style and performance measurement”. Journal of Portfolio Management (Winter), 7-19. Treynor, J., Mazuy, K. (1966): “Can mutual funds outguess the market”. Harvard Business Review, 44, pp. 131-36.

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