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What's in a (Green) Name? The Consequences of Greening Fund Names on Fund Flows, Turnover, and Performance

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We examine a sample of U.S. mutual funds and find that, between 2003 and 2018, 28 funds have changed their name to a sustainability-related appellation. Following the name change, we observe three main outcomes: (i) an increase in fund flows, (ii) a significant rise in portfolio turnover, and (iii) no substantial change in fund betas and alpha.
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What’s in a (Green) Name?
The Consequences of Greening Fund Names on Fund Flows, Turnover, and Performance
Forthcoming in the Finance Research Letters
Sadok El Ghoul
Campus Saint-Jean, University of Alberta
8406, Rue Marie-Anne-Gaboury (91 Street), Edmonton, AB T6C 4G9, Canada
Phone: 780-465-8725
Fax: 780-465-8760
elghoul@ualberta.ca
Aymen Karoui*
Glendon College, York University
2275 Bayview Avenue, Toronto, YH 330, Canada
Phone: 416 736 2100#88154
Fax: 416-487-6851
aymenkar@yorku.ca
May 30, 2020
* Corresponding author
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Whats in a (Green) Name?
The Consequences of Greening Fund Names on Fund Flows, Turnover, and Performance
Abstract
We examine a sample of U.S. mutual funds and find that,
between 2003 and 2018, 28 funds have changed their names to a
sustainability-related appellation. Following the name change, we
observe three main outcomes: (i) an increase in fund flows, (ii) a
significant rise in portfolio turnover, and (iii) no substantial change
in fund betas and alpha.
JEL classification: G11, G23, M14
Keywords: Mutual fund flows; Socially responsible investing; Fund names; Fund performance;
Turnover; Sustainability
3
1. INTRODUCTION
Since the 2007-2008 financial crisis, socially responsible investing (SRI) has emerged as
the fastest-growing category in the U.S. mutual fund industry. The assets under management of
U.S. funds incorporating environmental, social, and corporate governance (ESG) factors have
registered a tenfold increase between 2007 and 2016.
1
Further confirming the keen interest in this
category, a recent poll published by Morgan Stanley research in 2017 shows that 75% of U.S.
investors are interested in SRI.
2
By contrast, the mutual fund industry has grown at a much slower
rate and its assets under management registered an increase of merely 35% over the 2007-2016
period.
3
This stark difference in growth rates may have created an incentive for fund managers to
switch from conventional strategies toward the more promising socially responsible segment. A
recent report published by Morningstar (Hall, 2018) confirms this intuition and points out the new
practice of repurposing conventional funds toward sustainable strategies. Our paper sheds light on
this phenomenon. Specifically, our paper examines the process of changing names to
sustainability-related appellations (i.e., greening fund names) and its implications in terms of fund
flows and portfolio characteristics.
4
To take advantage of the flourishing SRI sector, mutual fund families may either launch
new funds or simply transform existing ones. The second alternative is, however, cheaper and
faster to implement. More importantly, it allows a fund to be withdrawn from a declining segment
1
The assets under management of U.S. funds incorporating ESG factors increased from $202 billion in 2007 to $2,597
billion in 2016. See the report on “US Sustainable, Responsible and Impact Investing Trends, 2016” (SIF, US, 2016,
p. 14).
2
https://www.morganstanley.com/ideas/sustainable-socially-responsible-investing-millennials-drive-growth.
3
The U.S. mutual fund assets under management increased from $11,995 billion in 2007 to $16,353 billion in 2016.
See https://www.icifactbook.org/deployedfiles/FactBook/Site%20Properties/pdf/2019/19_fb_table1.pdf.
4
The white paper of Hall (2018) focuses on funds adopting sustainable policies and examines the outcomes of such
adoptions. Our paper instead focuses on fund name changes. Historical fund name changes are only available in the
CRSP mutual fund database, and not in the Morningstar database.
4
and repositioned into a more dynamic one. Hall (2018) indeed shows that, in 2017, about 40% of
the assets under management of ESG funds are in repurposed funds. Hence, the phenomenon of
repurposing funds is on the rise and is therefore worth exploring.
