Employment Networks and Mutual Fund Performance A thesis presented by

Employment Networks and Mutual Fund Performance
A thesis presented
by
Michael Buckley
To
Applied Mathematics
in partial fulfillment of the honors requirements
for the degree of
Bachelor of Arts
Harvard College
Cambridge, Massachusetts
April 1, 2010
ABSTRACT
This paper explores the role of social networks in the portfolio allocation
decisions and performance of mutual fund managers. Using a novel dataset, I examine
the past employment networks of both mutual fund managers and firm executives. I find
that fund managers place larger bets and perform significantly better on firms when they
are connected to a senior executive of that firm through overlapping employment
networks. A portfolio of connected stocks outperforms a portfolio of unconnected stocks
by over 10% per year. The results suggest that fund managers may be benefiting from
improved information secured as a result of their networks.
*I would like to thank my adviser Lauren Cohen, along with John Beshears, Richard Townsend,
Jeremy Hoon, Zach Frankel, and an anonymous private wealth management adviser.
As social websites such as Facebook, LinkedIn, and Twitter have gained
popularity over the last few years, the relevance of social networks in our lives has
become increasingly apparent. It has also become clearer that these social networks are
not passive, but can in fact influence our lives enormously. Indeed, anecdotal evidence
for the effects of social connections is abundant. For instance, a recent article in the
Washington Post references the role of the “old-boy network in Washington.”1 In this
paper, I attempt to empirically document social networks and their effects. In particular, I
examine a specific network created through employment experiences. I define the
network as follows: when an individual joins a firm, he or she is permanently connected
to this firm and other individuals who worked for this firm. Over an individual’s entire
career, he or she may be connected to many different firms and thus many different
groups of people. I refer to this entire network of connections as the individual’s
employment network.
I examine employment networks in a specific setting: the mutual fund space. In
making investment decisions, mutual fund managers rely on information and research.
Superior performance can result from the ability to accurately interpret new information
before it is fully disseminated into the market.2 Therefore, advantages in acquiring
information should give managers the opportunity to act before the information is fully
reflected in prices. If a fund manager is connected to a firm executive through his or her
employment network, we might expect the manager to have an advantage in gathering
information about the firm. I test this hypothesis by developing a dataset on the
employment backgrounds for a number of mutual fund managers and firm executives.
1
The Washington Post, March 28, 2010, “Why did health-care reform pass? Nancy Pelosi was in charge.”
Fama (1965) points out that investors who can interpret new information quickly are precisely those who make
markets more efficient. 2
1 Using portfolio holdings data, I find that fund managers place larger bets and perform
significantly better on firms when they are connected to a senior executive of that firm
through employment networks. A portfolio formed by buying “connected” holdings and
shorting “unconnected” holdings yields over 10% per year. Additionally, I find that a
portfolio formed by buying connected holdings and shorting unheld connected stocks
yields a similar return of over 10% per year.
The paper proceeds as follows: Section I discusses relevant research related to the
role of social networks in financial markets. Section II outlines the sources and uses of
data. In Section III, I discuss the methodology and results for pooled OLS regressions in
order to determine any differences in portfolio weighting of connected and unconnected
holdings. In Section IV, I assess whether connected and unconnected holdings differ by
performance. In Section V, I outline several additional tests for robustness. Section VI
includes a small discussion on two additional types of connections: gender and ethnicity.
Finally, Section VII concludes.
I.
Literature Review
There has been a recent influx of literature dealing with the role of social
networks in finance. In general, researchers have succeeded in identifying networks and
documenting their effects. For instance, Kuhnen (2009) finds that connections between
fund directors and advisory firms, developed through past business relations, lead to
mutual preferential hiring. Similarly, Hwang and Kim (2009) analyze social connections
based on shared education experiences and other observable characteristics. They find
that connections between CEOs and directors impact executive compensation, CEO
turnover, and earnings management, illustrating that social ties can interfere with a
2
director’s role in a similar manner as financial and familial ties. In the private equity
universe, Rider (2009) finds that prior education and employment affiliations between
investors of different firms increases the likelihood that the firms will engage in coinvestments in the future.
