Finance, Markets and Valuation
DOI:
10.46503/QLUV5221
Corresponding author
Gerhard Lechner
Received: 21 Nov 2017
Revised: 12 Dec 2017
Accepted: 13 Dec 2017
Finance, Markets and
Valuation
ISSN 2530-3163.
Finance, Markets and Valuation Vol. 4, Num. 1 (January-June 2018), 1–14
Relationship between mutual funds and hedge funds
performance in dierent periods
Relación entre la performance de los fondos de inversión
y los hegde funds en diferentes periodos
Gerhard Lechner
ID
1
, Benjamin Fauster
2
1
Faculty of Economics and FH Joanneum Graz, University of Applied Sciences. Austria Email:
gerhard.lechner@fh-joanneum.at
2
Joanneum Graz, University of Applied Sciences. Austria
JEL: G11; G15; G23; C58
Abstract
The hedge fund literature has already shown that hedge funds and mutual funds follow a dierent strategy.
One result of the literature was that mutual funds herd into or out of stocks following the herd of hedge
funds one quarter later. The aim of this paper is to find out whether herding behavior of mutual funds have
changed aer the financial crisis. Our paper compares mutual funds and equity hedge funds in general
(not only large hedge funds). The hypothesis is that mutual funds are not herding to equity hedge funds
as strong as before the crisis. We use OLS regressions and correlation analysis to test the aforementioned
hypothesis. We found that the monthly returns of hedge funds and mutual funds have synchronized in
developed markets aer the financial crisis. Therefore, the argument that mutual funds herd hedge funds
is at least not as strong as before. The improving eectiveness and price informativeness could be an
explanation for this changing environment.
Keywords: Equity Hedge; Hedge fund performance; Mutual fund performance; Equity Indices; Herd behav-
ior.
Resumen
La literatura dedicada a los hedge fund ya ha demostrado que éstos y los fondos de inversión siguen
estrategias diferentes.Así, se ha demostrado que los fondos de inversión en su conjunto compran o venden
las mismas acciones que los hedge funds pero con un retraso de un trimestre. El objetivo del presente
trabajo es comprobar si este comportamiento gregario de los fondos de inversión se ha modificado tras la
crisis financiera. Con esta intención se comparan los fondos de inversión y los hedge funds en general (no
solo los hegde funds de gran tamaño). La hipótesis es que los fondos de inversión ya no siguen en tropel
a los hedge funds con la misma intensidad que en los tiempos previos a la crisis. Para comprobar si se
verifica esta hipótesis empleamos regresiones OLS y análisis de correlación. Como resultado, encontramos
que las rentabilidades mensuales de los hedge funds y los fondos de inversión se han sincronizado en los
mercados desarrollados tras la crisis. Por lo tanto, el argumento de que los fondos de inversión siguen la
How to cite: Lechner, G., Fauster, B. (2018) Relationship between mutual funds and hedge funds
performance in dierent periods. Finance, Markets and Valuation 4(1), pp. 1–14.
1
Finance, Markets and Valuation Vol. 4, Num. 1 (January-June 2018), 1–14
estela de los hedge funds ya no es igual de válido que en el pasado. Este cambio podría explicarse como
consecuencia de una mayor eficiencia y mayor información de los precios.
Keywords: Equity Hedge; Hedge fund; Fondo de inversión; Índice bursátil; Comportamiento gregario.
1 Introduction
Hedge funds were the top performers in the investment universe in the 1990s and at the
beginning of the new millennium. However, aer the financial crisis hedge funds lose some
momentum in comparison to other asset classes and many institutional investors sold their
hedge funds exposure. Brown (2016) even spoke of a crisis in the hedge funds industry, because
investors significantly reduced their exposure in July 2016. The author also made clear that the
reason for the weakness is not hard to find, because the S&P 500 had an annualized return of
14.5% from January 2009 through March 2016 and the HFRI Asset Weighted Composite Index
and the Dow Jones Credit Suisse Hedge Fund Index recorded an annualized return (aer fees)
of only 6.1% in the same period. There are still not many papers in literature which discuss the
weakness of the hedge funds industry aer the financial crisis. Lechner y Beinhauer (2017) found
that the Dodd Frank Act was definitely a moment from where hedge funds have lost momentum.
