Emerging Markets Finance and Trade
ISSN: 1540-496X (Print) 1558-0938 (Online) Journal homepage: http://www.tandfonline.com/loi/mree20
Effects of Transparency on Herding Behavior:
Evidence from the Taiwanese Stock Market
Kuei-Yuan Wang & Yu-Sin Huang
To cite this article: Kuei-Yuan Wang & Yu-Sin Huang (2018): Effects of Transparency on Herding
Behavior: Evidence from the Taiwanese Stock Market, Emerging Markets Finance and Trade, DOI:
10.1080/1540496X.2018.1504289
To link to this article: https://doi.org/10.1080/1540496X.2018.1504289
Published online: 26 Sep 2018.
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Emerging Markets Finance & Trade, 1–20, 2018
Copyright © Taylor & Francis Group, LLC
ISSN: 1540-496X print/1558-0938 online
DOI: https://doi.org/10.1080/1540496X.2018.1504289
Effects of Transparency on Herding Behavior: Evidence
from the Taiwanese Stock Market
Kuei-Yuan Wang1,2 and Yu-Sin Huang1
1
Department of Finance, Asia University, Taichung, Taiwan; 2Department of Medical Research, China
Medical University Hospital, China Medical University, Taichung, Taiwan
ABSTRACT: This study combines the concepts of information asymmetry from classical finance theory and
herding behavior from modern behavioral finance theory to investigate whether herding behavior exists in
the Taiwan stock market. Scores from the Information Disclosure and Transparency Ranking System
(IDTRs) are incorporated into the nonlinear model proposed by Chang, Cheng, and Khorana (2000). The
empirical results reveal that herding behavior is prevalent in the Taiwan stock market and the implementation of the IDTRs has effectively discouraged such behavior. In addition, the empirical results of this study
reveal that the lower level of transparency, the more prevalent of herding behavior in the Taiwan stock
market. The empirical results confirm the government’s efforts to increase the transparency of listed firms
in order to reduce information asymmetry and prevent investors from engaging in herding behaviors.
KEY WORDS: herding behavior, Taiwanese stock market, transparency
Since Christie and Huang (1995) proposed the cross-sectional standard deviation (CSSD) model as a
method for examining investors’ herding behavior, numerous studies have reported the occurrence of
herding behavior in stock markets in various countries (Chiang and Zheng 2010; Choe, Kho, and Stulz
1999; Hwang and Salmon 2004; Jlassi and Bensaïda 2014). Other studies have examined the causes of
herding behavior in stock markets; for example, Chiang et al. (2013) reported a relationship between
herding behavior and information asymmetry, whereas Ramli, Agoes, and Setyawan (2016) not only
confirmed the effect of information asymmetry on herding behavior but also asserted that domestic
investors follow foreign investors in making investment decisions because of the information asymmetry between domestic and foreign investors.
However, few studies have examined the factors that discourage herding behavior. In addition, the
evaluation results of Information Disclosure and Transparency Ranking System (IDTRs) are major
applied in the corporate governance research (Hsu, Lai, and Li 2016; Lee and Lee 2015; Lee, Lee, and
Wang 2017). This study combines the concepts of information asymmetry of classical finance theory
and herding behavior of modern behavioral finance theory to investigate whether herding behavior
exists in the Taiwan stock market, especially after introducing IDTRs by the financial supervisor
institution, and becomes our first purpose. Although, Chung, Judge, and Li (2015) stated that the main
objectives of information disclosure are to reduce information asymmetry and facilitate the accurate
evaluation of firm value or potential. This study investigates the Taiwan stock market to verify whether
increased information transparency can reduce information asymmetry, thereby discouraging herding
behavior in the Taiwan stock market, and becomes our second purpose.
There are three reasons for selecting the Taiwan stock market to explore. First, since the Taiwan
government relaxed trading restrictions on foreign institutional investors in 2000, foreign investors
have shown increasing interests in the Taiwan stock market. Second, Taiwan stock market is dominated by domestic individual investors as opposed to institutional and foreign investors (Demier,
Kutan, and Chen 2010). Demier, Kutan, and Chen (2010) asserted that most individual investors tend
Address correspondence to Kuei-Yuan Wang, Department of Finance, Asia University, 500, Lioufeng Rd.,
Wufeng, Taichung 41354, Taiwan. E-mail: gueei5217@gmail.com
Color versions of one or more of the figures in the article can be found online at www.tandfonline.com/mree.
2
K.-Y. WANG AND Y.-S. HUANG
to lack professional knowledge and cannot access information accurately and easily. The resulting of
information asymmetry may compel individual investors to follow the investment decisions of
informed domestic and foreign institutional investors. Therefore, the Taiwan stock market is suitable
for examining whether information asymmetry is a factor of herding behavior.
Second, Taiwan Stock Exchange and Taipei Exchange commissioned the Securities and Futures
Institute (SFI) executed the IDTRs in 2003. The main objectives of this system are to increase transparency
among domestic firms, protect investors’ rights, facilitate the development of capital markets, and
incentivize domestic firms to elevate their disclosure quality to international standards (Securities and
Futures Institute 2013). Third, because Chung, Judge, and Li (2015) claimed that the IDTRs meets
international standards. Therefore, this study considers that IDTRs have more public creditability.
In summary, this study modified the nonlinear model proposed by Chang, Cheng, and Khorana
(2000) and incorporated the evaluation results of IDTRs to our model to explore whether the
implementation of the IDTRs effectively discouraged herding behavior in the Taiwan stock market.
This study also further investigates whether the lower level of transparency, the more prevalent of
herding behavior in the Taiwan stock market.
The major contributions of this study are as follows: First, this study found that introducing IDTRs
by the government could migrate investors’ herding behavior in the Taiwan stock market. This
empirical finding confirms the government’s efforts. Second, since introducing IDTRs might migrate
investors’ herding behavior, this empirical findings might guide a direction to the government of the
emerging markets while stabilizing their stock markets. Third, since this study found that the lower
level of transparency, the more prevalent of herding behavior in the Taiwan stock market. It also
provides some implications to the management of corporations while stabilizing their stock prices.
Forth, similarly, since herding behaviors in the lower group of transparency are more prevalent than
the one of higher group, this anomaly makes stock prices more unstable. The results of this study can
be recommended to investors, allowing investors to make investment decisions according to their
preferences. Fifth, prior literature mainly examined whether herding behavior exists, and its causes.
This study further tries to explore how to migrate investors’ herding behaviors, and guides an
interesting direction to the future research. Sixth, because IDTRs are implemented by the financial
supervision institution, the evaluation results are closer to the real market than the data generated by
the academic research models. Then, this study introduces the evaluation results into our analysis
would let our empirical findings have more public credibility than some other literature that used the
data from some folk research institutions or academic research models.
The remainder of this article is organized as follows: Literature Review presents a literature review
of herding behavior and the IDTRs, Methodology addresses the methodology of this study, Results
discusses the empirical results, and Conclusions are the conclusions and recommendations.
Literature Review
Herding Behavior
The earliest study of herding behavior in economics was The General Theory of Employment, Interest
and Money by Keynes (1936). The book states that investors do not make decisions based solely on
available information but rather observe other investors’ decision-making behaviors and follow suit.
