The Effects of the Organizational Structure

on Asset Management

Massimo Massa * Lei Zhang **

Abstract:

We study how the strategies and performance of an asset management company are affected by its internal

organizational structure. Relying on Stein’s (2002) theory of organizations, we argue that a more hierarchical

structure reduces the incentives to collect “soft” information and to engage in proximity investment. This

should lower portfolio concentration, increase managerial herding and reduce performance. We use

information on the organizational structure of all the US mutual funds and insurance-managed funds

investing in US corporate bonds. We show that more hierarchical structures invest less in firms located close

to them and deliver lower performance. An additional layer in the hierarchical structure reduces the average

performance by 24 basis points per month. At the same time, more hierarchical structures tend to herd more

and to hold less concentrated portfolios. We also find that changes in fund structure quickly find their way

into the behavior of fund managers. Overall, the organizational structure affects performance slightly more

for mutual funds than insurance-managed funds, while it impacts proximity investment, herding and

portfolio concentration more for insurance-managed funds than mutual funds.

JEL classification: G23, G30, G32

Keywords: mutual funds, organization structure, performance, herding, proximity investment.

∗ Finance Department, INSEAD. Please address all correspondence to Massimo Massa, INSEAD, Boulevard de Constance, 77300 Fontainebleau France, Tel: +33160724481, Fax: +33160724045 Email: massimo.massa@insead.edu. We thank an INQUIRE EUROPE Grant to make this research possible.

1

The Effects of the Organizational Structure

on Asset Management

Abstract:

We study how the strategies and performance of an asset management company are affected by its internal

organizational structure. Relying on Stein’s (2002) theory of organizations, we argue that a more hierarchical

structure reduces the incentives to collect “soft” information and to engage in proximity investment. This

should lower portfolio concentration, increase managerial herding and reduce performance. We use

information on the organizational structure of all the US mutual funds and insurance-managed funds

investing in US corporate bonds. We show that more hierarchical structures invest less in firms located close

to them and deliver lower performance. An additional layer in the hierarchical structure reduces the average

performance by 24 basis points per month. At the same time, more hierarchical structures tend to herd more

and to hold less concentrated portfolios. We also find that changes in fund structure quickly find their way

into the behavior of fund managers. Overall, the organizational structure affects performance slightly more

for mutual funds than insurance-managed funds, while it impacts proximity investment, herding and

portfolio concentration more for insurance-managed funds than mutual funds.

JEL classification: G23, G30, G32

Keywords: mutual funds, organization structure, performance, herding, proximity investment.

2

1. Introduction

The mainstream literature on mutual funds has devoted scarce attention to the way the

management company is structured and how this structure affects investment strategies and

performance. Instead, the focus has been primarily on the “human drivers” of performance.

Managerial ability and information, compensation structure, trading costs and family affiliation

have been thoroughly investigated. Only recently, the focus has been extended to more

organizational issues such as whether a fund is managed by a team or a single manager (Bar et al.,

2005, Massa et al., 2005,) and whether management has been outsourced (Chen et al., 2005, Del

Guercio et al., 2007).

However, the broader question of how the internal organizational structure of the fund affects

its strategies and performance has not yet been fully explored. Let us consider a stylized case. A

fund is organized as a multi-layer hierarchical (“vertical”) structure. At the top there is the CEO

of the fund, below the Head of Fixed Income and below the Portfolio Manager. Do we expect this

structure to deliver better performance than a flatter one consisting of just one portfolio manager?

A more vertical structure lends itself to better risk management by helping reduce managerial

moral hazard and lowering the incentives to take (un)necessary risk. However, a more vertical

structure, by reducing the discretion of the portfolio manager, also lowers his incentives to collect

difficult-to-transfer information (“soft information”) — i.e., the one based on direct personal

interaction with the managers of the firm (Stein, 2002) — and to engage in proximity investment.

In the asset management industry performance is positively related to the collection of soft

information on the firms located close-by (Coval and Moskowitz, 1999 and 2001, Chen et al., 2004).

Therefore, more vertical structures, by reducing proximity investment and collection of

information, should deliver worse performance. Moreover, by forcing the “codification” of the

information passed-on to the superiors and by reducing the direct attribution of the fund

performance to the portfolio managers, a vertical structure would increase the incentives of the

manager to herd with the other fund managers. Both higher herding and more limited collection of

soft information should make more hierarchical funds have less concentrated portfolios.

These observations suggest the existence of a relation between organizational structure and

investment strategies and performance. In this paper, we study these factors by focusing on two

main players of the asset management industry: the mutual funds and the funds managed by

insurance companies (both life insurance and property and damage insurance). We use information

on portfolio composition, performance as well as organizational structure of all the US mutual

funds and insurance-managed funds investing in US corporate bonds (the “funds”). For each fund

we have information on its organizational structure. We know the identity, the functions and the

roles of all the members of the management team. Each member is characterized in terms of his

3

functional attributions (e.g., Chairman, CEO, CFO, Fixed Income Head, Portfolio Manager,

Trader, Analyst) as well as his areas of competence (e.g., market sector, credit sector, geographical

focus). This allows us to construct a measure of organizational structure: “hierarchy”. Hierarchy is

defined in terms of the number of layers, from the top down, in which the organization is

structured. For example, a structure presided by the CEO, with a fixed income head and two

portfolio managers has a hierarchy equal to 3. This also allows us to control for the potential

confounding effects due to heterogeneity in the degree of specialty, qualification, competence of

functional attribution existing within the structure.

We start by focusing on the determinants of the organizational structure. If the overall

objective of the financial family managing the fund (mutual fund family or insurance family) is not

limited to performance maximization, but is also geared to risk/managerial moral hazard control,

we can explain the choice of hierarchy on the basis of the characteristics of the financial family the

fund belongs to. This lets us identify the exogenous determinants of fund hierarchy.

We then relate hierarchy to proximity investment. We show that more hierarchical structures

tend to invest less in firms located close to them. An additional layer in the structure increases the

average holdings-weighted manager-bond distance by 6%. At the same time, hierarchy increases

the tendency to herd more and to hold less concentrated portfolios. An additional layer in

hierarchy increases herding by 16% and reduces portfolio concentration by 48%.

This has a direct impact on performance: more vertical structures display worse performance.

An additional layer in hierarchy reduces the average performance by 24 basis points per month.

Overall, the organizational structure affects performance slightly more for mutual funds than

insurance firms, while it impacts proximity investment, herding and portfolio concentration more

for insurance companies than mutual funds. These findings indicate that the organizational

structure is important in determining asset management strategies as well as performance. This is

broadly consistent with Stein’s (2002) theory of organizations.

Our findings contribute to several strands of literature. First, we relate to the recent literature

on the choice between team and sole management as well as the one between internal performance

generation and outsourcing. Massa et al. (2005) show that the choice between team and managers

is a mere marketing strategy, while Baer et al., (2005) argue that teams have a production

technology that differs from that of single managers. Chen et al. (2005) and Del Guercio et al.

(2007) investigate the effects of outsourcing on the incentives and performance of mutual funds,

showing that funds managed externally significantly under-perform those run internally. We

complement these findings by focusing on the overall organizational structure of the funds.

Moreover, our analysis is not limited to the mutual fund industry but also focuses on the insurance

industry. This is, to our knowledge, the first paper that also analyzes the strategies and

4

performance of the insurance companies and directly compares them to the mutual funds. Also, we

focus on a hitherto relatively unexplored area: the funds specialized in corporate bonds.

Second, we relate to the vast literature on mutual fund performance. Its goal has been to

determine whether it is possible to identify some measures that identify consistently

overperforming funds (e.g., Brown and Goetzmann, 1995, Elton, et al., 1996, Carhart, 1997). Our

paper contributes to this literature by presenting evidence for one of the drivers of fund

performance — the fund organizational structure. Again, our findings have bearings not only for the

mutual fund industry but for the overall asset management industry, including the insurance one.

Third, we relate to the literature on proximity investment (Coval and Moskowitz, 1999, 2001,

Chen et al., 2004). Our findings help to explain the positive relationship between proximity

investment and performance already documented in the mutual fund literature by showing that

one of the important factors that induce some funds to invest in close-by bonds is the

organizational structure of the fund.

Fourth, we relate to the literature on herding. Lakonishok et al. (1992), Grinblatt et al. (1995)

and Wermers (1999) document herding among pension fund and mutual fund managers. We

contribute to this literature in two ways. First, we provide evidence of herding for the insurance

companies and in the bond market. Second and more importantly, we show how the organizational

structure of the fund affects herding.

Finally, we relate to the literature on the economics of mutual fund families. Nanda et al.

(2004) document the positive spillover that having a ‘star’ fund provides to all the funds belonging

to the same family and the strategies played by the families to generate star funds. Khorana and

Servaes (1999) study the determinants of mutual fund stars, while Mamaysky and Spiegel (2001)

provide a first equilibrium model of the mutual fund industry, arguing that families generate funds

to allow investors to overcome their hedging needs. More recently, Guedj and Papastaikoudi

(2004) show that performance persists at the family level, especially large fund families, suggesting

that families purposefully allocate resources across funds in an unequal way, while Gaspar et al.

(2004) provide evidence of cross-fund subsidization at the family level. We contribute by explaining

the organizational structure of the fund with the characteristics of the family the fund belongs to.

Moreover, we extend the definition and analysis of “family coordination” to the families of

insurance-managed funds. This is, to our knowledge, the first paper to tackle this topic.

The rest of the paper is organized as follows. Section II describes our main hypotheses and the

empirical approach. Section III discusses the data and provides summary statistics. Section IV

presents the empirical results. A brief conclusion follows.

5

2. Main Hypotheses and Empirical Approach

To study the link between organizational structure and strategies, we loosely rely on Stein’s (2002)

model of organizations. The model posits that different structures perform differently in terms of

generating information about investment projects and allocating capital to these projects. Vertical

hierarchies make it more difficult to transfer information and tend to bureaucratically produce the

wrong kind of information. Therefore, hierarchical structures are better in the case of hard

information, while flat structures are better in the case of soft information. “A decentralized

approach — with small, single-manager firms — is most likely to be attractive when information

about projects is “soft” and cannot be credibly transmitted. In contrast, large hierarchies perform

better when information can be costlessly “hardened” and passed along inside the firm.” Evidence

of this has been found in the banking industry, with small banks better able to collect and act on

soft information than large ones (Berger et al., 2005).

In the case of the asset management industry, we observe different degrees of hierarchy across

management firms. This is even more true if we consider different classes of asset managers such as

mutual funds and insurance companies. The heterogeneity is greater for mutual funds than life

insurance and property and damage insurance companies.

We argue that the choice of the structure is affected by the overall objectives of the firm and it

is not limited to performance maximization — i.e., profits maximization in Stein’s model — but also

geared to risk/managerial moral hazard control. While these are outside of Stein’s model, they are

likely to shape the decision of the mutual fund family. A more hierarchical structure allows a

better control of managerial behavior. By making it necessary to “codify” the information passed-

on to the superior, a vertical structure may enhance the effectiveness of risk management.

These considerations suggest the existence of an optimal degree of hierarchy within each asset

management organization that is dictated by the trade-off between performance and risk

management. While we cannot observe what drives the specific optimum for each mutual fund or

insurance family, we can use family specific characteristics to explain (instrument) the level of

hierarchy of the funds belonging to the family and then relate it to observable fund policies. In

particular, we will focus on three policies: proximity investment, portfolio concentration and

herding and their relation to performance.

We start with proximity investment. The asset management industry relies on both hard and

soft information. The former is related to information about macro-variables as well as objective

fundamentals of the value of the firm. It is easy to store and transmit in impersonal ways. The

latter is more related to the less tangible information that the asset manager collects by interacting

with the managers of the firm (e.g., Coval and Moskowitz, 1999, 2001). Indeed, given that soft

information is based on direct personal interaction, it is mostly related to investment in close-by

6

companies. We therefore expect the structures more likely to engage in proximity investment to be

the less hierarchical ones. This defines our first restriction.

H1: Less hierarchical structures invest more in closely-located companies.

Stein (2002) posits that, in the case of soft information, the advantage of decentralization

relative to hierarchy is high-powered research incentives. Indeed, managers are more motivated to

generate information if they can directly act on it (and be remunerated accordingly). We also know

that the availability of more information induces the manager to tilt his portfolio towards the set

of assets over which he has more information. Evidence of this has been found by Kacperczyk et al

(2005) who document a positive relation between portfolio concentration and information for

mutual funds. We therefore argue that managers in less hierarchical structures tend to hold more

concentrated portfolios. This allows us to define our second restriction.

H2: Less hierarchical structures display a higher degree of portfolio concentration.

A more vertical structure makes it necessary to “codify” the information passed-on to the

superiors and reduces the direct attribution of performance to the portfolio manager. This reduces

the incentives to collect information, makes the managers more likely to adopt more

“conventional” investment behavior, and translates in higher herding with other investment

managers. This defines our third restriction.

