bachelor's thesis_samuel_robert_walser_s12463873

Zurich University of Applied Sciences

ZHAW

School of Management and Law

Department of Banking, Finance, Insurance

Size and Performance of Swiss Pension Funds

Bachelor’s Thesis

FS 2016

Author:

Samuel Robert Walser

Motorenstrasse 21, 8005 Zurich

Class BF_PiE

[email protected]

Student-ID: S12463873

Submitted to:

Regina Anhorn

Submission date:

May 26, 2016

Declaration of authorship

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help of others and that all sources are clearly referenced. I further declare that I will not supply

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Student’s name (in block letters)

SAMUEL ROBERT WALSER

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…………………………………………..

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Pension Funds”.

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Bachelor thesis “Size and Performance of Swiss Pension Funds”

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The student, Samuel Robert Walser, confirms with his signature on the present document that

he will not use the information received from Zurich University of Applied Sciences for any

other purpose than for his bachelor thesis “Size and Performance of Swiss Pension Funds” and

that this information will not be made available to any third party at any time without the ex-

press permission of Zurich University of Applied Sciences.

The term “third party” refers to anyone not involved in the supervision or evaluation of this

bachelor thesis.

Winterthur, May 26, 2016 ……………………………………...

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Size and Performance of Swiss Pension Funds I

Management Summary

Even though the Swiss occupational pension scheme attracts worldwide attention, little empiri-

cal research on the relation between size and performance was conducted. On the one hand, data

availability is not comparable with other countries and, on the other hand, the studies compiled

by consultants and public authorities confine themselves to the presentation of raw data.

Larger pension funds are broadly perceived as superior investors and beneficiaries of economies

of scale. The aim of this thesis was to analyze whether the difference in annual as well as cumu-

lative returns between small and large schemes is statistically significant in Switzerland. Anoth-

er aspect of examination was whether larger plans achieve lower cost rates and if a lower cost

rate translates into higher net performance.

To test the statistical difference in performance between the two subsamples, 3284 return rec-

ords provided by Complementa Investment Controlling AG for the period 2001 to 2014 were

analyzed using three statistical methods. A simple regression analysis and a Welch’s t-test were

complemented by a Wilcoxon-Mann-Whitney test. The relationships between both cost and net

performance, and size and cost, were tested with a simple regression based on 543 cost records

for the years 2013 and 2014. An interpretation of the results was reached through using research

on the Swiss pension fund system and considering variations in the investment behavior be-

tween large and small plans.

Although a direct explanation of net performance by the size of a scheme is impossible using a

simple regression, both the Welch’s t-test and the Wilcoxon-Mann-Whitney test provided strong

evidence that differences in returns were statistically significant over the period 2005 to 2014,

with the exception of 2011. Both tests indicated most pronounced differences in returns when

dividing the subsamples at a scheme size of approximately CHF 400-500 Million. Beyond a

plan size of CHF 700 Million, this difference was considered insignificant. The regression anal-

ysis further indicated that net returns are not explained by cost, but weak evidence was observed

that size explains a lower cost rate, indicating that economies of scale can be realized.

The analysis provides a reliable indication of a continued outperformance of larger plans with

assets above CHF 700 Million. In the past, due to a higher allocation to property, smaller plans

proved to be relatively resilient in down-trending markets. The introduction of new accounting

rules in 2005, as well as a convergence in the allocation to property between small and large

plans, diminished this advantage. To further understand the variation in returns, the effect of the

home bias should be taken into account. Moreover, the inclusion of risk parameters in future

studies would increase the informative value of the analysis.

Size and Performance of Swiss Pension Funds II

Table of Contents

List of Figures ............................................................................................................................ IV

List of Tables .............................................................................................................................. V

Index of Abbreviations ............................................................................................................. VI

1 Introduction ......................................................................................................................... 1

1.1 Problem ......................................................................................................................... 1

1.2 Objective and Research Questions ................................................................................ 2

1.3 Boundary ....................................................................................................................... 2

1.4 Research Methods ......................................................................................................... 2

1.5 Structure ........................................................................................................................ 3

2 Current state of Research ................................................................................................... 4

3 Swiss Pension Funds ........................................................................................................... 6

3.1 Swiss Pension Fund Landscape ..................................................................................... 6

3.2 Consolidation Trend ...................................................................................................... 9

3.3 Regulation ................................................................................................................... 11

3.4 Public Pension Funds .................................................................................................. 14

3.5 Small Pension Funds and Property .............................................................................. 16

4 Small versus Large Pension Funds .................................................................................. 18

4.1 Performance Driver ..................................................................................................... 18

4.1.1 Strategic Asset Allocation ................................................................................... 18

4.2 Cost Drivers ................................................................................................................ 22

4.2.1 Economies of Scale ............................................................................................. 22

4.2.2 Active / Passive Share ......................................................................................... 22

4.2.3 Share of Internal versus External Management................................................... 23

4.2.4 Alternatives and Derivatives ............................................................................... 25

5 Data..................................................................................................................................... 26

5.1 Data Provision ............................................................................................................. 26

5.2 Assumptions ................................................................................................................ 26

5.3 Descriptive statistics .................................................................................................... 27

5.3.1 Mean Return for Three Sample Sizes .................................................................. 28

5.3.2 Median Return for Three Sample Sizes .............................................................. 28

5.3.3 Mean and Cumulative Return for Five Sample Sizes ......................................... 29

5.3.4 Median and Cumulative Return for Five Sample Sizes ...................................... 31

6 Data Analysis ..................................................................................................................... 34

6.1 Return Analysis ........................................................................................................... 34

6.1.1 Regression Analysis ............................................................................................ 34

Size and Performance of Swiss Pension Funds III

6.1.2 Welch’s T-Test .................................................................................................... 35

6.1.3 Wilcoxon-Mann-Whitney Test............................................................................ 36

6.2 Cost Analysis............................................................................................................... 38

7 Combined Results and Findings ...................................................................................... 39

7.1 Results Data Analysis .................................................................................................. 39

7.1.1 Size and Performance Regression Analysis ........................................................ 39

7.1.2 Welch’s T-Test .................................................................................................... 40


7.1.4 Outperformance Larger Pension Funds over Smaller Pension Funds ................. 42

7.1.5 Cost and Performance Regression Analysis ........................................................ 43

7.1.6 Size and Cost Regression Analysis ..................................................................... 44

7.2 Findings ....................................................................................................................... 45

7.2.1 Size and Performance Regression Analysis ........................................................ 45

7.2.2 Welch’s T-Test .................................................................................................... 45


7.2.4 Outperformance of Larger Pension Funds over Smaller Pension Funds ............. 47

7.2.5 Cost and Performance Regression Analysis ........................................................ 48

7.2.6 Size and Cost Regression Analysis ..................................................................... 49

8 Discussion ........................................................................................................................... 50

9 Conclusion .......................................................................................................................... 53

Bibliography .............................................................................................................................. 56

Glossary ...................................................................................................................................... 61

Appendix A - Formula sheet .................................................................................................... 62

Size and Performance of Swiss Pension Funds IV

List of Figures

Figure 1: Breakdown of Swiss pension funds by their administrative form ................................. 7

Figure 2: Breakdown of Swiss pension fund market by size (assets under management) ............ 8

Figure 3: Asset allocation of Swiss pension funds between 1995 and 2014 ................................. 9

Figure 4: Forecast number of pension funds based on data of the Federal Bureau of Statistics . 10

Figure 5: Investment guidelines and diversification potential .................................................... 12

Figure 6: Coverage Ratio of Swiss pension funds by type of pension fund ................................ 15

Figure 7: Development of Swiss property prices between 1986 and 2013 ................................. 16

Figure 8: Asset allocation of Swiss pension funds between 2011 and 2014 ............................... 19

Figure 9: Breakdown of property investments between 2011 and 2014 ..................................... 20

Figure 10: Share of foreign investments of Swiss pension funds between 2011 and 2014 ........ 21

Figure 11: Indexed investments in percentage of overall assets between 2005 and 2014 .......... 23

Figure 12: Share of internally and externally managed assets as of end 2013 ............................ 24

Figure 13: Representativeness of data by means of number of pension funds ........................... 27

Figure 14: Mean return for three sample sizes ............................................................................ 28

Figure 15: Median return for three sample sizes ......................................................................... 29

Figure 16: Mean and cumulative return for five sample sizes .................................................... 30

Figure 17: Median and cumulative return for five sample sizes ................................................. 31

Figure 18: Cumulative median performance between 2004 and 2014, excluding 2008 ............. 32

Figure 19: Regression analysis size and performance - residual plots ........................................ 39

Figure 20: Regression analysis Cost and Performance - residual plots ....................................... 43

Figure 21: Regression analysis size and cost - residual plots ...................................................... 44

Figure 22: March 2016 UBS Risk / Return index for the last 36 months ................................... 52

Size and Performance of Swiss Pension Funds V

List of Tables

Table 1: Regression analysis for net returns on size between 2004 and 2014 ............................ 39

Table 2: Results Welch's T-Test ................................................................................................. 40

Table 3: Results Wilcoxon-Mann-Whitney Test ......................................................................... 41

Table 4: Out- and underperformance table.................................................................................. 42

Table 6: Regression analysis for net returns on TER for 2013 and 2014 .................................... 43

Table 7: Regression analysis for TER on size for 2013 and 2014 .............................................. 44

Size and Performance of Swiss Pension Funds VI

Index of Abbreviations

< Smaller than

> Larger than

AuM Assets under Management

bn Billion

Bps Basis Points

BVV2 Ordinance on Occupational Retirement, Survivors' and Disability Pension Plans

DB Defined Benefit Pension Plan

FX Foreign exchange

GDP Gross Domestic Product

LPP Swiss Federal Law on Occupational Old-age Survivors’ and Disability Pension Plan

m Million

OECD Organization for Economic Cooperation and Development

p.a. Per annum

SAA Strategic Asset Allocation

TAA Tactical Asset Allocation

TER Total Expense Ratio

U.K. United Kingdom

U.S. United States

Size and Performance of Swiss Pension Funds 1

1 Introduction

With approximately 80% of pension funds below CHF 300 Million worth of assets, smaller

pension funds make up for a considerable number of Swiss pension funds (Bundesamt für

Statistik, 2015, p. 15). There is an abundance of variables that impact the performance of pen-

sion funds, many of which are driven by the factor size. It is expected that larger schemes do

reap the economies of scale both in terms of more efficient administration and lower asset man-

agement cost. Further, a more experienced management team enables investments in bespoke

alternative assets that offer better return expectations. A smaller plan on the other side might

react more quickly to market events and are not refrained by liquidity limitations. Apart from

the factor size, the plan type (defined benefit or defined contribution) as well as the coverage

ratio might explain a variation in performance.

The presented thesis does not intend to provide reliable information as to which of those varia-

bles affect the performance of Swiss pension funds but to analyze whether large pension funds

in fact are able to outperform smaller pension funds. By using different statistical methods,

3284 net returns reported during the years 2001 and 2014 are analyzed in relation to pension

fund size. The impact of the variable size on the performance is evaluated both within a particu-

lar year and on a cumulative basis. The scientific research conducted is expected to provide

explanations to the results of the data analysis and address any possible anomalies. The ultimate

aim of the thesis shall therefore be to conclude whether there is a statistical difference in returns

of smaller and larger pension funds and which size would be needed to stay competitive going

forward.

1.1 Problem

The current low interest rate environment poses a considerable challenge to financial intermedi-

aries including pension schemes. This is heightened by the fact that pension funds are legally

required to achieve certain returns for those employees and retirees insured under its scheme.

Early indications show that the asset-weighted return of Swiss pension funds over the year 2015

was 0.4%, an unsatisfactory figure given that the average target return that would keep the cov-

erage ratio at the same level is as high as 2.90% (Swisscanto, 2016, p. 5). The very low yielding

interest rate environment that has prevailed now for several years coupled with the lift of the

EUR/CHF floor and rather sluggish economic growth has triggered an increase in public aware-

ness of pension funds’ cost structure in Switzerland. The subsequent trend towards increased

regulation and related costs combined with mounting public and political pressure for reform of

the financing and performance plans inevitably begs the question whether small pension funds

still have a right to exist.


General perception would lead someone to conclude that small pension funds need to disappear

due to disadvantageous cost structures, a lack of expertise when it comes to alternative invest-

ments, weak bargaining power as well as an inability to invest in a diversified well-structured

property portfolio. Whether such a straightforward assumption can be made or not shall be re-

searched throughout the course of this thesis.

1.2 Objective and Research Questions

The main goal of the presented bachelor thesis is to analyze whether a statistically significant

difference between returns of large and small pension funds can be identified. With a considera-

ble number of pension institutions disappearing every year, the aspect of economies of scale is

expected to be very strong.

Based on the provided data as well as the scientific research conducted, the following research

questions have been formulated.