The authors found a relatively high correlation of hedge funds to the CRB commodity index
and to the MSCI Emerging market equity index. It was also interesting that the correlations
of most hedge funds strategies increased with the S&P 500. Caglayan y Ulutas (2013) already
found a strong link between Emerging Market equities, Emerging Market currencies and the
future return of global macro hedge funds. However, the weakness on commodity markets and
Emerging Market equities was just one explanation or cause according to the study. There is
also an increasing general market eiciency and hedge funds were one of the largest profiteers
of weaker market eiciency.
Field-specific literature reveals many studies about the performance of hedge funds and
mutual funds. For example, Liang (1999) examined the performance of hedge funds and mutual
funds. His results demonstrated the relatively good performance of hedge funds. The risk-return
profile and the Sharpe Ratio of hedge funds were better than those of mutual funds. However,
his study was about the 1990s where hedge funds had golden times. There have already been
done some studies about the relationship of hedge funds and equity mutual funds. For example,
Eling y Faust (2010) compared the performance of hedge funds and mutual funds in emerging
markets. The reason for their study was the limited possibility of Emerging Market hedge funds
to be short of certain instruments like Emerging Market equities or Emerging Market bonds
(Government and Corporate bonds). The question was what could be the added value of hedge
funds if their ability to be short in the markets was limited. The authors used five dierent
performance measurement models to show whether Emerging Market hedge funds could
generate an alpha in comparison to Emerging Market mutual funds. The results showed that
Emerging Market hedge funds were able to generate alpha, but Emerging Market mutual funds
could not outperform the benchmark. According to the study the reason for the outperformance
of Emerging Market hedge funds was that these funds were more active in changing their asset
allocation than mutual funds. However, the data period from February 1995 until 2008 did
not consider aer crisis development. Another reason why Emerging Market hedge funds
outperformed mutual funds could be that hedge funds are able to mix their portfolio with
bonds and equities. Mutual funds are oen either specialized on equities or bonds.
A study of Jiao y Ye (2014) examined whether there is a relationship in the equity trading
Gerhard Lechner and Benjamin Fauster 2
Finance, Markets and Valuation Vol. 4, Num. 1 (January-June 2018), 1–14
strategy of mutual funds and large hedge funds. They found that mutual funds and large hedge
funds herd aer each other. According to the study mutual funds herding measure is positively
related to last quarters hedge fund herding. That means hedge funds buy or sell equities in
quarter i followed by mutual funds in the quarter i+1. The study used a sample period from
2000:Q1-2007:Q2. Another result of the study was that when mutual funds followed hedge
funds a significant price impact was the consequence, whereas hedge funds herding did not
destabilize prices. The top 30% of mutual funds following hedge funds does so persistently and
this group was also responsible for price reversals in equity markets. The limitation of this study
was that the authors only used long equity investment of hedge funds. Jiao (2012) has already
examined the relationship between hedge funds activities in the equity markets and equity
prices. In the expansion period from 2000 until 2009 hedge fund trading in equities predicted
increasing stock returns one quarter ahead and return reversals in the second year.
The aim of this paper is to analyze the relationship between hedge funds and mutual funds
returns in dierent periods starting from 2000 to April 2017. We want to analyze three dierent
periods, namely, from January 2000 to December 2006, from January 2007 to December 2008
and from January 2009 to April 2017. The target is to show that hedge fund performance weak-
ened in relation to mutual fund performance aer the financial crisis. A weaker performance
of hedge funds in relation to mutual funds would be furthermore an indication that herding
behavior in the form which was described by Jiao y Ye (2014) has weakened. However, we await
that the month on month correlation between hedge funds and equity mutual funds have
increased aer 2009, because of improved price informativeness (Bai, Philippon, y Savov, 2016)
and regulations (Dodd Frank Act, Solvency II) which hit hedge funds (Lechner y Beinhauer, 2017).
The latter argument is our main contribution to the literature, because the influential times of
hedge funds for the market development are likely to be over. At least when we take the time
period aer the financial crisis the influence of hedge funds on market trends have diminished.