Although network sociology has clearly been applied to the financial markets in
some form, relatively few studies have attempted to merge these concepts with portfolio
decisions and investment performance. In a study of venture capital firms, Hochberg,
Ljungqvist, and Lu (2007) find that well-connected firms enjoy significantly better
returns than their peers, where network connections are defined by relationships formed
through prior syndicate investments. More similar to this paper, Cohen, Frazzini, and
Malloy (2008) find that mutual fund managers place larger bets and perform better on
firms when they are connected to a board member through a common education
experience. They also find similar results in the performance of sell-side equity analysts
who were connected to executives through education networks (Cohen, Frazzini and
Malloy 2009). In a similar study, Tang (2009) finds that fund managers who previously
worked as sell-side analysts place larger bets and perform better on stocks which they
covered as analysts. I attempt to add to this literature by identifying a new channel
through which social connections between fund managers and firm executives may be
formed: prior employment.
II.
Data
This study required a significant amount of data collected from a number of
sources. First, I constructed a sample of 206 mutual funds using Morningstar, beginning
with the universe of Morningstar funds from 2003 to 2008. I restricted the universe of
3
funds to those that were actively managed and possessed at least 80% of their holdings in
domestic equities. Additionally, I immediately excluded any funds in which there was no
biographical information available for any managers over the time period. I included
both single-manager and multi-manager funds, for a total of 429 unique managers. For
the fund managers, I collected employment information from biographies provided on
Morningstar’s monthly discs. I supplemented the Morningstar biographies with data
collected from Zoominfo.com,3 specifically the untabulated data in its original source
form. I matched the managers from Morningstar to Zoominfo.com by using the
biographies and full names. When relevant, I also included biographical data from the
official websites of mutual funds and from manager data circulated to a private wealth
management adviser. The additional sources were necessary since manager biographies
often did not list past employment or only listed recent employment in the financial
services sector.
Next, I collected a variety of information pertinent to firms. I gathered mutual
fund holdings data from CRSP, which was available either quarterly or semi-annually. I
matched the Morningstar funds to CRSP primarily by using the fund names and tickers. I
then assembled a list of 5594 firms which were held at any point by any mutual fund in
the sample. For these firms, I obtained monthly stock return data from CRSP and
balance sheet items from Compustat. From the latter, I specifically collected market value
of equity and the items necessary to calculate book equity.4 Next, I obtained a list of firm
executives from Execucomp. Because of this, my sample consists of the executives for
all firms that were part of the S&P1500 at any point between 2003 and 2008 and which
3
Zoominfo.com is a search engine which provides biographical data that is aggregated from other web sources.
Book equity was calculated using the method outlined by Kenneth French here:
http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/Data_Library/variable_definitions.html
4
4
were also held by one of the mutual funds in the sample at any point during the time
period. This step reduced my sample of firms from a total of 5,594 to 1914. Next, I
executed a variety of string searches for the chief executive officer, chief financial
officer, and chairman of the board for each firm, which ultimately left 7,124 executives.
The string searches often yielded executives who possessed one of these titles for a
subsidiary company, as opposed to the main firm traded on a stock exchange. Regardless,
all of the executives in the sample are senior executives who possess the ability to convey
important information to mutual fund managers.
Additionally, I collected executive biographical information from Forbes.com.5
Although not explicitly stated, it is likely that only executives of larger firms have
biographies on the website, and so the resulting sample is probably slightly skewed
towards larger firms. I matched executives between Forbes and Execucomp using the full
name, the current employer, and the biography when there was any ambiguity. From an
original sample of 7124 executives, I was left with 4689 which were available on
Forbes.com. This step also reduced the number of firms in my sample from 1914 to 1549.
From the biographical information, I constructed an aggregated list of all firms at
which any individual ever worked. I did not include board memberships in this list, so
that any findings would be completely orthogonal to the research which has been done on
connections through board memberships. Using this aggregated list of all past employers,
I employed Capital IQ and executed web searches to trace the corporate structures of
each firm. This was necessary in order to determine a firm’s parent corporation, but was
often difficult since many firms in the list were private. To illustrate the process, consider
if someone once worked for a subsidiary of IBM. The fact that the firm was a subsidiary
5
People.forbes.com contains a searchable database of executives by name or by ticker. 5
of IBM was not always included in the biography. Thus, I used Capital IQ and web
searches to determine if a firm has a parent corporation, which in this case is IBM. I then
considered this firm to be IBM in the aggregated list. Additionally, I had to account for
historical corporate events such as mergers and acquisitions. For instance, two firms that
once were completely different may have become the same firm through a merger or
acquisition. In the aggregated list, I considered two firms to be the same firm if a merger
or acquisition occurred before the beginning of my sample period, or 2003. If a mutual
fund manager and firm executive are only connected through a past employer because of
a merger immediately before 2003, at the very least they may be aware of this merger and
thus the connection. On the other hand, this connection is likely not nearly as strong as
one developed by two individuals who worked for the same firm at the same time, or
even different firms that merged closer to the individuals’ employment dates at the firms.