1. Can larger pension funds in Switzerland outperform smaller ones over the long run?

2. And if so, where would the threshold lie in terms of size to stay competitive?

3. Do pension funds with a higher cost base achieve a lower performance than the rest?

4. Are the advantages of the size of a pension fund reflected in the TER?

A detailed analysis of the differences in the investment behavior among plans from different

size groups together with an understanding of the regulatory developments and the structure of

the Swiss pension fund system shall allow for an interpretation of the findings.

1.3 Boundary

In order to test the research question, a set of 3284 return records are analyzed. The analysis

does not explain any of the asset management components such as security selection, asset allo-

cation or market timing. The sheer size of the sample is expected to provide a reliable indication

as to whether there is a meaningful relation between size and performance of Swiss pension

funds. It is known that the reliability of the reported net performance figures should be consid-

ered with prudence. Not every pension fund in Switzerland uses the same method when measur-

ing its performance. Not only the chosen performance measurement method, but also the overall

assets that the return is measured against may vary (Brandenberger, 2008, p. 26).

1.4 Research Methods

Descriptive statistics of the dataset provide a first indication in regards to the presented topic.

The research questions shall be principally answered, however, using a quantitative approach. A

regression analysis between size and net performance, size and cost as well as cost and net per-


formance are complemented by a Wilcoxon-Mann-Whitney test and a Welch’s t-test for the

statistical significance of the difference in returns. In order to come up with meaningful recom-

mendations, the importance of Swiss singularities, such as regulation, small plans and property

as well as the role of public pension funds, need to be considered. By analyzing available litera-

ture, the respective driver of cost and performance will also be assessed.

This approach should provide both a reliable result to the research questions and offer a consid-

erable foundation to discuss the result and formulate meaningful recommendations to the Swiss

pension fund industry.

1.5 Structure

In the first part, section 2, the current state of research referring to performance discrepancies

between larger and smaller plans is assessed. The scientific research conducted comprises the

international as well as the Swiss pension fund market. The Swiss pension fund landscape and

its idiosyncratic dynamics are described in section 3. In section 4, the main differences of small

and large pension funds are analyzed in relation to the potential impact on cost and perfor-

mance. In the subsequent section 5, the return and cost data provided by Complementa Invest-

ment-Controlling AG are described. Section 6 sets out to analyze the data by means of different

statistical tools and to prove the statistical significance of the difference in return of small and

large plans. Moreover, the impact of size on cost as well the impact of cost on net performance

are analyzed in this section. The presentation and discussion of the findings in sections 7 and 8

is followed by a conclusion of the thesis in section 9.


2 Current state of Research

Identifying the relationship between size and performance of pension funds has been the subject

of several studies that have been compiled in the past. Predominantly U.S. and Canadian pen-

sion plan samples were used due to a higher degree of detail in reporting. This section gives an

overview of what has been researched and found so far.

Analyzing a set of 842 international pension plans between the years of 1990 and 2008, Dyck

and Pomorski (2011) documented significant positive scale economies. Larger plans were found

to outperform smaller ones by 43-50 bps per year in terms of their net abnormal returns. Com-

pared to the first quintile, the largest quintile plans manage 13 times more active assets internal-

ly. Another reason stated was that “larger plans devote significantly more assets to alternatives,

where scale and negotiating power matter most and obtain superior performance in these asset

classes” (Dyck & Pomorski, 2011, pp. 3-4). As opposed to Dyck and Pomorski (2011), An-

danov, Bauer and Cremers (2012) employed risk-adjusted returns. A sample of 557 U.S. pen-

sion funds reporting in the years between 1990 and 2010 revealed “larger pension funds do not

manage to transfer their lower investment costs into higher net returns” (p. 24). Size induced

liquidity limitations are said to be the main detractor of performance and can be identified in all

three asset management components (asset allocation, market timing and security selection).

Smaller pension funds managed to report both higher total returns and higher market timing

returns. There are two reasons for the considerably higher market timing returns. First, a more

flexible regulation on asset allocation bandwidths allowed smaller plans to exploit across asset

class momentum. Second, rebalancing for smaller pension plans had a much lower market im-

pact (Andonov, Bauer, & Cremers, 2012, p. 24).

Another study on U.S. DB funds found that the largest 30% have costs of about 15 bps per year,

versus average costs of 40 bps for the smallest. The study also found that “fund size and liquidi-

ty, as well as their interaction, are critical drivers of pension fund performance” (Bauer,

Cremers, & Frehen, 2010, p. 25). Further, fund size and performance are documented to be

strongly negatively associated. Also, for less liquid investments, this negative association is

stronger. While larger scale brings cost advantage, liquidity limitations seem to allow only

smaller funds to outperform their benchmarks.

Considering the diseconomies of scale that is mainly caused by liquidity issues faced by larger

plans, one tends to conclude that smaller pension funds indeed have a right to exist speaking

globally.

In Switzerland, Swisscanto (2015), in their annual study, found that pension funds with assets

below CHF 50m realize a performance, which is 10% lower than the rest of the sample. Institu-


tions with assets above CHF 50m, however, do not provide any significant deviations in per-

formance that relate to their size. Analyzing the past ten years, increasing correlation between

size and performance is prevalent. Economies of scale from lower administration costs and pro-

fessional asset management are mentioned as a potential reason (Swisscanto Vorsorge AG,

2015, p. 58).

The insights gained contradict the results of an earlier study released in 2006. Performance data

between the years 2000 and 2005 suggested a substantial outperformance of plans with assets

below CHF 50m over plans with assets above CHF 5bn. The prevalent question was, whether

small-sized pension funds had an advantage in the investment decision-making, which is mani-

fested in higher efficiency and speed that compensate for the disadvantages in portfolio diversi-

fication. The conclusion was that the reported net performance is by no means related to risk

capacity of the plan. An underfunded pension plan with a higher average insured age and more

retirees will not be able to take as much risk as another plan. Moreover, no information on the

calculation of the net return is known, consequently decreasing the reliability of the data. It has

been seen as probable that the investment results of some pension funds during the pre-FER-261

era have revalued their property portfolio. As main driver of a differentiated investment strate-

gy, the author considers professional expertise, time and access to high-quality information

which is expected to be easier to access for largger pension funds (Stucki, 2006, pp. 36-37).

Another Swiss pension fund consultant finds, despite systematic differences in the asset alloca-

tion between small and large plans, realized returns hardly differ in size in the long run. Whilst

plans exceeding CHF 800m achieved annualized returns of 2.8% between the period of 2001

and 2014, both smaller (<CHF 200m) and medium plans (CHF 200-800m) only lagged by 1

basis point. It was further determined that larger pension funds proved better to tap the potential

of the asset allocation than smaller ones, thereby gaining 30 bps. In contrast, smaller schemes

positioned their asset mix more advantageously (Complementa, 2015, p. 43).

In order to compare findings from Swiss pension fund studies to similar studies carried out in

other countries, the distinguishing characteristics of the Swiss pension fund market need to be

acknowledged. The following section gives a short overview of the dynamics of the Swiss pen-

sion fund market and provides an update on the regulatory framework.

1 Explained in Glossary


3 Swiss Pension Funds

This section is introduced by a brief description of the Swiss pension fund landscape with a

focus on the plan breakdown by size. Afterwards, the trend towards a further consolidation of

the Swiss pension landscape is described. To conclude this section, Swiss singularities that need

to be considered when analyzing the returns are presented.

3.1 Swiss Pension Fund Landscape

The pension fund market in Switzerland is of considerable importance. As of 2013, pension

assets relative to the GDP were 122%. On a global scale, only the Dutch pension fund market

with 170% of GDP and the United Kingdom Pension fund market with 131% of GDP have a

higher ratio (Markets Tools, 2014, p. 9).

The Federal Statistics Office publishes an annual pension fund statistic whereby solely public

and private pension institutions in Switzerland are questioned. The most recent pension fund

statistic was published in 2015 and describes the situation as of December 31st, 2013. The aim

of the pension fund statistic is primarily to depict the structure and the development of the oc-

cupational pension in Switzerland (Bundesamt für Statistik, 2015, p. 9).

After a positive investment year in 2013, the undercoverage could be reduced to CHF 33.4bn

representing a decrease of 11.7%. Only 5.1% of the private-law pension funds have exhibited a

coverage ratio of below 100%. The number of defined-benefit pension schemes has continued to

decrease. Only 10.6% of the actively insured persons belonged to a defined-benefit pension

scheme at the end of 2013 (Bundesamt für Statistik, 2015, p. 10).

A typical feature of the Swiss occupational pension is the extremely uneven distribution of as-

sets, both in regards to the number of insured persons and the balance sheet sum. This imbal-

ance is to be attributed to the dominance of small businesses. Ever increasing requirements for

the management of pension institutions as well as a growing number of legal provisions meant

that smaller, newly founded companies decided against founding their own independent pension

fund. Instead, they decided to become part of a collective- or community institution (Bundesamt

für Statistik, 2015, p. 36).

Community institutions are primarily created by banks, insurance companies or fiduciary com-

panies. Mostly small businesses that are unrelated to each other use community institutions. A

collective institution, on the other hand, is predominantly used by an association. The member

of an association is therefore not obliged to create its own pension fund (Bundesamt für

Statistik, 2015, p. 36).


Figure 1: Breakdown of Swiss pension funds by their administrative form

Note. Own illustration based on data from Bundesamt für Statistik, 2015, p. 13

The current structure of the occupational pension scheme is such that the majority of the 1957

plans are represented by plans of single employers and other plans. Other plans consist of col-

lective and community institutions of public-law corporations that are accessible for semi-public

companies or companies that relate somehow to a canton, municipality or the federal govern-

ment. Also to this group belong group institutions or holding companies or other combinations

of multiple employers (Bundesamt für Statistik, 2015, p. 37). The vast majority of the approxi-

mately 3.9m actively insured people are with other schemes (38%), with a collective institution

(37%) or with a community institution (21%). Out of 346’528 employers, the big bulk is part of

a collective institution (62%) or a community institution (35%) (Bundesamt für Statistik, 2015,

p. 13).

More important for the presented bachelor thesis is the breakdown of Swiss pension funds by

means of their size. Size in this context is measured by the amount of assets on their balance

sheet.


Figure 2: Breakdown of Swiss pension fund market by size (assets under management)

Note. Own illustration based on data from Bundesamt für Statistik, 2015, p. 15

With 536 out of 1957 pension plans, the subgroup sized between CHF 30m and 100m repre-

sents the largest part (27%). Sixty-seven percent of all pension funds are sized below CHF

100m and 85% are sized below CHF 300m. This shows small pension funds continue to repre-

sent by far the biggest group of Swiss pension plans. Evaluating this situation by means of the

number of insured persons, only 20% are insured by a plan sized below CHF 300m. Even more

noticeable is the importance of bigger pension funds when the breakdown is made by the per-

centage of total assets of the Swiss pension fund market. More than 50% of the CHF 720bn are

managed by schemes that exceed CHF 3bn of assets and the subgroup CHF 0-300m represents

merely 14% (Bundesamt für Statistik, 2015, p. 15).

Hence, whilst smaller pension plans represent a sizeable part of the total number of pension

funds, they make up a small portion of overall pension assets and insured persons. This situation

is underscored when examining the schemes with assets below CHF 100m. For every 100 pen-

sion plans in Switzerland, 67 are sized below CHF 100m. Out of 100 insured persons, merely 9

are with a plan smaller than CHF 100m of AuM. And lastly, out of CHF 100 worth of pension

assets, only CHF 6 are invested with a plan in this size group.


Figure 3: Asset allocation of Swiss pension funds between 1995 and 2014

Note. Original source from Complementa, 2015, p. 27, has been adapted to the English language

As the above chart illustrates, the asset structure of the second pillar in Switzerland is not domi-

nated by any asset class during the observation period. With exception of the year 2000, fixed

income represents the largest share of overall assets. After the turn of the millennium, Swiss

pension funds have started to build up some exposure to alternative investments. This share has

increased continuously and currently reaches approximately 7%. Also, a substantial increase in

foreign investments from 19.8% in 1995 to 45.5% in 2014 could be observed. However, the

unhedged currency risk was broadly reduced from 28.7% in 2006 to 18.8% in 2014

(Complementa, 2015, p. 27).

3.2 Consolidation Trend

The biannual pension fund statistics published by the Federal Bureau of Statistics in Switzerland

lists the official number of pension schemes in the country. Analyzing the data, it can be de-

duced that the compound annual growth rate2 of Swiss pension funds in the period between

1998 and 2013 has been -4.017%. Assuming the number of pension schemes continued to de-

crease at the same rate, another 688 pension institutions would disappear by the year 2025.

2 CAGR (Compound annual growth rate) calculation as per Appendix A, Formula Sheet

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3.3 Regulation

In order to comprehend possible differences in returns between smaller and larger plans in the

appropriate context, the regulatory environment and its evolution in recent years also needs to

be considered. This subsection attempts to understand certain deviations in the investment strat-

egy between smaller and larger plans.