H3: Less hierarchical structures herd less.

Finally, we consider the implication for performance. The existing literature (e.g., Chen et al.,

2004) suggests that funds investing in close firms — i.e., funds specialized in soft information — tend

to outperform their peers. A more hierarchical structure lowers the incentives to collect soft

information and to invest in geographically close assets — the ones more likely to provide superior

performance (Chen et al., 2004). Both effects concur to reduce performance. This defines our final

restriction.

H4: Less hierarchical structures outperform their peers.

It is worth noting that we will focus on bond funds. Recent findings have discovered evidence

of proximity investment also for this class of assets. Massa et al., (2007) document the existence of

local bias and proximity investment in bond investing by both mutual funds and insurance

companies. This happens on a scale similar if not greater than in the case of equity. In the case of

bonds, soft information is mostly about financial conditions and distress of the firm, as opposed to

movements in the government yield curve. We will see that this is confirmed in our findings as

most of the performance benefits of a flatter organizational structure — i.e., the one more related to

7

the collection of soft information and proximity investment — are concentrated in the investment in

low quality bonds.

3. Data, Construction of Main Variables and Summary Statistics

The main dataset is the Lipper’s eMAXX fixed income database. eMAXX contains details of fixed

income holdings for nearly 20,000 U.S. and European insurance companies-managed funds, U.S.,

Canadian and European mutual funds, and U.S. public pension funds. It provides information on

quarterly ownership of more than 40,000 fixed-income issuers with $5.4 trillion in total fixed

income par amount from the first quarter of 1998 to the second quarter of 2005. Moreover, it has

detailed information on the structure of the fund managing entity, including the functions, roles,

competences and areas of specialty of its members. The accuracy of the Lipper data (the part on

mutual funds) has been double checked using CRSP mutual funds and Morningstar. In our analysis,

we will mostly focus on the behavior of funds managed by either mutual fund families or insurance

companies. We will also consider a residual category that includes public pension funds and

variable annuities. In the case of mutual funds, we focus on the fund, aggregating the holdings of

its different classes (i.e., A, B, C, ..). In the case of insurance-managed funds we analyze them in

aggregate as well as we separately consider life insurance and property and damage insurance funds.

This allows us to control for the differences in constraints and goals of these different managing

entities.

We start by defining our proxy for the degree of hierarchy. Hierarchy is defined as the distinct

number of layers of the structure of the fund. We consider 6 layers. The first layer is made of the

Chairman, President and CEO; the second layer is made of the CFO and CIO; the third layer is

made of the Bond Department Head and the Fixed Income Head; the fourth layer is made of the

portfolio managers (Portfolio Manager General, Portfolio Manager Balanced, Portfolio Manager

Convertibles, Portfolio Manager Equity/Preferred Stocks, Portfolio Manager High Yield, Portfolio

Manager Investment Grade Corporate Bond, Portfolio Manager Private Placements, Portfolio

Manager Short-Term/ Money Market). The fifth layer is made of Traders and the sixth layer is

made of Research Analysts. It is possible that a person has multiple job functions. Therefore, if the

distinct number of hierarchies exceeds the number of employees, we use the number of employees

as vertical layer.

We also consider a set of control variables. We start with a variable that controls for the

degree of “employee specialty”. This proxies for the number of “competences” represented in the

organization. We want to separate the effect of hierarchy from the fact that funds are managed by

experts in many fields. Indeed, the degree of specialty of the asset managers may directly affect

performance. A higher degree of employee specialty affects managerial behavior. It increases the

ability to beat the peers and potentially discourages herding. Also, the presence of different areas

8

of specialty would make it less optimal to concentrate on few bonds and to invest just mostly in

closely located assets. At the same time, the presence of managers specialized in different areas

may also make it more difficult to reach consensus — e.g., a high-yield expert is likely to view the

markets from a perspective very different from that of a credit-derivative expert.

We define our variable of specialty in the following way. First, we consider the areas of

competence. They are: Market Sector, Credit Sector, Geographical Focus and Job Title. Market

Sector comprises the following areas of specialty: Asset Backed Securities, Corporate Bonds,

Government Bonds, Mortgage Backed Bonds, Local/regional Bonds, US firms investing non-

domestically, Combination of the above. Credit Sector comprises the following areas of specialty:

Any Corporate Sector, Any Municipal Sector, Any Specialty, Canadian Dollar, Euro, U.S. Dollar,

and Combination of Above. Geographical Focus comprises the following areas of specialty: Any

Country, Canada, Emerging Markets, Euroland, Any State/Territories, United States, Non-

domestic, and Combination of Above. Finally, Job Title is defined as above. That is, it comprises:

Chairman, President, Chief Executive Officer, Chief Financial Officer, Chief Investment Officer,

Bond Department Head, Fixed-Income Head, Portfolio Manager-General, Portfolio Manager-

Balanced, Portfolio Manager-Convertibles, Portfolio Manager-Equity/Preferred Stock, Portfolio

Manager-High Yield, Portfolio Manager-Investment Grade Corp Bonds, Portfolio Manager-Private

Placements, Portfolio Manager-Short-Term Bonds and Money Market. Then, we calculate the

number of total combinations of the above four sectors. Finally, we define the number of specialties

as the number of total combinations of the above four sectors. We define employee specialty as the

number of specialties divided by the number of employees. Therefore, our proxy of specialty

controls for heterogeneity in the degree of specialty, qualification, competence of functional

attribution existing within the structure.

Some descriptive statistics are reported in Table I, Panel A1. Hierarchy ranges between 1 and

41 and on average is higher for mutual funds than for insurance funds. Employee specialty ranges

between 0.5 and 7 and is higher for insurance funds. If we consider the overall sample, we see that

the number of single hierarchy funds is way greater than that of multiple hierarchy funds. That is,

in the sample there is a big imbalance in favor of one-layer funds. This may be a concern if it

implies a lack of cross-sectional variation within the hierarchy variable. To address this issue, we

resort to a matching sample technique, where, for each multi-hierarchy fund, we match it with

some other single-hierarchy fund similar in terms of fund type (if insurance or mutual fund) and

size, but different in terms of fund structure.

We construct two matching samples, one within fund family and one across fund families. Both

matching samples are more balanced in terms of fund hierarchy. Each one of the two matching

procedures has some advantages. By matching within fund family, we have better control for the

1 We drop outliers such as funds with more than 4 vertical layers which represent less than 0.05% of the sample.

9

unobserved common — presumably collected at the family level — information set of fund managers,

and for the potential coordinated behavior among funds of the same family (e.g., “cross-fund

subsidization”). Indeed, one other concern may be the fact that our analysis does not directly

control for some unobserved fund/family characteristics. However, matching within fund family

may not deliver the best match in terms of fund size, liquidity or other characteristics (Chen et.al.,

2004). Matching across fund families (within the same fund type), instead, allows us to better

control for size-related fund characteristics. We therefore jointly use these two matches as a way of

guaranteeing the robustness of our results.

The “matching within fund family” sample is constructed as follows. For each multi-hierarchy

fund, we first select another single hierarchy fund from the same fund family and most similar in

terms of fund size, and then we combine the matched single hierarchy funds with the original

multi-hierarchy funds. The “matching across fund family” sample is constructed similarly except

that the matched single-hierarchy fund is chosen from different fund families but belonging to the

same fund type (mutual funds, insurance companies, etc.).

We provide descriptive statistics of the different samples. Panel A1 is based on the full sample,

while Panel A2 and Panel A3 are for the matching sample within and across fund families

respectively. The number of observations (fund-quarter) is given in the parentheses. We report all

the institutions as well as separately report the results for funds owned by life insurance companies,

mutual funds, property insurance companies and other institutions (annuities and pension funds).

It is interesting to note that, mutual funds in general are more likely to have a higher degree of

hierarchy than funds owned by life insurance companies, but they have a lower degree of employee

specialty. Funds owned by property insurance companies have even less hierarchical structures

than life insurance companies. This may reflect the fact that property insurance companies are in

general smaller, even if it is also true that the size of funds owned by mutual funds is smaller than

that of life insurance companies.

Overall, the statistics suggest that hierarchy is not only related to the size of the fund, but it

also changes with the characteristics of the asset management firm. This will provide cross-

sectional variation for our tests and will help us explain the choice of hierarchy and employee

specialty on the basis of the characteristics of the financial family the fund belongs to. This lets us

identify the exogenous components of hierarchy and employee specialty.

Next we describe our main control variables. To control for portfolio turnover, we construct a

measure that captures how frequently a fund rotates its portfolio. Let us denote the set of bond

issues held by fund i by Q. The turnover ratio of fund i at quarter t is:

10

∑

∑

=

−

=−

+

+−= Q

1k

1t,i,kt,i,k

Q

1kt,k1t,i,kt,k,i

t,i

2VV

)R1(VVTurnover

，

where tkR , and tkiV ,, represent the return and the par amount of bond issue k held by investor i at

quarter t. This definition follows those commonly used to assess overall equity portfolio rotation

(Barber and Odean, 2000, Gaspar et al., 2005).

To control for the effect due to the existence of a team as opposed to a sole portfolio manager

(Ruenzi, 2005, Massa et al., 2005), we define a team dummy. This is a dummy variable equal to 1

if the fund has more than 1 portfolio managers and 0 otherwise.

The return of the fund is constructed as the cumulative monthly fund return during each

quarter. Fund monthly return refers to the raw return from its bond portfolio. The data on bond

returns are obtained from Bloomberg. For the case of the mutual funds, we also use the CRSP

return data. The results do not differ from the ones we report. The volatility of the fund returns is

defined as follows. For each fund at month t, we calculate return volatility as the standard

deviation of monthly returns in the prior 20 months.

We also include variables meant to capture fund size and family size. They are the logarithm of

the par-amount of bond holdings of each fund (fund family). To alleviate the concern that part of

the investment purpose of the insurance companies is asset-liability matching, we include a variable

representing fund portfolio maturity. It is the logarithm of the value-weighted average maturity of

all the bonds held by each fund. To control for location effects, we include a Financial Center

Dummy. It takes a value of 1 if the fund is located at either of the following cities: New York,

Chicago, Los Angeles, Boston and San Francisco.

Finally, we also control for the fraction invested by the fund in investment-grade bonds. This is

defined as the fraction invested in investment-grade bonds with S&P’s bond rating not below BBB.

The detailed definitions of these variables can also be found in the Appendix. We report the

summary statistics of the control variables in Panel B of Table I. In general, funds run by life

insurance companies have larger size, lower turnover ratio and higher portfolio maturity than those

of funds owned by mutual fund families.

With the measure of organizational structure and other control variables at hand, our next step

is to see the impact of fund hierarchy on portfolio strategies and fund performance. In particular,

we will look at fund proximity investing, portfolio concentration and herding. We briefly report the

summary statistics of those variables in Panel B of Table I. The detailed definitions of these

variables are given in the next section as well as in the Appendix.

11

4. Empirical Results

We start our analysis with the relation between organizational structure and proximity investing.

Then, we link it to portfolio concentration and herding. Finally, we focus on how the

organizational structure affects fund performance.

A. Organizational Structure and Proximity Investing

Restriction H1 posits that funds characterized by a less hierarchical structure invest more in

closely-located companies. For each fund, we define its distance from the firms whose bonds it

holds ( tiDis , ). It measures the distance between the fund and its bond portfolio. If we denote the

set of bond issues held by fund i by Q and tjiw ,, be the fraction invested in bond issue j, the fund-

bond distance is:

))]cos()cos()cos()sin()sin(arccos(*3963log[ ,,, jijijiQj

tjiti lonlonlatlatlatlatwDis −+= ∑∈

where ( ilat , ilon ), ( jlat , jlon ) are the (latitude, longitude) for fund i and bond issuer j in radian

degrees.2

We start with some univariate analysis. We report the results in Table II, Panel A. We break

down the sample into 4 different levels of hierarchy: from the lowest (1 layer) to the highest (4

layers). We then report the sample mean of fund portfolio distance at different levels of fund

hierarchy. The number of observations appears in parenthesis. We report the results for the

original sample and the two matching samples described earlier. We also provide univariate tests of

fund portfolio distance regarding to single vs. multi- fund hierarchy. Multi-hierarchy means the

number of fund hierarchies is greater than 1. The results show a monotonic increase in fund

portfolio distance as the number of layers increases. This holds regardless of the sample. A four-

layer fund tends to invest in bonds of firms on average 240 (175 and 210) km further away than a

one-layer fund in the case of the overall sample (sample based on matching within family and

sample based on matching across families).

We now move on to the multivariate analysis. We estimate:

titititi XHierarchyDis ,1,,, εδβα +×+×+= − , (1)

2 Information on bond issuer locations is from Compustat and SDC global new issue database. Since Lipper only provides county information of the managing firm, we use the location of the managing firm as the fund location. The county level coordinates (latitude, longitude) are from the Gazetteer Files of Census 2000.