Thus, the methods I used to sort through corporate structures may increase the
number of connections at the possible expense of the quality of some connections. In
light of the relatively low number of connections,6 these methods seem justified and
unavoidable. In order to avoid the issue of corporate structures, one could possibly
construct time series employment data on individuals and executives where the exact
dates of employment were included. This is resource intensive and outside the scope of
the analysis, however, but could be of further interest. Next, I determined each firm’s
fiscal year from Compustat and used this in conjunction with the executive employment
data, which had unspecific employment dates, such as “2007.” Whenever there were two
executives with the same title and the same year, I assumed that the more recent
executive took over the position midway through the fiscal year.
6
On average roughly 1.54% of fund assets are connected stocks. See Table III.
6
Ultimately, I restricted my sample to after January 2003 because it became more
difficult to collect biographical data on managers farther in the past. Moreover, I did not
include 2009 in the analysis because the executive data from Execucomp was not as
complete at the time of compilation. Since the first mutual fund holdings report date in
my sample is not until March 2003, the final sample of mutual funds and firms lies
between March 2003 and December 2008. Table I gives the final sample sizes along with
additional summary statistics, such as the past employers with the most connections. The
data reveal that past employers of mutual fund managers are more concentrated than past
employers of firm executives. For instance, PricewaterhouseCoopers is the most
connected past employer of firm executives, but is connected to only 4.12% of the firms
in my sample on average each year. In contrast, Bank of America, the most connected
past employer of fund managers, is connected to nearly 11% of funds on average each
year. This makes sense given that fund managers tend to originate in financial services.
Furthermore, for the main analysis, I use a filtered sample of funds which
includes only those in the top fifty percentile of total connections within fund holdings.
This removes a number of funds which have an extremely low number of connections. In
fact, 63 of the original 206 funds possessed zero connections within their holdings over
the entire time period. A low number of connections can result from the fact that certain
managers may simply have a limited potential to form connections through their
employment networks. For instance, many Fidelity managers began their careers at
Fidelity as analysts, and so they have few if any additional firms in their employment
backgrounds. Since funds were chosen prior to viewing the employment background in
biographies, this was an unavoidable consequence. On the other hand, using the filtered
7
sample helps attenuate a measurement issue with regard to the data collection. When a
manager possesses a large number of connections in the sample, it indicates that the
manager’s employment network is likely more accurate and complete. For many fund
managers, biographical data was difficult to find, and so it is likely that biographical data
is often incomplete. Thus, when a manager possesses a very low number of connections,
it could indicate that I have not been able to completely discern the manager’s
employment network, which could lead some connected stocks to be misclassified as
unconnected stocks, attenuating any holdings or performance differences. Additionally, if
a fund possesses more connections in holdings, then the managers may be more
cognizant of the connections and thus may be more likely to utilize them. If a fund
manager has only a few fund-firm-quarter connections in their holdings over the six year
period, they may not even be aware of them, given that a fund holds almost one hundred
stocks each quarter. By removing observations such as these, I remove observations that
may be false positives from the point of view of the manager, a priori any results.
It would have been possible to restrict the sample based on total connections, as
opposed to connections only within holdings. However, simply because a fund has
connections to firms in the entire firm universe does not mean this fund has the capability
to invest in these firms. Therefore, I attempt to restrict the sample to funds that are
connected to firms and also that have the ability to invest in them. For all of these
reasons discussed, I believe this filtered sample to be the most appropriate for analysis.
Nonetheless, I include brief results for a few filtered and unfiltered samples in Section V.
8
III.
Results of Portfolio Holdings
First, I explore whether or not portfolio managers are influenced in their holdings
decisions by their employment networks. Specifically, I seek to compare the portfolio
weights of connected stocks with the portfolio weights of unconnected stocks, in order to
determine if either group is treated differently by managers. I define a binary variable
CONNECTED for every fund-firm-quarter observation in fund holdings. In a given
quarter, a fund is connected to a firm if any of the fund managers have overlapping
employment networks with any of the senior executives at the firm. More simply, if any
of the fund managers worked at a firm where one of the senior executives also worked,
then I define this to be a connection.