Swiss Federal Law on Occupational Old-age Survivors’ and Disability Pension Plan (LPP)

obliges the pension plans to appoint an independent and recognized inspection body. This or-

ganization audits the management, the accounting as well as the financial situation of the re-

spective pension plan. In addition, an independent and certified pension fund expert audits the

underlying plan periodically on the following two aspects:

1. Can the pension plan at any time offer the security to meet its liabilities?

2. Do the regulatory, actuarial provisions on the benefits and financing comply with the

legal requirements?

Such expert audits need to be conducted annually in the event that a pension fund is underfund-

ed. The pension fund supervisory authority then requests on an annual basis a report on the

business activity and inspects the reports of the control authority as well as the pension fund

experts. The pension fund supervisory authority is vested with the power to take necessary

measures to address any shortcomings. The nine cantonally or regionally organized supervisory

authorities report to the federal supervisory authority of the pension plans (Bundesamt für

Statistik, 2015, p. 34).

Swiss pension schemes are subject to certain investment rules, influencing the diversification

potential of a portfolio. On the one hand, Article 55 of the Ordinance on Occupational Retire-

ment, Survivors' and Disability Pension Plans (BVV 2) defines the maximum allocation for

certain asset classes. Accordingly, mortgages and equities are both limited to 50%, the alloca-

tion to property to 30% and investments into alternative assets to 15% of overall assets. Un-

hedged FX exposure is limited to 30%. On the other hand, pension schemes are not allowed to

exercise short selling (Verordnung über die berufliche Alters-, Hinterlassenen- und

Invalidenvorsorge BVV2, 2016, pp. 38-43).


Figure 5: Investment guidelines and diversification potential

Note. Original source from Brändle et al., 2014, p. 23, has been adapted to the English language

The above illustration demonstrates that due to these legal boundaries, the possibility for action

and diversification potential are reduced. The theoretically achievable efficient portfolios lie on

a curve considerably below the efficient frontier for portfolios without restrictions. Increasing

the allocation to property or alternative investments would hence shift the above curve to the

left and project a more advantageous risk/return profile.

In practice, the return of Swiss pension funds are plotted below the red efficient frontier. There

are several reasons that would explain this situation. The individual pension fund regulations

can impose additional limits to certain investments. Moreover, the prevalent “home bias” of

Swiss pension funds decreases the risk/return profile. The tendency to allocate a higher weight

to Swiss investments is due to an old guideline in BVV2 that has been lifted only in 2009

(Brändle et al., 2014, p. 23).

Several amendments have been made to BVV2, referring to Article 53, which focuses on per-

mitted investments. The annual financial statements as per end of 2015 are audited for the first

time with the new rules. Several assets that were assigned to debts, and therefore were not lim-

ited to a certain level, have been reassigned to alternative investments. Mezzanine debt, asset

backed securities, credit linked notes, all kinds of loans (bank loan, senior secured loans) as well

as international mortgages are now subject to the 15% cap on alternatives (Scherer, Reichlin,

Neubert, & Ottinger, 2014, pp. 4-11).


A study published by ZHAW School of Management and Law, based on a survey of 35 pension

plans in Switzerland, revealed that increased pressure from regulators to reduce the allocation to

alternatives would have negative impacts on pension funds’ ability to achieve higher returns. A

third of the questioned consider the current regulation as too strict and that more responsibility

should be executed by the respective institutions. What bothered the participants of the survey

most, however, was a lack of strategy when dealing with regulation. An initial increase of the

allocation to alternatives to 15% has been followed a few years later in 2014 by new provisions

for a stricter regulation. Further, a subdivided regulation by i.e. size is very much welcomed

since a large pension fund with its own portfolio management requires a different approach than

a small scheme that hires only external managers (Anhorn & Moor, 2015, pp. 31-32).

If pension institutions are eager on allocating a higher share to individual asset classes or assets

than the prescribed investment guidelines in Article 55, a reference to Article 50, paragraph 4

can be made. The chosen investment portfolio must thereby comply with the statutory security

and risk distribution, which must be disclosed in the notes of the annual financial statements

(BVV2, Article 50, paragraph 4). Should the requirements for a deviation from the prescribed

investment guidelines not be fulfilled, the pension fund authority may demand an amendment to

the current asset allocation (BVV2, Article 50, paragraph 5). It is estimated that a significant

number of larger pension funds makes use of the stated optionality, helping them to deliver the

return goals.

On a separate note, the Swiss legislator has changed the reporting of asset management costs in

the 2013 annual report of Swiss pension funds. The desire to increase transparency of the asset

management fees has been well received. The regulator aims not only to enhance transparency

but also comparability of asset management related costs. Product providers are therefore the

main subjects of this change (Brändle et al., 2014, p. 35).

If cost data is unavailable, the respective products are qualified as non-transparent and need to

be listed in the annex of the annual report (BVV2, Article 48a, paragraph 3). In the report on the

reform of the 2020 retirement arrangement, the intention is to incentivize providers to decrease

the asset management fee (Bundesamt für Sozialversicherungen, 2013, p. 84).

Exchange traded Swiss equities have a combined market value of CHF 1300bn, whereby 8% or

CHF 100bn is assumed to be owned by Swiss pension funds (Schöchli, 2015). Effective as of

2015, Swiss pension schemes have to implement the ordinance against excessive compensation

or “Minder-Initiative”. According to the Ordinance against excessive remuneration of exchange

listed stock companies, Swiss pension schemes are obliged to execute the votes attached to

Swiss shares in the interest of the insured people. Preliminary surveys reveal that many of the

larger pension schemes already fulfilled the demands of the initiative ahead of the implementa-


tion. Especially for smaller pension funds though, the compilation of a well-founded opinion as

well as the reporting requirements bring about significant additional costs (Schmitz, Döhnert, &

Zöbeli, 2015, p. 5). According to industry experts, the new regulation has caused a shift among

smaller plans towards collective investment schemes (Schöchli, 2015). Otherwise, the board of

trustees of several smaller pension funds is expected to allocate mandates to so-called voting

rights advisors (Tagesanzeiger, 2014). The decisive questions will be whether the shift towards

collective investment schemes or the external hire of a voting right advisor is the better solution

from the perspective of the insured person.

Besides implementation costs, the Minder-Initiative also causes an increase of the ongoing costs

of Swiss pension funds. This comes at a time where administration costs are already very close-

ly observed (Brändle et al., 2014, p. 8).

In the light of the recent developments in regards to regulation, it can be well observed to what

extent the regulator can influence the pension plans performance. It is also important to notice

that certain provisions have different effects for smaller and larger plans. In the following sub-

sections 3.4 and 3.5, two Swiss singularities are addressed. The findings will further help to

draw adequate conclusions from the results of the data analysis.

3.4 Public Pension Funds

As the expression suggests, public-law pension plans are exclusively for employees of the gov-

ernment, the cantons, the municipalities or other public-law institutions. On occasion, employ-

ees of charitable or semi-public institutions may also fall within this category. In recent years,

the government has increasingly allocated employee pension funds to private law pension plans

(Bundesamt für Statistik, 2015, p. 36).

Public-law pension funds are numbered among the oldest and largest pension funds in Switzer-

land. The large employers have established some sort of pension scheme in the 19th century. The

preferred treatment that amongst other included a state guarantee and a number of scandals has

damaged the reputation of this type of pension funds. Disadvantages such as long and tedious

decision paths as well as inadequate division of labor among political and pension fund organs

have been reduced in the latest LPP revision in December 2010 (Swisscanto Asset Management

AG, 2011, pp. 35-36).

As of end 2013, overall public pension assets in Switzerland are expected to make up for ap-

proximately 25% of overall pension assets (Markets Tools, 2014, p. 11). As compared to other

countries, the public sector does not enjoy a higher importance. For the P7 countries, Australia,

Canada, Japan, Netherlands, Switzerland, U.K. and U.S., the average contribution of the public


sector to overall pension funds’ assets is 34% (Markets Tools, 2014, p. 11). Therefore, the role

of public pension funds nowadays should not be considered a Swiss singularity.

Due to its nature, most public pension funds in Switzerland belong to subsample large. As com-

pared to private plans, public pension funds exhibit significantly lower funding levels. The low

coverage levels are, however, not a result of lower net performance. The following figure shows

that the coverage ratios of public and private pension funds are at different levels but have de-

veloped very similarly between the years 1994 and 2011.

Figure 6: Coverage Ratio of Swiss pension funds by type of pension fund

Note. Original source from Complementa, 2012, p. 26 has been adapted to the English language

Rather, the differences in the technical interest rate, the type of pension plan (defined contribu-

tion or defined benefit) as well as the minimum interest rate could have an impact on the fund-

ing situation. The technical interest rate that is applied by defined benefit pension plans tends to

be lower (Swisscanto Asset Management AG, 2007, p. 53). According to their investment be-

havior, structural differences between public and private pension funds are hardly identifiable.

The public-law pension funds still have higher mortgage quotas and allocate slightly more to

equities and property. Moreover, public pension funds have a lower allocation to bonds and

liquidity than their private-law peers (Bundesamt für Statistik, 2015, p. 16).


3.5 Small Pension Funds and Property

Property prices have been rising in Switzerland since the late nineties and formed an integral

component of the performance of the individual plans. With an annual growth rate of 7.1%

since 1996, Swiss property was a strong positive contributor to overall performance of 5.33%3.

Figure 7: Development of Swiss property prices between 1986 and 20134

Note. Illustration based on Complementa, 2014, p. 62

Comparing allocations to direct property in Switzerland between small, mid- and large-sized

plans5, no significant distinction between the plans is identifiable. This result is rather unex-

pected since efficiently building and maintaining a structured property portfolio is capital in-

tense and requires substantial know-how. The main reason why smaller pension funds manage

to hold quite a considerable share of direct property is the fact that to a large extent the positions

are self-occupied (SVIT Schweiz, 2014, p. 2).

Overall allocation to property as of end 2013 reaches 16.7%. Small (CHF <100m) and mid-

sized (CHF 100-1000m) pension funds allocate 21.4% and 22.0%, respectively, to this asset

class whereas large plans exceeding the size of CHF 1bn only allocate 15.9%. The difference of

approximately 5 percentage points arises in the varying allocation to indirect Swiss property.

3 Annualized performance of BVG-40 Index between the years 1994 and 2014 (Pictet Asset Management, 2015, p. 3) 4 The SWX IAZI Investment Real Estate Performance Index includes direct investment properties in Switzerland and is calculated on a quarterly basis (IAZI AG - CIFI SA, 2016) 5 Small: <CHF 100m assets; Medium: CHF 100-1000m assets ; Large: >CHF 1000m assets


Plans with assets above CHF 1bn invest 6.2% less to indirect Swiss property than their mid-

sized counterparts (Complementa, 2014, p. 42). Over all size groups the share of indirect prop-

erty investments increased tenfold between 1995 and 2011 from 0.7% to 7.0% of overall assets

and the share of direct property has considerably decreased from 20.3% to 9.9% (Complementa,

2012, p. 35).

The relative attractiveness in regards to the risk/return profile of adding property to a simple

bond and equity portfolio is demonstrated in the following example. Assuming a portfolio con-

sists of 60% CHF bonds and 40% Swiss equities. According to long-term return of the Credit

Suisse strategy consultant, the portfolio would deliver long-term return potential of 2.8% p.a.

and a volatility of 5.5% p.a. Adding 15% direct property exposure and reducing the share of

bonds to 53% and the share of equities to 32% can achieve the very same return potential. For

the portfolio including property the inherent risk is reduced by 1.1 percentage points (Brändle et

al., 2014, p. 37).

Having invested in a diversified property portfolio had another positive effect. Not only did the

allocation to property provide stable attractive returns at a lower volatility but it also provided

resilience during both the burst of the IT bubble in 2001 as well as the financial crisis in 2008

and its aftermath. This Swiss singularity would assumingly have a very positive impact on the

performance of small- and mid-sized pension funds.

This section provided an overview of the structure of the Swiss pension fund industry. The pen-

sion fund system is represented by a considerable number of small schemes, which are increas-

ingly becoming part of collective and community institutions. Moreover, certain regulatory

provisions are seen to impact larger and smaller plans differently. The role of public pension

funds in the context of the research questions is considered as unimportant but the tendency of

smaller plans to allocate more to property is expected to play an instrumental role for the inter-

pretation of the test results.


4 Small versus Large Pension Funds

When analyzing the difference in performance between smaller and larger pension schemes two

main influence factors need to be taken into account. Firstly, there is the performance drivers,

which are mainly affected by the individual decisions on the asset mix. Secondly, there is the

cost structure of a pension fund that may have an impact on the final performance. These two

factors are highly interconnected. A good example is the use of private equity investments,

which can offer considerably higher return expectations but come at a higher cost rate.

4.1 Performance Driver

In the years between 2011 and 2014 PPC Metrics analyzed 41 small-, medium-, and large-sized

pension funds in Switzerland. The goal of their study was to identify the link between asset

allocation decisions, broken down into SAA, TAA and security selection, and performance.