Cooper et al. (2005) examine name changes among U.S. mutual funds and find strong
evidence that funds that change their names to reflect current hot styles attract additional flows.
Their study considers changes to value/growth and small/large styles. We translate this idea to the
SRI context and examine the effects of adopting a green appellation on fund flows, turnover,
and performance. We verify whether the greening process is beneficial for fund flows. Next, we
test whether name changes are cosmetic. Non-cosmetic name changes should exhibit a surge in
portfolio turnover and an increase in fund exposure to the MSCI KLD 400 social index (i.e., an
SRI index). Lastly, we investigate whether a name change alters the risk exposure and performance
of the fund.
To test the aforementioned relationships, we examine a sample of U.S. domestic mutual
funds and find that, over the period 2003-2018, 28 funds have changed their names to greener
appellations. The trend seems to have accelerated in the last three years of the sample, as 17 out of
the 28 name changes have taken place between 2016 and 2018. The most frequent name changes
include the words ‘sustainable’, ‘ESG’, ‘green’, and ‘impact’.
Next, we examine the change in fund characteristics surrounding the date of the name
change. In a univariate analysis, we find that fund flows increase in the year following the greening
of the fund name by 1.18% per month or 14.16% per year. This result is robust to using alternative
measures of fund flows, such as excess flows. Our findings are in line with Cooper et al. (2005),
who find a 28% increase in fund flows in the year following a fund name change to a currently hot
style. The regression analysis confirms this result for the full sample. Yet, the relationship between
5
name changes and fund flows is more pronounced for funds with high turnover and high exposure
to the MSCI KLD 400 social index. Hence, investors do not seem to direct their flows to funds
with cosmetic name changes.
Next, we find that a name change is also accompanied by a surge in fund turnover, going
from 0.71 one year before the name change to 1.03 one year afterwards, thereby signaling an
overhaul of the portfolio. There is also an increase in the exposure to the MSCI KLD 400 social
index, albeit a statistically insignificant one. Hence, the changes in fund names are, on average,
not cosmetic changes (i.e. greenwashing) but actually involve substantial portfolio rebalancing.
Lastly, we examine whether the rebalancing of the equity fund portfolios significantly changes
their exposure to common risk premia. Using the Carhart (1997) model, we find no evidence of a
change in either the factor loadings (i.e. MKT, HML, SMB, and MOM) or the risk-adjusted
performance (i.e. alpha) of the fund three years after the name change.
Overall, our results are consistent with a simple story: greening a fund name is beneficial
for fund flows and is accompanied by substantial rebalancing of the portfolio. The rebalancing
activity, however, does not significantly alter either the exposure of the portfolio to the common
risk premia or its risk-adjusted performance (i.e. alpha).
Our paper makes two contributions to the mutual fund literature. First, we contribute to the
literature on the relationship between fund names and fund flows. Green and Jame (2013) find that
funds with more fluent names attract more flows. Jacobs and Hillert (2015) and Doellman et al.
(2019) show that alphabeticity influences investor decisions, with funds at the top of alphabetical
listings attracting more inflows. Cooper et al. (2005) show that changing a fund name to reflect a
currently hot style brings additional flows to the fund. Espenlaub et al. (2017) find that investors
respond positively to superficial fund-name changes, with increased fund flows. In line with these
6
papers, we show that a change to a sustainability-related fund name captures investor attention and
attracts additional fund flows. We therefore extend the literature on behavioral biases associated
with fund names and examine how greening fund names impacts mutual fund flows.
Second, we add to the literature on SRI funds. Existing literature has mostly focused on a
dichotomous comparison between the characteristics of SRI funds and conventional funds (e.g.,
Statman, 2000; Schröder, 2004; Statman, 2006; Bauer et al., 2007; Renneboog et al., 2008; Benson
and Humphrey, 2008). Our paper sheds light on the process of repurposing conventional funds
into SRI funds. We find that, although the repurposing process involves, on average, significant
rebalancing of the portfolio and is therefore not a cosmetic change per se, it has no impact on either
the performance of the fund or its exposure to the most common risk factors. However, the
repurposing process brings a valuable outcome in terms of fund inflows, and this result is more
pronounced for funds with high turnover and high exposure to the MSCI KLD 400 social index.