However, one argument should not be underestimated. The performance of Emerging Mar-
ket equities was very weak aer the financial crisis and hedge funds probably increased their
exposure in this illiquid asset class. Aiken, Kilic, y Reid (2016) demonstrated that hedge funds
are not able to find the right timing for Emerging Market equities and conversely Caglayan y
Ulutas (2013) found a positive relation between hedge funds future returns and their exposure
to Emerging Market equities and Emerging Market currencies. Caglayan y Ulutas emphasized
that only directional hedge fund strategies have the right timing ability in investing in Emerging
Market equities.
2 Methodology and data
Contrarily to Eling y Faust (2010) our focus will be on the comparison of the performance
of worldwide hedge funds and equity mutual funds. We dierentiate between three dierent
periods. The first period is from January 2000 until December 2006 which displays the period
before the financial crisis. This period was very favorable for hedge funds according to many
studies in the literature. The second period we use is the time immediately before and at the
financial crisis from January 2007 to December 2008 and the third period consists of the time
aerthe financial crisis fromJanuary 2009 tillApril 2017which became a more andmorediicult
time for hedge funds. For all data we used monthly return data. All data are net returns which
means that fees are already extracted. For hedge funds we used the data of CISDM which are
open for the public. Therefore, our study can be compared for example with Capocci y Hübner
(2004); Chen y Chen (2009); Ding y Shawky (2007); Eling y Faust (2010); Kouwenberg (2003).
Gerhard Lechner and Benjamin Fauster 3
Finance, Markets and Valuation Vol. 4, Num. 1 (January-June 2018), 1–14
The study of Jiao y Ye (2014), which is very relevant for our paper used data from Thomson
Financials CDA/Spectrum 13 F and the study of Lechner y Beinhauer (2017) used Hedgefund
Research (HFR) data. Furthermore, Jiao y Ye (2014) used quarterly returns instead of monthly
returns and they focused on large hedge funds. Additionally, as mentioned this study used only
long equity investments of hedge funds. Therefore, our study is not directly comparable with
this study. The data for mutual funds, equity indices, three month EURIBOR and three month
US LIBOR are from Bloomberg. As a first step we filtered out funds which do not exist for the
whole period from January 2000 to April 2017. Furthermore, we only used retail tranches of
equity mutual funds. Finally, we selected 458 European, 414 North American and 146 Emerging
Market equity funds. For the classification of the region we used management focus of the
funds and not the place of residence. We calculated equal weighted indices for each region
with monthly returns of the funds. Concerning the performance data of hedge funds there are
some biases which are already very well known in literature. For example, D. G. Kaiser (2009)
and Lhabitant (2009) discussed the survivorship bias, the backfilling bias, the selection bias
and multi-period sample bias.
We want to use a very simple model to test whether mutual funds are following the trend of
hedge funds. A simple OLS regression with the following equation will be tested:
Y
t
= β
Long Short Equity
X
1t
+ β
Equity Market Neutral
X
2t
+ β
Merger Arbitrage
X
3t
+ β
Distressed
X
4t
+ Const . +
(1)
For
Y
t
we use dierent mutual fund indices. The hypothesis is now that the R square of the
third period is higher than the R square of the first period. If this hypothesis is correct, we can
conclude that hedge funds explain the performance better than before the crisis. This indicates
a higher relationship between mutual funds and hedge funds aer the crisis. Therefore, the
result that mutual funds follow hedge funds strategy one quarter later has weakened in the third
period. We only use the equity related hedge funds strategies like Long/Short Equity, Equity
Market Neutral, Merger Arbitrage and Distressed Securities because the collinearity statistics
displayed a collinearity eect when we included the Equal Weighted Composite Index. However,
as mentioned, the limitation of our model is that we do not use the data of large hedge funds
as Jiao y Ye (2014). That means if our hypothesis is right the trading strategy of hedge funds and
mutual funds is not as dierent . Furthermore, we could simply check the correlation between
hedge funds and equity mutual funds. If they increased in the period aer the financial crisis,
then, the thesis of the herd behavior of mutual funds has weakened.
3 Results
This chapter is organized as follows. First, we want to present the descriptive statistics for
our three periods where we get a first overview about the performance of hedge funds and
mutual funds. Second, we present the correlations of the dierent periods and finally, we show
the results of the regressions of the first and the third period.