Even though several plans managed to outperform the strategy, the study implied that pension

funds are unable to generate any added value net of costs (Skaanes & Kunkel, 2014, p. 6). The

findings suggest that pension funds can either realize higher performance via a change of the

SAA or by reducing costs.

With that in mind, the subsequent subsection will take a closer look at the differences in the

strategic asset allocation of smaller and larger pension funds in Switzerland.

4.1.1 Strategic Asset Allocation

The data retrieved from the Complementa Risk Check up 2012 suggest that the size of a pension

fund is the main driver of the investment strategy. Neither the legal type (public-law, private-

law), nor the type of plan (defined benefit, defined contribution) materially influenced the asset

allocation. This is not obvious since the legislator foresees the investment strategy of a pension

fund to reflect its risk capacity as represented by the coverage ratio (Complementa, 2012, p. 39).


Figure 8: Asset allocation of Swiss pension funds between 2011 and 2014

Note. Own illustration based on data from various Complementa Risk studies 6

The above chart illustrates how the strategic asset allocation of small, mid-sized and large pen-

sion funds has developed over the past 4 years. The subgroup “small” consists of plans sized

below CHF 100m, the subgroup “medium” of plans sized between CHF 100m and 1000m, and

the subgroup “large” of plans above that level. For 2014, Complementa lowered the threshold to

subgroup “large” from CHF 1000m to CHF 800m. Another observation is that small pension

funds tend to have a higher allocation to liquidity than mid- to large-sized plans. Back in 2011,

the allocation to fixed income with 44.7% has been the highest for large plans. Pension funds of

all size groups have reduced their exposure to fixed income, led by large plans with a reduction

of more than 5%. As demonstrated in the 2014 asset allocation, equity exposure with around

30% of overall assets for all plans does not correlate with the plan size. Most of the reduction in

the fixed income piece has been compensated by an equal increase in the allocation to equities.

The largest deviation in the respective allocation is attributable to the allocation to property.

Further, a high correlation between plan size and exposure to alternative investments is appar-

ent. While the allocation to alternatives has not materially increased during the course of the

past 4 years, large plans allocate twice as much to alternatives as small plans with medium-sized

plans being somewhere in between. Whereas small pension funds allocate slightly above 1% to

6 Complementa, 2015, p. 42 / Complementa, 2014, p. 42 / Complementa, 2013, p. 40 / Complementa, 2012, p. 34. Please also note that for year 2014, Complementa defined the limit between medium and large schemes at CHF 800m.

7.4 7.2 8.2 7.6 6.6 6.9 7 6.1 6.3 6.6 7.8 6.1

40.4 37.6 37 38.6 39.4 36.5 35.9 36.144.7 41.2 40.1 39.6

25.827.4 29.7 30 27.3 28.4 30.4 30.8

25.6 28.9 29.6 29.9

22.8 22.521.4 20.2

20.9 22.7 22 21.6 16.3 15.8 15.9 16.6

3.2 3.6 3.5 3.3 5.7 5.5 4.5 5.5 7 6.8 6.5 7.2

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2011 2012 2013 2014 2011 2012 2013 2014 2011 2012 2013 2014

asset class in % of overall assets

Liquidity Fixed Income Equities Property Alternative Assets Other

small schemes(< CHF 100m)

medium schemes(CHF 100m ‐ CHF 1000m)

large schemes(>CHF 1000m)


hedge funds, their exposure to private equity investments is only around 0.3%. Larger pension

funds on the other hand allocate over 2% to hedge funds and approximately 1.4% to private

equity (Complementa, 2015, p. 42).

Figure 9: Breakdown of property investments between 2011 and 2014


The allocation to property is highest for small and mid-sized pension funds. The difference in

this allocation between small and large pension funds has, however, decreased substantially.

While the gap in the 2011 allocation was as high as 6.5%, it is now only 3.6%. A decrease in the

allocation of small schemes to direct property from 13.1% in 2011 to 9.1% was the main reason

for this development. The data also indicates that mid- and large-sized pension funds managed

well to maintain their quota of direct property. Despite of international diversification in the

equity sleeve, Swiss pension funds are cautious to build up exposure to international property.

What the breakdown fails to show are potential investments in international REIT’s which are

allocated to the equity part here (Davidson, 2012, p. 217). The positive effects of diversification

of a higher geographical distribution of the property portfolio have been suggested by various

studies. The revised BVV2 rules allow for a overall 10% allocation to international property

representing a third of the overall property exposure (Davidson, 2012, p. 218). Potential reasons

mentioned for the underallocation are good performance of Swiss property in the past, less

knowledge and higher volatility. With decreasing returns and elevated valuations in Switzer-

land, the share of international property is set to rise.


13.1 12.3 119.1 10.4

12.79.1 10.3 9.8 9.6 8.8 9.2

9.2 9.39.4

9.79.1

8.811.6 9.9

5 4.6 5.4 5.4

0.5 0.91

1.4 1.41.2 1.3 1.4

1.6 1.6 1.6 2

0

5

10

15

20

25

2011 2012 2013 2014 2011 2012 2013 2014 2011 2012 2013 2014

Investment in % of overall pension fund assets

Direct Swiss Property Indirect Swiss Property International Property

medium schemes(CHF 100m ‐ CHF 1000m)

small schemes(< CHF 100m)

large schemes(>CHF 1000m)


Figure 10: Share of foreign investments of Swiss pension funds between 2011 and 20148


The most striking discrepancy in the investment strategy between smaller and larger pension

funds, however, is their varying allocation to foreign investments. While foreign investments

contribute 46.7% to overall portfolio of large pension funds, this share is only 30.6% for small

plans. This pronounced “home bias” of small pension funds has diminished in the last few

years. Although at the same time, the larger plans have also further increased their share of for-

eign investments. The tendency of smaller pension funds to invest less in foreign assets is

prevalent across all asset classes (Complementa, 2015, p. 43). Whereas the U.S. stock exchange

makes up for almost half of the global market capitalization, the Swiss market represents merely

3%. Hence, a Swiss investor investing 70% of its equity allocation in the Swiss market, under-

weights the rest of the world by 67%. The cost of home bias is expressed in a lower degree of

diversification by industries and hence also an inferior risk/return profile (Schäfer, 2014, para.

4). Analyzing returns of the members of the S&P Global 1200 equity index between the years

1990 and 2012 and assuming an 80% weighting of the Swiss market, the cost of the home bias

is found to be 1.1% p.a. (Agnesens, 2014).

Hence, the most noticeable differences in the investment strategy between smaller plans and

larger plans relate to property, alternative investments and foreign investments in general.


24.2%

31.9%

39.5%

26.7%

31.3%

42.5%

27.3%

33.6%

44.8%

30.6%

35.4%

46.7%

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

45.0%

50.0%

small (<CHF 100m) medium (CHF 100‐1000m) large (>1000m)

Percentage

of overall assets

2011 2012 2013 2014


4.2 Cost Drivers

Relative to the coverage ratio, the performance and the investment structure, the matter of ad-

ministration costs has grown in importance. The administration costs can inform how efficient

the savings of the insured person are translated into pension benefits. A certain relation between

various pension fund characteristics and the cost of administration is identifiable. Based on the

official Swiss pension fund statistics, Gerber and Weber (2007) provide a thorough investiga-

tion of the investment behavior and costs of Swiss pension funds. They detect that public pen-

sion funds, defined contribution plans, as well as larger funds generally have lower costs of

administration (p. 18). In an international comparison, Switzerland has a very competitive cost

structure with 30bps of administration costs in relation to the overall assets. OECD links the

differences in administration costs to various reasons. Countries with a dissipated sector struc-

ture and many small schemes, exhibit higher costs than countries with larger plans (Brändle et

al., 2014, p. 8).

Since the success of a change in SAA is dependent on many variables that cannot be controlled,

the importance of cost discipline is evident. When deciding on increasing allocation to a certain

asset class, the diversification aspect, however, is substantially more important than costs

(Brändle et al., 2014, p. 36). This shows that costs are considered especially within a certain

asset class rather than on the overall asset allocation.

4.2.1 Economies of Scale

The most recent study of Swisscanto implied that Swiss pension funds can benefit from econo-

mies of scale. Relative differences between small and large institutions are especially high for

audit, pension fund expert and oversight. These costs vary between CHF 125 per beneficiary for

the smallest and CHF 6 per beneficiary for the largest. Administration costs and cost for asset

management define the total costs. Here the range varies from CHF 1150 per beneficiary for the

smallest plans to less than CHF 750 for the largest ones (Swisscanto Vorsorge AG, 2015, pp.

77-78).

4.2.2 Active / Passive Share

Ambachtsheer, Capelle and Scheibelhut (1998) contend that size and a high degree of passive

asset management are positively associated with pension fund performance. Another study has

compared the fees, expenses and trading costs society pays to invest in the U.S. stock market

with an estimate of what would be paid to get passive exposure. Between the years 1980 and

2006, the average difference in return between active and passive investing into the U.S. stock

market was 67 bps (French, 2008, p. 1561). According to the author a general misperception


about investment opportunities in active investing is the main reason why the share of active

investments is still so high.

The share of indexed solutions correlates with the size of the pension fund. In the past decade

the share of passively managed assets has increased significantly. Both unsatisfactory results

with classical investments and cost pressure have supported this development (Swisscanto

Vorsorge AG, 2015, pp. 48-50). The following graph illustrates that the smallest plans have

always had the lowest use of indexed solutions while the largest plans were exposed the highest.

Thus instead of converging, the allocations have further diverged. While the gap between the

allocations to indexed solutions of the smallest and largest plans was 14% in 2005, it reached

23.5% in 2014. For the remainder, increase in the use of passive instruments could be observed

over time, only the plans sized between CHF 50m and 100m have shown some peculiar devia-

tions which is most probably explained by changes in the database.

Figure 11: Indexed investments in percentage of overall assets between 2005 and 2014

Note. Own illustration based on data retrieved from Swisscanto Vorsorge AG, 2015, p. 50

The rise of the core-satellite approach underlines this trend. The core portfolio consists of pas-

sively managed investments. In relatively inefficient markets such as emerging markets, small

caps or alternatives, actively managed solutions are employed. Typically 75-80% of the assets

are covered in the core (Anhorn & Moor, 2015, p. 24).

4.2.3 Share of Internal versus External Management

The recent ordinance that tries to implement the Initiative by Minder came into effect on Janu-

ary 1st, 2015 and is expected to cause a shift from internal to external management. Swisscanto

identified across all sizes a high share of externally managed assets varying between 55% and

0

5

10

15

20

25

30

35

40

2014201320122011201020092008200720062005% of overall asset invested passively

CHF <50 M CHF 50‐100M CHF 100‐500MCHF 500‐1000M CHF 1000‐5000M CHF >5000M


76%. Interestingly, no correlation between the size of the pension fund and the share of exter-

nally managed assets could be detected (Swisscanto Asset Management AG, 2014, p. 42).

Figure 12: Share of internally and externally managed assets as of end 2013

Note. Own illustration based on data retrieved from Swisscanto Asset Management AG, 2014

Looking closer at the type of investment, certain patterns are identifiable. The share of assets

invested in, for instance, investment foundations, decreases with the increasing size of the pen-

sion plan. The breakdown of the type of assets by pension fund size based on the 2015 Swiss-

canto study has revealed that the smallest subsample invested with 35% of its overall assets

almost twice as much in investment foundations than the largest subsample with 16%. The same

applies for multi-asset mandates and structured products. The largest subgroup decided against

investing in mixed-asset mandates at all. On the other hand, increasing size of the pension fund

tends to be associated with higher allocation to single-asset class mandates (Swisscanto

Vorsorge AG, 2015, p. 48).

Overall, the trend towards collective investment schemes continues. In 2013, the allocation of

Swiss pension funds to collective investments has increased from 46.5% to 49.6%. In other

words, every second franc is invested in an investment foundation, investment fund or struc-

tured product reflecting overall assets of CHF 357.5bn. Ten years ago, only a quarter of the

assets were invested in collective investments (Bundesamt für Statistik, 2015, p. 16).

44.4

25.4 23.829.6

39.2 39.9

55.76

74.6 76.170.4

60.9 60.1

0

10

20

30

40

50

60

70

80

90

100

CHF <50m CHF 50‐100m CHF 100‐500m CHF 500‐1000m CHF 1000‐5000m CHF >5000m

Share of overall assets in

%

Share of externally managed assets in % of overall assetsShare of internally managed assets in % of overall assets


4.2.4 Alternatives and Derivatives

According to a study on asset management costs by the Swiss Federal Office for Social Securi-

ty, alternative investments with a relative share of 6.4% of overall assets make up for approxi-

mately one third of overall asset management fees. Hedge funds, followed by private equity

investments, were mentioned as main drivers of costs (Bundesamt für Sozialversicherungen,

2013, p. 84).