In a variety of pooled OLS regression specifications, I consistently find that
managers place larger bets on firms to which they are connected; these results are both
statistically and economically significant. With controls, I find that funds invest on
average an additional 24 basis points in connected stocks compared to unconnected
stocks. In each regression, the units of observation are fund-firm-quarter. The dependent
variable is the percent of total net equity assets of the security within the fund portfolio in
a given quarter. The independent variable is the binary variable CONNECTED. In
different regression specifications, I include different control variables. Specifically, I
experiment with combinations of the momentum factor (Carhart (1997)) and the three
Fama - French factors (1993).
Using these controls is motivated by the fact that
“connectedness” may in fact be correlated with observable factors. Indeed, when size is
considered, the magnitude of the effect drops significantly from roughly 35 basis points
to 23 basis points. This implies that funds may overweight connected stocks simply
because connected firms tend to be larger than the average firm. One explanation is that
9
there may be more connections at larger firms, since the employment numbers are so
much larger. Additionally, this effect is likely an unintended consequence of the fact that
employment data was easier to find for larger firms, given that it originated from
Forbes.com. Furthermore, I also include quarter fixed effects in most specifications and
experiment with additional fixed effects such as firm, firm-quarter, and industry code, the
latter of which is based on the Fama-French (1997) 49-industry codes. In each regression
I cluster standard errors by quarter. Table II illustrates that the additional control
variables have little effect on the results of the regression. Indeed, all of the results tell a
similar story: managers place larger bets on stocks within their employment network.
Across my sample managers invest on average 121 basis points of their portfolio in each
unconnected stock, and thus the 24 basis points difference I found is nearly a 20%
difference. I also find that these results are robust within sub-periods of the time series.
(Table IV) Furthermore, I cluster standard errors by fund-firm in unreported tests and
find statistical significance at the 5% level for each specification. Although the average
stock portfolio weight seems high, given the propensity for mutual funds to hold often
more than 100 stocks, some of this difference can be attributed to the fact that I was more
likely to find data on larger firms. In addition, I suspect the particular funds in the sample
possessed fewer stocks than the average mutual fund.
One might expect these results for a variety of reasons. First, managers may tend
to overweight stocks within their employment networks simply because of familiarity
bias.7 Additionally, managers may invest in executives with overlapping employment
networks because they believe this background to be indicative of positive future
7
Cohen, Frazzini, and Malloy (2008) discuss this explanation with respect to connections through education networks;
they mention the related results of Huberman (2001) who finds that shareholders in Regional Bell Operating Company
tend to live in its service areas.
10
performance. Due to his or her own self-image, a fund manager might simply have
greater respect for executives with whom he or she shares employment experiences.
Thus, the manager may tend to overweight these executives because of a belief about
positive future performance. It would be difficult to disentangle the relative strength of
past employers; therefore, I cannot completely discount this as at least a partial
explanation. Next, managers may overweight connected stocks because these stocks
correlate closely with another attribute that might lend itself to overweighting. In
particular, connected stocks might be concentrated in an industry that performed
especially well during this period. However, the momentum control helps address this
concern, and the results on portfolio performance discussed in the next section tend to
refute each of the reasons just mentioned.
Indeed, managers may overweight connected stocks because they have better
information on these stocks. This improved information could be the result of many
factors related to overlapping employment networks. First, it might be easier to gather
evaluative information on the executives, since the fund manager and firm executive are
more likely to have mutual contacts. In this case, the fund manager may feel more
confident on his assessment of a stock and its executives, and thus might be more likely
to place larger bets in connected stocks. Larger bets on a firm could result in extreme
overweighting and underweighting. In the latter case, some connected stocks could be
weighted to zero and drop completely out of the sample due to a manager’s negative
conviction; this would further enlarge the average portfolio weight of connected stocks.
Second, it might be easier for a fund manager to gather public information on a connected
firm, due to improved access to the firm’s executives. Alternatively, corporate executives
11
might provide better, more timely information to fund managers within their employment
network. This could be the indirect result of being comfortable during a discussion or
could be the direct result of intentionally favoring someone within their employment
network. An executive might feel a strong allegiance to a past employer, and thus might
be inclined to assist anyone who also worked for that employer. In addition, an
employment connection between a manager and executive raises the possibility that the
pair may actually know each other, which could facilitate communication and possibly
favoritism.8 The potential positive effects of these social connections are grounded in
homophily, or the idea that people tend to associate with, or have affinity for, those who
are most similar to them. (McPherson, Smith-Lovin, and Cook 2001)
IV.