12

where tiDis , represents the fund-bond distance of fund i at quarter t, t,iHierarchy is fund

hierarchy and 1, −tiX is the vector made of the other control variables defined above. We add the

fund type dummies across all specifications.

We report the results in Table II, Panel B for the entire sample. In Panels C and D, the sample

is based on the matching sample within and across fund families respectively. We include funds

owned by life insurance companies, mutual funds, property insurance companies and other

institutions (annuities and pension funds). Column (1) reports the results from an OLS regression

with standard errors clustered at fund level.

To address the possible endogeneity of fund structures, in Column (2), we implement an IV

regression, where family level structures are chosen as instruments. We instrument fund structure

variables using the following variables: family hierarchy (median of fund hierarchy within a

family), family employee specialty (median of employee specialty within a family), family team

(median of team dummy within a family), the interaction of family hierarchy with a financial

center dummy and the interaction of family employee specialty with a financial center dummy.

This latter variable resembles the instrument used by Chen et al. (2005) for the degree of

outsourcing. The intuition is the following. We know that the location in a financial center will

have a direct impact on proximity investment and performance, while the interaction with the

structure of the family needs not be so. At the same time, the interaction with the structure of the

family helps to explain the structure of the fund. Indeed, the incentive of the management family

to include many layers or many different areas of specialty depends on the availability of people.

Availability is higher in financial centers than in rural areas. Therefore, the desire of the family to

set up a specific structure is constrained by the location of the fund.

(Unreported) results show that the instruments help explain the organizational structure. Also,

they do not affect the dependent variable in the second stage through a channel different from the

impact on the instrumented variable. At the bottom of each IV specification we report the

Hansen’s J statistic (p-value). It always fails to reject the null, providing evidence for the quality of

our instruments.

As additional robustness check, In Column (3), we provide Fama-Macbeth (1973) estimates at

the fund level, while in Column (4), we provide the results of Fama-Macbeth estimates at the

family level. That is, we have first calculated family averages of all the variables. Column (5) and

(6) are estimated in the same way as in Column (3), but the sample is based on the funds owned

by life insurance companies and mutual fund families.

The results indicate that there is a strong positive relation between the average distance of the

firms in which the fund invests and fund hierarchy. This holds across the different specifications as

13

well as for different sub-samples. It also appears that the impact of the organizational structure on

insurance firms is higher than mutual funds.

The results are not only statistically significant but also economically relevant. An increase of

one layer in hierarchy raises the average distance (holding weighted distance) of the firms in which

the fund invests by 6% (Column (2) of Panel B). This supports our first hypothesis (H1), showing

that proximity investment is directly affected by the type of structure of the fund. If we consider

the other variables, we see that being managed by a team or by a sole manager does not affect the

decision to invest in closer firms. This suggests that our structure variables do not just proxy for

the mere fact that a fund is team-managed.

The other control variables are consistent with intuition. Being located in a financial center

increases the investment in closer firms. The same is true in the case the fund is more risk-

conscious and restricts itself to high-grade bonds. In the latter case, high risk prudence causes

funds to shorten their investment distances.

It is also interesting to note that funds that rotate their portfolio a lot (i.e., “high-turnover”

funds) are more likely to invest further away. This can be explained with the higher liquidity need

of these funds, not easy to meet in a more limited local area. There is scarce evidence in favor of an

impact of the degree of employee specialty.

As a further robustness check, for each fund, no matter whether it is single-hierarchy or multi-

hierarchy, we match it with some other fund similar in type, geographical location and size, but

different in terms of fund family and hierarchy. Then, we run regressions based on the differences

between the original fund and the matched fund. The idea is that funds located closely are more

likely to face a homogenous information set, and using differences, we can effectively cancel the

unobservable factors away. The matching procedure is as follows: for each fund-quarter we first

choose all the other funds of the same fund type but from different fund families and having

different fund hierarchy. Then we pick 20 funds located most closely and narrow them down to 10

according to similarity in fund size. From those 10 funds we select the final one with the smallest

geographical distances to the original fund. If there is more than one matched fund left meaning

that they are located at the same place, we choose the most similar one in terms of fund size. All

the variables except the financial center dummy, including both the dependent and independent

variables, are the differences between the original fund and its matched peer. The results are

reported in Panel E. We still find a strong positive relation between the difference in portfolio

distances and the difference in fund hierarchy.

Finally, it is worth mentioning that one possible criticism of our measure of distance is that it

measures the total distance that the fund in question resides from the bond issuers represented in

the portfolio. However, it may be that that it is not total distance that matters but how many

14

issuers are within a certain radius from this investor. We therefore also use an alternative approach

in which we define a radius (300 km) and close bonds are the ones of the firms located within the

radius and distant bonds the ones of the firms located outside the radius. We then assign a value of

0 to the close bonds and 1 to the distant ones. The (unreported) results based on this methodology

are consistent with the reported ones.

Overall our findings hold across different specifications as well as for different sub-samples.

They also hold in the specification based on “differenced” variables with another similar fund

located close-by. These findings show that proximity investment is directly affected by the type of

structure of the fund. We now move on to see if fund hierarchy affects other portfolio strategies

such as portfolio concentration and herding.

B. Organizational Structure and Portfolio Concentration

We now consider the impact of the organizational structure on portfolio concentration (H2). We

know that the availability of more information induces the manager to tilt the portfolio towards

the set of few assets over which he has more information (e.g., Kacperczyk et al., 2005). We

therefore expect that a more vertical hierarchy, by reducing the incentive to collect soft

information, reduces the degree of portfolio concentration. Restriction H2 posits that funds

characterized by a more hierarchical structure should display a lower degree of portfolio

concentration.

As in the previous analysis, we start with some univariate statistics. First, we define our

measure of portfolio concentration: t,iHerfin . It captures the degree of portfolio concentration in

bonds of fund i at quarter t. If we denote the set of bond issues held by fund i by Q and tjiw ,, be

the fraction invested in bond issue j, fund herfindahl is ∑∈

=Qj

2t,j,it,i wHerfin . We then break down

the sample into 4 different levels of hierarchy: from the lowest (1 layer) to the highest (4 layers).

In Table III, Panel A, we report the sample mean of fund portfolio concentration. We report

the results for the original sample and the two matching samples described earlier as well as tests

of fund portfolio differences in portfolio concentration between single and multi- fund hierarchy.

The results show a monotonic decrease in portfolio concentration as the number of layers increases.

This holds regardless of the sample. A four-layer fund tends to have a degree of concentration

equal to just 26% (41% and 37%) of the concentration of a one-layer fund in the case of the overall

sample (matching within family and matching across families).

We then employ a multivariate specification and estimate:

titititi XHierarchyHerfin ,1,,, εδβα +×+×+= − , (2)

15

where t,iHerfin is the degree of portfolio concentration in bonds of fund i at quarter t, tiHierachy ,

is fund hierarchy and 1t,iX − is the vector of control variables as defined above.

We report the results in Table III, Panel B for the entire sample. In Panels C and D, the

sample is based on the matching sample within and across fund families respectively and with the

same specifications as in Panel B. Column (1) reports the results from an OLS regression with

standard errors clustered at the fund level. In Column (2), we report the results of an IV

regression, where family level structures are chosen as instruments. 3 The standard errors are

clustered at the fund level. In Column (3), we provide Fama-Macbeth (1973) estimates at the fund

level, while in Column (4), we provide the results of Fama-Macbeth estimates at the family level.

Column (5) and (6) are estimated in the same way as in Column (3), but they are restricted to the

funds managed by life insurance companies and mutual fund families. As in the previous

specifications, in Panel E, the regressions are based on the difference in fund concentration and the

difference in fund hierarchy between the original fund and another similar fund located close-by.

The matching procedure is the same as described before.

The results show a strong and consistent negative correlation between hierarchy and portfolio

concentration. This holds across the different specifications (OLS, IV and Fama-MacBeth) as well

as for the different sub-samples. An increase of one layer in the hierarchy reduces concentration by

48% (Column (2) of Panel B). The analysis based on the matching sample delivers consistent

results. These findings support H2 and are consistent with the previous ones on proximity

investment. They confirm an overall picture in which a more hierarchical structure reduces soft

information collection. It is also interesting to note a strong negative correlation between the

degree of employee specialty and portfolio concentration. This also holds across the different

specifications (OLS, IV and Fama-MacBeth). An increase of one layer in the degree of specialty

reduces concentration by 6% (Column (2) of Panel B).

Overall our findings show that the structure of the fund affects the degree of portfolio

concentration. We now move on to herding.

C. Organizational Structure and Herding

We argued that a higher hierarchy would stifle fund managers’ incentive to collect soft information

and would induce them to invest more in line with their peers (H3). To address this issue, we

study the relation between managerial herding and fund structure.

3 We instrument the proxies for fund structure using the following variables: family hierarchy (median of fund hierarchy within a family), family employee specialty (median of employee specialty within a family), family team (median of team dummy within a family), the interaction of family hierarchy with financial center dummy and the interaction of family employee specialty with financial center dummy.

16

We define a variable (Herdingi,t) which proxies for the tendency of a fund to follow the trading

behavior of its peers or to go against it. We employ the same methodology used by Lakonishok,

Shleifer and Vishny (1992) and Grinblatt, Titman and Wermers (1995). A detailed description of

the construction of this variable is reported in the section Variables Definitions at the end of the

paper. In Panel B of Table I, we report some descriptive statistics of the measure of herding. The

mean in our sample is 1.3%. It is higher than the findings of 0.84% by Grinblatt, Titman and

Wermers (1995). This can be seen as evidence that bond funds herd more with each other than

equity funds.

We start with some univariate analysis. We break down the sample into 4 different levels of

hierarchy: from the lowest (1 layer) to the highest (4 layers). We then report the sample mean of

fund herding. We also provide univariate tests of fund herding regarding to single vs. multi- fund

hierarchy. The results are reported in Table IV, Panel A. The results show a monotonic increase in

fund herding propensity as the number of layers increases. This holds regardless of the sample. A

four-layer fund tends to herd on average 51% (33% and 53%) more than a one-layer fund in the

case of the overall sample (matching within family and matching across families).

We then move on to the multivariate analysis and estimate:

titititi XHierarchyHerding ,1,,, εδβα +×+×+= − , (3)

where Herdingi,t represents the herding measure as defined above of fund i at quarter t,

t,iHierarchy is fund hierarchy and 1, −tiX are other control variables. The other variables are

defined as before. We include fund type dummies in all the specifications.

We report the results in Table IV, Panel B for the entire sample. In Panels C and D, the

sample is based on the matching sample within and across fund families respectively and with the

same specifications as in Panel B. In Panel E, we report the results of the specification based on

differences. We include funds owned by life insurance companies, mutual funds, property insurance

companies and other institutions (annuities and pension funds). The layout of the columns is the

same as in the previous analysis.

The results show a positive relation between hierarchy and herding. This holds across the

different specifications as well as for different sub-samples. An increase of one layer in the

hierarchy raises herding by 16% (Column (2) of Panel B). This is in line with H3. Hierarchy, by

reducing the incentives to collect soft information, translates in more herding.

It is also interesting to note a negative relation between herding and the degree of employee

specialty. An increase of one layer in the degree of employee specialty reduces herding by 12%

(Column (2) of Panel B). In the case of employee specialty, the effect of reduction of risk taking is

17

more than offset by the availability of more areas of specialty that increases the ability to beat the

peers and therefore discourages herding. This would be consistent with the fund relying more on its

private information because it has more areas of specialty available.

D. Organizational Structure and Performance

We now focus on performance. Restriction H4 posits that funds characterized by a less hierarchical

structure deliver higher performance. To address this issue, we study the relation between

performance and fund structure.

We start by defining the measure of performance ( t,iAlpha ) of fund i in month t. It is

constructed in the following way. First, for each fund-month (i,t), we estimate the monthly factor

loadings by running the following regression:

,UMDuHMLhSMBs)rr(mCVc

RSrTSt)rr(barr

s,is1t,is1t,is1t,is,rfs,m1t,is1t,i

s1t,is1t,is,rfs,dj1t,i1t,is,rfs,i

ε++++−+

+++−+=−

−−−−−

−−−−(4)

where 1ts30t −≤<− and we require a minimum of 25 observations for each regression. The

dependent variable is the monthly return of fund i in month s less the risk-free rate sfr , . The

independent variables include 8 factors: the excess return of Dow Jones Corporate Bond Index over

the risk-free rate ( s,fs,dj rr − ), the yield difference between the twenty-year constant maturity

treasury bonds and the two-year constant maturity treasury bonds (gs20-gs2, term spread, TS)

(Colin-Dufresne et. al., 2001), the yield difference between Moody’s BAA corporate bond index and

the thirty year constant maturity treasury bonds (Baa-gs30, risk spread, RS)4, the yield difference

between the five year constant maturity treasury bonds and the average yield of two year and ten

year constant maturity treasury bonds (gs5-(gs2+gs10)/2, curvature spread, CV), the excess return

of market return over the risk-free rate ( sfsm rr ,, − ), the return difference between small and large

capitalization stocks (SMB), the return difference between high and low book-to-market stocks

(HML), the return difference between stocks with high and low past returns (UMD). The return

data on Dow Jones Corporate Bond Index is from Dow Jones’ website. The data on treasury bond

yields and Moody’s Baa corporate bond yields are from the FRED database at the Federal Reserve

Bank of Saint Louis. The data on risk-free rate, market return, SMB, HML and UMD are from

Kenneth French’s website.