Herding behavior is a behavior that investors mimic other investors’ actions or market consensus to make
their investment decisions rather than using information they owned (Banerjee 1992; Bikhchandani and
Sharma 2000; Demier, Kutan, and Chen 2010; Nofsinger and Sias 1999). Some herding behavior theories
have been developed from information perspective. According to the information-related herding theory,
informed investors’ behaviors are perceived as useful information by individual investors who lack information (Demier, Kutan, and Chen 2010; Froot, Scharfstein, and Stein 1992; Shleifer and Summers 1990). The
information cascade theory states that other individuals’ behaviors convey information to observant investors
EFFECTS OF TRANSPARENCY ON HERDING BEHAVIOR
3
who ignore their own information and follow others’ decisions, thereby triggering herding behavior
(Banerjee 1992; Bikhchandani, Hirsheifer, and Welch 1992; Demier, Kutan, and Chen 2010).
Hsieh (2013) asserted that the causes of herding can generally be separated into two types:
information-driven and behavior-driven. Information-driven herding may indicate that investors are
facing similar decision-making problems and receiving related private information, whereas behaviordriven herding occurs when investors follow other investors’ actions. Zhou and Lai (2009) stated that a
higher proportion of information-based trading increases the likelihood of herding by investors.
Numerous studies have pointed out that individual investors have difficulty accessing sufficient
information about small companies and behavior-driven herding affects small companies’ more than
large companies (Hsieh 2013; Lakonishok, Vishny, and Vishny 1992; Shyu and Sun 2010; Sias 2004).
According to Choi and Skiba (2015), some studies have asserted that herding is not driven by
fundamental information and destabilizes stock prices, whereas other studies have argued that herding is
driven by fundamental information and enables stock markets to be more efficient because stock prices
adjust to new information quickly. In addition, Choi and Skiba (2015) emphasized that informationbased explanations for herding in financial markets include investigative herding and information
cascades. Investigative herding occurs when investors react similarly to the correlated market signals,
whereas information cascades occur when investors’ ignore private information in favor of making
decisions by observing others. Non-information-based explanations for herding include reputationrelated reasons, characteristic herding, and fads. Because information regarding a stock’s fundamental
value is of secondary concern to investors, non-information-based explanations are more likely to
destabilize prices; for example, when managers of portfolios ignore private information and choose to
mimic others’ investment decisions, they choose to follow the crowd even if doing so involves making
wrong decisions, which results in reputational herding. However, Scharfstein and Stein (1990) asserted
that although reputational herding is rational, the blame of wrong investment decisions is shared because
such decisions are not made by a single manager. In addition, characteristic herding occurs when similar
stock characteristics attract investors and investors might make similar investment decisions.
Caparrelli, D’Arcangelis, and Cassuto (2004) found that herding behavior is prevalent in large and
growing Italian firms, and it is migrated in bear markets. Hwang and Salmon (2004) found that herding
reveals in both U.S. and South Korean stock markets no matter in the bear or bull markets. Tan et al. (2008)
indicated that herding behavior appears in A shares in the Shanghai and Shenzhen stock exchanges no matter
in bull or bear markets. However, herding behavior is not prevalent in B shares of these two markets. But,
Yao, Ma, and He (2014) obtained opposing findings that herding behavior in B shares in these two markets.
Demier, Kutan, and Chen (2010) employed the nonlinear model proposed by Chang, Cheng, and Khorana
(2000) (CCK model hereafter) and the state space model developed by Hwang and Salmon (2004) to measure
herding behavior in various sectors of the Taiwan stock market. The empirical results revealed that herding is
prevalent in most Taiwanese industries, especially during the bear markets. Hsieh (2013) reported the
presence of institutional and individual herding in the Taiwan stock market and revealed that institutional
herding is stronger than individual herding. Huang, Lin, and Yang (2015) reported an enhanced presence of
herding in the Taiwan stock market during the 2007–2008 financial crisis.
To sum up, herding behavior is a behavior that investors often mimic other investors’ decisions or
market consensus while making their investment decisions rather than using their own information.
And, herding behaviors exists in the Taiwan stock market.
Information Disclosure and Transparency Ranking System (IDTRs)
Taiwan Stock Exchange and GreTai Securities Market commissioned the SFI to carry out annual
Information Transparency and Disclosure Ranking evaluation from 2003. The main goals of this rating
are to improve transparency of firms, to protect the rights of investors, to develop the capital market, and to
internationalize local companies, bringing them on track with international practices. The SFI’s annual
rating consists of a complete set of corporate information that released by the company. The rating is
conducted once a year and the results are reported in the following year. The rating includes companies that
4
K.-Y. WANG AND Y.-S. HUANG
have been traded on the stock market or over-the-counter for at least a year, excluding companies where the
responsible party has known integrity issues or companies that are poorly managed (SFI 2013).
Although the first year of the IDTR included only 62 indicators, there have been amendments and
augmentations over the years. In the eighth and ninth ratings, there were 114 indicators, and in the eleventh
evaluation, there were 109 indicators. The rating indicators can be divided into five main types, which are
legal compliance (indicators 1–12), timeliness of disclosure (indicators 13–33), the disclosure of financial
forecasts (indicators 34–37), the disclosure of annual report information (indicators 38–87), and information disclosure on the web (indicators 88–109) (Securities and Futures Institute 2013).
In the first and second years of the evaluation, to encourage the IDTRS, only the top third of
companies were publicized. In the third year, the companies were assigned into five grades: A+ (over
80), A (60–79), B (50–59), C (45–49), C− (under 45). Owing to the steady improvements, the results
are assigned into seven grades: A++ (over 85), A+ (80–84), A (70–79), and A− (60–69), B (50–59), C
(45–49), C− (under 45) (Securities and Futures Institute 2013).
Lee and Lee (2015) used the evaluation results of IDTRs as a proxy for transparency to examine
whether transparency could migrate the accruals anomaly. Hsu, Lai, and Li (2016) also took the
evaluation results of IDTRs as a proxy of transparency to examine the influence of R&D intensity
and institutional ownership on transparency in the high-tech, mid-tech, and traditional industries. The
empirical findings show that transparency promotion and the increase of institutional ownership are
statistically significantly related. Lee, Lee, and Wang (2017) took the empirical results of IDTRs as a
proxy to information disclosure, and found that companies review by the industry-specialized chief
accountant have higher grades of IDTRs than the ones that review by the non-industry-specialized chief
accountant. Besides, the industry-specialized chief accountant could increase the credibility of firms’
transparency. According to the above literature, this study found that the evaluation results of IDTRs are
major applied in the corporate governance research, and are often used to measure the transparency.
Methodology
Research Hypotheses
Since the purposes of introducing IDTRs by the government is to promote the transparency of the whole
stock market, and further to protect investors’ rights. In practice, from the evaluation results of IDTRs, we
can found that the number of companies receiving an A Grade of transparency increased from 144 to 175
from 2005 to 2014. In addition, the number of companies receiving a C Grade decreased from 141 to 16.
That is, samples are more and more transparency in the Taiwan stock market. Therefore, introducing the
IDTRs by the government has effectively enhanced the information transparency in Taiwan stock market.