2. DATA AND SAMPLE SELECTION
We rely on the CRSP mutual fund database to retrieve information on U.S. domestic funds.
In particular, we extract the following fund characteristics: monthly returns, total net assets (TNA),
expense ratio, turnover ratio, and inception date. We compute monthly fund flows using the
following formula:
     

(1)
where TNAj,t and TNAj,t-1 are the total net assets of fund j in months t and t-1, respectively, and
Rj,t is the return of fund j in month t. We obtain Carhart’s factors from Kenneth French’s website
and the MSCI KLD 400 social index returns from Morningstar Direct.
7
We extract historical fund names from the CRSP mutual fund database to identify fund
name changes. To be recorded as a name change, a fund should have retained the same CRSP fund
number but changed its name. Moreover, since some funds have more than one share class, we
follow Cooper et al. (2005) and select the oldest share class of the fund.
We follow the approach of Nofsinger and Varma (2014) to identify funds that have
switched to a sustainability-related appellation. The new name should include one of the following
keywords: environment, ESG, ethical, governance, green, impact, responsible,
social, SRI, sustainable, and sustainability.
5
6
****Insert Table 1 about here****
Table 1 reports the frequency distribution of the name changes in our sample. The most
frequent change is to include the word sustainable, with 12 funds including this word in their
new name. ESG comes next with a frequency of seven observations, followed by ‘green’ and
‘impact’ with six and five observations, respectively.
To further confirm our initial intuition that the change to appellations reflecting social
responsibility is a recent phenomenon, we report a histogram of the frequency of fund name
changes across years. Figure 1 shows that the name changes start in 2003 and increase substantially
over the most recent period. This is in line with the recent report published by Morningstar (Hall,
2018), which also shows that name changes to greener appellations are expected to increase in the
upcoming years.
5
As suggested by Nofsinger and Varma (2014), we also considered the keywords faith, religion, Christian,
Islam, Baptist’, and Lutheran, but could not find new fund names that included any of these keywords.
6
We also considered the reverse process, i.e., the removal of sustainability-related appellations. However, we could
find only six cases, which limited the possibilities of undertaking statistical tests. This indicates that the trend is toward
investing in socially responsible funds, not divesting of them.
8
****Insert Figure 1 about here****
3. METHODOLOGY AND EMPIRICAL TESTS
3.1. Name-change funds vs. other funds
We follow Cooper et al. (2005) and compare funds that changed their names to the rest of
the sample in the month of the name change. Several interesting results emerge. First, funds
register negative fund flows in the month preceding the name change. This is also the case for
longer horizons that extend to 6 or 12 months. Hence, a decline in fund flows could be the driver
of fund name changes. This is not the case for the rest of the sample, where mean flows are positive
and median flows are higher than in the sample of name-change funds. Second, performance as
measured by past returns over the last month or six months is positive for name-change funds.
Hence, poor performance does not seem to be a motive for changes in fund names. Third, other
fund characteristics, including the turnover ratio, TNA, expense ratio, and fund age, are
comparable across name-change funds and the rest of the sample.
7
****Insert Table 2 about here****
3.2. Fund characteristics surrounding fund name changes: Univariate analysis
To assess the impact of name changes on fund characteristics, we measure the mean return,
flows, expense ratio, and turnover ratio one year before and one year after the name change. Table
3 reports the results of our analysis. For returns, the difference is not statistically significant.
However, there is a significant increase in the fund flows after the name changes. The increase in
7
In unreported results, we also compared a sample of name-change funds and a matching sample of funds (using a
propensity score matching approach similar to Cooper et al., 2005). We find, however, no differences in fund
characteristics between the two groups in the year preceding the name change. Hence, name-change funds do not have
different characteristics. The greening of the fund name seems to be a response to changes in the mutual fund industry
rather than to specific fund factors.
9
the mean is 1.18% per month or 14.14% per year. The t-statistic of the difference in means is
significant at the 1% level.
In order to control for passive changes in fund flows, we compute adjusted fund flows.