3.1 Descriptive Statistics
First we want to demonstrate the dierent results of the descriptive statistics for the three
dierent periods which we used.
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Finance, Markets and Valuation Vol. 4, Num. 1 (January-June 2018), 1–14
Min (%) Max (%) Mean (%) St. dev. (%) Skew. Kurt. Max. DD (%) Sharpe Ratio
Euro Stoxx 50 -18.64 14.27 -0.06 5.47 -0.46 1.65 -61.60 -0.29
MSCI Emerging Markets -15.69 13.55 0.93 6.01 -0.47 -0.28 -49.66 0.28
S&P 500 -11.00 9.67 0.04 4.12 -0.26 0.34 -46.28 -0.27
FONDS EUROPE -13.35 10.63 0.37 4.57 -0.66 0.76 -53.57 -0.02
FONDS EM -14.08 10.79 0.94 5.27 -0.59 -0.23 -41.62 0.38
FONDS NA -10.33 8.04 -0.03 4.15 -0.44 -0.01 -47.11 -0.33
CISDM Equal Weighted -3.14 7.89 0.77 1.78 0.38 2.10 -5.81 0.98
CISDM Long/Short Equity -3.38 6.50 0.61 1.77 0.19 0.83 -6.96 0.64
CISDM Equity Market -1.33 2.76 0.59 0.56 0.40 3.61 -1.46 2.01
CISDM Merger Arbitrage -2.00 2.41 0.57 0.76 -0.41 1.60 -2.34 1.38
CISDM Distressed Securities -1.52 4.18 0.98 1.12 0.15 0.29 -2.56 2.28
Table 1. Descriptives 01/2000-12/2006
Note: This table provides the descriptive statistics of our sample for the first period from January 2000 until December 2006. Mean denotes the average monthly return of the indices and funds
while St. Dev. denotes the monthly standard deviation of the sample. “Skew. and “Kurt. represent the third and fourth moment of the return distribution. The Sharpe Ratio shows the annualized
Sharpe Ratio, whereby we used the 3 month EURIBOR for Eurostoxx 500 and for FONDS EUROPE as the risk free rate. For all other variables we used the 3 month LIBOR as risk free rate. “Max.DD”
represents the maximal drawdown and Min and Max represent the minimum and maximum monthly return which we can observe in the data sample.
Gerhard Lechner and Benjamin Fauster 5
Finance, Markets and Valuation Vol. 4, Num. 1 (January-June 2018), 1–14
In Table 1 the descriptive statistics of the first period shows that concerning the mean
Emerging Market equities outperformed European and US stocks. If we take the S&P 500 as
a benchmark for North American mutual funds, we read out of the figures that mutual funds
were not able to outperform the benchmark.
The performance of the European stocks was mitigated in this period, because of the break-
down of the IT bubble 2001/02. The CISDM Distressed Securities index even outperformed the
MSCI Emerging Market which was the strongest performer in the equity universe. Hedge funds
performance was generally very strong in this period. The relatively low standard deviation
of hedge funds leads to a very high Sharpe Ratio which made an investment in hedge funds
more and more attractive. Even Emerging Market mutual funds were not able to keep pace with
hedge funds concerning the risk-return profile. The higher volatility of equity indices and equity
mutual funds in comparison to hedge funds is mirrored in the maximum and minimum returns.
Table 2 represents the data period of the financial crisis. The crisis on the financial markets
started approximately in summer 2007 and ended at the end of 2008. Concerning the monthly
return all hedge fund indices outperformed mutual funds and equity indices. However, the
survivorship bias has to be considered in this context.
Many hedge funds went bankrupt during the financial crisis and this had no impact on
the performance of hedge fund indices. The expectation would be that the performance was
overestimated. D. Kaiser y Haberfelner (2011) found that the survivorship and backfilling bias
has increased since the financial crisis. The liquidation bias also increased strongly in the
aermath of the crisis and it can account for an overestimate of performance of over 10 percent
p.a. The study also confirmed the already known fact that funds of hedge funds should be less
prone of data bias eects. The study of D. Kaiser y Haberfelner (2011) used the database TASS,
which is very oen used for an analysis of hedge fund performance. On the contrary to this
Xu, Liu, y Loviscek (2010) used CISDM data which are comparable with our data as well as a
dierent sample period than D. Kaiser y Haberfelner (2011) and they found that the survivorship
bias was lower in 2008 compared with previous years.