The degree of FX hedges correlates to a large extent with the size of the pension fund. Even

though smaller plans tend to have a stronger “home bias”, their FX risk of 20% is higher than

the 15.6% of the larger plans (Complementa, 2011, pp. 14-15). The reduced FX risk of larger

pension funds is accompanied by a substantially higher use of currency overlays to hedge their

currency risk. Whereas the smallest subgroup (CHF <50m) does currency overlays for 5.2% of

their international bond quota, the largest subgroup (CHF >5000m) uses this option for 69.2%

(Swisscanto Vorsorge AG, 2015, p. 54).

As aforementioned in subsection 3.5, the addition of property to the overall asset allocation

contributes very positively from a risk-return perspective. With a focus on cost, however, the

optimized portfolio is to be questioned. According to a study compiled by the Federal Bureau of

Statistics, the cost factor for the administration and management of a direct property portfolio is

1.61% (Brändle et al., 2014, p. 37).

Ultimately, it can be said that internal management as well as the deployment of passive strate-

gies help the larger plans to drive down costs considerably. The higher allocation to alternatives

as well as higher usage of derivate instruments, especially for currency hedges, however, brings

the level of cost more in line with smaller plans. The plans that actively maintain a portfolio of

direct property are believed to exhibit a significantly higher cost rate.


5 Data

In this section the provided data is described by means of descriptive statistics. The observations

served as the main motivation for the conducted statistical significance tests.

5.1 Data Provision

The data used for this thesis has been provided by Complementa Investment Controlling AG, a

St. Gallen based pension fund consultant founded in 1984. Due to its long existence, the com-

pany was able to share net return figures for up to 319 pension funds in Switzerland from 1996

until 2014, as well as TER data for the years 2013 and 2014. The pension fund size as of 2014

complemented the data set. For 2014, the sample provided represents aggregated total assets of

CHF 486.3bn, which makes up 68% of overall pension fund assets in Switzerland, therefore

highly representative (Complementa, 2015, p. 8). A questionnaire that is sent on an annual basis

to Swiss pension funds builds the basis for the data. Since the investment pattern is much more

aligned after the year 2000 than in the nineties, only data starting from 2001 is analyzed in de-

tail.

5.2 Assumptions

This thesis in its core sets out to analyze whether there is a statistical difference between the

return distribution of smaller and larger pension funds. The respective asset mix of every pen-

sion fund is a substantial driver of pension funds’ performance. In practice, each pension

scheme would have its own benchmark that reflects the strategic asset allocation it is measured

against. A test of the performance against the individual benchmarks would therefore provide

information about a set of potential return differentiators. Such return differentiators could be an

increased ability in security selection or timing or an enhanced cost structure due to economies

of scale or better negotiation skills. Another manifestation of economies of scale could be in-

vestments in non-traditional assets that promise a more favorable risk/return profile but require

dedicated investment professionals.

In addition to the aforementioned return differentiators, the result of the analysis will also par-

tially explain the effects of the asset allocation decision since the individual benchmark perfor-

mance is not available for the examined sample.

The definition of size of a pension fund finds two forms. On the one hand, the number of bene-

ficiaries and, on the other hand, the asset base of the plan. In the present thesis, the size is de-

fined by the asset base. In order to approximate the asset base of any pension fund in a particu-

lar year, the achieved return is discounted from the subsequent years’ asset base. Gaps in the


return series of a single pension fund lead to the cancellation of the previous years’ return fig-

ures. Consequently, pension plans recording a return figure in 2001, also recorded return figures

in all subsequent years up to 2014.

The simple discounting of the net return does not account for possible changes in assets that are

caused by insurance, administration or developments in the number of insured people

(Holzbauer, 2006, pp. 111-112). Nevertheless, this method is believed to provide a reliable ap-

proximation of the asset base.

5.3 Descriptive statistics

The awareness level of the Complementa Risk Study has grown continuously. In combination

with a strong pace of consolidation in the sector, this means that the reliability of the analyzed

data has also increased during the course of the years.

Figure 13: Representativeness of data by means of number of pension funds

Note. Own illustration. Number of recorded plans in Switzerland retrieved from publication of the Federal Bureau of Statistics (Bundesamt für Statistik, 2010 / Bundesamt für Statistik, 2015 / Bundesamt für Statistik, 2006)

When describing the discrete return of pension funds by size, Complementa, in their most recent

risk study, divided the sample into three groups. Small pension funds have a size up to CHF

200m, mid-sized plans lie between CHF 200m and CHF 800m and large ones above that level.

These thresholds compare with median assets of CHF 259.4m in the year 2014, a 33rd percentile

of CHF 137.2m and a 66th percentile of CHF 629.2m. Both the median as well as the 33rd and

66th percentile are very similar for the asset sizes during the last ten years. The chosen bin limits

are therefore reasonable to describe the respective discrete returns of small-, medium- and large-

sized pension funds in Switzerland.

0

500

1000

1500

2000

2500

3000

3500

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014Number of pension Funds

number of plans represented in data set number of recorded plans in Switzerland


5.3.1 Mean Return for Three Sample Sizes

Figure 14: Mean return for three sample sizes

Note. Own illustration based on data from Complementa Investment Controlling AG

This chart illustrates that the mean of the largest subsample has surpassed the mean of the entire

sample in every year after 2003 with the exception of 2008. Of the years that provided positive

returns, only in 2003 did the large plans not manage to outperform the mean. Not only did large

plans achieve a higher mean return than the overall sample, they were also the best performing

subsample after 2003, with the exception of 2008 and 2009. It can further be observed that

small plans underperformed the overall sample in each year after 2004 with the exception of

2008.

5.3.2 Median Return for Three Sample Sizes

Unlike arithmetic mean, the median calculation method is not sensitive to outliers. It therefore

provides an important confirmation or rejection of the previous findings.

The presentation of median returns shows a very similar pattern with the median of the largest

sample size outperforming the overall median in every year except for the years 2002, 2003,

2008 and 2009. Unlike the arithmetic mean approach, using the median approach also shows an

outperformance of mid- and large-size pension funds in the years 2001 and 2002. This is to be

attributed to big outliers that have considerably distorted the mean value. The smallest subsam-

ple has underperformed the median 11 out of 14 times and represented the worst performing

subsample in the same number of years.


Figure 15: Median return for three sample sizes

Note. Own illustration based on data from Complementa Investment Controlling AG

5.3.3 Mean and Cumulative Return for Five Sample Sizes

In order to attain a more detailed breakdown of the realized returns per size of pension fund, the

number of subsamples was increased to five. The bin ranges have been selected such that the

largest (S5) and the smallest (S1) subsample in 2014 make up roughly 50% of the overall sam-

ple and that the remaining participants are equally represented in sizes 2 to 4. The resulting bin

ranges were therefore: 0-100m (S1), 100-200m (S2), 200-600m (S3), 600-2000m (S4) and

>2000m (S5).


Figure 16: Mean and cumulative return for five sample sizes

Note. Own illustration based on data from Complementa Investment Controlling AG10

Again, a sustainable pattern can only be identified after 2003 if five subsamples are identified.

In the years between 2004 and 2014, subsample S4 and S5 outperformed the average pension

fund in every year except for 2008. A new finding of this approach is that pension funds that

have an asset base between CHF 600m and CHF 2000m managed to achieve the highest mean

return in 6 out of the last 11 years. In comparison, the largest subsample performed the best in

only 3 out of the last 11 years. It can further be acknowledged that the smallest size group per-

sistently underperformed the overall mean since 2004, where they lagged behind their counter-

parts 9 out of 11 times. In the size groups 2 and 3 (pension funds sized between CHF 100m and

CHF 600m) no consistent pattern can be identified. The number of outperforming years is al-

most equal to the number of underperforming years.

When visualizing the cumulative performance starting in 2001 it can be observed that subsam-

ple S4 and S5 managed to achieve a considerably higher performance despite the higher draw-

down in 2008. Among the two, subsample S4 suffered more in 2008 but managed to catch up in

recent years. In absolute terms, the cumulative performance for size groups S4 and S5 were

49.36% and 49.63%, respectively, as compared to S1 (44.55%), S2 (45.76%) and S3 (45.96%).

10 S1 represents schemes <CHF 100m, S2 schemes between CHF 100 and 200m, S3 schemes between CHF 200 and 600m, S4 schemes between CHF 600 and 2000m and S5 schemes above that. S1-S5 refer to primary vertical axis and Cumulative S1-Cumulative S5 to secondary vertical axis


If the year 2008 as well as the years before 2004 are excluded from the calculation, a much

stronger divergence between smaller and larger pension funds should be identified.

There are two key observations that can be made. First, the cumulative return of the mean per-

formance is significantly higher for subsample S4 (81.27%) and S5 (80.67%). Second, subsam-

ple S1 with 68,32% achieves lower returns in the long run than subsamples S2 and S3 that de-

livered cumulative returns of 73.33% and 74.81%, respectively.

5.3.4 Median and Cumulative Return for Five Sample Sizes

To recap, the median returns of the respective subsamples are compared to the median of the

entire data set in a particular year to provide for a confirmation or rejection of the previous dis-

coveries.

Figure 17: Median and cumulative return for five sample sizes


Similar to the analysis with three subsamples, the pattern that was observed when describing the

arithmetic mean is not only confirmed but can be extended to the years prior to 2004. The larg-

est size groups managed to outperform the overall sample median in 12 out of the last 14 times.

Next to 2008, S4 lost relative performance in the year 2001 and S5 slightly underperformed in

2005. Subsample 4 was the best performing subsample in 7 out of 14 times and S5 in 5 out of

14 times.



By dividing the overall sample into five instead of three subsamples it can be observed that size

is not necessarily the driver of the divergence. Pension plans sized between CHF 600m and

CHF 2000m achieve, with 51.37%, a slightly higher cumulative performance than the largest

size group which achieved 50.61% over the past 14 years. Using median returns instead of mean

returns substantially reduced the drawdown for the second largest subsample in 2008. Whereas

mean returns for S4 detracted 15.06% (in relative terms losing 1.61%), median returns only

decreased by 13.30% which equals a relative annual underperformance of only 46bps.

For an alternative observation of the cumulative performance, the years before 2004 as well as

the year 2008 were stripped off. The cumulative performance values resulted in the following

numbers. Subsample 1 achieved 67.23%, considerably lagging S2 and S3 with returns of

73.36% and 72.08%, respectively. More apparent than in the previous calculation, S4 surpassed

S5 with 79.87% as compared to 77.73%.

Figure 18: Cumulative median performance between 2004 and 2014, excluding 2008


If the year 2008 for both calculation methods (arithmetic mean and median returns) is excluded,

the difference for the smallest and largest subsample in the cumulative performance of the past

11 years exceeds 10 percent.

To conclude this section it can be said that by means of descriptive statistics, a considerable

divergence in performance between large- and small-sized Swiss pension funds can be ob-



served. Whereas mean return figures in early years were more prone to erroneous results due to

a stronger impact of outliers coupled with a smaller sample size, a clear pattern after 2003 has

developed. It can be further identified that the divergence is particularly accentuated between

the plans sized below CHF 100m and the ones above CHF 600m. In down-trending markets

such as 2001 and 2008 the smallest plans proved to be very resilient. Using median returns, they

outperformed the broad sample by 67bps in 2001 and 77bps in 2008. The description of the raw

data further reveals an outperformance of the second largest subsample (CHF 600-2000m) over

the largest schemes. Whether the observations made are statistically significant shall be the

main subject of the following section.


6 Data Analysis

This section is divided into two subsections, Return analysis 6.1 and Cost analysis 6.2. The re-

turn analysis sets out to provide answers for the research questions one and two, whereby the

results of the cost analysis is meant to answer research questions 3 and 4 (see subsection 1.2).

6.1 Return Analysis

To examine whether small pension funds’ returns differ statistically from large pension funds’

returns, three different statistical approaches are used. First, a regression analysis is used to ana-

lyze if the size (asset under management) of pension funds can explain variations in their re-

turns. Second, two location tests are used to analyze if small and large pension funds’ returns

have a different distribution. In the second step, on the one hand, the parametric t-test is de-

ployed to test if the mean return of small pension funds differs from the mean return of large

pension funds. The t-test assumes returns are normally distributed. In order to drop this assump-

tion, the Wilcoxon-Mann-Whitney test is used on the other hand. While the t-statistic tests dif-

ferences in the mean, only the Wilcoxon-Mann-Whitney statistics tests the hypothesis that two

independent samples were drawn from the same population. If the two samples mainly differ in

the mean and if the returns are sufficiently normally distributed, the two tests should yield simi-

lar results. All tests are implemented using Microsoft Excel.

The observed differences in the returns of small pensions funds are statistically significant.In

the t-test and the Wilcoxon-Mann-Whitney test, only two return distributions or two mean val-

ues can be tested against each other. Therefore, a whole set of thresholds dividing larger plans

from smaller plans have been defined. In total, 19 thresholds ranging from CHF 50m to CHF

5000m were identified. Hence, 19 significance tests have been carried out for both tests, the t-

test as well as the Wilcoxon-Mann-Whitney test, for every year between 2001 and 2014. To

complement the analysis, the respective cumulative performances since 2001 and in the last 10

years have been tested. In total, 608 significance tests were conducted, building the basis of the

presented thesis.

