An Empirical Study of Herding Behavior of Mutual Funds in...

An Empirical Study of Herding Behavior of Mutual Funds in China
CHU Ying, QIN Ping
School of Management, Huazhong University of Science & Technology, Wuhan, P.R. China, 430074
Art Department,Hubei University of Education, Hubei, P.R. China, 430205
[email protected]
Abstract We check herding behavior of Chinese mutual funds in this paper, and the results show:
there exists significant herding behavior in stock markets of China, and buying effect and selling effect
are basically equal; the herding behaviors on stocks with high involvement of funds are even more
significant, and the buying effect of these stocks is weaker than selling effect, while the buying effect of
stocks with less involvement of funds are stronger than selling effect; the herding behaviors on stocks
with a large amount in circulation are most significant, and buying effect of these stocks is weaker than
their selling effect, while buying effect of other stocks is stronger than their selling effect, the results on
stocks with a small amount in circulation have no big difference with other stocks.
Keywords Herding Behavior; Mutual Funds; LSV Method
1 Introduction
Empirical studies on herding behavior have attracted lots of scholars since the 1990s. One of the major
directions in the study of herding is taking some kind investors, such as mutual funds and pension funds,
as the targets, studying their portfolio changes and transaction information to judge whether herding
exists in markets.
Krous (1972) et al. first use 229 monthly data of mutual funds and bank trust institutions from Jan.
1968 to Sep. 1969 to test institutional group behavior. They find that mutual funds tend to imitate the
strategies of their more successive counterparts, and they call it " Follow leaders" Strategy. Lakonishok,
Shleifer and Vishny (1992) use the percentage of investors that are in one-side markets to study whether
there exists herding between managers of stock funds exemption from duty in America, and they further
test this based on classifications according to sizes of shares, historical performance and asset scale of
funds, etc. They find that these funds do not exhibit significant herding but there exists a little herding in
transactions of stocks of small companies. Grinblat, Titman and Wemers (1995) study on portfolios of
274 mutual funds in a period of 1974-1984 and get a similar result. Generally, LSV method is most
widely used for its simplify and the availability of data. Wemers (1999) takes all the funds in American
stock markets between 1975-1994 as the object of the study, and find herding is very obvious for total
funds based on a more further study, and the herding tendencies of different funds have great differences.
Recent years some domestic scholars study the herding. Shi Donghui (2001) analyzes the herding
behavior of Chinese investment funds, and the results show that more than 75% of funds are at the same
side in markets, herding is very serious. Yuan ke and Chen Hao (2003) test completely the herding of
investment funds in stock markets of Shanghai and Shenzhen by referring to the method (LSV method)
of Lakonishok (1991), and find there exists strong herding between mutual funds in Chinese stock
markets. Qi Bin et al. (2006) use the classic LSV method and the method extended by Wermers to study
empirically behaviors of institutional investors of which the presentatives are mutual funds in China.
The results suggest that there exists significant herding between mutual funds and the herding has
following characteristics: using a strategy of operation with positive and negative feedback at the same
time; significant herding on stocks with large or small amount in circulation; significant herding of
growing funds, etc. Liu Chengyan et al. (2007) study the behavior of QFII in A-share markets. The
results show that QFII has obvious herding behavior, and after the reform of non-tradable shares their
herding is more significant. The samples used in domestic literature are mostly ended before 2001. And
during the period before 2001, stock markets in China are mostly at a stage of rising without a lasting
decreasing period; therefore, studies based on samples in such a period have no generality.
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Since Jun. 2000, mutual funds in our country are required to disclose their holding position semiannually. The completeness of the disclosure provides conditions for us to study herding more
accurately, which is the biggest difference of our study and previous studies on the same problem. For
the unavailability of complete data, previous studies only have to calculate approximately by using the
data of top ten holding position disclosed by mutual funds. The deficiency of such studies is obvious and
will inevitably lead to error. For example, when a fund increase its position of one stock, a bigger
increase of position of another stock make the previous one out to the top ten holding position, which
will be easily misunderstood to be holding a short position. Moreover, they will ignore small changes of
funds position. Aiming at above defects, this paper will use data of all stocks that funds hold positions
on to test herding of mutual funds and the results will be more convincible.