The standard deviation of hedge funds is still lower than that of mutual funds and equity
indices, but almost no strategies were able to outperform the risk free rate. The mean of mutual
funds underperformed their benchmarks with the exception of North American mutual funds.
Most interesting about the results of the data period aer the financial crisis, which are
represented in Table 3, is the fact that the mean of hedge funds performed relatively weak
against equity indices and completely underperformed against mutual funds. This fact is
dierent to the period until 2007. However, when we look at the risk-return profile (Sharpe
Ratio) the performance of hedge funds is still better than that of mutual funds. These results
confirm the comment of Brown (2016) who argued against the statement that the hedge fund
industry is in a state of crisis.
3.2 Correlations
Table 4 displays the correlations between equity indices, equity mutual funds and hedge
funds. The correlations of European and North American mutual funds to hedge funds are very
similar to each other.
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Finance, Markets and Valuation Vol. 4, Num. 1 (January-June 2018), 1–14
Min (%) Max (%) Mean (%) St. dev. (%) Skew. Kurt. Max. DD (%) Sharpe Ratio
Euro Stoxx 50 -14.69 5.43 -1.99 5.47 -1.11 0.60 -46.14 -1.54
MSCI Emerging Markets -27.50 11.00 -1.50 9.37 -1.00 1.14 -60.60 -1.25
S&P 500 -16.94 4.75 -1.73 5.07 -1.31 2.14 -42.15 -1.74
FONDS EUROPE -16.20 4.79 -2.10 5.55 -1.18 0.78 -48.29 -1.99
FONDS EM -22.01 8.84 -1.55 7.91 -0.99 0.64 -55.24 -1.54
FONDS NA -13.40 5.78 -1.56 4.79 -0.78 0.19 -38.21 -1.86
CISDM Equal Weighted -7.90 3.03 -0.43 2.76 -1.25 1.43 -21.12 -1.68
CISDM Long/Short Equity -5.40 2.42 -0.29 2.22 -0.77 -0.29 -17.04 -1.41
CISDM Equity Market -2.10 1.70 0.29 0.77 -1.24 3.01 -2.79 -0.25
CISDM Merger Arbitrage -2.63 2.13 0.17 1.45 -0.48 -1.00 -5.65 -1.25
CISDM Distressed Securities -10.59 1.58 -0.66 2.57 -2.78 9.67 -21.22 -2.67
Table 2. Descriptives 01/2007-12/2008
Note: This table provides the descriptive statistics of our sample for the second period from January 2007 until December 2008. Mean denotes the average monthly return of the indices and funds
while St. Dev. denotes the monthly standard deviation of the sample. “Skew. and “Kurt. represent the third and fourth moment of the return distribution. The Sharpe Ratio shows the annualized
Sharpe Ratio, whereby we used the 3 month EURIBOR for Eurostoxx 500 and for FONDS EUROPE as the risk free rate. For all other variables we used the 3 month LIBOR as risk free rate. “Max.DD”
represents the maximal drawdown and Min and Max represent the minimum and maximum monthly return which we can observe in the data sample.