Table 1. Summary of the evaluation results of the information disclosure and transparence
ranking system (IDTRs) during 2005–2014.
Year
Rating
A++
A+
A
A−
B
C
C−
Total
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
–
11
144
–
334
141
–
630
–
12
171
–
326
106
20
635
–
11
167
–
308
131
34
651
–
29
222
–
362
48
4
665
–
28
222
–
332
94
9
685
–
34
241
–
324
96
10
705
15
19
111
173
314
83
5
720
20
32
132
197
292
75
6
754
39
42
115
204
273
86
14
773
60
48
175
289
208
16
1
797
Source: Securities and Futures Institute (2015). Data from: 2005–2014 Information disclosure and transparency ranking results in
Taiwan [dataset]. Securities and Futures Institute Website. Accessed on 2nd May 2015. http://www.sfi.org.tw/cga/cga2.
EFFECTS OF TRANSPARENCY ON HERDING BEHAVIOR
5
Table 1 shows the evaluation results of the Information Disclosure and Transparency Ranking
System (IDTRs) during 2005–2014. Data is from the website of Securities and Futures Institute.
Chung, Judge, and Li (2015) proposed that increasing information transparency can reduce
information asymmetry, whereas Choi and Skiba (2015) indicated that asymmetry causes information
cascades.1 Therefore, increasing transparency can reducing information asymmetry, and further reducing information cascades. Since information cascades reduces, investors might not follow others’
investment decisions, and herding behavior might be discouraged.
In summary, this study infers that the implementation of the IDTRs could enhance transparency and
reduced information asymmetry. Consequently, investors do not need to follow others’ decisions like
the lacking information anomaly before. Therefore, information cascade reduces, and herding behavior
is migrated. Then, this study proposes the first hypothesis as follows:
H1: Introducing IDTRs by the government could migrate the herding behaviors in the Taiwan stock market.
If companies discloses less information, investors have less information to make investment
decisions. From the information cascade views, observant investors might follow others’ decisions
to make their investment decisions, then herding behavior is produced. Therefore, this study infers that
the lower level of transparency, the more prevalent of herding behavior in the Taiwan stock market,
and proposes the second hypothesis as follows:
H2: The lower level of transparency, the more prevalent of herding behavior in the Taiwan stock market.
Sample Selection and Source
This study takes the listed firms that joined the IDTRs as samples to explore. The number of samples
of each group in each year show in the Table 2. Daily return data is from Taiwan Economic Database
(TEJ), and transparency is adopted by the IDTRs results published by the SFI. This study defines the
year of 1993–2002 as the period before the implementation of the IDTRs, and the year of 2005–2014
as the period after the implementation of the IDTRs. The reason that this study ignores the period of
2003–2004 is that seldom firms join this evaluation system owing to the promotion nature of this
stage. Furthermore, this study denotes the evaluation results of IDTRs of A++, A+, A and A− as the
group with higher transparency, and the ones of C and C− as the group with lower transparency. Then,
this study further uses these groups to explore whether the lower level of transparency, the more
prevalent of herding behavior in the Taiwan stock market.
Table 2 shows the number of samples in each sample year. Before represents 1993–2002 years
that they are the periods before implementing IDTRs by the government, and After represents
2005–2014 years that they are the periods after implementing IDTRs by the government. High
denotes the group with higher transparency, which evaluation grades are A++, A+, A and A−.
Table 2. Number of samples in each year.
Panel A: Information Disclosure and Transparence Ranking
Before
Year
1993
1994
1995
1996
N
203
225
267
307
After
Year
2005
2006
2007
2008
N
630
635
651
665
Panel B: Transparency
High
Year
2005
2006
2007
2008
N
155
183
178
251
Low
Year
2005
2006
2007
2008
N
141
126
165
52
System
1997
342
2009
685
1998
394
2010
705
1999
463
2011
720
2000
525
2012
754
2001
577
2013
773
2002
689
2014
797
2009
250
2009
103
2010
275
2010
106
2011
318
2011
88
2012
381
2012
81
2013
400
2013
100
2014
572
2014
17
6
K.-Y. WANG AND Y.-S. HUANG
Low denotes the group with lower transparency, which evaluation grades are C and C−. N
represents the observations.
Herding Tests
Christie and Huang (1995) proposed that herding occurs when investors frequently abandon their own
investment decisions in favor of following the collective actions of the market. Because most investors
make similar investment decisions, individual returns do not stray far from the market return.
Therefore, Christie and Huang (1995) developed the CSSD model (CH model hereafter), and indicated
that herding behavior can be examined by exploring whether CSSD significantly small in the extreme
large positive or negative return distributions.
However, Chang, Cheng, and Khorana (2000) asserted that the CH model is too stringent. Rational asset
pricing models predict not only that stock return dispersions are an increasing function of market return, but
also that the relationship between the two is linear. However, if investors herd irrationally, the increasing
linear relationship between dispersion and market return no longer holds. Demier, Kutan, and Chen (2010)
proposed that using the linear CH model to measure herding may overlook the price comovement between
the returns on individual assets and those of the market; for example, observing a drop in all asset prices
along with the market portfolio on any given day causes the dispersion measure to take a lower value for
that day to provide support for the existence of herding. However, this might be a result of a common
reaction to unanticipated news on that day among investors, which a test would fail to detect. The linear CH
model failing to determine the price comovement may lead to inaccurate results. By contrast, the nonlinear
CCK model allows for nonlinear comovement between return dispersions and market returns while testing
for the existence of herding by examining the additional nonlinear term in the model. Therefore, the
nonlinear CCK model provides a more flexible and accurate approach to testing for herding formation than
does the linear CH model. The CCK model is described as follows:
CSAD ¼
Pn Ri;t Rm;t i¼1
n1
where, Ri;t represents the individual stock return rate; Rm;t represents the equal-weighted portfolio
return rate; n represents the observations.
CSADf ;t ¼ α þ β1;f Rm;t þ β2;f ðRm;t Þ2 þ εt
(1)
CSADh;t ¼ α þ β1;h Rm;t þ β2;h ðRm;t Þ2 þ εt
(2)
where, f = 1 denotes the period of 1993-2002 years before the implementation of the IDTRs; f = 0
denotes the period of 2005–2014 years after the implementation of the IDTRs; h = 1 denotes the group
with higher transparency of grade A++,A+, A, A−; h = 0 denotes the group with lower transparency of
grade C, C−; and the presence of statistically significantly negative β2,f (β2,h) indicates that herding
behavior exists.2
To measure asymmetric investor behavior under various market conditions, Chiang and Zheng
(2010) modified the CCK model by adding Rm,t to the right-hand side. In addition, Yao, Ma, and He
(2014) added a 1-day lag of cross-sectional absolute deviation of returns (CSAD) on the right-hand
side of the CCK model to improve its explanatory power. Therefore, this study adopts these two
approaches to modify the CCK model as follows:
(3)
CSADf ;t ¼ α þ γ1;f Rm;t þ γ2;f Rm;t þ γ3;f ðRm;t Þ2 þ γ4;f CSADf ;t1 þ εt
EFFECTS OF TRANSPARENCY ON HERDING BEHAVIOR
CSADh;t ¼ α þ γ1;h Rm;t þ γ2;h Rm;t þ γ3;h ðRm;t Þ2 þ γ4;h CSADh;t1 þ εt
7
(4)
Subsequently, this study employs the CCK model, modified CCK model, and Chow test to test H1
and H2.