2 Empirical Model
Data used in our study is all from the fund database of website www.huaan.com.cn, covering all funds
of closed end and open-end. The sample period is from 31, Dec. 2000 to 31, Dec. 2003, during which all
funds disclose the stock portfolios that they hold semi-annually. We define increase and decrease of
stock position by comparing funds' positions of consecutive periods. The "stock—semi-annual" data of
that the plus of the number of funds increasing position on the stock and the number of funds decreasing
position is greater than or equal to five are sampled. There are totally seven periods. We handle the data
of delisted stocks and stocks that have supplement issues, right offerings, dispatch and stocks with
shares increased by convertible bonds as follows: first, for newly issued stocks, we will use their
transaction data only after the new stocks have experienced two complete sample periods (for example,
only the data during the second half year of 2001 of stock listed in Apr. 2000 will be used), which
because issuing stocks in Chinese stock markets has a characteristics, that is, because of the price spread
between primary and secondary markets, almost all funds will be active in share subscription and most
funds sell shares of new stocks for cash in a very short time. Many new stocks are more active than
others in a certain time after listed, and many funds are involved in these abnormal transactions, which
should not be considered in our study. Second, the situations of right offerings and supplement issuances
are similar to that of new issued stocks, so the handling of those data is the same as that of new issued
stock. Third, for dispatch and shares increased by convertible bonds, they are different from stocks of
right offerings and supplement issuances and they do not have to buy these stocks actively. Therefore,
after dispatch and bonds converted to stocks, the increase of position does not mean funds have
optimistic perspectives on these stocks, so they should be covered in our study. They are handled in the
same way as that of above two kinds of stocks. Fourth, transactions of delisted stocks are relatively
abnormal, especially before delisting. Because of existence of third board market, some institutional
investors bet on that the stocks will be listed again one day and buy a large amount of them. Such stocks
are few, and they have little effect on the results, so we do not cover them in our study.
The models used in empirical study of herding behavior on institutional investors are mainly models of
LSV, GTW, and Wermers model.
LSV is a measure of herding put forward by Lakonishok, Shleifer and Vishny (1992). They take the
tendency of buying (selling) some stock of funds managers as the measure of herding. This indicator
measures traders' trading mode and their relations of buying (selling) the same stocks to some extent.
What LSV contributes most is they put forward the measuring indicator HM (i ) of herding, and the
definition is
HM (i) = B(i ) / ( B(i ) + S (i ) ) − p (t ) − AF (i )
(1)
B(i)——number of fund managers increasing stocks' position at t;
S(i)——number of fund managers decreasing stocks' position at t;
P(t)——expected ratio of the number of fund managers increasing position to the number of fund
managers increasing or decreasing position at time t, that is, the times of increasing position on all
stocks for any fund manager divided by the times of increasing position and decreasing position on all
stocks for any manager at time t.
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AF(i)——adjusting factor, which settle down the problem of B (i) / ( B (i ) + S (i) ) − p(t ) greater
than zero under the assumption of no herding exists, the definition is
AF (i ) = E B(i) / ( B(i ) + S (i ) ) − p (t )
(2)
That is, AF(i) is the expectation of B (i) / ( B (i ) + S (i) ) − p(t ) . Because B(i) follows a binomial
distribution with parameters n (n=B(i)+S(i)) and p, i.e. B~B(n, p). And n and p are known, so AF(i) can
be calculated. To calculate herding of all funds in whole market, we only have to calculate the
arithmetic average of HM (i, t ) of all "stock—period" Samples, labeled as HM , the greater the value,
the more serious the herding between funds.
LSV model has relatively big flaws in measuring herding in following two aspects: first, when
measuring herding on some stock, uses the number of investors on buying (selling) side instead of
quantities every investor buy (sell). In fact, this method ignores following situation: for one stock, the
numbers of investors on buying side and on selling side are basically equal, but the amount of shares
bought by investors on buying side are far larger than the amount sold by investors on selling side, or
vice versa. Herding definitely exists under such situation, but LSV method can not measure the herding.
Second, LSV method cannot recognize the transaction mode during periods. For example, LSV can only
test whether intertemporal herding exist on one stock, but cannot tell whether a fund are keeping herding.
Grinblatt, Titman and Wermers (1995, GTW) improve the LSV model. They define
HFi =
Bi − S i
Bi + S i
， HV
i
=
BVi − SVi
BVi + SVi
(3)
where Bi and S i are numbers of mutual funds that are buying and selling stock i, BVi and SVi are
transaction quantities of mutual funds that are buying and selling stock i. The values of
HFi and HVi are between [0 1]. If both of them are greater than zero significantly, then herding exists.