Gerhard Lechner and Benjamin Fauster 7
Finance, Markets and Valuation Vol. 4, Num. 1 (January-June 2018), 1–14
Min (%) Max (%) Mean (%) St. dev. (%) Skew. Kurt. Max. DD (%) Sharpe Ratio
Euro Stoxx 50 -13.79 14.69 0.51 5.11 -0.21 0.05 -29.68 0.23
MSCI Emerging Markets -14.78 16.66 0.71 5.82 0.37 0.62 -38.51 0.31
S&P 500 -10.99 10.77 1.05 4.01 -0.33 0.54 -18.62 0.86
FONDS EUROPE -11.23 14.74 0.92 4.06 -0.10 0.93 -22.89 0.72
FONDS EM -11.60 14.90 0.87 4.63 0.17 0.81 -23.29 0.57
FONDS NA -8.68 9.82 1.11 3.47 -0.30 0.43 -14.61 1.08
CISDM Equal Weighted -4.59 6.23 0.59 1.63 -0.02 1.74 -9.26 1.18
CISDM Long/Short Equity -4.00 4.30 0.57 1.58 -0.60 1.02 -9.39 1.18
CISDM Equity Market -1.37 2.79 0.41 0.61 0.46 2.19 -2.29 2.18
CISDM Merger Arbitrage -1.03 1.44 0.44 0.50 -0.27 -0.21 -1.07 2.85
CISDM Distressed Securities -2.40 4.91 0.69 1.30 -0.11 0.64 -9.03 1.79
Table 3. Descriptives 01/2009-03/2017
Note: This table provides the descriptive statistics of our sample for the third period from January 2009 until April 2017. Mean denotes the average monthly return of the indices and funds while St.
Dev. denotes the monthly standard deviation of the sample. “Skew. and “Kurt. represent the third and fourth moment of the return distribution. The Sharpe Ratio shows the annualized Sharpe
Ratio, whereby we used the 3 month EURIBOR for Eurostoxx 500 and for FONDS EUROPE as the risk free rate. For all other variables, we used the 3 month LIBOR as risk free rate. “Max.DD” represents
the maximal drawdown and Min and Max represent the minimum and maximum monthly return which we can observe in the data sample.
Gerhard Lechner and Benjamin Fauster 8
Finance, Markets and Valuation Vol. 4, Num. 1 (January-June 2018), 1–14
Euro
Stoxx 50
MSCI EM S&P 500 Mutual
Funds
Europe
Mutual
Funds EM
Mutual
Funds NA
Equal
Weighted
Equity
Long/
Short
Equity
Market
Neutral
Merger
Arbitrage
Distressed
Securities
Euro Stoxx 50 1.00
MSCI EM 0.68 1.00
S&P 500 0.82 0.75 1.00
Mutual Funds Europe 0.94 0.78 0.80 1.00
Mutual Funds EM 0.70 0.96 0.75 0.83 1.00
Mutual Funds NA 0.84 0.75 0.94 0.88 0.82 1.00
Equal Weighted 0.62 0.79 0.65 0.75 0.79 0.72 1.00
Equity Long/Short 0.65 0.77 0.69 0.77 0.78 0.75 0.96 1.00
Equity Market Neutral 0.38 0.40 0.33 0.43 0.40 0.38 0.67 0.69 1.00
Merger Arbitrage 0.50 0.55 0.47 0.60 0.54 0.55 0.68 0.70 0.63 1.00
Distressed Securities 0.43 0.65 0.49 0.55 0.61 0.49 0.78 0.76 0.51 0.55 1.00
Table 4. Correlations 01/2000-12/2006
Note: This table indicates the correlations of equity indices, equity mutual indices and hedge fund indices in the period from Januar y 2000 to December 2006.
Gerhard Lechner and Benjamin Fauster 9
Finance, Markets and Valuation Vol. 4, Num. 1 (January-June 2018), 1–14
Euro
Stoxx 50
MSCI EM S&P 500 Mutual
Funds
Europe
Mutual
Funds EM
Mutual
Funds NA
Equal
Weighted
Equity
Long/
Short
Equity
Market
Neutral
Merger
Arbitrage
Distressed
Securities
Euro Stoxx 50 1.00
MSCI EM 0.83 1.00
S&P 500 0.92 0.82 1.00
Mutual Funds Europe 0.96 0.86 0.92 1.00
Mutual Funds EM 0.85 0.98 0.84 0.90 1.00
Mutual Funds NA 0.93 0.79 0.96 0.95 0.84 1.00
Equal Weighted 0.80 0.95 0.82 0.89 0.97 0.82 1.00
Equity Long/Short 0.80 0.89 0.75 0.88 0.93 0.80 0.95 1.00
Equity Market Neutral 0.54 0.70 0.46 0.62 0.74 0.50 0.76 0.85 1.00
Merger Arbitrage 0.76 0.66 0.76 0.83 0.70 0.80 0.73 0.79 0.62 1.00
Distressed Securities 0.75 0.81 0.83 0.82 0.82 0.78 0.88 0.74 0.47 0.66 1.00
Table 5. Correlations 01/2007-12/2008
Note: This table indicates the correlations of equity indices, equity mutual indices and hedge fund indices in the period from Januar y 2007 to December 2008. In bold are the correlations, which
have increased in comparison to the period from 2000 to 2006.