Then, using the estimated loadings, we calculate fund alpha in month t by:

4 Moody's includes bonds with remaining maturities as close as possible to 30 years. Moody’s drops bonds if the remaining life falls below 20 years, if the bond is susceptible to redemption, or if the rating changes.

18

.UMDuHMLhSMBs)rr(mCVc

RSrTSt)rr(brr

t1t,it1t,it1t,it,rft,m1t,it1t,i

t1t,it1t,it,rft,dj1t,it,rft,it,i

−−−−−

−−−

−−−−−

−−−−−−=α (5)

We start with some univariate analysis. We break down the sample into 4 different levels of

hierarchy: from the lowest (1 layer) to the highest (4 layers). We then report the sample mean of

fund alpha. We also provide univariate tests of difference in performance between single and multi-

fund hierarchy. The results are reported in Table V, Panel A. They show a monotonic decrease in

fund performance as the number of layers increases. This holds regardless of the sample. A four-

layer fund has a performance 41 bp (39 bp and 38 bp) lower than a one-layer fund in the case of

the overall sample (matching within family and matching across families).

We them move on to the multivariate analysis and estimate:

titititi XHierarchyAlpha ,1,,, εδβα +×+×+= − , (6)

where t,ialpha is fund performance, t,iHierarchy is fund hierarchy and 1t,iX − is the vector of the

control variables defined as above. The results are reported in Table V. The analysis in Panel B is

based on the entire sample, while Panel C and Panel D are based on the matching sample within

and across fund families respectively with the same specifications as in Panel B. Panel E is based

on a “differenced” specification as defined in the above. The layout of the columns is the same as

in the previous specifications.

The results show a strong negative relation between performance and fund structure. Hierarchy

reduces performance. This holds across the different specifications (OLS, IV and Fama-MacBeth)

as well as for the case of the matched sample. One additional layer reduces fund alpha by 24 bp

(Column (2) of Panel B).

In the previous section, we found that hierarchy is negatively related to proximity investment.

We know that proximity investment is positively related to performance (Coval and Moskowitz,

1999 and 2001, Chen et al., 2004). Is it the case that the negative relationship between performance

and hierarchy is just due to the lower proximity investment? To address this issue, in Panel B

(column (7)-(10)) we include a dummy variable taking a value of 1 if the fund portfolio distance is

below the sample median in each quarter and 0 otherwise, and we interact it with fund hierarchy

and employee specialty. The interaction is always significantly negative for hierarchy. This suggests

that for funds investing in close firms the negative impact of hierarchy is stronger and is consistent

with wasting hard-to-transfer soft information.

If we consider the other variables, we notice the strong positive relation between performance

and the fraction invested in high-quality bonds. This suggests that constraints on the ability to

choose risky assets do not hamper performance. It is interesting to note that also the degree of

19

specialty is significant and reduces performance. However, in this latter case, if we break the

sample into mutual funds and insurance funds, we see that the result is mostly due to the

insurance funds, while the effect on mutual funds is scarce.

Overall, these findings provide evidence of the “dark side” of the fund organizational structure:

a negative relation between performance and hierarchy. We have argued that this is due to the fact

that hierarchy slows the flow of information within the organization. If this is the case, we expect

that most of the impact of hierarchy is concentrated in the investment in low quality bonds.

Indeed, these are the ones in which the lack of diffused and widespread information about the

company makes soft information more relevant. For instance, soft information of the company

which may result in a potential rating downgrade should matter more for low-quality bonds than

for high quality bonds.

We test this issue, by studying whether the impact of hierarchy is more pronounced for the

investment in high-quality bonds or for low-quality bonds. In particular, we indentify the “high

rated bonds” in the fund portfolio and compare the impact of fund hierarchy on the fund

performance of investing in high rated bonds with that of investing in low rated bonds. High rated

bonds refer to bonds with Moody’s credit rating above A3. Low rated bonds are bonds with

Moody’s credit rating from B3 to BBB1. For each fund, we estimate two portfolio alphas

separately. The first is based on the value-weighted return of investing in high rated bonds, while

the second is based on the return of investing in low rated bonds. Performance estimation is the

same as the one defined above.

We report the results in Table V, Panel F. From Column (1) to Column (4) we stack the high

and low rated alphas together and create a rating category dummy which equals 1 if it is a low

rated alpha and 0 otherwise. Our focus is the interaction term of fund hierarchy and the rating

category dummy. We also add the interaction of employee specialty and the rating category

dummy as additional controls. The standard errors are clustered at the fund level (Column (1)) as

well as at the family level (Column (2)). Column (3) and (4) are estimated in the same way as in

Column (1) but only based on funds owned by life insurance companies and mutual fund families.

In Column (5) and (6) we run Fama-Macbeth regressions separately for the low rated alpha and

the high rated alpha.

The results show that the strong negative correlation between performance and fund hierarchy

is mostly concentrated in the low-rated bonds. This confirms our intuition that hierarchy mostly

affects the flow of information mostly for low quality bonds. The interaction term of fund hierarchy

and the low rating dummy of bond quality is always significant and negative. One additional layer

of hierarchy reduces performance by 12 bp (Column (2) of Table V) more in the case of low quality

bonds than for high quality bonds.

20

E. Robustness Checks

We now consider a robustness check. All the previous specifications are based on the relationship

between hierarchy and fund behavior (portfolio concentration, herding, proximity investment) and

performance. We now test whether the same specifications hold on changes. That is, we investigate

whether changes in fund behavior and performance is related to a change in the degree of hierarchy

of the fund. We therefore focus on the subsample (fund-quarter) where the fund changes its

hierarchical structure from quarter t-1 to quartet t. We estimate the following regression:

t,i1t,it,it,it,i mentFundManageXHierarchymentFundManage ε∆δ∆βα∆ ++×+×+= − , (7)

where t,imentFundManage∆ represents the change in fund behavior (portfolio distance, herding,

and portfolio concentration) as well as the change of fund performance. We consider two measures

of performance: the raw return (cumulative, quarterly) of the fund and the change of fund alpha

(cumulative, quarterly) respectively from quarter t -1 to quarter t. t,iHierarchy∆ is the change of

fund hierarchy and tiX ,∆ are the changes of other control variables from quarter t-1 to quarter t. A

more detailed definition of these variables is reported in the Appendix. 1,imentFundManage − is the

lagged dependent variable at quarter t-1. The standard errors are clustered at fund level and we

always include time dummies and fund type (e.g., life insurance, property insurance, ..) dummies.

The results are reported in Table VI. They are consistent with the previous ones. An increase

in the degree of hierarchy reduces proximity investment and portfolio concentration, while it raises

fund herding. Overall, this implies a lower performance. These findings are not only statistically

significant, but also economically relevant. An additional layer lowers the average distance from

firms whose bonds it invests in by 25 km, reduces portfolio concentration by 5% and raises fund

herding by 7%. It reduces fund performance by 14 bp in the case of raw returns and 23 bp in the

case of alpha (quarterly). These results provide an additional robustness check. They also show

that changes in the fund structure quickly find their way into the behavior of the fund managers.

V. Conclusion

We study how the internal organizational structure affects fund’s strategies and performance. We

focus mostly on mutual funds and insurance-managed funds. We argue that a more hierarchical

structure reduces the incentives to collect “soft” information and proximity investment. This

reduces the incentive to concentrate the investment in few bonds and makes the manager more

likely to herd. The net result is lower performance.

21

We show that that funds with more hierarchical structures tend to invest less in firms located

close to the funds. This has a direct negative effect on fund performance: more vertical structures

are characterized by worse performance. Funds with a more vertical structure tend to herd more

with the other funds and to hold less concentrated portfolios. We also find that changes in the fund

structure quickly find their way into the behavior of the fund managers.

These findings are consistent with Stein’s (2002) theory of organizations. They also indicate

that the organizational structure is an important determinant of fund strategies and performance.

22

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24

Variable Definitions Fund Hierarchy For each fund we first define fund vertical layer according to the employee’s job title in the following way:

Vertical Layer Job Name Job Code 1 Chairman CHR 1 President PRE 1 Chief Executive Officer CEO 2 Chief Financial Officer CFO 2 Chief Investment Officer CIO 3 Bond Department Head BDH 3 Fixed-Income Head HFI 4 Portfolio Manger-General PMG 4 Portfolio Manger-Balanced PMB 4 Portfolio Manger-Convertibles PMV 4 Portfolio Manger-Eq/Pref Stock PME 4 Portfolio Manger-High Yield PMH 4 Portfolio Manger-Inv Grade Corp Bd PMJ 4 Portfolio Manger-Pvt Placements PMP 4 Portfolio Manger-Short-Term/MM PMT 5 Trader-General TBG 6 Credit/Research Analyst-General RAG

Then fund hierarchy is counted as the distinct number of vertical layers. It is possible that a person can be entitled with multiple job functions, so in the cases where the distinct number of vertical layers exceeds the number of employees, we use the number of employees as the fund hierarchy. Employee Specialty For each fund we first determine the number of specialties according to the employee’s area of focus. We mainly look at the following sectors:

1: Market Sector 2: Credit Sector 3: Geographical Focus 4: Job Title

Asset Backed Any Corporate Sector Any Country Chairman Corporate Any Municipal Sector Canada President Government Any Specialty Emerging Markets Chief Executive Officer Mortgage backed Canadian Dollar Euroland Chief Financial Officer Local/Regional Euro Any State/Terr. Chief Investment Officer US firms investing non-domestically

U.S. Dollar United States Bond Department Head

Combination of Above Combination of Above Non-domestic Fixed-Income Head Combination of Above Portfolio Manger-General Portfolio Manger-Balanced Portfolio Manger-Convertibles Portfolio Manger-Eq/Pref Stock Portfolio Manger-High Yield Portfolio Manger-Inv Grade Corp Bd Portfolio Manger-Pvt Placements Portfolio Manger-Short-Term/MM

The number of specialties is counted as the number of total combinations of the above four sectors. Then we define employee specialty as the number of specialties divided by the number of employees. Team Dummy We define a team dummy equals 1 if the fund has more than 1 portfolio managers and 0 otherwise.

25

Fund Portfolio Distance Fund portfolio distance measures the distance between the fund and its bond portfolios. If we denote the set of bond

issues held by fund i by Q and tjiw ,, be the fraction of fund i invested in bond issue j, the fund portfolio distance

is defined as:

))]cos()cos()cos()sin()sin(arccos(*3963log[ ,,, jijijiQj

tjiti lonlonlatlatlatlatwDis −+= ∑∈

,

where ( ilat , ilon ), ( jlat , jlon ) are the (latitude, longitude) for fund i and bond issue j in radian degrees.

Information on locations of bond issuers is obtained from Compustat and SDC global new issue database. Since

Lipper only provides county information of the managing firm, we utilize location of the managing firm as the

location of the fund. The county level coordinates (latitude, longitude) are obtained from the Gazetteer Files of

Census 2000.

Fund Portfolio Concentration Fund portfolio concentration represents the fund’s concentration ratio (herfindal) of its bond portfolio. If we denote

the set of bond issues held by fund i by Q and tjiw ,, be the fraction invested in bond issue j, fund portfolio

concentration is defined as:

∑∈

=Qj

tjiti wHerfin 2,,, .

Fund Herding Fund Herding represents the tendency of a fund to “follow the crowd or to go against it”. We follow the same

methodology used by Lakonishok, Shleifer and Vishny (1992) and Grinblatt, Titman and Wermers (1995). The first

step is to define a measure of investor herding at the bond level. Let tkB , ( tkS , ) be the number of funds buying

(selling) bond issue k at quarter t, then the herding measure (UHM) is expressed as:

|][||][| ,,,,, tktktktktk pEpEpEpUHM −−−= ,

where )/( ,,,, tktktktk SBBp += is the proportion of funds trading issue-quarter k, t which are buyers. We use the

proportion of all trades by funds that are purchases during quarter t to proxy for ][ ,tkpE . The first part represents

the “extra” number of funds trading a bond issue during a given quarter as the proportion of the total number of

funds buying that issue-quarter minus the expected proportion of buyers. The second term is an adjustment factor

allowing for random variation around the expected proportion of buyers under the null hypothesis of cross-sectional

independence among fund trades. The expectation in the second term is calculated by assuming that tkB , follows

a binomial distribution with parameter ( tktk SB ,, + ) and ][ ,tkpE .