6.1.1 Regression Analysis

In a first step, a regression analysis between the size (independent variable X) and the net return

(dependent variable Y) is conducted. The regression analysis provides insights as to whether a

linear dependency between the size of Swiss pension funds and the achieved returns can be

observed. The equation for the regression analysis is as follows:


whereby is the Y-intercept and is the slope of the line (or the change in net return for eve-

ry unit change in AUM). In a simple linear equation the effect of all factors, other than the X

variable (AUM), are assumed to be part of the random error term, labeled as . Hence, the ran-

dom error represents all the influences on Y that are not represented by the linear relationship

between Y and X (Newbold, Carlson & Thorne, 2013, pp. 418-422).

The null and alternative hypotheses have been defined accordingly:

: 0 t

: 0

6.1.2 Welch’s T-Test

The unequal variances t-test or Welch’s t-test is a two-sample location test used to test the hy-

pothesis that two populations have equal means. Welch’s t-test has been derived with the help

of Student’s t-test and is more reliable when the two samples have unequal variances and une-

qual sample sizes (Ruxton, 2005). Given evidence that smaller pension funds have a higher allo-

cation to real estate and therefore a higher exposure to assets not market-to-market, one can

argue that the return distribution will most likely have different variances. Similar to the Stu-

dent’s t-test, Welch’s t-test also assumes that the sample is normally distributed i.e the pension

fund returns are normally distributed.

The Welch T-Statistic is defined as

where , are the observed sample means and are the sample variances and and

are the sample sizes. In the Microsoft Excel calculations all of the components to the t-value

have been calculated separately in a first stage. In the second stage, the components were

plugged into the formula to solve the above equation.

Under the null hypothesis that both means are equal, the difference of the observed means is

distributed with the mean being zero. The hypothesis of equal means has to be rejected if the

observed difference is significantly different from zero, i.e. highly unlikely to be observed under

the null hypothesis. To test the observed t-statistic, the t-distribution with degrees of freedom

is used. The parameter v of the t-distribution is derived by the Welch–Satterthwaite equation

(Welch, 1947, pp. 28-35).


In the Microsoft Excel model, v has been calculated as follows:

Where and . The p-value for the two-tailed Student’s t-distribution is

then calculated with an Excel function that incorporates the degrees of freedom and the t-value

as calculated before.

The null and alternative hypotheses for this test are as follows

:

:

6.1.3 Wilcoxon-Mann-Whitney Test

Describing the data in section 5 has revealed that outliers can potentially distort the mean val-

ues. The Wilcoxon-Mann-Whitney test which is also known as the Wilcoxon rank sum test does

not assume a normal distribution and is not sensitive to outliers as this test relies on the ranked

order of the observations only and not on their absolute magnitude. The statistic tests the hy-

pothesis that two independent samples were drawn for the same population. With the null hy-

pothesis that the two samples were drawn from the same population tested against the alterna-

tive hypothesis that they were not. Under the assumption the distribution of the two samples do

not differ in their higher moments, a rejection of the null hypothesis implies that the two distri-

butions differ in central tendency. The Wilcoxon-Mann-Whitney test is therefore often referred

to as the nonparametric version of the parametric t-test (Wild, n.d., p. 1).

For the Wilcoxon-Mann-Whitney test the null and alternative hypotheses are the following

:

:

There are two basic representations of the test. The Mann-Whitney U-Test statistic (Mann &

Whitney, 1947, pp. 50-60) and the Wilcoxon rank sum statistic (Wilcoxon, 1945, pp. 80-83).

Both statistics are based on the assumption that the null hypothesis rank should be randomly

distributed over the combined sample. For the implementation in Excel the Wilcoxon rank sum

test was used.


The Wilcoxon test statistics is defined by

,

Where represents the Rank of the i-th observation in the combined sample with

observations. In the context of this work, , represents the rank sum of either all m smaller

or n larger pension funds in the combined sample of all pension funds. With the help of

Excel each pension fund return in a specific year is given a rank: the worst return has rank 1, the

second worst return has rank 2, and so on. For the test statistic the sum of the ranks for two

groups are calculated by simple addition.

Example:

Return: 4.60 4.64 4.65 4.67 4.85 4.90 5.03 5.30 Big/Small: Big Small Small Small Big Big Small Big Rank: 1 2 3 4 5 6 7 8

= 16

The logic behind the test is that if there is a systematic difference between big and small pension

fund returns, the more smaller or bigger returns ranks will belong to one group. For large sam-

ples, W is approximately normally distributed. In that case, the standardized value is

, ;

In the example , would be approximately distributed as ; .

Depending on the threshold used to group pension funds as big or small, the sample sizes and

of the two groups can be significantly different. In order to account for the different sample

sizes when approximating with the normal distribution function in Excel the following adjust-

ments have to be made. This will ensure the correct tail p-value is always obtained.

∗

∗


With ∗

it follows that:

∗ ∗ nm

For the normal approximation the mean of the two ∗and ∗ is used

,∗ ;

Since ∗ ∗ , the mean used in the normal approximation is the mean of the two

values of W. The absolute value of the z statistic calculated will be the same whichever value of ∗ and ∗ is used.

For the two-sided test the p-values are obtained by using Excel’s cumulative normal distribution

function. If the assumption of equal higher moments holds, the T-test and the Wilcoxon test

should show similar results. In the context of the research question, a resulting p-value of below

0.05 indicates the return distribution of the two populations is different.

6.2 Cost Analysis

The newly introduced rules on cost transparency have enabled a reliable analysis of the TER

data. For the years 2013 and 2014, 544 TER records have been provided and build the basis for

answering research question three and four. The total expense ratio gives information on the

total costs associated with managing and operating an investment fund. The costs consist pri-

marily of management fees and additional expenses such as trading fees, legal fees, auditor fees

and other operational expense (Investopedia, n.d.b).

The available data allows analyzing on the one hand whether the size of a pension fund explains

the cost. On the other hand, the impact of cost on net performance can be analyzed. In order to

test the former, a regression analysis between the total expense ratio (independent variable X)

and the net return (dependent variable Y) is conducted. For the latter, a regression analysis be-

tween the size of the pension fund (independent variable X) and the TER (dependent variable

Y) is conducted. The regression analysis will give information on whether a linear dependency

between the respective X and Y variables can be observed. The equations for the regression

analysis are as follows:


7 Combined Results and Findings

The first part of this section sets out to present the results of the various tests that have been

conducted.

7.1 Results Data Analysis

7.1.1-7.1.4 presents the results of the return analysis and 7.1.5-7.1.6 presents the results of the

cost analysis. In the second part, the findings are introduced.

7.1.1 Size and Performance Regression Analysis

Table 1: Regression analysis for net returns on size between 2004 and 2014

Coefficients Standard Error P‐value Observations R Square

Intercept 4.021945274 0.136942776 6.8808E‐166 2855 0.000727425

Beta 5.26511E‐05 3.65346E‐05 0.149657793

Note. The data are adapted from the model output of Microsoft Excel.

Figure 19: Regression analysis size and performance - residual plots

Note. Output of Microsoft Excel based on data from Complementa Investment Controlling AG.

y = 5E‐05x + 4.0439R² = 0.0007

‐40

‐30

‐20

‐10

0

10

20

30

0 5000 10000 15000 20000 25000 30000 35000 40000

Net Returns in %

Size in Million CHF



This table presents the results of the Welch’s t-test. The Welch’s t-test is a two-sample, two-tailed t-test with the assumption of unequal variances and unequal sample sizes.

Table 2: Results Welch's T-Test

BreakPoint13 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Cumulative Perfor‐mance since 2001

Cumulative Perfor‐mance last 10 Y

50 0.344 0.083 0.319 0.799 0.072 0.157 0.011 0.480 0.014 0.117 0.413 0.528 0.880 0.028 27.774% 12.546%

100 0.129 0.506 0.156 0.466 0.003 0.003 0.001 0.037 0.008 0.016 0.201 0.033 0.991 0.023 32.976% 16.832%

200 0.435 0.997 0.912 0.924 0.009 0.047 0.001 0.003 0.011 0.000 0.420 0.005 0.122 0.175 16.217% 17.795%

300 0.462 0.724 0.622 0.337 0.035 0.012 0.000 0.003 0.022 0.000 0.756 0.001 0.037 0.091 48.058% 15.639%

400 0.190 0.460 0.960 0.406 0.051 0.003 0.001 0.017 0.030 0.000 0.463 0.001 0.006 0.039 12.953% 7.112%

500 0.415 0.687 0.846 0.279 0.005 0.012 0.001 0.008 0.018 0.004 0.488 0.000 0.012 0.078 14.006% 8.164%

600 0.861 0.752 0.537 0.362 0.030 0.015 0.001 0.015 0.050 0.001 0.612 0.003 0.016 0.016 17.490% 3.769%

700 0.869 0.664 0.648 0.682 0.045 0.070 0.002 0.030 0.123 0.001 0.644 0.003 0.015 0.016 19.047% 8.732%

800 0.572 0.508 0.822 0.772 0.073 0.142 0.002 0.030 0.245 0.008 0.517 0.016 0.052 0.024 16.756% 11.776%

900 0.755 0.531 0.914 0.406 0.078 0.145 0.006 0.050 0.164 0.005 0.488 0.157 0.044 0.044 25.972% 17.499%

1000 0.732 0.926 0.931 0.423 0.083 0.134 0.007 0.049 0.136 0.046 0.397 0.059 0.285 0.101 50.430% 38.223%

1250 0.810 0.955 0.584 0.360 0.074 0.240 0.013 0.093 0.127 0.031 0.429 0.053 0.421 0.113 47.210% 30.208%

1500 0.845 0.933 0.584 0.425 0.300 0.287 0.016 0.072 0.333 0.062 0.306 0.204 0.346 0.078 54.279% 29.259%

2000 0.930 0.325 0.868 0.539 0.138 0.372 0.011 0.457 0.092 0.157 0.877 0.042 0.358 0.288 46.656% 40.156%

2500 0.799 0.285 0.316 0.909 0.147 0.385 0.012 0.726 0.091 0.262 0.921 0.012 0.137 0.685 29.443% 23.908%

3000 0.837 0.450 0.477 0.538 0.598 0.375 0.045 0.889 0.174 0.019 0.707 0.052 0.326 0.490 35.280% 12.713%

3500 0.634 0.547 0.264 0.477 0.776 0.874 0.112 0.974 0.258 0.089 0.732 0.076 0.256 0.847 48.571% 11.552%

4000 0.732 0.553 0.200 0.525 0.740 0.775 0.101 0.152 0.249 0.120 0.812 0.045 0.500 0.932 66.608% 24.930%

5000 0.429 0.372 0.387 0.726 0.455 0.148 0.150 0.216 0.293 0.316 0.225 0.268 0.571 0.997 73.463% 21.680%

Note. The data are adapted from the model output of Microsoft Excel. Input data was provided by Complementa Investment Controlling AG

13 The BreakPoint in Million CHF defines the border between the two subsamples that are compared against each other



This table presents the results of the Wilcoxon-Mann-Whitney test for the statistical difference in the return distribution between two subsamples.