Gerhard Lechner and Benjamin Fauster 10
Finance, Markets and Valuation Vol. 4, Num. 1 (January-June 2018), 1–14
The correlations between mutual funds and Equity Market Neutral and Merger Arbitrage
are relatively low in comparison to other hedge fund strategies. Nevertheless, the data do
not confirm results of Jiao y Ye (2014). However, Jiao y Ye (2014) only considered large hedge
funds, which are probably also part of CISDM indices. We also tested the correlations of the
bear market from 2001 to 2002 and found that the correlations are much higher in this period.
Capocci, Corhay, y Hübner (2005) investigated the performance of hedge funds in bull and bear
markets. Their results show that almost all hedge funds follow the market more closely in the
bearish period from 2001 to 2002. However, this study used completely dierent hedge funds
strategies and a dierent database (TASS).
The findings of Capocci y cols. (2005) are confirmed in table 5 where the crisis period is
shown. The correlations between mutual funds and hedge funds increased, whereby especially
for Distressed Securities and Merger Arbitrage the correlations to mutual funds have increased
significantly. The correlation between mutual funds and Equity Market Neutral have also
increased, but remained relatively low in comparison to other strategies.
Table 6 displays that correlations between mutual funds and hedge funds increased in the
aer crisis period. There are only few exceptions. The correlation between mutual funds and
Merger Arbitrage decreased in comparison to the period before 2007. The strongest increase in
the correlation to mutual funds recorded the Equal weighted hedge fund index. An increase in
correlations show that herd behavior between hedge funds and mutual funds is on the rise, but
hedge funds are not a forerunner. It is logical that the correlation between hedge funds and
equity indices has also increased in most cases. Especially the correlations to the MSCI Emerging
Market have increased considerably which supports the papers of Caglayan y Ulutas (2013) and
Lechner y Beinhauer (2017) who stressed the relationship between hedge fund performance
and Emerging Market equity performance. The hedge funds which we selected for our data
analysis are probably ver y active in Emerging Markets equities.
3.3 Regressions
In this section we depict the results of our regression model of equation 1. The hypothesis
is that the R square should have risen aer the financial crisis. This means that mutual funds
and hedge funds are related more closely than before the crisis with the consequence that the
forerunner role of hedge funds has decreased aer the financial crisis.
The results show that the R square was rising for Mutual Funds Europe and Mutual Funds
North America. For Emerging Markets mutual funds we did not find an increase of the R square.
However, the model also shows that aer the financial crisis we only have one significant
variable for Europe (Long/Short Equity) instead of three before the crisis. In North America the
situation is similar.
Our hypotheses that the explanation value of our model increased aer the crisis have
been confirmed for Mutual Funds in Europe and North America. For us this shows that the
forerunner role of hedge funds has decreased. In the descriptive statistics in tables 1-3 we
could already show that mutual funds performed better than hedge funds. This together with
the fact that correlations were rising confirms that hedge funds do not have the forerunner
role in comparison to mutual funds. Only from a risk adjusted basis we still found that hedge
funds are the best performers. Concerning Emerging Markets hedge funds could still be a
forerunner to mutual funds because the results of our model show a decreasing explanation of
the performance of hedge funds through mutual funds.