The second step is to define the signed herding measure (SHM) which indicates the tendency of whether fund i is

following the crowd or going against it in trading bond k. This is calculated as:

][ ,,,,,,,, tktkitktkitki UHMIEUHMISHM ×−×= ,

where tkiI ,, is an indicator variable:

26

=0 if |][||][| ,,,, tktktktk pEpEpEp −<− ; tkiI ,, =1 if |][|][ ,,,, tktktktk pEpEpEp −>− and fund i is a buyer

of bond k, or if |][|])[( ,,,, tktktktk pEpEpEp −>−− and fund i is a seller of bond k; tkiI ,, =-1 if

|][|][ ,,,, tktktktk pEpEpEp −>− and fund i is a seller of bond k, or if |][|])[( ,,,, tktktktk pEpEpEp −>−−

and fund i is a buyer of bond k. Additionally, we impose the restriction that 0,, =tkiSHM is there are fewer than

5 funds traded bond k during quarter t. Under the assumption that the number of buyers of bond k is binomially

distributed, the expectation term ][ UHMIE × can be calculated by the following formula:

∑∑−>−−−>−

−−−=×|][|)(:

,|][|:

,,,,,

)Pr()()12()Pr()()12(][tktktktk ppEppp

tkppEppp

tk ppUHMpppUHMpUHMIE ,

where Pr(p) is the probability of ( tktk SB ,, + )p occurrences assuming a binomial distribution with parameter

( tktk SB ,, + ) and ][ ,tkpE .

Finally, if we denote the set of bond issues held by fund i by Q and the fraction invested in bond k by tkiw ,, , the

herding measure of fund i at quarter t is:

∑∈

−−=Qk

tkitkitkiti SHMwwHerding ,,1,,,,, )( .

Fund Return: Fund monthly return refers to the raw investment return from its bond portfolio. The data on bond returns are obtained from Bloomberg. Fund Quarterly return refers to cumulative monthly returns in each quarter. Fund Performance For each fund-month (i,t), we first estimate the monthly factor loadings by running the following regression:

,)()( ,1,1,1,,,1,1,1,1,,,1,1,

,,

sistististisrfsmtistististisrfsdjtiti

srfsi

UMDuHMLhSMBsrrmCVcRSrTStrrbarr

ε++++−++++−+

=−

−−−−−−−−−

where 130 −≤<− tst . We require a minimum of 25 observations for each regression. The dependent variable is

the monthly return of fund i in month s less the risk-free rate sfr , . The independent variables include 8 factors: the

excess return of Dow Jones Corporate Bond Index over the risk-free rate ( sfsdj rr ,, − ), the yield difference between

the twenty-year constant maturity treasury bonds and the two-year constant maturity treasury bonds (gs20-gs2,

term spread, TS), the yield difference between Moody’s BAA corporate bond index and the thirty year constant

maturity treasury bonds (Baa-gs30, risk spread, RS), the yield difference between the five year constant maturity

treasury bonds and the average yield of two year and ten year constant maturity treasury bonds (gs5-(gs2+gs10)/2,

curvature spread, CV), the excess return of market return over the risk-free rate ( sfsm rr ,, − ), the return difference

between small and large capitalization stocks (SMB), the return difference between high and low book-to-market

stocks (HML), the return difference between stocks with high and low past returns (UMD). The return data on Dow

Jones Corporate Bond Index are from the Dow Jones’ website. The data on Treasury Bond yields and Moody’s Baa

corporate bond yields are from the FRED database at the Federal Reserve Bank of Saint Louis. The data on

risk-free rate, market return, SMB, HML and UMD are obtained from Kenneth French’s website. Then fund alpha

at month t is estimated by the following equation:

27

.ˆˆˆ)(ˆˆˆˆ)(ˆ1,1,1,,,1,1,1,1,,,1,

,,,

ttittittitrftmtittittittitrftdjti

trftiti

UMDuHMLhSMBsrrmCVcRSrTStrrb

rr

−−−−−−−− −−−−−−−−−−

−−=α

Fund Return Volatility: For each fund at month t, we calculate return volatility as the standard deviation of monthly returns during the past 12 months. Fund Portfolio Maturity: logarithm of the value-weighted average maturity of all the bonds held by each fund. Fund Size: logarithm of the par-amount of bond holdings held by each fund Family Size: logarithm of the par-amount of bond holdings held by each fund family (managing firm) Number of funds: the number of funds in each fund family Fraction in Investment-grade bonds: the fraction of bond portfolio invested in investment-grade bonds with S&P’s bond rating not lower than BBB. Financial Center Dummy: dummy variable taking a value of 1 if the fund is located at either of the following cities: New York, Chicago, Los Angeles, Boston and San Fransciso.

28

Table I Summary Statistics

This table presents summary statistics and univariate tests of the main variables used in the subsequent analysis.

Our primary database is Lipper’s eMAXX fixed income database. It contains information on quarterly bond

holdings of major U.S. insurance companies (life and property), mutual funds, annuities and pension funds from the

first quarter of 1998 to the second quarter of 2005. It also provides information on the fund employee’s job title,

market sector, credit sector and geographical focus which enables us to characterize the organizational structures.

Panel A: Summary Statistics of Fund Structure

Panel A reports the sample mean of fund hierarchy and employee specialty characterizing fund structures. The

detailed definitions can be found in the appendix. We separately report the results for mutual funds as well as funds

managed by life insurance companies, property insurance companies and other institutions (annuities and pension

funds). We construct two matching samples, one within fund family and one across fund families. The matching

procedure is performed as follows. The “matching within fund family” sample is constructed as follows. For each

multi-hierarchy fund, we first select another single hierarchy fund from the same fund family and most similar in

terms of fund size, then combine the matched single hierarchy funds with the original multi-hierarchy funds. The

“matching across fund families” sample is constructed similarly except that the matched single-hierarchy fund is

chosen from different fund families but belonging to the same fund type (mutual funds, insurance companies,

pension funds etc.). Panel A1 is based on the full sample, while Panel A2 and Panel A3 are for the matching sample

within and across fund families respectively. The number of observations (fund-quarter) is given in the parentheses.

Panel A1: Full Sample

All

Institutions Life

Insurance Mutual Fund

Property Insurance

Others

Fund Hierarchy 1.1139 1.1381 1.1943 1.0558 1.0939 (83998) (21105) (19934) (33030) (9929) Employee Specialty 1.5505 1.6232 1.5481 1.5709 1.3330 (83998) (21105) (19934) (33030) (9929)

Panel A2: Matching within Fund Family

All

Institutions Life

Insurance Mutual Fund

Property Insurance

Others

Fund Hierarchy 1.6260 1.7416 1.6141 1.5296 1.5636 (11911) (3507) (4328) (2930) (1146) Employee Specialty 1.2056 1.1238 1.2973 1.1614 1.2226 (11911) (3507) (4328) (2930) (1146)

Panel A3: Matching across Fund Family

All

Institutions Life

Insurance Mutual Fund

Property Insurance

Others

Fund Hierarchy 1.5937 1.6282 1.6411 1.4693 1.6316 (16440) (4971) (5855) (4072) (1542) Employee Specialty 1.4740 1.4854 1.5403 1.3937 1.3977 (16440) (4971) (5855) (4072) (1542)

29

Panel B: Summary Statistics of Fund Characteristics In Panel B we report the sample mean of fund characteristics with the number of observations (fund-quarter) given

in parenthesis. We separately report our results for mutual funds as well as funds managed by life insurance

companies, property insurance companies and other institutions (annuities and pension funds). The detailed

definition of each variable can be found in the Appendix.

All

Institutions Life

Insurance Mutual Fund

Property Insurance

Others

Fund Size 10.4484 11.1481 10.8299 9.5883 10.9423 (83998) (21105) (19934) (33030) (9929) Family Size 13.7109 14.1441 13.8344 13.0935 14.5010 (83998) (21105) (19934) (33030) (9929) Log(Number of Funds) 2.3669 2.2896 2.4010 2.3027 2.6717 (83998) (21105) (19934) (33030) (9929) Fund Turnover 0.2694 0.2082 0.3775 0.2320 0.3132 (83998) (21105) (19934) (33030) (9929) Fund Portfolio Maturity 1.7495 1.8738 1.7734 1.6507 1.7500 (83998) (21105) (19934) (33030) (9929) Fund Return Volatility 0.0130 0.0131 0.0137 0.0121 0.0147 (83998) (21105) (19934) (33030) (9929) Fund Return (Quarterly) 0.0171 0.0178 0.0153 0.0172 0.0183 (83998) (21105) (19934) (33030) (9929) Fraction: Investment-grade Bond 0.7848 0.8333 0.6221 0.8829 0.6806 (83998) (21105) (19934) (33030) (9929) Financial Center Dummy 0.3044 0.2307 0.3852 0.2752 0.3970 (83998) (21105) (19934) (33030) (9929) Fund Portfolio Distance 6.6528 6.6213 6.7820 6.5540 6.7870 (81342) (21245) (18876) (31333) (9888) Fund Portfolio Concentration 0.0680 0.0496 0.0573 0.0925 0.0469 (83998) (21105) (19934) (33030) (9929) Fund Herding 0.0129 0.0101 0.0137 0.0147 0.0126 (68606) (18575) (18086) (22769) (9176) Fund Performance (Monthly) -0.0001 0.0009 -0.0027 0.0009 -0.0033 (160670) (45911) (30650) (71315) (12794)

30

Table II Fund Hierarchy and Portfolio Distance

This table relates fund hierarchy to portfolio distance. Panel A summarizes the sample mean of fund portfolio

distance at different levels of fund hierarchy. We also provide univariate tests of fund portfolio distance regarding

to single vs. multi- fund hierarchy. Multi-hierarchy means the number of fund hierachies to be greater than 1. We

consider the overall sample as well as the “matching within fund family” and the “matching across fund families”.

We report the results for the full sample and the two matching samples separately. Both two tailed T-test and

Wilconxon rank-sum test are performed to test the differences. The number of observations (fund-quarter) is given

in the parentheses. In Panels B-E, we report the results of the multivariate analysis. We estimate:

titititi XHierarchyDis ,1,,, εδβα +×+×+= − ,

where tiDis , represents fund portfolio distance of fund i at quarter t, tiHierachy , is fund hierarchy and 1, −tiX

are other control variables. The definitions are detailed in the appendix.

The analysis in Panel B is based on the full sample. We add the fund type dummies across all specifications.

Column (1) is based on OLS regressions with standard errors clustering at fund level. In Column (2), we report the

results of an IV regression, where family level structures are chosen as instruments. Specifically, we instrument fund

structure variables using the following variables: family hierarchy (median of fund hierarchy within a family),

family emplolyee specialty (median of employee specialty within a family), family team (median of team dummy

within a family), the interaction of family hierarchy with financial center dummy and the interaction of family

employee specialty with financial center dummy. Hansen’s J statistic (p-value) is reported to examine the quality of

instruments. The standard errors are clustered at fund level. Column (3) provides Fama-Macbeth (1973) estimates

at the fund level, while Column (4) provides the results of a Fama-Macbeth estimates at the family level. Column

(5) and (6) are estimated in the same way as in Column (3) but only based on funds owned by life insurance

companies and mutual fund families. Panel C and Panel D are based on the matching sample within and across fund

families respectively with the same specifications as in PanelB.

Panel E uses a “differenced” variable approach, where for each fund we match it with some other fund similar

in fund type, geographical location and size, but different in terms of fund family and fund hierarchy. The matching

procedure is as follows: for each fund-quarter we first choose all the other funds of the same fund type but from

different fund families and having different fund hierarchy. Then we pick 20 funds located most closely and narrow

them down to 10 according to similarity in fund size. From those 10 funds we select the final one with the smallest

geographical distances to the original fund. If there is more than one matched fund left meaning that they are

located at the same place, we choose the most similar one in terms of fund size. All the variables except the financial

center dummy, including both the dependent and independent variables, are the differences between the original

fund and its matched peer.

For the sake of brevity we only report the coefficients of fund hierarchy from Panel C to Panel E. ***, ** and

* represent significance levels at 1%, 5% and 10% respectively with t-statistics given in parentheses.