Portfolio Performance Results
I directly test the hypothesis that managers may have advantages in gathering
information about their connected stocks. If improved information is truly flowing to the
manager, then we would expect to see managers perform better on connected stocks than
unconnected stocks. In order to test this hypothesis, I construct two monthly calendartime portfolios from the fund-firm-quarter observations: one based on connected stocks
and one based on unconnected stocks. Since holdings data is only available quarterly or
semiannually, I assume that holdings do not change between reports.9 I then create
connected and unconnected calendar-time sub-portfolios for each fund. For each subportfolio, I calculate value-weighted monthly returns, using the monthly returns of the
stocks and weighting by fund’s dollar holdings in each stock. I rebalance sub-portfolios
each calendar quarter based on the most up to date holdings data. Next, I value-weight
8
9
See Tang (2009) for a discussion on direct, personal connections versus social connections.
See Cohen, Frazzini and Malloy (2008), Tang (2009), and Grinblatt and Titman (1989).
12
the sub-portfolio monthly returns across funds, weighting each by the fund’s total net
equity assets. In each quarter a large number of funds have zero connections, thus it is
important to note that these funds are not included when weighting the connected
portfolio within a given quarter. The result is two calendar-time portfolios, one based on
connected stocks and one based on unconnected stocks. With the portfolios I compute
risk-adjusted returns, using Carhart’s (1997) four-factor model, which adds his
momentum factor to the three Fama and French (1993) factors. I also compute raw
returns, which are summarized in Table III along with the main results.
The results are consistent with the hypothesis: the connected portfolio has a
higher risk adjusted alpha and a higher annual raw return than the unconnected portfolio.
The effect is both statistically (1%) and economically significant, with an annual riskadjusted alpha of approximately 12.16%. In addition, a portfolio constructed by buying
connected holdings and shorting unconnected holdings yields a statistically significant
(2%) risk-adjusted alpha of approximately 10.91%. Therefore, I find conclusive evidence
that managers outperform the relevant benchmarks on connected holdings. Additionally,
the effect of employment network connections appears larger than the effect of education
network connections documented by Cohen, Frazzini and Malloy (2008). This suggests
that connections forged through work environments may be more effective than
connections forged through common educational backgrounds, at least in this setting. The
annual average number of past employers (3530) is much greater than that of past
academic institutions (341);10 consequently, on average, there should be fewer
connections to each past employer than connections to each educational institution. Thus,
there may be more solidarity between managers and executives who are connected
10
See Table I in Cohen, Frazzini and Malloy (2008).
13
through employment networks. This is implied by in-group bias, or preferential treatment
towards members of one’s own group, which has been shown to increase with the
distinctiveness of a group. (Gerard and Hoyt 1974) The greater solidarity could help
explain the greater effect. Additionally, the outperformance on connected holdings does
not come at the expense of greater risk; the Sharpe ratio on connected holdings is .79
versus a Sharpe ratio of .20 for the unconnected holdings. Finally, the fact that mutual
fund managers do in fact earn abnormal returns on connected holdings indicates that
managers are not necessarily acting irrationally by placing larger bets on connected
stocks.
I perform an additional test on each sample to rule out the possibility that all
connected stocks, held and not held, earn abnormal returns. This could be a concern, for
instance, if mutual fund managers happen to be connected to outperforming firms. For
instance, if a mutual fund manager was previously employed by a very elite firm, then
one might expect current executives who previously worked there to outperform.
Additionally, mutual fund manager connections might simply be concentrated in
industries that performed abnormally well during the sample period. Therefore, I
construct a portfolio comprised of purchasing connected stocks in which funds hold and
shorting connected stocks in which funds do not hold, where the latter stocks are value
weighted within each fund sub-portfolio by their market weight. This long-short portfolio
yields a risk-adjusted alpha of 10.66%, which directly refutes the two proposed
explanations. Ultimately, this result strengthens the conclusion that fund managers are
truly obtaining improved information on connected stocks, enabling the managers to
selectively invest in them. Additionally, if mutual funds were capable, we might even
14
see them shorting a batch of these connected stocks that are not held, in which received
information on the corporation may have indicated negative future performance.
V.