Sensitivity Analysis
To test whether H1 and H2 are supported in up or down markets, this study defines when Rm,t > 0
represents the up market, and when Rm,t < 0 represents the down market. Besides, this study adopts
the dummy variable approach in Chiang and Zheng (2010) and incorporates the perspective of Yao,
Ma, and He (2014) to modify the CCK model. The CCK model further modified in this study is
expressed as follows:
up
2
down
CSADf ;t ¼ α þ γup
1;f DRm;t þ γ2;f DðRm;t Þ þ γ3;f ð1 DÞRm;t
2
þ γdown
4;f ð1 DÞðRm;t Þ þ γ5;f CSADf ;t1 þ εt
up
2
2
down
down
CSADh;t ¼ α þ γup
1;h DRm;t þ γ2;h DðRm;t Þ þ þγ3;h ð1 DÞRm;t þ γ4;h ð1 DÞðRm;t Þ
þ γ5;h CSADh;t1 þ εt
(5)
(6)
where, DRup is that D times Rup. D is a dummy variable. D = 1 represents the up market (Rm;t > 0);
up down
down
D = 0 represents the down market (Rm;t < 0); If γup
2;f (γ4;f ) and γ2;h (γ4;h ) are significantly negative,
then they represent that herding behavior exist in the up (down) market.
Numerous studies have reported that herding is more prevalent than usual during a financial
crisis (Economou, Katsikas, and Vickers 2016; Huang, Lin, and Yang 2015; Jlassi and Bensaïda
2014). Since the Asian financial crisis and subprime mortgage crisis exist in our sample period,
this study refers to Kaminsky and Schmukler (1999) and Erkens, Hung, and Matos (2012) to
define the Asian financial crisis as the period of 1997–1998, and subprime mortgage crisis as the
period of 2007–2008, and the others defines as the normal market period. This study follows the
modified CCK model mentioned by Chiang and Zheng (2010) to examine whether the hypothesis
1 and 2 can be supported under normal market and financial crisis conditions, the modified
equations are as follows.
CSADf ;t ¼ α þ γ1;f DRm;t þ γ2;f DRm;t þ γ3;f DðRm;t Þ2 þ γcrisis
4;f ð1-DÞRm;t
2
crisis
þ γcrisis
5;f ð1-DÞRm;t þ γ6;f ð1-DÞðRm;t Þ þ γ7;f CSADf ;t1 þ εt
(7)
crisis
CSADh;t ¼ α þ γ1;h DRm;t þ γ2;h DRm;t þ γ3;h DðRm;t Þ2 þ γcrisis
4;h ð1-DÞRm;t þ γ5;h ð1-DÞRm;t 2
þ γcrisis
6;h ð1-DÞðRm;t Þ þ γ7;h CSADh;t1 þ εt
(8)
where, D is a dummy variable. D = 1 represents the normal market; D = 0 represents the financial
crisis
crisis period; If γ3;f (γcrisis
6;f ) and γ3;h (γ6;h ) are significantly negative, then they represent that herding
behavior exist in the normal market (financial crisis).
Christie and Huang (1995) indicated that herding behavior are easier to appear in the extreme
market. This study took the 10% upper and lower tails of the trading volume as the criteria of the
extreme market movements, and utilized Equations 3 and 4 to examine whether the hypothesis 1 and 2
can be supported under the extreme market conditions.
8
K.-Y. WANG AND Y.-S. HUANG
65
Grade
60
55
50
45
2005
2006
2007
2008
Year
2009
2010
2011
2012
Figure 1. The average scores of IDTRs in Taiwan during 2005–2012.
Source: Securities and Futures Institute (2013).
Results
Descriptive Statistics
The main objective of the IDTRs is to increase the transparency of companies and serve as a reference
for investor’ decisions and rights. Figure 1 illustrates the annual mean IDTRs scores between 2005 and
2012 and demonstrates that the mean score increased annually between 2005 and 2012. Therefore, the
implementation of the IDTRs has effectively increased transparency among Taiwanese companies,
thereby reducing information asymmetry.
Table 3 demonstrates that the average CSAD scores before and after the implementation of the
IDTRs are 0.0166 and 0.0142, respectively, and the average CSAD scores for the low and high
transparency groups are 0.0146 and 0.0135, respectively. The minimum CSADs of all four variables
are positive and approach zero. The observations of Rm indicates that the maximum and minimum of
any Rm fall within the range of (−0.0644, 0.0635), which correlates with the maximum quota change
of 7% regulated by the Taiwan Securities and Exchange Act.
Table 3 shows the descriptive statistic of the daily CSAD and the equal-weighted portfolio
return rate (Rm). Mean represents the average of CSAD or Rm. Std. represents the standard
Table 3. Descriptive statistic.
Information disclosure and transparence ranking system
CSAD
Rm
Mean
Std.
Min
Max
N
ADF
Mean
Std.
Min
Max
N
ADF
Transparency
Before
After
Low
High
0.0166
0.0053
0.0055
0.0393
2,735
(−10.10)***
0.0004
0.0148
−0.0644
0.0602
2,735
(−45.47)***
0.0142
0.0039
0.0066
0.0351
2,483
(−6.37)***
0.0005
0.0127
−0.0622
0.0629
2,483
(−43.12)***
0.0146
0.0045
0.0042
0.0364
2,483
(−6.20)***
0.0006
0.0125
−0.0608
0.0620
2,483
(−41.76)***
0.0135
0.0038
0.0053
0.0359
2,483
(−6.39)***
0.0005
0.0127
−0.0630
0.0635
2,483
(−44.37)***
Note: ***, **, * represents the significant level of 1%, 5%, 10%, respectively.
EFFECTS OF TRANSPARENCY ON HERDING BEHAVIOR
9
deviation. Min and Max represents the minimum and maximum. N represents the observations.
T-values of ADF tests show in the parentheses, ***, **, * represents the significant level of 1%,
5%, 10%, respectively; Before represents the period of 1993–2002 that is before the IDTRs
implementation. After represents the period of 2005–2014 that is after the IDTRs implementation. Low represents the relatively lower transparency group with the evaluation grades of C and
C−. High represents the relatively higher transparency group with the evaluation grades of A++,
A+, A and A−.
Effects of the IDTRs on Herding Behavior
Table 4 shows that in Panel A, the γ3,Before and γ3,After coefficients in the modified CCK model are
−5.3830 and −4.8402, respectively. Both coefficients reach the statistically significant level, indicating
the presence of herding behavior in the Taiwan stock market before and after the implementation of the
IDTRs. This study uses the Chow test to confirm that γ3,After exceeds γ3,Before by 0.5428, which is a
significant difference. Therefore, the implementation of the IDTRs effectively discourages investors from
engaging herding behavior. In Panel B, the β2,Before and β2,After coefficients are significantly negative in
the CCK model, indicating a significant level of herding behavior before and after the implementation of
the IDTRs. In addition, the Chow test results reveal that β2,After is significantly larger than β2,Before,
thereby indicating a reduction in herding behavior between 1993–2002 and 2005–2014. The consistency
of the empirical results between Panels A and B demonstrates that the implementation of the IDTRs
effectively discouraged investors’ herding behaviors. Therefore, H1 is supported.