31

Panel A: Univariate Results

Fund Portfolio Distance by Fund Hierarchy Full Sample Matching Within

Family Matching Across

Family 1 6.6451 6.6916 6.6709 (73278) (5837) (8465) 2 6.7182 6.7267 6.7224 (6616) (4880) (6682) 3 6.7310 6.7286 6.7449 (1242) (1005) (1267) 4 6.8235 6.8191 6.8235

(206) (189) (206) T-test: Multiple vs. Single Hierarchy 12.71*** 4.29*** 7.49*** Wilconxon Test: Multiple vs. Single Hierachy 11.55*** 3.00*** 6.34***

Panel B: Full Sample All Institutions Life Mutual OLS IV FM FM Family FM FM (1) (2) (3) (4) (5) (6) Fund Hierarchy 0.0384*** 0.0547*** 0.0381*** 0.0339*** 0.0610*** 0.0295*** (3.79) (3.97) (6.69) (5.06) (6.38) (3.73) Control Variables Employee Specialty 0.0067 0.0097 0.0065*** 0.0073*** -0.0056*** 0.0038 (1.09) (1.05) (3.90) (2.79) (-2.69) (1.16) Team Dummy -0.0132 -0.0025 -0.0134* 0.0265** -0.0465*** 0.0164 (-0.89) (-0.09) (-1.76) (2.03) (-3.08) (1.41) Fund Size 0.0272*** 0.0273*** 0.0261*** 0.0366*** 0.0207*** 0.0072*** (7.68) (7.70) (19.26) (10.86) (13.29) (4.11) Family Size -0.0076 -0.0072 -0.0066** -0.0069* -0.0064** 0.0041* (-1.60) (-1.51) (-2.31) (-1.89) (-2.07) (1.77) Portfolio Maturity 0.0067 0.0064 -0.0033 -0.0041 -0.0331*** -0.0342** (0.82) (0.78) (-0.52) (-0.30) (-3.12) (-2.13) Log(Number of Funds) -0.0083 -0.0082 -0.0102** 0.0080 -0.0333*** 0.0066 (-1.03) (-0.98) (-2.08) (1.39) (-5.79) (1.21) Fund Turnover 0.0277*** 0.0274*** 0.0209 0.0509** 0.0401*** 0.0045 (2.95) (2.91) (1.44) (2.63) (3.20) (0.32) Fund Return Volatility 0.5668 0.5650 0.1902 -1.0858 3.1276* -3.6036** (0.48) (0.48) (0.19) (-0.78) (1.85) (-2.40) Fund Return -0.1019 -0.0980 0.2944 0.3971 -0.2654 0.1410 (-0.92) (-0.89) (1.22) (1.25) (-0.48) (0.44) Fraction in Investment-grade Bonds -0.3158*** -0.3139*** -0.3153*** -0.3631*** -0.3720*** -0.3487*** (-13.65) (-13.49) (-20.64) (-16.75) (-10.50) (-21.06) Financial Center Dummy -0.0915*** -0.0905*** -0.0897*** -0.1200*** -0.0693*** -0.0981*** (-6.91) (-6.76) (-21.93) (-23.66) (-12.54) (-20.35) Const 6.8434*** 6.8128**** 6.8479*** 6.6748*** 6.8473*** 6.9682*** (107.03) (98.62) (277.11) (273.76) (134.52) (214.12) Time Dummies Y Y - - - - Fund Type Dummies Y Y Y - - - Hansen J (p-value) - 0.30 - - - - (Average) R-squared 0.0791 0.0788 0.0862 0.0681 0.0674 0.0786 Number of observations 81342 81342 81342 24061 21245 18876

32

Panel C: Matching Within Family All Institutions Life Mutual OLS IV FM FM Family FM FM (1) (2) (3) (4) (5) (6) Fund Hierarchy 0.0267** 0.0601*** 0.0243*** 0.0298*** 0.0021 0.0419*** (2.07) (3.27) (3.70) (2.66) (0.19) (3.79) Control Variables Y Y Y Y Y Y Time Dummies Y Y - - - - Fund Type Dummies Y Y Y - - - Hansen J (p-value) - 0.64 - - - - (Average) R-squared 0.1055 0.1023 0.1382 0.1795 0.1864 0.1479 Number of observations 11911 11911 11911 4893 3507 4338

Panel D: Matching Across Family All Institutions Life Mutual OLS IV FM FM Family FM FM (1) (2) (3) (4) (5) (6) Fund Hierarchy 0.0417*** 0.0624*** 0.0413*** 0.0498*** 0.0724*** 0.0395*** (3.89) (4.28) (5.77) (6.69) (6.73) (3.05) Control Variables Y Y Y Y Y Y Time Dummies Y Y - - - - Fund Type Dummies Y Y Y - - - Hansen J (p-value) - 0.86 - - - - (Average) R-squared 0.0892 0.0880 0.1210 0.1255 0.1340 0.1300 Number of observations 16645 16645 16645 9147 4971 5962

Panel E: Regression based on Differenced Variable All Institutions Life Mutual OLS IV FM FM Family FM FM (1) (2) (3) (4) (5) (6) Difference in Fund Hierarchy 0.0394*** 0.0248*** 0.0366*** 0.0305*** 0.0334*** 0.0307*** (10.29) (4.32) (4.90) (3.54) (4.18) (3.27) Difference in Control Variables Y Y Y Y Y Y Time Dummies Y Y - - - - Fund Type Dummies Y Y Y - - - Hansen J (p-value) - 0.83 - - - - (Average) R-squared 0.0441 0.0425 0.0891 0.1275 0.0890 0.1569 Number of observations 61252 61252 61252 20886 17463 15721

33

Table III Fund Hierarchy and Portfolio Concentration

This table relates fund hierarchy to portfolio concentration. Panel A summarizes the sample mean of fund portfolio

concentration at different levels of fund hierarchy. We also provide univariate tests of fund portfolio distance

regarding to single vs. multi- fund hierarchy. Multi-hierarchy means the number of fund hierachies to be greater

than 1. We consider the overall sample as well as the “matching within fund family” and the “matching across fund

families”. We report the results for the full sample and the two matching samples separately. Both two tailed T-test

and Wilconxon rank-sum test are performed to test the differences. The number of observations (fund-quarter) is

given in the parentheses. In Panels B-E, we report the results of the multivariate analysis. We estimate:

titititi XHierarchyHerfin ,1,,, εδβα +×+×+= − ,

where tiHerfin , represents the portfolio herfindal of fund i at quarter t, tiHierarchy , is fund hierarchy and

1, −tiX are other control variables. The definitions are detailed in the appendix.

The analysis in Panel B is based on the full sample. We add the fund type dummies across all specifications.

Column (1) is based on OLS regressions with standard errors clustering at fund level. In Column (2), we report the

results of an IV regression, where family level structures are chosen as instruments. Specifically, we instrument fund

structure variables using the following variables: family hierarchy (median of fund hierarchy within a family),

family emplolyee specialty (median of employee specialty within a family), family team (median of team dummy

within a family), the interaction of family hierarchy with financial center dummy and the interaction of family

employee specialty with financial center dummy. Hansen’s J statistic (p-value) is reported to examine the quality of

instruments. The standard errors are clustered at fund level. Column (3) provides Fama-Macbeth (1973) estimates

at the fund level, while Column (4) provides the results of a Fama-Macbeth estimates at the family level. Column

(5) and (6) are estimated in the same way as in Column (3) but only based on funds owned by life insurance

companies and mutual fund families. Panel C and Panel D are based on the matching sample within and across fund

families respectively with the same specifications as in Panel B.

Panel E uses a “differenced” variable approach, where for each fund we match it with some other fund similar

in fund type, geographical location and size, but different in terms of fund family and fund hierarchy. The matching

procedure is as follows: for each fund-quarter we first choose all the other funds of the same fund type but from

different fund families and having different fund hierarchy. Then we pick 20 funds located most closely and narrow

them down to 10 according to similarity in fund size. From those 10 funds we select the final one with the smallest

geographical distances to the original fund. If there is more than one matched fund left meaning that they are

located at the same place, we choose the most similar one in terms of fund size. All the variables except the financial

center dummy, including both the dependent and independent variables, are the differences between the original

fund and its matched peer.

For the sake of brevity we only report the coefficients of fund hierarchy from Panel C to Panel E. ***, ** and

* represent significance levels at 1%, 5% and 10% respectively with t-statistics given in parentheses.

34

Table III (Cont’d)

Panel A: Univariate Results

Fund Portfolio Concentration by Fund Hierarchy Full Sample Matching Within

Family Matching Across

Family 1 0.0761 0.0385 0.0461 (77649) (5748) (8233) 2 0.0448 0.0300 0.0360 (6916) (4755) (6528) 3 0.0212 0.0152 0.0163 (1304) (953) (1207) 4 0.0197 0.0157 0.0169

(213) (164) (178) T-test: Multiple vs. Single Hierarchy -27.87*** -14.44*** -17.38*** Wilconxon Test: Multiple vs. Single Hierachy -48.13*** -24.64*** -24.73***

Panel B: Full Sample All Institutions Life Mutual OLS IV FM FM Family FM FM (1) (2) (3) (4) (5) (6) Fund Hierarchy -0.0221*** -0.0329*** -0.0221*** -0.0272*** -0.0319*** -0.0063*** (-13.20) (-14.40) (-23.40) (-35.18) (-18.60) (-5.98) Control Variable Employee Specialty -0.0022*** -0.0042*** -0.0021*** -0.0045*** -0.0043*** 0.0007 (-3.24) (-4.17) (-11.19) (-16.90) (-12.25) (1.37) Team Dummy -0.0040* -0.0026 -0.0033*** -0.0042** -0.0022 -0.0079*** (-1.73) (-0.62) (-3.58) (-2.29) (-0.71) (-5.79) Fund Size -0.0236*** -0.0237*** -0.0234*** -0.0213*** -0.0219*** -0.0163*** (-38.44) (-38.60) (-69.54) (-23.41) (-58.32) (-22.08) Family Size 0.0034*** 0.0031*** 0.0033*** -0.0010 0.0042*** -0.0001 (4.60) (4.24) (15.48) (-1.34) (13.59) (-0.20) Portfolio Maturity -0.0093*** -0.0091*** -0.0140*** -0.0130*** -0.0118*** -0.0267*** (-7.45) (-7.32) (-8.88) (-6.97) (-4.83) (-10.91) Log(Number of Funds) -0.0024** -0.0028** -0.0021*** 0.0011 -0.0033*** 0.0029*** (-2.08) (-2.34) (-6.45) (1.38) (-5.44) (3.86) Fund Turnover 0.0062*** 0.0063*** 0.0073*** 0.0065** 0.0096** -0.0010 (4.33) (4.43) (3.60) (2.18) (2.40) (-0.58) Fund Return Volatility 0.2200 0.2238 1.0221*** 1.2464*** 0.3674 0.6780** (1.17) (1.19) (3.78) (3.68) (0.88) (2.49) Fund Return 0.0349* 0.0343* 0.0214 -0.0386 0.0245 0.0083 (1.84) (1.81) (0.28) (-0.39) (0.37) (0.07) Fraction in Investment-grade Bonds 0.0017 0.0011 0.0063* -0.0045 -0.0304*** 0.0238*** (0.47) (0.31) (1.67) (-1.40) (-6.28) (4.86) Financial Center Dummy -0.0061*** -0.0062*** -0.0056*** 0.0021** -0.0165*** 0.0064*** (-3.26) (-3.31) (-6.25) (2.60) (-12.01) (6.79) Const 0.2898*** 0.3093*** 0.3100*** 0.3592*** 0.3352*** 0.2782*** (30.50) (30.38) (46.38) (89.84) (58.26) (32.70) Time Dummies Y Y - - - - Fund Type Dummies Y Y Y - - - Hansen J (p-value) - 0.35 - - - - (Average) R-squared 0.3021 0.2998 0.3114 0.3296 0.3757 0.2424 Number of observations 83998 83998 83998 24606 21105 19934

35

Table III (Cont’d)

Panel C: Matching Sample (Within Family) All Institutions Life Mutual OLS IV FM FM Family FM FM (1) (2) (3) (4) (5) (6) Fund Hierarchy -0.0109*** -0.0179*** -0.0109*** -0.0132*** -0.0125*** -0.0048*** (-11.05) (-12.06) (-16.19) (-12.39) (-11.53) (-5.93) Control Variables Y Y Y Y Y Y Time Dummies Y Y - - - - Fund Type Dummies Y Y Y - - - Hansen J (p-value) - 0.75 - - - - (Average) R-squared 0.2375 0.2159 0.2715 0.3439 0.4204 0.2527 Number of observations 11576 11576 11576 4734 3367 4218

Panel D: Matching Sample (Across Family) All Institutions Life Mutual OLS IV FM FM Family FM FM (1) (2) (3) (4) (5) (6) Fund Hierarchy -0.0129*** -0.0190*** -0.0129*** -0.0150*** -0.0151*** -0.0076*** (-15.34) (-15.20) (-25.78) (-24.76) (-13.72) (-8.66) Control Variables Y Y Y Y Y Y Time Dummies Y Y - - - - Fund Type Dummies Y Y Y - - - Hansen J (p-value) - 0.46 - - - - (Average) R-squared 0.2679 0.2664 0.2976 0.3273 0.4026 0.2785 Number of observations 16146 16146 16146 8904 4809

Panel E: Regression based on Differenced Variable All Institutions Life Mutual OLS IV FM FM Family FM FM (1) (2) (3) (4) (5) (6) Difference in Fund Hierarchy -0.0175*** -0.0196*** -0.0180*** -0.0209*** -0.0220*** -0.0060*** (-19.53) (-11.56) (-15.33) (-11.92) (-10.62) (-4.52) Control Variables Y Y Y Y Y Y Time Dummies Y Y - - - - Fund Type Dummies Y Y Y - - - Hansen J (p-value) - 0.41 - - - - (Average) R-squared 0.0850 0.0848 0.1107 0.1527 0.1479 0.1277 Number of observations 62498 62498 62498 21246 17677 16226

36

Table IV Fund Hierarchy and Herding

This table related fund hierarchy to herding. Panel A summarizes the sample mean of fund herding at different

levels of fund hierarchy. We also provide univariate tests of fund herding regarding to single vs. multi- fund

hierarchy. Multi-hierarchy means the number of fund hierachies to be greater than 1. We consider the overall

sample as well as the “matching within fund family” and the “matching across fund families”. We report the results

for the full sample and the two matching samples separately. Both two tailed T-test and Wilconxon rank-sum test

are performed to test the differences. The number of observations (fund-quarter) is given in the parentheses. In

Panels B-E, we report the results of the multivariate analysis. We estimate:

titititi XHierarchyHerding ,1,,, εδβα +×+×+= − ,

where tiHerding , represents fund herding tendency of fund i at quarter t, tiHierachy , is fund hierarchy and

1, −tiX are other control variables. The definitions are detailed in the appendix.