First, we compute excess flows relative to all fund flows (i.e., flows of the name-change fund
minus the average flows across all funds). The difference in mean-adjusted flows between the
post- and pre-name-change periods is equal to 1.11% per month and is statistically significant at
5%. Next, we compute excess fund flows relative to matching funds (i.e., flows of the name-change
fund minus the flows of matching funds). Our matching procedure is based on the propensity score
matching (PSM) approach and is similar to that described in Cooper et al. (2005). Each name-
change fund is matched with the non-name-change fund that has the closest propensity score, using
the following characteristics measured one year prior to the name change: age, TNA, expense ratio,
and style. The difference in the mean-adjusted flows, between the post- and pre-name-change
periods, is equal to 1.83% per month and has a t-statistic equal to 4.26. Hence, changing the fund
name increases funds’ raw flows and excess flows.
8
Another interesting result is the surge in the turnover ratio. The latter moves from a mean
(median) of 0.79 (0.41) a year before to 1.03 (0.54) a year after the name change, thereby reflecting
an overhaul of the portfolio. Hence, the change in name coincides with a churning of the portfolio,
most probably to reflect a change in the investment policy of the fund, and is therefore not a merely
cosmetic change.
****Insert Table 3 about here****
3.3. Fund characteristics surrounding fund name changes: Multivariate analysis
8
We considered one, three, and five matching funds for each name-change fund. The results remain very similar
irrespective of the number of matching funds.
10
To further investigate the differences in fund flows, from before to after the name change,
we conduct a multivariate regression in which we control for fund characteristics: returns, expense
ratio, turnover ratio, TNA, past flows, and fund age (as defined in section 2). The test variable is
a dummy variable that takes the value of 0 in the period preceding the change in name, and 1
thereafter. To be in line with the univariate analysis, we consider a panel regression that uses an
interval from one year before to one year after the name change and includes only funds that have
changed name:
       
         
Panel A of Table 4 reports the results for Equation (2). As in our univariate analysis, we
use three different measures of fund flows: monthly flows, monthly excess flows relative to all
funds, and monthly excess flows relative to matching funds. Confirming our univariate analysis
results, we find that name changes are associated with increases in fund flows. The coefficient on
the name-change dummy is positive and statistically significant for the three measures of fund
flows.
In a similar way to the regression setting of Equation (2), we examine the effects of changes
in fund names on the fund turnover ratio:

    
         
****Insert Table 4 about here****
11
The last specification of Panel A of Table 4 reports the results for Equation (3). The
coefficient on the name-change dummy is positive and significant at the 1% level, thereby showing
that name changes are accompanied by a surge in the turnover ratio.
3.4. Responses of fund flows to cosmetic vs. non-cosmetic name changes
To distinguish between flow responses to cosmetic and non-cosmetic name changes, we
sort funds by their turnover ratio and exposure to an SRI index, namely, the MSCI KLD 400 social
index. The two measures are proxies for a fund’s alignment with socially responsible values. We
therefore split our sample into high- (above-median) and low-turnover (below-median) fund
groups and into high- and low-βKLD fund groups. Next, we estimate Equation (2) for the two
subgroups and report the results in Panel B of Table 4. We find that investors react strongly to
name changes in the high-turnover and high-βKLD groups, which contain the funds that have made
non-cosmetic changes. For the low-turnover and low-βKLD groups, the coefficient of the name
change becomes insignificant, indicating there is no relationship between fund flows and name
changes. Hence, investors seem to be able to distinguish between cosmetic and non-cosmetic
changes, and direct their flows to the non-cosmetic-change group.
9
3.5. Responses of fund flows to name changes before and after the financial crisis
Previous research has shown that the SRI sector took off in the aftermath of the 2008-2010
financial crisis (SIF, US, 2016). To further examine the shift in investor behavior, we split our
sample into pre-crisis (i.e. before 2010) and post-crisis (after 2010) periods. The last two
specifications of Panel B of Table 4 report the regression results from Equation (2) for the two
subsamples. In line with this intuition, we find that the sensitivity of fund flows to name changes
9
We also sorted funds into low- and high-R-square groups and the results were very similar to those of βKLD.
12
is significant only in the post-crisis period. Hence, the SRI appellation appears to have become
more attractive only subsequent to the financial crisis.