Table 4 shows the comparison effects of IDTRs implementation on herding behaviors in the Taiwan stock
market. Panel A shows the empirical results of Equation 3, and Panel B shows the empirical results of
Equation
1.
3
is
as
follows:
Equation
2
CSADf ;t ¼ α þ γ1;f Rm;t þ γ2;f Rm;t þ γ3;f ðRm;t Þ þ γ4;f CSADf ;t1 þ εt , and Equation 1 is as follows:
CSADf ;t ¼ α þ β1;f Rm;t þ β2;f ðRm;t Þ2 þ εt . CSAD denotes the CSAD. f = 1 represents the period that
before the IDTRs implementation; otherwise, f = 0 represents the one after it. Rm;t represents the equalweighted portfolio return rate on t day. T-value shows in the parentheses, and F-value shows in the square
Table 4. The effects of IDTRs implementation on herding behaviors in the Taiwan stock market.
Panel A: Modified CCK Model
Α
γ1,Before
0.0020
−0.0040
(10.55)***
(−1.05)
Α
γ1,After
0.0031
−0.0295
(17.26)***
(−7.90)***
Chow test
Panel B: CCK Model
Α
β1,Before
0.0133
0.4406
(74.83)***
(18.35)***
Α
β1,After
0.0116
0.4046
(98.73)***
(22.63)***
Chow test
γ2,Before
γ3,Before
0.2973
−5.3830
(20.39)***
(−15.49)***
γ2,After
γ3,After
0.2866
−4.8402
(22.99)***
(−16.32)***
γ3,After - γ3,Before
γ4,Before
adj-R2
0.7544
0.6988
(69.61)***
γ4,After
adj-R2
0.6646
0.6647
(53.28)***
[15.31]***
β2,Before
−6.6128
(−11.46)***
β2,After
−5.1847
(−12.17)***
β2,After - β2,Before
Note: ***, **, * represents the significant level of 1%, 5%, 10%, respectively.
N
2735
N
2483
0.5428
adj-R2
0.1645
N
2735
adj-R2
0.2808
N
2483
[83.52]***
1.4281
10
K.-Y. WANG AND Y.-S. HUANG
Table 5. The effects of transparency on herding behaviors in the Taiwan stock market.
Panel A: Modified CCK Model
α
γ1,Low
0.0035
−0.0132
(18.00)***
(−2.78)***
α
γ1,High
0.0033
−0.0238
(18.24)***
(−6.20)***
Panel B: CCK Model
Chow test
α
β1,Low
0.0113
0.5025
(83.96)***
(24.21)***
α
β1,High
0.0110
0.3748
(94.72)***
(21.22)***
Chow test
γ2,Low
0.3603
(23.01)***
γ2,High
0.2741
(21.17)***
γ3,Low
−5.7809
(−14.96)***
γ3,High
−4.3434
(−14.21)***
γ3,Low - γ3,High
β2,Low
−6.6623
(−13.02)***
β2,High
−4.4733
(−10.69)***
β2,Low - β2,High
γ4,Low
0.6073
(45.96)***
γ4,High
0.6315
(47.75)***
adj-R2
0.6244
N
2483
adj-R2
0.6211
N
2483
adj-R2
0.3038
−1.4375
N
2483
adj-R2
0.2725
N
2483
[11.12]***
[48.97]***
−2.1890
Note: ***,**,* represents the significant level of 1%, 5%, 10%, respectively.
brackets. ***, **, * represents the significant level that reaches the 1%, 5% or 10%, respectively. N represents
the observations.
Effects of Transparency on Herding Behavior
Table 5 shows that in Panel A, the γ3,Low and γ3,High coefficients in the modified CCK model are
−5.7809 and −4.3434, respectively. Both coefficients exhibit statistical significance, indicating a strong
presence of herding behavior in both transparency groups. This study uses the Chow test to confirm
that γ3,Low exceeds γ3,High by −1.4375, which is a significant difference. Therefore, the lower level of
transparency, the more prevalent of herding behavior in the Taiwan stock market, and increasing
transparency effectively prevents investors from engaging in herding behavior. In Panel B, the β2,Low
and β2,High coefficients are significantly negative in the CCK model, indicating that herding
behaviors exist in both transparency groups. The Chow test results reveal that β2,Low is significantly
less than β2,High, thereby indicating that the lower level of transparency, the more prevalent of herding
behavior in the Taiwan stock market. That is, increasing transparency discourages investors’ herding
behaviors. Therefore, H2 is supported.
Table 5 shows the effects of transparency on herding behaviors in the Taiwan stock market. Panel A
shows the empirical results of Equation 4, and
Panel B shows the ones of Equation 2. Equation 4 is as
follows: CSADh;t ¼ α þ γ1;h Rm;t þ γ2;h Rm;t þ γ3;h ðRm;t Þ2 þ γ4;h CSADh;t1 þ εt , and Equation 2 is as
follows: CSADh;t ¼ α þ β1;h Rm;t þ β2;h ðRm;t Þ2 þ εt . CSAD denotes the CSAD. h = 1 represents the
group of higher transparency; otherwise, h = 0 represents the one of lower transparency. Rm;t denotes
the equal-weighted portfolio return rate on t day. T-value shows in the parentheses, and F-value shows
in the square brackets. ***, **, * represents the significant level that reaches the 1%, 5% or 10%,
respectively. N represents the observations.
Herding Behavior in Up and Down Markets
Table 6 shows investors’ herding behaviors in up and down markets in Taiwan before (1993–2002)
and after (2005–2014) the implementation of the IDTRs. The γup2,Before, γdown4,Before, γup2,After, and
EFFECTS OF TRANSPARENCY ON HERDING BEHAVIOR
11
Table 6. The effects of IDTRs implementation on herding behaviors in the upper and down zone
in the Taiwan stock market.
α
0.0020
(10.51)***
α
0.0031
(17.11)***
Chow test
γup1,Before
0.3318
(19.79)***
γup1,After
0.2744
(19.02)***
γup2,Before
−6.7508
(−15.21)***
γup2,After
−5.5251
(−13.87)***
[13.69]***
Chi-square
γdown3,Before
γdown4,Before
γ5,Before
−0.2647
−4.1337
0.7549
(−15.68)***
(−9.63)***
(69.95)***
γdown3,After
γdown4,After
γ5,After
−0.2995
−4.3181
0.6655
(−20.34)***
(−12.02)***
(53.39)***
γup2,After - γup2,Before
1.2256
γup2,Before - γdown4,Before
2.6171 [24.20]***
adj-R2
0.7013
N
2735
adj-R2
0.6655
N
2483
γdown4,After - γdown4,Before
−0.1844
γup2,After - γdown4,After
1.2070 [6.62]***
Note: ***, **, * represents the significant level of 1%, 5%, 10%, respectively.