The analysis in Panel B is based on the full sample. We add the fund type dummies across all specifications.

Column (1) is based on OLS regressions with standard errors clustering at fund level. In Column (2), we report the

results of an IV regression, where family level structures are chosen as instruments. Specifically, we instrument fund

structure variables using the following variables: family hierarchy (median of fund hierarchy within a family),

family employee specialty (median of employee specialty within a family), family team (median of team dummy

within a family), the interaction of family hierarchy with financial center dummy and the interaction of family

employee specialty with financial center dummy. Hansen’s J statistic (p-value) is reported to examine the quality of

instruments. The standard errors are clustered at fund level. Column (3) provides Fama-Macbeth (1973) estimates

at the fund level, while Column (4) provides the results of a Fama-Macbeth estimates at the family level. Column

(5) and (6) are estimated in the same way as in Column (3) but only based on funds owned by life insurance

companies and mutual fund families. Panel C and Panel D are based on the matching sample within and across fund

families respectively with the same specifications as in Panel B.

Panel E uses a “differenced” variable approach, where for each fund we match it with some other fund similar

in fund type, geographical location and size, but different in terms of fund family and fund hierarchy. The matching

procedure is as follows: for each fund-quarter we first choose all the other funds of the same fund type but from

different fund families and having different fund hierarchy. Then we pick 20 funds located most closely and narrow

them down to 10 according to similarity in fund size. From those 10 funds we select the final one with the smallest

geographical distances to the original fund. If there is more than one matched fund left meaning that they are

located at the same place, we choose the most similar one in terms of fund size. All the variables except the financial

center dummy, including both the dependent and independent variables, are the differences between the original

fund and its matched peer.

For the sake of brevity we only report the coefficients of fund hierarchy from Panel C to Panel E. ***, ** and

* represent significance levels at 1%, 5% and 10% respectively with t-statistics given in parentheses.

37

Table IV (Cont’d) Panel A: Univariate Results

Fund Herding by Fund Hierarchy Full Sample Matching Within

Family Matching Across

Family 1 0.0128 0.0142 0.0129 (61056) (5090) (7300) 2 0.0134 0.0140 0.0139 (6200) (4344) (6082) 3 0.0146 0.0157 0.0154 (1156) (872) (1143) 4 0.0193 0.0189 0.0198

(194) (172) (192) T-test: Multiple vs. Single Hierarchy 3.49*** 0.38 3.44*** Wilconxon Test: Multiple vs. Single Hierachy 5.76*** 1.09 4.64***

Panel B: Full Sample All Institutions Life Mutual OLS IV FM FM Family FM FM (1) (2) (3) (4) (5) (6) Fund Hierarchy 0.0014*** 0.0021*** 0.0013*** 0.0016*** 0.0015** 0.0009** (4.31) (4.61) (4.47) (4.90) (2.45) (2.30) Control Variables Employee Specialty -0.0007*** -0.0010*** -0.0006*** -0.0007*** -0.0009*** -0.0007*** (-5.79) (-6.79) (-7.43) (-5.91) (-6.00) (-4.52) Team Dummy -0.0003 -0.0011* -0.0002 0.0006 0.0010 -0.0003 (-0.73) (-1.84) (-0.43) (1.33) (1.49) (-0.53) Fund Size -0.0002* -0.0001 -0.0002* -0.0003 -0.0002* 0.0004** (-1.80) (-1.43) (-1.77) (-1.46) (-1.74) (2.00) Family Size 0.0003** 0.0002 0.0003** 0.0005*** 0.0003* 0.0003 (2.51) (1.63) (2.32) (3.28) (1.90) (1.08) Portfolio Maturity 0.0010*** 0.0010*** 0.0014*** 0.0000 0.0000 0.0036*** (4.93) (4.87) (3.38) (0.10) (0.03) (4.82) Log(Number of Funds) 0.0007*** 0.0006*** 0.0006*** 0.0009*** 0.0004 0.0003 (3.60) (3.24) (3.53) (3.81) (1.30) (0.64) Fund Turnover 0.0087*** 0.0086*** 0.0098*** 0.0116*** 0.0177*** 0.0054*** (19.22) (19.18) (10.12) (9.45) (7.08) (5.18) Fund Return Volatility 0.0488 0.0493 0.0300 0.0103 -0.0879 0.0981* (1.48) (1.50) (0.60) (0.14) (-1.33) (1.68) Fund Return 0.0134** 0.0134** 0.0061 0.0061 0.0126 -0.0101 (2.52) (2.53) (0.41) (0.38) (0.71) (-0.51) Fraction in Investment-grade Bonds 0.0023*** 0.0023*** 0.0011 0.0011 -0.0007 0.0008 (3.77) (3.70) (0.95) (0.74) (-0.32) (0.65) Financial Center Dummy 0.0025*** 0.0024*** 0.0024*** 0.0017*** 0.0014*** 0.0016*** (7.20) (7.11) (7.35) (5.56) (2.80) (3.16) Const -0.0014 0.0052*** 0.0017 0.0036** 0.0050 -0.0127*** (-0.91) (3.16) (0.82) (2.20) (1.52) (-3.28) Time Dummies Y Y - - - - Fund Type Dummies Y Y Y - - - Hansen J (p-value) - 0.26 - - - - (Average) R-squared 0.0717 0.0712 0.0757 0.1047 0.1092 0.0695 Number of observations 68606 68606 68606 20957 18575 18086

38

Panel C: Matching Within Family

All Institutions Life Mutual OLS IV FM FM Family FM FM (1) (2) (3) (4) (5) (6) Fund Hierarchy 0.0010** 0.0022*** 0.0010*** 0.0016*** 0.0012 0.0013** (2.45) (3.17) (2.78) (3.54) (1.45) (2.31) Control Variables Y Y Y Y Y Y Time Dummies Y Y - - - - Fund Type Dummies Y Y Y - - - Hansen J (p-value) - 0.31 - - - - (Average) R-squared 0.0826 0.0790 0.1231 0.1833 0.2093 0.1651 Number of observations 10478 10478 10478 4555 3075 3932

Panel D: Matching Across Family All Institutions Life Mutual OLS IV FM FM Family FM FM (1) (2) (3) (4) (5) (6) Fund Hierarchy 0.0013*** 0.0025*** 0.0011*** 0.0013*** 0.0025*** 0.0004 (3.24) (4.28) (2.88) (3.56) (3.52) (0.50) Control Variables Y Y Y Y Y Y Time Dummies Y Y - - - - Fund Type Dummies Y Y Y - - - Hansen J (p-value) - 0.16 - - - - (Average) R-squared 0.0810 0.0783 0.1147 0.1347 0.1896 0.1345 Number of observations 14717 14717 14717 8304 4410 5504

Panel E: Regression based on Differenced Variable All Institutions Life Mutual OLS IV FM FM Family FM FM (1) (2) (3) (4) (5) (6) Difference in Fund Hierarchy 0.0008*** 0.0010*** 0.0009*** 0.0008*** 0.0012*** 0.0015*** (4.17) (2.91) (3.24) (2.77) (2.77) (3.04) Control Variables Y Y Y Y Y Y Time Dummies Y Y - - - - Fund Type Dummies Y Y Y - - - Hansen J (p-value) - 0.41 - - - - (Average) R-squared 0.0285 0.0283 0.0756 0.0847 0.1498 0.0974 Number of observations 47731 47731 47731 16965 13980 13922

39

Table V: Fund Hierarchy and Performance

This table relates fund hierarchy to performance. Panel A summarizes the sample mean of fund performance for different levels of fund hierarchy. We also provide univariate tests of

fund performance regarding to single vs. multi- fund hierarchy. Multi-hierarchy means the number of fund hierachies to be greater than 1. We consider the overall sample as well as

the “matching within fund family” and the “matching across fund families”. We report the results for the full sample and the two matching samples separately. Both two tailed T-test

and Wilcoxon rank-sum test are performed to test the differences. The number of observations (fund-quarter) is given in the parentheses. In Panels B-E, we report the results of the

multivariate analysis. We estimate: titititi XHierarchyAlpha ,1,,, εδβα +×+×+= − , where tiAlpha , represents the alpha of fund i at month t, tiHierachy , is fund

hierarchy and 1, −tiX are other control variables. The definitions are detailed in the appendix. The analysis in Panel B is based on the entire sample including life insurance companies,

mutual funds, property insurance companies and other institutions. We put fund type dummies in all specifications. Column (1) is OLS regression with standard errors clustering at

fund level. In Column (2), we have IV regressions, where family level structures are chosen as instruments. Specifically, we instrument fund structure variables using the following

variables: family hierarchy (median of fund hierarchy within a family), family employee specialty (median of employee specialty within a family), family team (median of team dummy

within a family), the interaction of family hierarchy with financial center dummy and the interaction of family employee specialty with financial center dummy. Hansen’s J statistic

(p-value) is reported to examine the quality of instruments. The standard errors are clustered at fund level. Column (3) provides Fama-Macbeth (1973) estimates at the fund level,

while Column (4) provides the results of a Fama-Macbeth estimates at the family level. From Column (7) to Column (10) we add the interaction term of fund hierarchy and a “close

investment” dummy. It equals 1 if the fund portfolio distance is below the sample median of the quarter and 0 otherwise. We also add the interaction term of employee specialty and

close investment dummy as additional controls. Panel C and Panel D are based on the matching sample within and across fund families respectively. Panel E is based on the

“differenced” variable as defined in the previous tables. For the sake of brevity we only report the coefficients of the fund hierarchy from Panel C to Panel E. In Panel F we compare

the impact of fund hierarchy on the fund performance of investing in high rated bonds with that of investing in low rated bonds. High rated bonds refer to bonds with Moody’s credit

rating above A3. Low rated bonds are bonds with Moody’s credit rating from B3 to BBB1. For each fund we estimate two portfolio alphas separately. One is based on the

value-weighted return of investing in high rated bonds while the other is based on the return of investing in low rated bonds. The estimation procedure is the same as described in the

appendix. From Column (1) to Column (4) we stack the high and low rated alphas together and create a rating category dummy which equals 1 if it is a low rated alpha and 0 otherwise.

Our focus is the interaction term of fund hierarchy and the rating category dummy. We also add the interaction of employee specialty and the rating category dummy as additional

controls. Standard erros are clustered at the fund level (Column (1)) as well as at the family level (Column (2)). Column (3) and (4) are estimated in the same way as in Column (1)

but only based on funds owned by life insurance companies and mutual fund families. In Column (5) and (6) we run Fama-Mecbeth regressions separately for the low rated alpha and

the high rated alpha.. ***, ** and * represent significance levels at 1%, 5% and 10% respectively with t-statistics given in parentheses.