Table 3: Results Wilcoxon-Mann-Whitney Test

BreakPoint14 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Cumulative Performance since 2001

Cumulative Performance last 10 Y

50 0.2450 0.0058 0.0709 0.4230 0.1470 0.3442 0.0175 0.6040 0.0117 0.3147 0.2742 0.0713 0.4718 0.0398 0.3709 0.0218

100 0.2422 0.5776 0.1733 0.2928 0.0111 0.0122 0.0021 0.1300 0.0078 0.0054 0.1471 0.0074 0.8411 0.0748 0.3941 0.0053

200 0.8608 0.9859 0.7953 0.3991 0.0392 0.0492 0.0010 0.0038 0.0438 0.0000 0.2761 0.0031 0.0872 0.1473 0.1169 0.0034

300 0.9172 0.6780 0.8748 0.0468 0.1031 0.0112 0.0000 0.0060 0.0939 0.0000 0.4496 0.0016 0.0235 0.1310 0.3155 0.0068

400 0.5005 0.4358 0.8232 0.0142 0.0625 0.0017 0.0001 0.0121 0.0891 0.0000 0.3793 0.0011 0.0029 0.0470 0.0637 0.0006

500 0.8829 0.7972 0.9224 0.0138 0.0057 0.0009 0.0002 0.0036 0.0267 0.0003 0.3627 0.0003 0.0087 0.1073 0.0713 0.0012

600 0.5987 0.8522 0.5783 0.0491 0.0561 0.0015 0.0002 0.0102 0.0769 0.0002 0.4601 0.0006 0.0153 0.0197 0.1269 0.0003

700 0.6022 0.7340 0.7435 0.1284 0.0779 0.0201 0.0006 0.0262 0.2393 0.0001 0.4385 0.0005 0.0096 0.0284 0.1352 0.0011

800 0.8966 0.5709 0.8732 0.1452 0.1746 0.0534 0.0010 0.0266 0.5667 0.0051 0.2696 0.0024 0.0412 0.0445 0.1066 0.0038

900 0.6820 0.6142 0.9753 0.0513 0.2012 0.0599 0.0043 0.0538 0.4001 0.0017 0.2672 0.0756 0.0302 0.0763 0.1774 0.0126

1000 0.6988 0.8582 0.9603 0.0631 0.2159 0.0557 0.0060 0.0529 0.3308 0.0140 0.1669 0.0174 0.2661 0.1810 0.3999 0.1133

1250 0.3168 0.8824 0.6631 0.0563 0.2122 0.1337 0.0135 0.0857 0.3208 0.0064 0.1807 0.0126 0.4499 0.2146 0.4155 0.0801

1500 0.3332 0.9617 0.6631 0.0863 0.7477 0.1759 0.0164 0.0617 0.5418 0.0210 0.1386 0.0745 0.3571 0.1770 0.5043 0.0756

2000 0.4894 0.4758 0.8597 0.1526 0.4023 0.2729 0.0102 0.3326 0.1449 0.0496 0.6027 0.0109 0.4839 0.3893 0.4520 0.2403

2500 0.6489 0.3814 0.4326 0.4682 0.3942 0.2901 0.0150 0.3961 0.0840 0.0410 0.6021 0.0020 0.1533 0.7628 0.3130 0.1113

3000 0.6027 0.4833 0.6433 0.2044 0.9027 0.2722 0.0680 0.5603 0.1094 0.0105 0.5630 0.0202 0.4184 0.6111 0.3371 0.0768

3500 0.8368 0.6139 0.6214 0.1566 0.8520 0.9871 0.1405 0.7239 0.2066 0.0563 0.5536 0.0242 0.3210 0.7941 0.4351 0.0899

4000 0.7069 0.6290 0.5541 0.1998 0.5737 0.8486 0.1111 0.5918 0.2033 0.0813 0.6161 0.0124 0.4634 0.8585 0.5615 0.2400

5000 0.8600 0.4251 0.8795 0.4042 0.5225 0.2663 0.1128 0.6881 0.8011 0.2327 0.2398 0.1048 0.5121 0.9161 0.6163 0.2133

Note. The data are adapted from the model output of Microsoft Excel. Input data was provided by Complementa Investment Controlling AG

14 The BreakPoint in Million CHF defines the border between the two subsamples that are compared against each other

Size and Perform

7.1.4 Outper

This table show

Table 4: Out- and

Note. The data ar

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42


7.1.5 Cost and Performance Regression Analysis

Table 5: Regression analysis for net returns on TER for 2013 and 2014


Intercept 6.752301026 0.171934535 1.6067E‐160 543 7.2979E‐05

Beta 0.061811205 0.311066892 0.842566509 Note. The data are adapted from the model output of Microsoft Excel.

Figure 20: Regression analysis Cost and Performance - residual plots


y = 0.0618x + 6.7523R² = 7E‐05

0

5

10

15

20

25

0 0.5 1 1.5 2 2.5

Net Perform

ance in

%

Total Expense Ration in %


7.1.6 Size and Cost Regression Analysis

Table 6: Regression analysis for TER on size for 2013 and 2014


Intercept 0.49868051 0.011807605 2.0901E‐173 543 0.006848

Beta ‐5.29404E‐06 2.74097E‐06 0.053949177 Note. The data are adapted from the model output of Microsoft Excel.

Figure 21: Regression analysis size and cost - residual plots


y = ‐5E‐06x + 0.4987R² = 0.0068

0

0.5

1

1.5

2

2.5

0 5000 10000 15000 20000 25000 30000 35000 40000

Total expense Ration in

%

Size in Million CHF


7.2 Findings

In the following part the results of the three statistical methods applied, Regression analysis,

Welch’s t-test and Wilcoxon-Mann-Whitney test, are presented.

7.2.1 Size and Performance Regression Analysis

The estimated coefficients for the regression of the performance and size are positive and in line

with the expectation of larger pension funds outperforming smaller pension funds. In order to

examine whether this effect is statistically significant, the R-square and p-values of the regres-

sion analysis have to be considered.

R-square or coefficient of determination provides a descriptive measure of the proportion of the

total variability that is explained by the regression model. In this case, the R-square of 0.07%

expresses the proportion of the performance which is explained by a variation in the AUM. The

p-value tests the null hypothesis that the coefficient is equal to zero. A p-value below 0.05

indicates a rejection of the null hypothesis.

Testing the relationship between the predictor variable AUM and the response variable net per-

formance with return data between 2004 and 2014, three statements can be made. Firstly, as

expected, there is a positive relationship between size and performance. Secondly, with a p-

value of 0.15, the null-hypothesis that there is no significant prediction of net returns by the size

of the pension fund, cannot be rejected. And lastly, the R-square indicates only 0.07% of the

returns of Swiss pension funds are explained by their size.

The scatter plot illustrates the variability of returns among small pension funds has been much

higher than for larger-sized plans. In conclusion, the conducted test suggests that the perfor-

mance is not directly explained by the size of the pension fund.


This test examined the arithmetic mean returns of the respective subsamples and reveals the

statistical difference in returns between small and large pension funds is significant. In this test

a significant statistical difference, which leads to the rejection of the null hypothesis is observed

when the p-value is smaller than the significance level of 0.05. The lower the p-value, the

more certainty can be attributed to the rejection of the null hypothesis.

In more detail, in the years between 2001 and 2004 no statistical difference between larger and

smaller pension funds can be identified irrespective of the breakpoint used. The calculated p-

values for these years are far from the targeted 0.05. With the exception of 2011, the years be-

tween 2005 and 2014 provided the basis to reject the null hypothesis with 85.7% of the p-values

using breakpoints between CHF 100m and 700m being below 0.05. Beyond a breakpoint size of


CHF 700m the difference in mean returns between larger and smaller plans is not as pronounced

and fades away with increasing breakpoint size. This implies the advantages of a certain size do

not matter much once a comfortable level of approximately CHF 700-800m in assets is reached.

Out of the 14 years analyzed, the performance of year 2011 was the flattest with a median return

of -0.14%. The lack of direction might have led return figures of larger and smaller plans being

more aligned to each other. Further, in seven out of the last ten years, no statistical difference

between pension funds with assets below CHF 50m and plans with assets above CHF 50m is

noticeable. That indicates the very small schemes proved to be very resilient relative to the ones

sized between CHF 50 and 100m.

As regards the difference of the cumulative performance between smaller and larger plans, the

result is different. Only using a breakpoint of CHF 600m leads to a rejection of the null hypoth-

esis at the 5% significance level for cumulative returns of the past ten years. Rather low p-

values of 0.071 to 0.087 can be observed for breakpoints between CHF 400 and 700m, too. In-

terestingly, the significance testing in 2007 points out that statistical differences in the means

between the two subsamples can be seen up to a breakpoint of CHF 3bn suggesting that in this

year especially the largest plans have delivered strong returns.

Focusing on the results for breakdowns between CHF 100m and CHF 700m, and excluding the

year 2011, reveals another insight. Out of 63 tests, 29 result in a p-value that is below 0.01. Put

differently, 46% of the tests reject the null hypothesis even at a 1% significance level. The high-

est conviction level of rejecting the null hypothesis is prevalent in the years 2007, 2010 and

2012.

The most consistent difference in the past ten years between subsample Small and subsample

Large could be observed at the breakpoint levels CHF 400m and CHF 500m. This finding is

based on simply adding up the p-values for every breakpoint level in the past ten years. The

calculated sum for breakdown level CHF 400m and CHF 500m are 0.611 and 0.626, respective-

ly, and reach values as high as 4.995 for a breakdown level of 3500.


For the non-parametric Wilcoxon-Mann-Whitney test that examines the return distribution also

a 5% significance level is applied. P-Values below 0.05 suggest a rejection of the null hypothe-

sis that the two subsamples small and large stem from the same distribution. As well as for the t-

test, 19 limits are defined that build the breakpoint between subsample small and subsample

large.


The observed pattern for this statistical test highly correlates with the t-test. Early years do not

reveal any significant difference in the respective return distribution and a breakpoint set at CHF

50m did not indicate a persistent difference in performance for the smallest analyzed subsample.

Similarly, beyond the breakpoint of CHF 800m, the conviction level of rejecting the null hy-

pothesis is considerably reduced with only three periods providing p-values below 0.05 as com-

pared to six for a breakpoint level of CHF 700m. Further, the test confirms that the years of

2007, 2010 and 2012 provided particular confidence that the two return distributions are statisti-

cally different. This finding is manifested in every p-value being below 0.01 for the breakpoints

CHF 100m up to CHF 800m. Similarly strong differences between smaller and bigger plans

could be observed in 2006.

As opposed to the t-test, the Wilcoxon-Mann-Whitney test starts to show statistical differences

in the various distributions already in the year 2004. The significance levels calculated for the

distributions of the cumulative performance figures are also in contradiction to the results

gained from the t-test. An analysis of the cumulative performance of the past ten years suggests

a strong rejection of the null hypothesis using breakdown levels between CHF 50m and CHF

900m. Even at a significance level of 0.01, the null hypothesis can be rejected for breakpoints

between CHF 100m and CHF 800m. The dispersion for the long-term performance figures is

expected to be substantially higher than the performance values within a particular year. The

level of distortion of the mean value, the basis for the t-test, is therefore also elevated suggesting

that an analysis of the cumulative values should be based on the Wilcoxon-Mann-Whitney test.

The highest level of conviction as measured by the sum of t-values of the last ten years are again

observed at a breakpoint of CHF 400m and CHF 500m. The resulting sum for the breakpoint

range from 0.516 for a breakpoint at CHF 500m up to 4.660 for a breakpoint set at CHF 3.5bn.

7.2.4 Outperformance of Larger Pension Funds over Smaller Pension Funds

Both the Welch’s t-test and the Wilcoxon-Mann-Whitney test provide information as to whether

the mean returns and the return distribution of the two subsamples, respectively, are statistically

different. They, however, do not indicate if the difference in the mean returns and the return

distribution, respectively, are positive or negative. In order to bring the findings of the conduct-

ed significance tests in context, an out- and underperformance table (see 7.1.4) has been built.

For each of the 19 breakpoints, the table shows the difference between the median returns of the

respective subsample Big and Small. The last two columns compare the cumulative returns be-

tween the subsamples for the last ten years on the one hand, and since 2001 on the other hand.

Further, the median returns of the overall sample are calculated each year as well as for the cu-

mulative periods.


When using CHF 400m for a division of small and large pension funds, the data reveals that

larger pension plans outperformed their smaller peers in 13 out of 14 years. The cumulative

outperformance over the last ten years reached considerable 6.14%. Similar outperformances

can be identified using breakpoints up to CHF 700m. For a breakpoint set at CHF 800m the

outperformance decreases to 4.03% before finding its minimum of 0.57% with a breakpoint of

CHF 2bn. Put differently, the outperformance of large over small is most pronounced when

using breakpoints between CHF 400m and 600m, and least pronounced when using breakpoints

around CHF 2bn. For the years 2002, 2003, 2005 and 2014 the returns of pension funds with

assets above CHF 2,5bn were even inferior to the returns of their smaller peers.

With the average pension fund declining 12.85% in 2008, the plans above CHF 1bn worth of

assets underperformed their smaller counterparts by approximately 1%. Out of the 14 analyzed

years, three periods (2001, 2002 and 2008) had observed negative median returns. In each of

these three periods the smallest subsample that consists of pension funds with assets of up to

CHF 50m outperformed their larger peers.

To conclude, the main findings are presented as follows. The Wilcoxon-Mann-Whitney test

suggests, with the exception of 2011, there is a high level of certainty that the distributions of

large and small pension funds between the years 2004 and 2014 are not equal, therefore reject-

ing the null hypothesis. The Welch’s T-test further confirms that the mean returns of smaller

and larger pension funds are not the same. Not only are the returns statistically different, but

also a persistent outperformance of larger over smaller schemes can be identified in the last ten

years. The significance of this difference is especially pronounced for breakpoints set at CHF

400m and CHF 500m and stays high for breakpoints up to CHF 700m. Beyond this level, the

positive contribution of returns generated by large plans diminishes. The year 2008 acted

against this trend and showed a statistically significant outperformance of smaller over larger

plans. Further, return figures in the years 2007, 2010 and 2012 indicated the strongest outper-

formance of large over small using both test methods.

7.2.5 Cost and Performance Regression Analysis

Testing the relationship between the predictor variable TER and the response variable net per-

formance, three statements can be made. Firstly, the Beta of 0.0618 reveals that there is a posi-

tive relationship between cost rate of a pension fund and its net performance. This is rather sur-

prising since higher costs would intuitively lead to a lower net performance. Secondly, with a p-

value of 0.84, the null hypothesis that there is no significant prediction of net returns by the size

of cost of the pension fund cannot be rejected. Finally, the R-square indicated that 0.00% of

the returns of Swiss pension funds are explained by its total expense ratio.