，
Based on LSV model, Wermers (1999) put forward another two indices besides indices of H (i ) :
herding indicator of buying side, BHM (i, t ) , and herding indicator of selling side, SHM (i, t ) . Their
definitions are
BHM (i, t ) = HM (i, t ) p (i, t ) > E[ p (i, t )]
(4)
SHM (i, t ) = HM (i, t ) p (i, t ) < E[ p (i, t )]
(5)
Calculate the arithmetic average, BHM and SHM , of BHM (i, t ) and SHM (i, t ) in the same way as
above context. Compare them, and we will see the differences between herding at buying and herding at
selling.
3 Test Results
The results of test for total are presented in Table 1.
Table 1. Herding Test for Whole funds in Stock Markets in China
Herding Indicator
2000.6.—2003.12.
（ ）
0.0955（907）
0.0936（1104）
0.0944 2011
HM
BHM
SHM
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Note: () are sample sizes.
The average, HM , in Table 1. is 0.0944. That is, when there are 100 funds are in transaction,
compared with markets without herding, there are 9.44 funds more that are on the same side of
transaction, which suggest there exists significant herding in transactions of funds in Chinese stock
market. Moreover, the values of BHM and SHM have minimal difference, which suggest herding of
buying side and herding of selling side are basically equal.
We compare above results with that of LSV (1992), GTW (1995), Wermers (1999) and Yuan Ke, Chen
Hao (2004) in Table 2.
Table 2. Comparison of Fund Herding Indicators
Ctry.
Results Source
（ ）
GTW（1995）
Wermers（1999）
Yuan Ke et al
（2004）
This Paper
（2004）
LSV 1992b
U
S
A
CHN
Period
Test Objects
HM
0.025
－
－
0.034
0.0298
0.0373
0.088
0.0052
0.0097
0.0944
0.0955
0.0936
769 funds of tax-exempt
0.027
1974—1984
274 mutual funds
1975—1994
2424 mutual funds
2000—2003
All funds of Closed-end and
open-end in China
All funds of Closed-end and
open-end in China
SHM
－
－
1985—1989
2000—2003
BHM
We can see through the comparison, HM in this paper is significantly greater than that of American
stock markets. Foreign scholars generally follow above scholars' results and conclude that herding
basically does not exist in American stock markets or the strength of herding is very weak. But studies
on our country's markets show that herding of mutual funds in China is significant. Compared with
Yuan ke, Chen Hao (2004), the value of HM in this paper is bigger, which suggest a more serious
herding, and herding of buying and herding of selling are basically equal, both values of them are close
to that of HM .
The results of test under classifications according to numbers of funds involved are presented in Table 3.
Table 3. Test under Classifications according to Numbers of Funds Involved
Funds Involved
5~10
11~20
21~40
Above 40
HM
BHM
（1110）
（589）
（269）
（43）
（471）
（270）
（143）
（23）
0.0899
0.0926
0.1077
0.1553
0.1160
0.1059
0.0898
0.1008
SHM
（639）
（319）
（126）
（20）
0.0632
0.0836
0.1138
0.1367
The numbers of funds involved reflect their participation enthusiasm. Table 3 shows that as the number
of funds involved increases, herding indicator HM also tends to increase, and the indicators BHM in
first two groups are greater than SHM , while BHM in the latter two groups are smaller than SHM .
We think these results are consistent with reality in Chinese stock markets. First, Most of the stocks with
many funds involved in are so called "core assets" by funds, and it seems that they are collusive bankers
when looking at their operations on these stocks. The consistency of funds actions on these stocks is
obvious higher than that on other stocks, so the herding indicators HM in latter two groups are greater
than that in first two groups. Second, for most stocks, funds' investment decisions are made after careful
analysis and research, because of similarity in information and analyzing tools, moves of funds when
buying stocks tend to converge, while when selling stocks, for short sell is not allowed, funds generally
are unwilling to follow others decision to sell stocks because their investment decisions are made based
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on analysis and research. Above truths make BHM greater than SHM in first two groups. But for
those "core assets" it's a different story. Funds seem be collusive bankers on these stocks but without
binding power, when prices of these stock rise high, funds are at prisoners' dilemma. Once one or
several funds sell these stocks, it will cause chain reactions between funds and finally leads to serious
herding. Thus selling herding are stronger than buying herding in the two groups with greater numbers
of involved funds.
Next we test by classify all samples into four groups according to circulation sizes of shares. The results
are presented in Table 4.