Panel A: Univariate Results Fund Performance by Fund Hierarchy Full Sample Matching Within Family Matching Across Family

1 0.0000 -0.0009 -0.0004 (147478) (10124) (13170) 2 -0.0017 -0.0012 -0.0018 (10623) (7615) (9926) 3 -0.0022 -0.0018 -0.0022 (2319) (1757) (2236) 4 -0.0041 -0.0039 -0.0038

(250) (216) (228) T-test: Multiple vs. Single Hierarchy -9.59*** -1.76* -5.84*** Wilconxon Test: Multiple vs. Single Hierachy -9.71*** -1.34 -5.78***

40

Panel B: Full Sample

All Institutions Life Mutual All Institutions Life Mutual OLS IV FM FM Family FM FM OLS FM FM FM (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Fund Hierarchy -0.0015*** -0.0024*** -0.0012*** -0.0011*** -0.0008*** -0.0012*** -0.0008** -0.0008*** -0.0005** -0.0007*** (-4.61) (-4.77) (-8.47) (-4.15) (-4.03) (-4.86) (-2.28) (-4.69) (-2.05) (-2.68) Fund Hierarchy * Close Investment Dummy

-0.0016*** -0.0011*** -0.0007*** -0.0017***

(-2.82) (-3.50) (-2.83) (-3.43) Close Investment Dummy 0.0005 0.0005** -0.0006** 0.0003 (1.23) (2.41) (-2.09) (0.95) Control Variables Employee Specialty -0.0001 -0.0002* -0.0001*** -0.0002*** -0.0001** -0.0001 -0.0002 -0.0002*** -0.0002** -0.0001 (-1.46) (-1.78) (-4.34) (-4.14) (-2.37) (-0.89) (-1.51) (-4.22) (-2.59) (-0.78) Employee Specialty * Close Investment Dummy

0.0001 0.0001* 0.0001* 0.0000

(0.54) (1.69) (1.83) (-0.16) Team Dummy 0.0005 0.0021*** 0.0005** -0.0005 -0.0006** 0.0003 0.0013* 0.0009** 0.0007** 0.0005 (1.23) (3.02) (2.44) (-1.03) (-2.04) (0.94) (1.87) (2.30) (2.23) (0.74) Fund Size -0.0001 -0.0001 -0.0001** -0.0004*** -0.0003*** 0.0000 -0.0001 -0.0001** -0.0003*** 0.0000 (-0.74) (-0.80) (-2.03) (-3.36) (-3.68) (0.20) (-0.77) (-2.05) (-3.62) (0.20) Family Size 0.0001 0.0001 0.0001 0.0003*** -0.0001 -0.0002* 0.0001 0.0001 -0.0001 -0.0003** (1.35) (1.15) (1.35) (3.27) (-0.92) (-1.67) (1.33) (1.36) (-0.96) (-1.99) Portfolio Maturity -0.0009*** -0.0009*** -0.0006 0.0010 0.0008 -0.0027*** -0.0009*** -0.0006 0.0008 -0.0026*** (-4.65) (-4.58) (-0.59) (0.84) (0.62) (-2.85) (-4.67) (-0.58) (0.61) (-2.79) Log(Number of Funds) -0.0004** -0.0005*** -0.0004*** -0.0008*** 0.0001 -0.0001 -0.0004** -0.0004*** 0.0001 0.0000 (-2.50) (-2.66) (-3.99) (-4.37) (0.67) (-0.37) (-2.42) (-3.92) (0.73) (-0.06) Fund Turnover -0.0004 -0.0004 -0.0004 -0.0002 0.0004 0.0001 -0.0004 -0.0004 0.0004 0.0000 (-1.28) (-1.28) (-1.34) (-0.46) (0.58) (0.17) (-1.29) (-1.28) (0.68) (0.07) Fund Return Volatility 0.5026*** 0.5027*** 0.1875 0.0410 0.0242 0.3159*** 0.5041*** 0.1866 0.0278 0.3334*** (12.59) (12.61) (1.55) (0.32) (0.17) (3.21) (12.62) (1.54) (0.19) (3.44) Fund Return -0.1008*** -0.1006*** -0.0625 -0.0478 -0.0240 -0.0806* -0.1009*** -0.0622 -0.0235 -0.0801* (-15.02) (-15.00) (-1.25) (-0.97) (-0.35) (-1.87) (-15.05) (-1.25) (-0.34) (-1.84) Fraction in Investment-grade Bonds 0.0116*** 0.0116*** 0.0084*** 0.0076*** 0.0044 0.0080*** 0.0118*** 0.0085*** 0.0045 0.0087*** (16.12) (16.14) (3.45) (3.08) (1.48) (3.18) (16.26) (3.56) (1.50) (3.56) Financial Center Dummy 0.0000 0.0000 0.0000 -0.0009*** -0.0005** 0.0001 0.0000 0.0000 -0.0006** 0.0001 (-0.15) (0.11) (-0.31) (-4.89) (-2.32) (0.32) (-0.09) (-0.22) (-2.48) (0.61) Const -0.0195*** 0.0076*** -0.0056 -0.0039 0.0015 0.0016 -0.0202*** -0.0062* 0.0010 0.0012 (-13.12) (4.82) (-1.64) (-1.23) (0.32) (0.39) (-13.53) (-1.80) (0.23) (0.28) Time Dummies Y Y - - - - Y - - - Fund Type Dummies Y Y Y - - - Y Y - - Hansen J (p-value) - 0.16 - - - - - - - - (Average) R-squared 0.3704 0.3699 0.2384 0.2468 0.2586 0.2681 0.3706 0.2401 0.2753 0.2613 Number of observations 160670 160670 160670 48170 45911 30650 160670 160670 45911 30650

41

Table V (Cont’d)

Panel C: Matching Within Family All Institutions Life Mutual OLS IV FM FM Family FM FM (1) (2) (3) (4) (5) (6) Fund Hierarchy -0.0013*** -0.0029*** -0.0013*** -0.0013*** -0.0012*** -00011*** (-4.21) (-3.43) (-7.91) (-3.90) (-4.50) (-3.15) Control Variables Y Y Y Y Y Y Time Dummies Y Y - - - - Fund Type Dummies Y Y Y - - - Hansen J (p-value) - 0.49 - - - - (Average) R-squared 0.3268 0.3238 0.3354 0.4208 0.3609 0.3884 Number of observations 19712 19712 19712 7133 6528 6118

Panel D: Matching Across Family All Institutions Life Mutual OLS IV FM FM Family FM FM (1) (2) (3) (4) (5) (6) Fund Hierarchy -0.0014*** -0.0021*** -0.0012*** -0.0012*** -0.0013*** -0.0011*** (-4.76) (-3.64) (-8.04) (-5.75) (-4.98) (-3.59) Control Variables Y Y Y Y Y Y Time Dummies Y Y - - - - Fund Type Dummies Y Y Y - - - Hansen J (p-value) - 0.34 - - - - (Average) R-squared 0.3550 0.3544 0.2911 0.3237 0.3222 0.3305 Number of observations 25560 25560 25560 14250 8794 8281

Panel E: Regression based on Differenced Variable All Institutions Life Mutual OLS IV FM FM Family FM FM (1) (2) (3) (4) (5) (6) Difference in Fund Hierarchy -0.0011*** -0.0013*** -0.0010*** -0.0011*** -0.0009*** -0.0015*** (-6.96) (-4.71) (-4.62) (-4.39) (-3.11) (-4.76) Difference in Control Variables Y Y Y Y Y Y Time Dummies Y Y - - - - Fund Type Dummies Y Y Y - - - Hansen J (p-value) - 0.93 - - - - (Average) R-squared 0.0315 0.0314 0.1689 0.1859 0.1993 0.2711 Number of observations 90659 90659 90659 35317 31630 14234

42

Table V (Cont’d)

Panel F: Interaction with Rating Category

OLS:

All Institutions

OLS: Life

OLS:

Mutual

FM: Low Rating Category

FM: High Rating

Category (1) (2) (2) (3) Fund Hierarchy 0.0000 0.0000 0.0003 0.0000 -0.0008*** -0.0001** (0.14) (0.13) (1.22) (-0.06) (-7.05) (-2.13) Fund Hierarchy * Rating Category

-0.0012*** -0.0012*** -0.0014*** -0.0013**

(-3.91) (-4.06) (-3.24) (-2.07) Control Variables Employee Specialty -0.0001* -0.0001 -0.0001 0.0000 -0.0001*** 0.0001*** (-1.73) (-1.38) (-1.30) (0.11) (-3.83) (3.01) Employee Specialty * Rating Category

0.0001 0.0001 0.0001 0.0001

(1.23) (0.95) (1.19) (0.35) Rating Category -0.0046*** -0.0046*** -0.0041*** -0.0032*** (-11.74) (-9.02) (-7.05) (-3.69) Team Dummy 0.0001 0.0001 -0.0002 0.0001 0.0004** -0.0001 (0.46) (0.44) (-0.65) (0.34) (2.20) (-0.84) Fund Size 0.0000 0.0000 0.0000 0.0001 -0.0001** 0.0001* (0.78) (0.64) (0.61) (1.41) (-2.21) (1.78) Family Size 0.0001* 0.0001 0.0000 -0.0001 0.0002*** 0.0000 (1.90) (1.44) (0.40) (-0.48) (2.90) (-0.81) Portfolio Maturity 0.0002* 0.0002 0.0004*** -0.0002 -0.0002 0.0009* (1.85) (1.46) (3.43) (-1.08) (-0.32) (1.79) Log(Number of Funds) -0.0003*** -0.0003** 0.0000 -0.0002 -0.0005*** 0.0001 (-3.16) (-2.31) (-0.30) (-0.85) (-5.40) (1.08) Fund Turnover 0.0002 0.0002 0.0002 0.0001 -0.0006 0.0007*** (1.16) (1.00) (0.52) (0.48) (-1.61) (2.70) Fund Return Volatility 0.1530*** 0.1530*** 0.1166*** 0.1776*** -0.0685 0.1859*** (8.02) (6.01) (3.49) (4.45) (-1.14) (4.60) Fund Return -0.0323*** -0.0323*** -0.0194*** -0.0500*** -0.0313 0.0129 (-10.08) (-8.56) (-3.40) (-7.59) (-0.93) (0.83) Fraction: Investment-grade Bonds 0.0065*** 0.0065*** 0.0057*** 0.0048*** 0.0050*** 0.0061*** (14.82) (12.56) (6.48) (6.06) (3.44) (4.16) Financial Center Dummy -0.0003** -0.0003 -0.0005** -0.0004 -0.0006** -0.0001 (-2.08) (-1.53) (-2.59) (-1.49) (-2.60) (-1.25) Const -0.0006 -0.0006 0.0009 0.0020 -0.0061*** -0.0095*** (-0.81) (-0.67) (0.68) (1.10) (-3.46) (-5.81) Time Dummies Y Y Y Y - - Fund Type Dummies Y Y - - Y Y Clustering at Fund Family Fund Fund - - (Average) R-squared 0.2771 0.2771 0.3113 0.1916 0.1357 0.2136 Number of observations 211662 211662 74506 38530 105831 105831

43

Table VI Changes in Fund Management with Changes in Fund Hierarchy

This table presents the results of regressing changes in fund management on changes in fund hierarchical structure.

We only focus on the subsample (fund-quarter) where the fund changes its hierarchical structure from quarter t-1

to quartet t. We estimate the following regression:

tititititi mentFundManageXHierarchymentFundManage ,1,,,, εδβα ++∆×+∆×+=∆ − ,

where from Column (1) to Column (5) timentFundManage ,∆ represents the change of fund portfolio distance,

the change of fund herding, the change of fund portolio concentration, the change of fund raw return(cumulative,

quarterly) and the change of fund alpha (cumulative, quarterly) respectively from quarter t -1 to quarter t.

tiHierachy ,∆ is the change of fund hierarchy and tiX ,∆ are the changes of other control variables from quarter

t-1 to quarter t. The definitions are detailed in the appendix. 1,−imentFundManage is the lagged dependent

variable at quarter t-1. The standard errors across all specifications are clustered at fund level and we always

include time dummies and fund type dummies. ***, ** and * represent significance levels at 1%, 5% and 10%

respectively with t-statistics given in parentheses.

ΔFund Portfolio Distance

ΔFund Herding

ΔFund Portfolio

Concentration

ΔFund Raw

Return

ΔFund Alpha

(1) (2) (3) (4) (5) Change in Fund Hierarchy 0.0182*** 0.0010*** -0.0024*** -0.0014*** -0.0023*** (2.96) (2.78) (-4.01) (-2.95) (-2.73) Control Variables Change in Employee Specialty -0.0081** -0.0003 0.0007* -0.0001 0.0014** (-2.01) (-0.89) (1.72) (-0.20) (2.06) Change in Team Dummy 0.0106 0.0005 -0.0020** -0.0007 -0.0013 (1.46) (1.09) (-2.64) (-1.24) (-1.19) Change in Fund Size 0.0289* 0.0046*** -0.0108** -0.0019** -0.0012 (1.85) (3.92) (-2.39) (-2.07) (-0.66) Change in Family Size -0.0384*** -0.0001 0.0000 0.0005 -0.0002 (-3.06) (-0.21) (-0.03) (0.81) (-0.21) Change in Portfolio Maturity 0.0163 0.0028** -0.0042 0.0015 -0.0001 (0.64) (2.53) (-1.46) (1.52) (-0.06) Change in Fraction in Investment-grade Bonds -0.3113*** 0.0025 0.0197 0.0164*** 0.0007 (-3.45) (0.39) (0.86) (2.81) (0.07) Lagged Dependent Variable -0.2435*** -0.5687*** -0.1030*** -0.7313*** -0.2630*** (-13.01) (-26.29) (-3.40) (-21.63) (-16.95) Const 1.3320*** 0.0119*** 0.0016 0.0369*** 0.0116*** (10.47) (4.05) (0.33) (17.11) (2.74) Time Dummies Y Y Y Y Y Fund Type Dummies Y Y Y Y Y R-squared 0.1418 0.3602 0.0767 0.8001 0.3380 Number of observations 5419 4613 5381 1986 1986