Gerhard Lechner and Benjamin Fauster 11
Finance, Markets and Valuation Vol. 4, Num. 1 (January-June 2018), 1–14
Euro
Stoxx 50
MSCI EM S&P 500 Mutual
Funds
Europe
Mutual
Funds EM
Mutual
Funds NA
Equal
Weighted
Equity
Long/
Short
Equity
Market
Neutral
Merger
Arbitrage
Distressed
Securities
Euro Stoxx 50 1.00
MSCI EM 0.65 1.00
S&P 500 0.79 0.77 1.00
Mutual Funds Europe 0.95 0.71 0.81 1.00
Mutual Funds EM 0.69 0.97 0.76 0.77 1.00
Mutual Funds NA 0.78 0.66 0.93 0.82 0.74 1.00
Equal Weighted 0.72 0.86 0.81 0.81 0.72 0.77 1.00
Equity Long/Short 0.76 0.77 0.85 0.85 0.88 0.84 0.93 1.00
Equity Market Neutral 0.50 0.38 0.43 0.53 0.79 0.45 0.55 0.57 1.00
Merger Arbitrage 0.56 0.47 0.56 0.57 0.42 0.52 0.53 0.59 0.34 1.00
Distressed Securities 0.54 0.67 0.61 0.65 0.46 0.60 0.82 0.79 0.40 0.40 1.00
Table 6. Correlations 01/2009-03/2017
Note: This table indicates the correlations of equity indices, equity mutual indices and hedge fund indices in the period from January 2009 to April 2017. In bold are the correlations which increased
in comparison to the period from 2000 to 2006.
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Finance, Markets and Valuation Vol. 4, Num. 1 (January-June 2018), 1–14
January 2000 - December 2006 January 2009 - April 2017
Regressors MF Europe MF North America MF Europe MF North America
Long/Short Equity 0.91*** 1,03*** 0.81*** 1.01***
(7.08) (8.06) 7.48 (9.18)
Equity Market Neutral -0.25** -0.32*** 0.05 -0.06
(-2.59) (-3.23) (0.78) (-0.95)
Merger Arbitrage 0.21** 0.18* 0.10 0.03
(2.08) (1.75) (1.53) (0.38)
Distressed -0.14 -0.25** -0.05 -0.19**
(-1.34) (-2.30) (-0.60) (-2.09)
Descriptive statistics
Adjusted R square 0.62 0.62 0.73 0.72
Durbin Watson 2.04 1.79 2.04 1.89
n 84 84 84 84
Table 7. OLS estimations with Mutual Funds as dependent variables
Note: * indicates significance at 10% level, ** indicates significance at 5% level, *** indicates significance at 1% level.
The dependent variables are Mutual Funds Europe (MF Europe) and Mutual Funds North America (MF North America).
In the first line we depict the standardized beta of hedge funds strategies which are the independent variables. The
t-statistics are displayed in parenthesis and n is the number of observations. We also tested the collinearity of the data
and did not find any significant collinearity.
Maybe, for Emerging Markets the price informativeness is still not as eective as in the
developed markets. Therefore, hedge funds still could be a forerunner of the whole market.
4 Conclusions
The basis for our study were the results of Jiao y Ye (2014) who found that mutual funds
and hedge funds herd aer each other with a lag of one quarter. However, the thesis of their
paper is only valid for large hedge funds. The target of our paper was to check the relationship
of mutual funds and hedge funds before and aer the crisis. The main dierence of our paper
in comparison to Jiao y Ye (2014) is that we do not limit hedge funds to “large hedge funds”.
Furthermore, we only addressed hedge fund strategies with a focus on equity investment.
Another dierence to the study of Jiao y Ye (2014) is that we used monthly data for our analysis.
Our findings demonstrate that herding behavior of mutual funds to hedge funds has decreased
aer the financial crisis. First, the performance of hedge funds was significantly weaker aer the
crisis. Second, correlations between mutual funds and hedge funds increased aer the crisis.
Finally, our model displays a higher explanation value of mutual funds through hedge fund
performance, which indicates that herding behavior has decreased. A weaker performance
together with a higher correlation to mutual funds proves that hedge funds have lost their
forerunner role at least in the developed markets. The reason for the loss of the forerunner
role could be that the market eectiveness has increased. For Emerging Markets our model
shows that the explanation of the hedge fund performance through Emerging Market mutual
funds decreased. That indicates still weaker market eectiveness in Emerging Markets. Hedge
Funds are more likely frontrunner in such markets. Nevertheless, the analysis does not show
that hedge funds are no longer interesting for investors, because the Sharpe Ratios of hedge
funds are still higher than those of mutual funds.
Gerhard Lechner and Benjamin Fauster 13
Finance, Markets and Valuation Vol. 4, Num. 1 (January-June 2018), 1–14
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