γdown4,After coefficients are all significantly negative. Table 6 reveals a significant presence of herding
behavior in up and down markets before and after the implementation of the IDTRs. The Chow test
results that γup2,After exceeds γup2,Before by 1.2256, which is a significant difference. Therefore, the
implementation of the IDTRs can discourage herding behavior in up markets. Although a significant
difference between γdown4,After and γdown4,Before was revealed, γdown4,After being lower than γdown4,Before
by −0.1844, and reveals that the implementation of the IDTRs failed to discourage herding behavior in
down markets. The chi-square test results in Table 6 verify whether asymmetry existed between
investors’ herding behaviors in up and down markets in Taiwan before and after the implementation of
the IDTRs. The test results reveal a significant difference between γup2,Before and γdown4,Before, by
2.6171, indicating the occurrence of asymmetrical herding behaviors in up and down markets before
the implementation of the IDTRs. Similarly, a significant difference between γup2,After and γdown4,After
by 1.2070 is observed, indicating the occurrence of asymmetrical herding behaviors in up and down
markets after the implementation of the IDTRs.
Table 6 shows the empirical results of IDTRs implementation on herding behaviors in up and down
markets in the Taiwan stock market. Before represents the period of 1993–2002 before implementing IDTRs,
and after represents the period of 2005–2014 years after it. Equation 5 is utilized, and shows as follows:
up
2
2
down
down
CSADf ;t ¼ α þ γup
1;f DRm; t þ γ2;f DðRm;t Þ þ γ3;f ð1 DÞRm;t þ γ4;f ð1 DÞðRm;t Þ þ γ5;f CSADf ;t1 þ
εt : CSAD denotes the CSAD. f = 1 represents the period that is before the IDTRs implementation; otherwise,
f = 0 represents the one after it. Rm;t denotes the equal-weighted portfolio return rate on t day. D is a dummy
variable. D = 1 represents Rm;t > 0 on t day; otherwise, D = 0 represented Rm;t < 0 on t day. T-value shows in the
Table 7. The effects of transparency on herding behaviors in the up and down markets in the
Taiwanese stock market.
α
0.0110
(29.64)***
α
0.0104
(31.33)***
Chow test
Chi-square
γup1,Low
γup2,Low
0.5539
−8.0128
(19.96)***
(−10.44)***
γup2,High
γup1,High
0.3964
−5.2041
(17.51)***
(−9.08)***
[25.17]***
γdown3,Low
γdown4,Low
−0.4610
−5.5636
(−18.43)***
(−8.92)***
γdown3,High
γdown4,High
−0.3783
−4.2961
(−17.54)***
(−8.10)***
γup2,Low - γup2,High
−2.8087
γup2,Low - γdown4,Low
−2.4492 [7.96]***
Note: ***, **, * represents the significant level of 1%, 5%, 10%, respectively.
γ5,Low
0.0170
(0.85)
γ5,High
0.0349
(1.77)*
adj-R2
0.3061
N
2483
adj-R2
0.2733
N
2483
γdown4,Low - γdown4,High
−1.2675
γup2,High - γdown4,High
−0.9080 [1.79]
12
K.-Y. WANG AND Y.-S. HUANG
parentheses, and F-value shows in the square brackets. ***, **, * represents the significant level that reaches
the 1%, 5% or 10%, respectively. N represents the observations.
Table 7 shows investors’ herding behaviors in up and down markets and in both transparency
groups. The γup2,Low, γdown4,Low, γup2,High, and γdown4,High coefficients are all significantly
negative. Table 7 demonstrates a significant presence of herding behavior under all four conditions. The Chow test results reveal that γup2,Low is significantly smaller than γup2,High by
−2.8087, which indicates that the lower level of transparency, the more prevalent of herding
behavior in the Taiwan stock market. That is, increasing transparency can discourage investors
from engaging in herding behavior in up markets. Similarly, γdown4,Low is significantly smaller
thanγdown4,High by −1.2675, which also indicates that the lower level of transparency, the more
prevalent of herding behavior in the Taiwan stock market. That is, increasing transparency can
discourage investors from engaging in herding behavior in down markets. In addition, the chisquare results demonstrate that in the low transparency group, herding behaviors in up and down
markets exhibit asymmetry. However, herding behaviors in up and down markets in the high
transparency group do not exhibit significant asymmetry, since the empirical results do not reach
the significant level.
Table 7 shows the effects of transparency on herding behaviors in up and down markets and in both
higher and lower transparency groups. Equation 6 is utilized, and shows as follows: CSADh;t ¼ α þ
up
2
2
down
down
γup
1;h DRm;t þ γ2;h DðRm;t Þ þ γ3;h ð1 DÞRm;t þ γ4;h ð1 DÞðRm;t Þ þ γ5;h CSADh;t1 þ εt CSAD
denotes the CSAD. h = 1 represents the group of higher transparency; otherwise, h = 0 represents the one of
lower transparency. Rm;t denots the equal-weighted portfolio return rate on t day. D is a dummy variable.
D = 1 represents Rm;t > 0 on t day; otherwise, D = 0 represents Rm;t < 0 on t day. T-value shows in the
parentheses, and F-value shows in the square brackets. ***, **, * represents the significant level that
reaches the 1%, 5% or 10%, respectively. N represents the observations.
Table 8 shows the empirical results of herding behaviors under normal market conditions and
during financial crises before (1993–2002) and after (2005–2014) the implementation of the IDTRs.
The γ3,Before, γcrisis6,Before, γ3,After, and γcrisis6,After coefficients are significantly negative, and reveal that
herding behaviors exist under all four conditions. The Chow test results reveal that γ3,After exceeds γ3,
crisis
crisis
Before by 1.8158, but γ
6,After is smaller than γ
6,Before by −1.8465. These results reveal that the
implementation of the IDTRs discouraged investors’ herding behavior under normal market conditions, but herding behavior increases during financial crises.
The chi-square test between γ3,Before and γcrisis6,Before demonstrates that herding behaviors in
the normal market or financial crisis are not significantly different before (1993–2002) the
implementation of the IDTRs. However, the chi-square test between γ3,After and γcrisis6,After are
significantly different by 2.9257, which indicates that after the implementation of the IDTRs,
herding behavior during financial crises is more severe than that under normal market conditions after the implementation of the IDTRs. Based on the financial intuition, in theory,
investors should have the similar attitudes toward the different financial crises, but the two
chi-square tests reveal that financial crises exert varying effects on investors’ herding behaviors.
This study refers to Kaminsky and Schmukler (1999) that, relative to other Asian stock
markets, Investors in the Taiwan stock market were not heavily directly damaged during the
1997–1998 Asian financial crisis. But, 2008 subprime mortgage crisis affected worldwide
investors including Taiwan stock market. This is the reason why herding behaviors in the
normal condition before the implementation of the IDTRs are more severe than those during the
financial crisis.
Table 8 shows the empirical results of IDTRs implementation on herding behaviors under
normal market and in the financial crisis in the Taiwan stock market. Before represents the
period of 1993–2002 before implementing IDTRs, and after represents the period of
2005–2014 years after it. Equation 7 is utilized, and shows as follows: CSADf ;t ¼ α þ
EFFECTS OF TRANSPARENCY ON HERDING BEHAVIOR
13
Table 8. The effects of IDTRs implementation and financial crises on herding behaviors in the
Taiwan stock market.