Table 4. Test under Classification according to Circulation Sizes of Shares
Size
smallest
smaller
larger
largest
HM
BHM
（314）
（1362）
（278）
（57）
（130）
（607）
（130）
（32）
0.0984
0.0937
0.0826
0.1482
0.1284
0.1155
0.0960
0.0940
SHM
（184）
（755）
（148）
（25）
0.0676
0.0753
0.0721
0.1347
Tables 4 shows that funds exhibit herding in each sample, and have a characteristic of "big in both ends
and pointed in middle". HM of the group with smallest circulation size is slightly bigger than that of
two groups in the middle, while HM of the group with largest circulation size is obviously greater than
that of other three groups. And we can see from the changes of BHM and SHM , except the fourth
group, the buying herding is stronger than the selling herding in the rest three groups, which is totally
different from studies of above scholars. We find after further analysis, that 35 out of 57 stocks in the
group with largest circulation size are held by more than 20 funds at the same time, which suggest that
data of this group have some overlaps with data in the latter two groups in Table 3, the percentage of
overlapped part is 61.40%, thus the herding behavior of funds on these samples are stronger than that of
other two groups, and selling herding is greater than buying herding. The herding in the group with
smallest circulation size has no significant difference with other two groups, which might because the
overall level of information disclosure in Chinese stock markets is relatively low. Information
dissemination of other stocks in markets is no better than that of small stocks, thus funds do not exhibit
more significant herding behavior on small stocks.
4 Cause Analysis
We use LSV model to test herding behavior of mutual funds in China in this paper. The results suggest
that there exists significant herding in Chinese stock market, buying herding and selling herding are
basically equal; the herding on stocks with high involvement of funds is more significant, and buying
herding on these stocks are weaker then the selling herding, while buying herding on stocks with low
involvement of funds are stronger than the selling herding; herding on stocks with large circulation size
are most significant, and buying herding on these stocks is weaker than the selling herding; the buying
herding on other stocks are stronger than selling herding, and the results on small stocks have no big
difference with that of other stocks.
There are many factors causing herding. Wermers (1999) summarizes the causes of herding and finds
four situations: first, managers do not want to risk their reputations so as to ignore their own private
information and follow decisions of the majority; second, the consistent actions of different managers
might because of the same or relative private information they've got or the same technical analysis
indicators they use; third, other managers probably obtain the same private information from
investments of managers who got more information in previous periods, finally leading to consistent
investment decisions; last, institutional investors probably have the same risk preference and thus have
the same investment decision.
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Herding exhibited by mutual funds in China is caused not only by above factors but also by some
characteristic factors owned only by Chinese stock markets. As an emerging market, investors' decisions
are inevitably affected by factors of policy, system arrangement, market structure and decision-making
process and so on. All these factors have probabilities of causing herding or strengthening it.
First, policy factors. For the uncertainty and instability of policies, funds are inclined to take "follow"
strategy, which leads to herding. Moreover, the policy of developing institutional investors abnormally
by the management of markets causes conflict between rapid expansion of funds' size and the limited
source of listed companies with high quality. This policy brings two outcomes: one is that many funds
with the same investment style chase the few blue chips; the other is to catch up with performance of
funds founded earlier, it's a rational choice for funds founder later to imitate first-movers. Both of above
outcomes lead to herding.
Second, market structure. For investment choices, large blue chips and listed companies of high
growing that attracts funds are scarce resources. Thus fund companies always choose stocks of
companies with high market acceptance and bring in the tendency of centralized holding. For fund
holders, the characteristics of institutionalized holders of funds is very obvious. Most investments of
institutional investors in Chinese stock markets are not long-term. Instead, they are devoted to chase
short-term price spread, and the phenomenon of checkout at the end of a year is serious, which mainly
because of the short-term of funding make fund managers have to reduce investment choices, and pay
more attention to stock return in a short-term (generally one year).
Third, similarity of investment decision-making process for fund companies. Many funds use the same
criteria to choose stocks and establish stock pools. Fund managers' choices are limited to stocks within
pools. This mode determines only those stocks with high acceptance will be choosed and pooled, while
unpopular stocks need to be analyzed carefully before being choosed. Analysts and fund managers are
all willing to choose stocks with high market acceptance.
The last, short-terming of fund performance evaluation. Fund companies evaluates fund manages
annually. In such an environment, fund managers bear great pressure and are likely to herd.
5 Conclusion
We use LSV model to test herding behavior of mutual funds in China in this paper. The results suggest
that there exists significant herding in Chinese stock market, buying herding and selling herding are
basically equal; the herding on stocks with high involvement of funds is more significant, and buying
herding on these stocks are weaker then the selling herding, while buying herding on stocks with low
involvement of funds are stronger than the selling herding; herding on stocks with large circulation size
are most significant, and buying herding on these stocks is weaker than the selling herding; the buying
herding on other stocks are stronger than selling herding, and the results on small stocks have no big
difference with that of other stocks.
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