Robustness
In this section I attempt to address some of the issues raised and discuss further
considerations. First, I explore the use of the main filtered sample versus other
possibilities. As Panel A of Table IV indicates, using several different filtered samples
yields similar results. As expected, the magnitude and significance of both the portfolio
weight and performance seem to increase as the sample becomes more filtered, which is
likely the result of improved data quality and more manager awareness of connections.
Next, Panel B outlines several additional tests on the main sample related to
portfolio weights.
The results clearly indicate that managers place larger bets on
connected stocks, and this is consistent throughout different sub-periods. Interestingly, I
find a much stronger effect in the earlier part of the period compared to the latter. During
this period, roughly 2003-2007, the stock market experienced a major boom as the value
of the S&P increased over 60%.11 Indeed, executives may have been more willing to
share positive information during the boom, which could have led managers to
overweight these stocks more generously.
Alternatively, the difference may be
attributable to manager actions in 2007 and 2008. The stock market stopped climbing in
2007 and experienced a massive fall in 2008 as part of the financial crisis.12 Thus,
managers may have had advanced knowledge about their connected firms which led them
to unwind those positions. However, the results on performance discussed next tend to
refute the latter explanation.
11
12
On January 6 2003, the S&P closed at 908.59, and on December 31, 2007, the S&P closed at 1475.25.
On October 5, 2007, the S&P closed at 1557.59, and on Dececmber 26, 2008, the S&P closed at 872.80.
15
Panel C displays several additional tests related to portfolio returns for connected
holdings. First, I split my sample of firms into the top half and bottom half by market
capitalization; I recalculate connected and unconnected portfolio returns, restricting the
portfolios to only large firms (top half) or only small firms (bottom half). I find similar
connection premiums of 10.23% for large firms and 11.00% for small firms, where the
connection premium is the annual four-factor alpha of a portfolio formed by buying
connected holdings and shorting unconnected holdings. However, the small firms result
is without statistical significance, which is consistent with the fact that information on
executives was more easily obtained for larger firms, making the CONNECTED variable
more reliable in this instance. In order to account for this, I perform a separate four-factor
regression on the full sample of firms using a portfolio of large cap stocks (top 20% of
NYSE/AMEX/NASDAQ) in place of the market portfolio, and I find a reliable
connection premium of 10.81%. Additionally, the inclusion of the SMB factor helps
account for any potential bias. Therefore, it does not appear the main results are driven by
abnormal returns to all larger firms.
Next, within different sub-periods of the sample, I find that connection premiums
are all positive, although without statistical significance, which is not surprising given the
smaller sample size. I find a much lower premium in the latter half of the period (3.37%),
which is consistent with the lower portfolio weights of connected stocks during the
period. This may indicate that the negative returns in 2008 extended even to connected
holdings, which would be expected given the discovery by Cohen, Frazzini, and Malloy
(2008) that abnormal information flow from executive to fund manager tends to only be
positive in nature. Alternatively, during this massive fall in the stock market, it is possible
16
that stocks were trading on reasons other than fundamentals; thus it may be a time period
in which information advantages are less likely to convert to abnormal returns.
Nonetheless, I cannot reject the null hypothesis that the abnormal returns from this period
are equal to zero.
Additionally, Panel C includes tests meant to determine whether or not connected
holdings display any performance persistence. Lags are introduced between the report
dates for fund holdings and the actual stock returns from CRSP. The connection premium
tends to persist notably with both a one month lag (8.01%) and a two month lag (5.84%),
the first of which is significant at the 6% level. These results imply that an investor could
profit by replicating a portfolio of connected stocks, since funds are required by the SEC
to file holdings within 60 days after a fiscal period end.13 Additionally, since the premium
tends to persist but also reduce with more lag time, it seems to reaffirm that the abnormal
returns on connected holdings are the result of information advantages, which would
presumably manifest at precise points in time, such as earnings announcements.
VI.
Gender and Ethnicity
In this section I briefly explore two other bases for social networks: gender and
ethnicity. The consideration of both of these is motivated by the principle of in-group
bias. In particular, women have been shown to display strong gender in-group bias
(Rudman and Goodwin 2004). In the context of mutual funds, we might expect women to
display even more bias, since they are undoubtedly the minority.14 Of the entire sample,
13
See the SEC website: http://www.sec.gov/investor/pubs/inwsmf.htm.