3.6. Factor loadings surrounding fund name changes
One way to verify whether a fund has significantly shifted its style after a name change is
to examine the factor loadings of the Carhart model (1997). To do so, we run the following
regression over an interval of 36 months before and 36 months after the name change:
        
where  is portfolio j’s net return in month t and Rf,t is the T-bill rate in month t. MKT, HML,
SMB, and MOM are the market, value, size, and momentum factors, respectively. The parameter
(alpha) denotes the risk-adjusted performance of portfolio j.
10
****Insert Table 5 about here****
Panel A of Table 5 reports the factor loadings of Equation (4) 36 months before and 36
months after the name change. Looking at the main market coefficient, we find no noticeable shift
in it. The same is true for the three other coefficients, HML, SMB, and MOM. We also find no
noticeable change in performance as measured by alpha.
Panel B of Table 5 reports similar regression results to Equation (4), except that the market
factor is replaced by the MSCI KLD 400 social index. We find that βKLD increases after the name
change, thereby signaling better alignment with social values. The increase is, however,
statistically insignificant.
10
In our sample of 28 name changes, we have 25 equity funds and 3 bond funds. In estimating Equation (4), we
removed the bond funds and estimated the model only for the equity funds.
13
4. CONCLUSION
The 2007-2008 financial crisis triggered important changes in the asset management
industry. Chief among these changes have been the surge in SRI and the slowdown of traditional
categories of mutual funds. This newly created context has offered an incentive for funds, in search
of additional flows, to migrate from traditional categories toward the fast-growing SRI category.
Our paper examines this repurposing process as captured by the change in fund name and its
consequences in terms of fund flows, turnover, and portfolio exposure.
Using a sample of U.S. mutual funds over the period from 2003 to 2018, we find that 28
funds changed their names to more sustainability-related appellations, and 17 out of the 28 did so
in the last three years of the sample, thereby signaling an emerging trend of switching toward the
sustainability category.
Our study provides three sets of results and a simple message. First, funds that adopt a
more sustainability-related appellation increase their flows by 1.18% per month (or 14.14% per
year) in the year after their name change. Second, funds that change their name proceed to carry
out a significant rebalancing of their portfolio, as gauged by the fund turnover which moves from
0.71 to 1.03, i.e. an increase of 0.32. Lastly, the rebalancing of the portfolio does not significantly
change the exposure to major risk premia, nor affect fund performance, following the name
change.
Our study presents new insights on a bourgeoning trend in the mutual fund industry. Like
any study, it is, however, not exempt from limits. For example, the number of name changes is
relatively small. However, given that this phenomenon is likely to persist, our paper paves the way
for future and more thorough research on the subject.
14
REFERENCES
Bauer, R., Derwall, J. and Otten, R., 2007. The ethical mutual fund performance debate: New
evidence from Canada. Journal of Business Ethics 70(2), pp. 111-124.
Benson, K.L. and Humphrey, J.E., 2008. Socially responsible investment funds: Investor reaction
to current and past returns. Journal of Banking & Finance 32, pp. 1850-1859.
Carhart, M.M., 1997. On persistence in mutual fund performance. The Journal of Finance 52(1),
pp. 57-82.
Cooper, M.J., Gulen, H. and Rau, P.R., 2005. Changing names with style: Mutual fund name
changes and their effects on fund flows. The Journal of Finance 60(6), pp. 2825-2858.
Doellman, T., Itzkowitz, J., Itzkowitz, J. and Sardarli, S., 2019. Alphabeticity bias in 401(k)
investing. The Financial Review 54(4), pp. 643-677.
Espenlaub, S., ul Haq, I. and Khurshed, A., 2017. Its all in the name: Mutual fund name changes
after SEC Rule 35d-1. Journal of Banking & Finance 84, pp. 123-134.
Green, T.C. and Jame, R., 2013. Company name fluency, investor recognition, and firm
value. Journal of Financial Economics 109(3), pp. 813-834.
Jacobs, H. and Hillert, A., 2015. Alphabetic bias, investor recognition, and trading
behavior. Review of Finance 20(2), pp. 693-723.