α
0.0020
(10.51)
***
α
0.0035
(18.57)
***
Chow test
Chi-square
γ1,Before
−0.0013
(−0.32)
***
γ1,After
−0.0307
(−6.30)
***
γ2,Before
γ3,Before
0.2990
−5.4733
(19.90)
(−15.16)
***
***
γ2,After
γ3,After
0.2455
−3.6575
(17.30)
(−9.22)***
***
[13.70]***
γcrisis4,
γcrisis5,
Before
Before
−0.0151
(−1.68)*
0.2814
(11.61)
***
γcrisis6,Before
γ7,Before
adj-R2
N
−4.7367
(−6.15)***
0.7553 0.6988 2735
(69.59)
***
γcrisis4,After γcrisis5,After γcrisis6,After
γ7,After
adj-R2
N
−0.0217
0.3631
−6.5832
0.6395 0.6708 2483
(−3.80)
(21.46)
(−16.55)
(49.06)
***
***
***
***
γcrisis6,After - γcrisis6,Before
γ3,After - γ3,Before
1.8158
−1.8465
γ3,After - γcrisis6,After
γ3,Before - γcrisis6,Before
−0.7366 [0.90]
2.9257 [32.87]***
Note: ***, **, * represents the significant level of 1%, 5%, 10%, respectively.
crisis
γ1;f DRm;t þ γ2;f DRm;t þ γ3;f DðRm;t Þ2 þ γcrisis
ð1
DÞR
þ
γ
ð1
DÞ
R
m;t
m;t þ
4;f
5;f
2
γcrisis
6;f ð1DÞðRm;t Þ þ γ7;f CSADf ;t1 þ εt CSAD denotes the CSAD. f = 1 represents the period that
before the IDTRs implementation; otherwise, f = 0 represents the one after it. Rm;t denotes the
equal-weighted portfolio return rate on t day. D is a dummy variable. D = 1 represents during
the normal market; otherwise, d = 0 represents in the financial crisis. T-value shows in the
parentheses, and F-value shows in the square brackets. ***, **, * represents the significant
level that reaches the 1%, 5% or 10%, respectively. N represents the observations.
Table 9 shows herding behaviors under normal market conditions and during financial crises in
the two transparency groups. The γ3,Low, γcrisis6,Low, γ3,High, and γcrisis6,High coefficients are all
significantly negative. Table 9 demonstrates a significant presence of herding behavior under all
four conditions.
The Chow test results reveal that γ3,Low is significantly smaller than γ3,High by −1.5867, and
γcrisis6,Low is significantly smaller than γcrisis6,High by −1.2781. Therefore, the lower level of
transparency, the more prevalent of herding behavior in the Taiwan stock market. That is,
elevating transparency can discourage investors from engaging in herding behavior under both
normal market and financial crisis conditions. The chi-square test results reveal that regardless of
transparency group, herding behavior is more apparent during financial crises than under normal
market conditions. From the financial intuition point of view, in general, investors intuitively
regard the financial crisis as bad news, then the herding behaviors might be more severe during
the financial crisis than in the normal market.
Table 9 shows the empirical results of herding behaviors under normal market and in the
financial crisis in the higher and lower transparency groups.
Equation 8 is utilized, and shows as
2
crisis
¼
α
þ
γ
DR
þ
γ
D
R
follows: CSAD
1;h
2;h
h;t
m;t
m;t þ γ3;h DðRm;t Þ þ γ4;h ð1 DÞRm;t þ
2
γcrisis
Rm;t þ γcrisis
5;h ð1 DÞ
6;h ð1DÞðRm;t Þ þ γ7;h CSADh;t1 þ εt CSAD denotes the CSAD. h = 1 repre-
sents the group of higher transparency; otherwise, h = 0 represents the one of lower transparency. Rm;t denotes the equal-weighted portfolio return rate on t day. D is a dummy variable.
D = 1 represents during the normal market; otherwise, d = 0 represents in the financial crisis.
T-value shows in the parentheses, and F-value shows in the square brackets. ***, **, *
represents the significant level that reaches the 1%, 5% or 10%, respectively. N represents the
observations.
γ1,Low
−0.0049
(−0.78)
γ1,High
−0.0261
(−5.22)***
γ2,Low
γ3,Low
0.3159
−4.5156
(17.78)***
(−8.84)***
γ2,High
γ3,High
0.2234
−2.9289
(15.12)***
(−7.11)***
[8.29]***
γ3,Low - γcrisis6,Low
3.2159 [23.31]***
γcrisis4,Low
−0.0197
(−2.74)***
γcrisis4,High
−0.0135
(−2.31)**
Note: ***, **, * represents the significant level of 1%, 5%, 10%, respectively.
Chi-square
Α
0.0039
(18.97)***
Α
0.0038
(20.06)***
Chow test
γcrisis5,Low
0.4416
(20.89)***
γcrisis5,High
0.3699
(21.14)***
γ3,Low - γ3,High
−1.5867
γcrisis6,Low
−7.7315
(−14.75)***
γcrisis6,High
−6.4534
(−15.93)***
Table 9. The effects of transparency and financial crisis on herding behaviors in the Taiwan stock market.
γ7,Low
adj-R2
0.5858
0.6289
(42.67)***
γ7,High
adj-R2
0.5973
0.6311
(43.17)***
γcrisis6,Low - γcrisis6,High
−1.2781
γ3,High - γcrisis6,High
3.5245 [45.19]***
N
2483
N
2483
14
K.-Y. WANG AND Y.-S. HUANG
(−0.57)
(8.31)***
α
(−0.30)
(10.15)***
(8.17)***
γ6,Before
0.2032
γ5,Before
−0.0023
0.0038
(5.68)***
γ2,Before
0.3118
γ1,Before
−0.0089
α
0.0095
γ3,Before
(−2.00)**
−1.2541
γ7,Before
(−5.99)***
−7.5872
γ4,Before
(13.19)***
0.4572
γ8,Before
(9.67)***
0.4996
N
276
N
276
adj-R2
0.6756
adj-R2
0.3341
Note: ***, **, * represents the significant level of 1%, 5%, 10%, respectively.
10% lower trail
10% upper tail
(6.54)***
0.0035
α
(7.84)***
0.0067
α
(−1.09)
−0.017901
γ5,After
(−1.39)
−0.0194
γ1,After
(0.97)
0.0517
γ6,After
(5.20)***
0.2525
γ2,After
(1.25)
1.6128
γ7,After
(−4.33)***
−4.6420
γ3,After
(17.44)***
0.6578
γ8,After
(12.14)***
0.5516
γ4,After
248
N
248
N
0.6582
adj-R2
0.4452
adj-R2
[10.55]***
Chow test
[8.69]***
Chow test
2.8669
γ7,After - γ7,Before
2.9452
γ3,After - γ3,Before
Table 10. The effects of IDTRs implementation on herding behaviors while the trading volume following in the extreme market movements in
the Taiwan stock market.