Gerard and Hoyt (1974) find that in-group bias tends to increase with the distinctiveness of the group. Therefore,
minority groups should tend to display relatively more in-group bias. 14
17
only 14% of fund managers are female and less than 6% of senior executives are female.
(Table V)
In general, I employ the same methodology used in the main study of this paper
and in the same time-frame, 2003-2008. I determined the genders of mutual fund
managers from their first names, and when there was any ambiguity, I used newspaper
article references. I collected the genders of 7124 executives from Execucomp. For the
analysis, within each quarter I restrict the sample of funds to those with at least one
female manager (female funds). I use a binary variable CONNECTED which equals 1
when at least two senior executives are female, and 0 otherwise. Thus, there is a
connection when at least one fund manager and two executives are female. I find that
female funds place an additional 50 basis points in connected holdings, compared to a
weighting of about 127 basis points in unconnected holdings. This difference is both
economically and statistically significant (10%). Interestingly, I find that male funds
(zero female managers) placed slightly less weight in stocks with two female executives,
significant at the 5% level. (Table V) Thus, women seem to place much larger bets on
firms in which multiple women hold senior executive positions. This could be a signal of
support, or could be the result of information advantages provided by female executives
to female managers.
I thus construct a long-short portfolio using the method outlined in Section IV,
which consists of buying connected holdings and shorting unconnected holdings. The
portfolio yields a four-factor risk-adjusted annual alpha of approximately 4.1%, but
without statistical significance. This is possibly a consequence of the fact that the number
of connections in the sample is very low: on average, only 29 funds each year are female
18
funds and female funds place roughly 1.2% of their assets in connected holdings. In a
separate configuration, I assessed connections between female fund managers and firms
with at least one female executive and failed to find any effects. This may have been a
result of the fact that annually nearly 13% of firms in the sample have at least one female
executive. Thus, these firms are relatively common, and are much less distinct than firms
with at least two female executives.
Last, I consider whether or not ethnic groups produce any of the characteristics
observed with employment networks. For both executives and fund managers, I used
Ancestry.com15 to find the ethnic origins of last names to proxy for an individual’s ethnic
background. However, I failed to find significant results using several different
definitions of connectedness. With a larger sample of funds and managers, this may have
been a more fruitful exercise.
VII.
Conclusion
The results from this paper indicate that mutual fund managers obtain information
advantages through their employment networks. Mutual fund managers place larger bets
on firms to which they are connected, and they also perform better on these holdings. A
portfolio formed by buying connected holdings and shorting unconnected holdings yields
over 10% per year, and these returns do not come at the expense of greater risk. To a
certain extent, the results may reveal that fund managers face lower costs in obtaining
some types of public information about firms. On the other hand, fund managers may
have access to private information by virtue of their employment networks. Significant
15
Ancestry.com includes a searchable utility that allows someone to find the meaning and history of surnames, which
includes the name’s origin.
19
publicity has surrounded insider trading recently16 and this paper arguably provides
empirical evidence that insider trading is both meaningful and prevalent, unconfined to
the isolated cases that become publicized.
Additionally, this study is related to the literature on actively-managed mutual
fund performance. Although my research does not speak to the broad performance of
funds, it does inform regarding the process by which managers may outperform on
individual stocks. Connected holdings comprise a relatively low percentage of a fund
portfolio, so their effect on aggregate performance is modest. However, connections
through education networks (Cohen, Malloy, and Frazzini 2008) and past business
dealings (Tang 2009) were shown to provide similar benefits for mutual fund managers.
Further research could include all three networks, along with other possible networks that
remain to be explored. I briefly explored two of these, gender and ethnicity, both of
which deserve further consideration. If networks are aggregated, the effect of connection
premiums may no longer be so modest. Of the very few managers who may have true
stock picking ability,17 it remains to be seen to what extent their returns are driven by
information advantages from social networks.
16
Many have recently been indicted in an insider-trading case involving New York based hedge fund, Galleon Group.
http://online.wsj.com/article/BT-CO-20100323-708873.html
17
An ongoing debate persists on whether or not actively managed mutual funds outperform the market. For instance,
Carhart (1997) finds managers do not outperform benchmarks and Wermers (2000) finds that managers can outperform
passive indices before expenses. 20
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22
Table I: Summary Statistics
23
Table II: Portfolio Weights of Connected vs. Unconnected Stocks
24
Table III: Returns of Connected Holdings
25
Table IV: Robustness
26
Table V: Gender Study
27