Hall, J., 2018. Morningstar report. Available at https://www.morningstar.com/lp/sustainable-
funds-landscape-report.
Nofsinger, J. and Varma, A., 2014. Socially responsible funds and market crises. Journal of
Banking & Finance 48, pp. 180-193.
15
Renneboog, L., Ter Horst, J. and Zhang, C., 2008. The price of ethics and stakeholder governance:
The performance of socially responsible mutual funds. Journal of Corporate Finance 14,
pp. 302-322.
Schröder, M., 2004. The performance of socially responsible investments: Investment funds and
indices. Financial Markets and Portfolio Management 18(2), pp. 22-142.
SIF, US, 2016. 2016 Report on US Sustainable, Responsible, and Impact Investing
Trends. Washington, DC: US SIF.
Statman, M., 2000. Socially responsible mutual funds. Financial Analysts Journal 56(3), pp. 30-
39.
Statman, M., 2006. Socially responsible indexes: Composition, performance, and tracking
error. Journal of Portfolio Management 32(3), pp. 100-109.
16
Figure 1
Fund name changes across years
Figure 1 reports the frequency distribution of fund name changes across years.
0
1
2
3
4
5
6
7
2003 2005 2008 2009 2010 2013 2015 2016 2017 2018
17
Table 1
Frequency distribution of name changes
Table 1 reports the frequency distribution of sustainability-related keywords included in the new
fund names. Some fund names may include multiple keywords.
Keywords
Frequency
Sustainable
12
ESG
7
Green
6
Impact
5
Responsible
1
Sustainability
1
Environment
0
Ethical
0
Governance
0
Social
0
SRI
0
Total
32
18
Table 2
Mean and median characteristics in the month of the name change
Table 2 reports average and median fund characteristics at the time of the name change for the
name-change funds and the other funds: monthly return, total net assets, monthly flow, expense
ratio, turnover ratio, fund return over the past 6 months, standard deviation over the past 12 months,
mean fund flow over the past 6 and 12 months, and fund age (in months).
Name-change funds
Other funds
Fund characteristics
Mean
Median
Mean
Median
Fund one-month-lagged flow (%)
-1.39%
-1.02%
0.65%
-0.29%
Fund six-month-lagged flow (%)
-1.28%
-0.82%
0.91%
-0.33%
Fund twelve-month-lagged flow (%)
-0.61%
-0.57%
0.81%
-0.30%
Fund one-month-lagged return (%)
1.58%
1.32%
0.70%
0.58%
Fund six-month-lagged return (%)
3.46%
7.26%
3.61%
3.42%
Expense ratio (%)
1.12%
1.02%
0.94%
0.90%
Turnover ratio (%)
77.3%
40.5%
79.7%
46.0%
Fund one-month-lagged total net assets
303.6
137.1
763.5
138.6
Fund age (in months)
213.5
206.5
224.8
220.0
Std dev. of fund returns over past 12 months (%)
0.108
0.089
0.098
0.081
19
Table 3
Fund characteristics before and after the name change
Table 3 reports the difference in fund characteristics (i.e. monthly return, monthly flow, monthly excess flow relative to all funds,
monthly excess flow relative to matching funds, expense ratio, and turnover ratio) in the year before and the year after funds change
their names to sustainability-related appellations. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.
One year before
One year after
After minus before
Mean
Median
Mean
Median
t-statistic
Monthly return (%)
1.03%
0.80%
0.60%
0.67%
-1.52
Monthly flow (%)
-0.42%
-0.65%
0.76%
-0.27%
3.10***
Monthly excess flow (over all funds) (%)
-1.66%
-1.69%
-0.55%
-1.47%
2.99**
Monthly excess flow (over matching funds) (%)
-1.72%
-0.53%
0.11%
0.12%
4.26***
Expense ratio
0.011
0.010
0.011
0.010
-0.08
Turnover ratio
0.719
0.410
1.037
0.540
3.52***
20
Table 4
Fund flow and turnover responses to fund name changes
Panel A of Table 4 reports panel regressions of (1) monthly flows, (2) monthly excess flows
relative to all funds, (3) monthly excess flows relative to matching funds, and (4) turnover on
lagged name-change dummy and lagged fund characteristics. Name-change dummy equals 0
before the name change and 1 afterwards. The regression considers only name-change funds and
is over the period 2003-2018. Fund characteristics are return, expense ratio, turnover ratio, total
net assets, flows, and fund age. Panel B splits specification (1) of Panel A into high (i.e. above-
median) and low (i.e. below-median) turnover groups, high (i.e. above-median) and low (i.e.