The scatter plot illustrates very few schemes have cost rates above 1%. The majority of pension

funds have a cost rate which lies somewhere between 0.2% and 0.6%. In conclusion, the con-

ducted test strongly suggests the performance is not directly explained by cost. Hence, the re-

search question: ‘Do pension funds with a higher cost base achieve a lower performance than

the rest?’ can be answered in the negative.

7.2.6 Size and Cost Regression Analysis

The available data further allows to test the relationship between the predictor variable AUM

and the response variable TER. If the assumption of economies of scale holds, an increase in

size of Swiss pension funds should lead to a lower cost rate as indicated by the TER. Three

statements can be made. Firstly, the negative Beta confirms, the costs are negatively related to

size. Secondly, with a p-value of 0.0539, the null hypothesis that there is no significant predic-

tion of TER by the size of the pension fund cannot be rejected. There is, however, weak evi-

dence that the null hypothesis does not hold. Finally, the R-square indicates 0.68% of the cost of

Swiss pension funds are explained by its size.

The conducted test suggests weak evidence that the TER is explained by the size of a pension

fund. In regards to the research question: ‘Are the advantages of the size of a pension fund re-

flected in the TER?’, no absolute answer applies. The scatter plot shows a very high dispersion

of TER among pension funds sized below CHF 1bn.


8 Discussion

Even though the simple linear regression suggests net performance of Swiss pension funds is

not directly explained by the size of a plan, there is strong evidence which indicates that returns

of large plans are statistically different from returns delivered by small plans. Both the regres-

sion analysis and the Welch’s t-test are sensitive to outliers, which is why the highest im-

portance shall be attributed to the Wilcoxon-Mann-Whitney test. Another disadvantage of the

conducted regression test is that it only looks at linear relationships between the dependent and

independent variables, assuming there is a linear relationship between them.

Many of the previous Swiss pension fund studies were based on the analysis of only a few

years’ return figures. The data set made available by Complementa Investment Controlling AG,

however, has facilitated the analysis of the performance of small and large pension funds for the

past 14 years. Hence, the findings provided information as to the behavior of Swiss Pension

Funds in both up- and down-trending markets. Analyzing not only the performance in particular

years, but also the cumulative performance, reveals that pension funds with CHF 700m or more

in assets consistently outperformed smaller ones during the past ten years, as characterized by

the Wilcoxon-Mann-Whitney test.

As stated earlier, differences in performance measurement methods might explain the lack of

consistency that would confirm the statistical difference in returns amongst small and large for

the period 2001 to 2004. Further, the considerable outperformance of the smallest subsample in

the years 2001, 2002 and 2008 could well be explained by their high allocation to direct, owner-

occupied property. By means of artificial revaluation of their property holdings as mentioned in

section 3.5, the smallest pension funds may have positively influenced their net returns. Before

2005, no provision for the valuation methods of property existed. Primarily large schemes with

considerable property holdings have adapted the Discounted Cash-Flow method prior to the

introduction of Swiss GAAP FER 26 in 2005. Nevertheless, a large number of schemes have

continued to value their property at acquisition cost with provisions for renovation and mainte-

nance. By doing so, returns over years could be smoothed. The deteriorating financial situation

after 2000 has, however, led to a liquidation of these reserves (Stuber & Wessa, 2005, p. 340).

The introduction of the Swiss GAAP FER 26 accounting rules in 2005 might therefore be the

principal reason for a missing statistical difference in returns of large and small pension funds

between the years 2001 and 2004.

In the years 2001, 2002 and 2008, when the median returns of Swiss pension funds were nega-

tive, equity markets have declined sharply and fixed income was the best performing asset class.

In 2008, a considerable difference in an international equity portfolio and the Swiss Perfor-


mance Index could be noticed. Whereas the SPI retreated 32.8%, an international portfolio fell

as much as 43.4% (BlackRock, 2016). The home bias amongst smaller pension funds might

therefore explain their outperformance during this year since no significant differences in the

allocation to fixed income between smaller and larger plans could be identified. The variation

might further be explained by the higher allocation of smaller pension funds to direct property.

Despite Swiss real estate trusts falling 22.7%15, direct property appreciated in value by 6.6%

during the course of 2008.

The second research question is principally based on the results gained from the Wilcoxon-

Mann-Whitney test and the Welch’s t-test. Both tests strongly indicate that the statistical differ-

ence in the returns between smaller and larger pension funds fades away if breakpoints above

CHF 700m are used. Having reached a comfortable size of around CHF 700m of assets, no con-

siderable upside in performance is expected. Furthermore, in the current low-interest rate envi-

ronment and rather sluggish equity markets, access to good hedge fund managers, private equity

investments or an international property portfolio can add the necessary alpha to the portfolio

allowing pension funds to meet the minimum interest requirements. For that, a dedicated in-

vestment team and a certain size are eminent.

As regards the relationship between the cost and net performance, the regression analysis (sub-

section 7.1.5) has revealed a positive relationship between the two variables. A possible expla-

nation might be that the higher TER is not only linked to a higher degree of external manage-

ment and active instruments, but also to a higher allocation to alternatives. Alternatives led by

private equity managed to positively influence pension funds’ performance in the past years and

the excess returns more than compensated for the higher cost rate. Overall, the findings of Zingg

(2009) are confirmed. Despite identifying a positive relationship between the two variables,

Zingg concludes that the effect of administration costs on performance is insignificant. As a

potential explanation he indicates that pension funds with high administration costs have proba-

bly more internal specialist know-how and a more sophisticated decision-making process. This,

in turn, lead to higher administration costs, but also higher performance (p. 69).

Looking ahead, the performance of very small pension funds in a bearish equity market is ex-

pected to be less resilient due to the reduction in allocation to direct property in recent years. As

illustrated in figure 9, the share of direct property of pension funds with assets below CHF

100m is, at 9.1%, even lower than their mid- and large-sized peers, at 10.3% and 9.2%, respec-

tively (Complementa, 2015, p. 42).

15 As per SWX SR Realestate TR Index (IAZI AG, 2010)


The analysis of the net returns, which forms the core part of the presented bachelor thesis, does

not account for a very important variable: the risk or volatility of the returns. Neither the ques-

tionnaire of the Swisscanto Risk Study nor the Complementa Risk Check-up incorporates this

variable. UBS AG, however, publishes on a monthly basis the Pension Fund Performance Index

that breaks down the standard deviation and the Sharpe ratio by pension fund size. The data is

based on Swiss pension funds, whose assets are held with UBS as their global custodian. The

standard deviation of returns is based on the variation of the last 36 monthly returns. The Sharpe

ratio is defined as the outperformance of the portfolio return over the risk-free rate in relation to

the standard deviation (UBS, 2015, p. 4).

Figure 22: March 2016 UBS Risk / Return index for the last 36 months

Note. Based on Schöttler, 2016, p. 216

As illustrated in figure 22, over the past three years pension funds above CHF 1bn of assets

achieved the highest annualized returns at the lowest standard deviation. The risk-adjusted re-

turn as measured by the Sharpe ratio is on average 0.93. Pension Funds with assets below CHF

300m have the lowest Sharpe ration with 0.81, followed by schemes sized between CHF 300m

and 1bn with 0.93. By far the strongest risk-adjusted performance provided the largest plans

with a Sharpe ratio of 1.17 (Schöttler, 2016, p. 2).

16 All numbers are in CHF, returns are annualized


9 Conclusion

The analysis of data from the annual Complementa Risk-Check up revealed in eight out of the

past ten years a statistically significant outperformance of larger over smaller schemes. Only the

years 2008 and 2011 have provided a different result. A potential explanation for the reversed

situation in 2008 is a stronger home bias and higher allocation to property of smaller schemes.

In 2011, a lack of direction in the market is seen to have avoided a confirmation of the statistical

difference in returns. The high confidence of the statistical difference in returns as shown by

both testing methods for the years 2007, 2010 and 2012 might be attributed also to the unfavor-

able effects of a higher home bias of smaller plans during these years. However, further research

needs to be conducted to support this thesis. The regression analysis between size and perfor-

mance failed to confirm a direct connection between two variables. Interestingly, the depiction

of median cumulative returns over time as shown by figure 18 has revealed that pension funds

sized between CHF 600m and CHF 2000m performed even better than the largest subsample.

Smaller pension funds have yet to adapt to the new market environment. The trend in the Swiss

pension fund industry suggests that roughly 4% of the plans in Switzerland are set to disappear

on an annual basis. Smaller plans will likely be the primary target of this consolidation trend. A

market environment with negative yielding government bonds, very low economic growth rates

and an abundance of geopolitical challenges requires a change in investment strategy and a

higher degree of diversification. The allocation to direct property, which was once the best

friend of smaller schemes, has decreased substantially.

As of the end 2014, the allocation to foreign investments of plans sized below CHF 100m is

approximately 16% lower than the allocation of plans with assets above CHF 800m

(Complementa, 2014, p. 42). According to the financial theory, the home bias leads to a lack of

international diversification and lower expected risk-return profiles, despite the presence of

transaction costs (PPCmetrics AG, 2015, p. 10).

To make up ground on the alternative space, smaller plans are confronted with a dilemma. If

they gain larger access to private equity, hedge funds or infrastructure investments, the better

risk/return profile usually comes at much higher costs than for their larger peers due to inferior

negotiating power. As the results of the regression analysis revealed, higher costs are not related

to a lower net performance and should therefore not be the main consideration. There is statisti-

cally weak evidence that a larger pension fund size explains variation in the total expense ratio

thereby approving the assertion that economies of scale can be realized.

The results further revealed that the statistical difference in returns between large and small

plans is highest if the two subgroups are split at a pension fund size of CHF 400-500m. The


majority of the test results have, however, revealed a statistical significance in the difference in

returns for a breakpoint set at CHF 700m. Thereafter, no comprehensive differentiation in the

net returns is observable. Put differently, the returns of a pension fund sized above CHF 700m

do not statistically differ from plans below CHF 700m. The findings do not provide clear evi-

dence as to what size is needed to stay competitive, but they suggest that plans with assets be-

low CHF 700m are expected to continue underperforming large plans, especially in up-trending

markets.

With around 1.5m insured persons, plans sized below CHF 300m are still strongly represented.

Public pressure and tightening in regulations might accelerate the consolidation trend. The re-

cent implementation of the Minder-Initiative has, for instance, led smaller pension funds to de-

crease their share of internally managed assets. The tedious voting procedure for Swiss equities

triggered a sale of these assets that potentially resulted in an increase of management costs. Fu-

ture regulation trends are probably more likely to affect smaller schemes, putting even more

pressure on them.

Based on the findings of the presented thesis, particularly Swiss pension funds with an asset

base of below CHF 500m are encouraged to become part of a community or collective institu-

tion. The few plans that are still able to keep up with the performance of the larger schemes over

the long run, represent an exception to the rule.

To potentially secure the viability of small pension funds in today’s market environment two

principal changes are suggested: regulation would need to use differentiated approaches for

different size groups and the smaller schemes need to follow the large ones in terms of exposure

to alternative investments and usage of indexed instruments.

It can be concluded that both the Wilcoxon-Mann-Whitney test as well as the Welch’s t-test

have confirmed that larger pension funds managed to outperform smaller ones over the past ten

years, with the exception of two years. The results proved to be statistically significant. The

costs of administration or management are believed to have a marginal effect on this result.

Future research would be required to find out why pension funds sized between CHF 600m and

2000m have performed even better than the largest subsample. Furthermore, the impact of a less

articulated home bias of larger pension funds should be investigated in detail. Ideally, an im-

plementation of the risk parameters is anticipated for future studies. This will help to better as-

sess risk-adjusted differences in performance between larger and smaller schemes.

With elevated real estate prices in Switzerland, negative yielding government bonds, a lack of

trust in the sustainability of the equity rally of the past years and falling commodity prices, the

pension funds with an experienced management team and access to alternative sources of in-


come will have an advantage in the new world of pension investments. With European property,

insurance linked securities, infrastructure, private equity, mortgages or loans emerging, the pen-

sion funds are prepared to start the next journey. A journey where the larger plans are set to

settle the race.


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Glossary

FER-26 The accounting rules for occupational pension schemes

Swiss GAAP FER 26 were introduced on January 1st, 2004.

Their application is mandatory for the financial year 2005

for the first time.

Total Expense Ratio The TER consists principally of the manager's annual

charge, but also includes the costs for other services paid

for by the fund, such as the fees paid to the trustee (or de-

positary), custodian, auditors and registrar (Financial

Times, n.d.).

REIT The REIT or Real Estate Investment Trust is a type of secu-

rity that invests in real estate through property or mortgages

and often trades on major exchanges like a stock. REITs

provide investors with an extremely liquid stake in real

estate (Investopedia, n.d. c).


Appendix A - Formula sheet

(Investopedia, n.d.b)

(UBS, 2015, p. 4)

# 1

(Investopedia, n.d. a)

bachelor's thesis_samuel_robert_walser_s12463873

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