below-median) βKLD groups, and pre-crisis (i.e. ≤2010) and post-crisis (i.e. >2010) periods. For
brevity’s sake, Panel B does not report control variable coefficients. Coefficients are displayed on
the first line and t-statistics in parentheses on the second line. ***, **, and * indicate significance
at the 1%, 5%, and 10% levels, respectively.
Panel A. Fund flow and turnover responses to fund name changes
Flowst+1
Excess flowst+1
(over all funds)
Excess flowst+1
(over matching
funds)
Turnover
ratiot+1
(1)
(2)
(3)
(4)
Name change dummy
0.008**
0.008*
0.023***
0.428***
(2.10)
(1.96)
(3.82)
(4.74)
Return
0.010
-0.007
0.025
-2.290*
(0.18)
(-0.12)
(0.29)
(-1.74)
Expense ratio
-1.044*
-1.199**
-1.771**
-141.0***
(-1.83)
(-2.12)
(-2.08)
(-12.28)
Turnover ratio
-0.000
-0.000
-0.002
(-0.16)
(-0.05)
(-0.58)
TNA
-0.000
-0.000
-0.000
0.000***
(-0.33)
(-0.28)
(-1.55)
(3.47)
Flowst
0.126
0.100
0.200
-6.982***
(1.35)
(1.08)
(1.44)
(-3.33)
Fund age
-0.000*
-0.000
-0.000
0.001***
(-1.79)
(-1.19)
(-1.48)
(2.76)
Intercept
0.016*
0.003
0.013
1.999***
(1.84)
(0.35)
(1.05)
(11.42)
Adj. Rsq.
0.021
0.015
0.050
0.319
N
496
496
496
496
21
Panel B. Fund flow response to fund name changes sorted on turnover, β KLD, and crisis
period
Low
turnover
ratio
High
turnover
ratio
Low
βKLD
High
βKLD
Pre-crisis
2010
Post-crisis
>2010
Name-change dummy
-0.003
0.017***
0.006
0.011*
0.007
0.011***
(-0.59)
(2.90)
(1.21)
(1.77)
(0.54)
(2.87)
Intercept
0.019
0.050***
0.026**
0.010
0.034
0.016*
(1.32)
(3.27)
(2.23)
(0.76)
(1.58)
(1.85)
Adj. Rsq.
0.009
0.082
0.051
0.009
-0.011
0.044
N
240
256
243
253
120
376
22
Table 5
Changes in fund factor loadings
Panel A of Table 5 reports the differences in the Carhart four-factor coefficients between 36
months before and 36 months after the fund name change. Panel B reports the same differences in
the Carhart four-factor coefficients, except that the market index is replaced by the MSCI KLD
400 social index. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.
Panel A. Differences in the Carhart four-factor coefficients
36 months before
36 months after
After minus before
Mean
Median
Mean
Median
t-statistic
α (in %)
-0.13
-0.12
-0.06
-0.16
0.26
βMKT
0.82
0.95
0.86
0.96
0.32
βHML
-0.28
-0.20
-0.05
-0.10
1.55
βSMB
-0.01
-0.06
0.08
0.00
1.27
βMOM
-0.07
-0.01
-0.05
-0.02
0.35
Panel B. Differences in the βKLD coefficient
36 months before
36 months after
After minus before
Mean
Median
Mean
Median
t-statistic
α (in %)
-0.11%
-0.06%
-0.24%
-0.18%
-0.56
βKLD
0.88
0.91
0.97
0.93
1.02
βHML
-0.28
-0.19
-0.18
-0.02
0.74
βSMB
0.09
0.08
0.15
0.06
0.89
βMOM
-0.08
-0.05
-0.06
-0.02
0.42
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