EFFECTS OF TRANSPARENCY ON HERDING BEHAVIOR
15
16
K.-Y. WANG AND Y.-S. HUANG
Herding Behavior During Extreme Market Movements
In Table 10, this study explores, while the trading volume following in the extreme 10% upper and
lower tails, whether herding behaviors exist. In addition, each subsamples are further divided into
two periods of before (1993–2002) and after (2005–2014) the implementation of the IDTRs. In
Table 10, the coefficients of γ3,After, γ3,Before and γ7,Before are all statistically significant negative.
These results show that, before implementing the IDTRs, when the trading volume are in both 10%
upper and lower tails, investors show herding behaviors. But, after implementing the IDTRs, when
the trading volume are in the 10% upper tail, investors show herding behaviors. However, in the
10% lower tail, investors do not show herding behaviors. Furthermore, from the financial intuition
viewpoint, this study suggests that when the trading volume is larger, the stock market fluctuates
more. And, when the markets are in the extreme fluctuation, investors, especially those who lack
information, are easier to follow the market or others to make investment decisions. Therefore,
herding behaviors are more prone to produce.
The phenomenon when the trading volume are in the extreme 10% tails before the implementation of
the IDTRs, investors show herding behaviors is consistent with the financial intuition viewpoint. However,
in the 10% lower tail after the implementation of IDTRs, the findings do not support the financial intuition
viewpoint. Kaminsky and Schmukler (1999) has indicated that Taiwan stock market was soaring in 1990s,
and implied that investors have consistent opinions in that period. Maybe this is why, in the 10% lower tail,
investors still have herding behaviors before the implementation of the IDTRs.
The Chow test results reveal that γ3,After is significantly larger than γ3,Before, which indicates that
the implementation of the IDTRs effectively discouraged herding behavior in the 10% upper tail.
Similarly, γ7,After is significantly larger than γ7,Before, which indicates that the implementation of the
IDTRs effectively discouraged herding behavior in the bottom 10% tail. Therefore, H1 is
supported.
This study took the 10% upper and lower tails of the trading volume as the criteria of the
extreme market movements. Table 10 shows the empirical results of IDTRs implementation on
herding behaviors while the trading volume following in the extreme 10% upper and lower tails.
Before represents the period of 1993–2002 before implementing IDTRs, and after represents the
period of 2005–2014 years
after it. Equation 3 is utilized, and shows as follows:
CSADf ;t ¼ α þ γ1;f Rm;t þ γ2;f Rm;t þ γ3;f ðRm;t Þ2 þ γ4;f CSADf ;t1 þ εt . CSAD denotes the CSAD. f
= 1 represents the period that before the IDTRs implementation; otherwise, f = 0 represents the
one after it. Rm;t denotes the equal-weighted portfolio return rate on t day. T-value shows in the
parentheses, and F-value shows in the square brackets. ***, **, * represents the significant level
that reaches the 1%, 5% or 10%, respectively. N represents the observations.
Table 11 shows the herding behaviors in the 10% lower and upper tails in both transparency
groups. γ3,Low and γ3,High are significantly negative, which indicates a significant presence of
herding behavior in the 10% upper tail in both transparency groups. γ7,Low is not significant, whereas
γ7,High is significantly positive. Therefore, herding behavior is not prevalent in the 10% lower tail in
either high or low transparency group. The above finding is in line with financial intuition. In
general, the larger the trading volume of the market, the more prone to heavy fluctuations. When the
market is in the heavy fluctuations, investors are prone to follow others to make decisions, and
herding behaviors produce.
The Chow test between γ3,Low and γ3,High reveals that the lower level of transparency, the more prevalent
of herding behavior in the Taiwan stock market. That is, increasing transparency can effectively discourage
herding behavior in the 10% upper tail, thereby supporting H2. However, the Chow test between γ7,Low and
γ7,High reveals no significant differences between the two variables. Therefore, increasing transparency
does not affect herding behavior in the 10% lower tail, and thus H2 is not supported.
This study took the 10% upper and lower tails of the trading volume as the criteria of the extreme
market movements. Table 11 shows empirical results of the herding behaviors while the trading volume
(−1.75)*
(6.21)***
(2.35)**
γ6,Low
0.1427
γ5,Low
(6.37)***
−0.0355
α
γ2,Low
0.3450
0.0037
(0.68)
(6.82)***
γ1,Low
0.0101
α
0.0058
(−0.28)
−0.4397
γ7,Low
(−4.66)***
(15.19)***
0.6290
γ8,Low
(13.31)***
γ4,Low
0.5615
γ3,Low
−5.5059
N
248
N
248
adj-R2
0.6133
adj-R2
0.5219
Note: ***, **, * represents the significant level of 1%, 5%, 10%, respectively.
10% lower trail
10% upper tail
(6.51)***
0.0034
α
(8.69)***
0.0074
α
(−0.84)
−0.0136
γ5,High
(−1.14)
−0.0159
γ1,High
(0.38)
0.0210
γ6,High
(4.11)***
0.2151
γ2,High
(1.85)*
2.4037
γ7,High
(−3.16)***
−3.7190
γ3,High
(17.32)***
0.6589
γ8,High
(10.61)***
0.5038
γ4,High
248
N
248
N
0.6540
adj-R2
0.3788
adj-R2
[1.18]
Chow test
[2.36]**
Chow test
−2.8434
γ7,Low - γ7,High
−1.7869
γ3,Low - γ3,High
Table 11. The effects of transparency on herding behaviors while the trading volume following in the extreme market movements in the
Taiwan stock market.
EFFECTS OF TRANSPARENCY ON HERDING BEHAVIOR
17
18
K.-Y. WANG AND Y.-S. HUANG
following in the extreme 10% upper and lower tails in both
higher and lower transparency groups.
Equation 4 is as follows: CSADh;t ¼ α þ γ1;h Rm;t þ γ2;h Rm;t þ γ3;h ðRm;t Þ2 þ γ4;h CSADh;t1 þ εt . CSAD
denotes the CSAD. h = 1 represents the group of higher transparency; otherwise, h = 0 represents the one
of lower transparency. Rm;t denotes the equal-weighted portfolio return rate on t day. T-value shows in
the parentheses, and F-value shows in the square brackets. ***, **, * represents the significant level that
reaches the 1%, 5% or 10%, respectively. N represents the observations.
Conclusions
This study combines the concepts of information asymmetry from classic finance theory and herding
behavior from modern behavioral finance theory to investigate whether herding behavior exists in the
Taiwan stock market. The empirical period is divided into the following two time periods: before
(1993–2002) and after (2005–2014) the implementation of the IDTRs. The empirical results reveal that
herding behavior has been ubiquitous in the Taiwan stock market before and after the implementation
of the IDTRs. The Chow test results demonstrate that the implementation of the IDTRs strongly
discouraged herding behavior in Taiwan, thereby supporting H1.
This study investigates listed stocks between 2005 and 2014 and divides the samples into high and
low transparency according to the annual IDTRs report published by the SFI. The results reveal the
presence of herding behavior regardless of transparency group. Subsequently, this study uses the Chow
test to verify whether a significant difference exists between the herding behaviors of these two
groups. The empirical results reveal that herding behavior has a stronger presence in the low
transparency group than in the high transparency group, thereby supporting H2.
Maybe information cascade theory could explain for the empirical findings. According to the
information cascade theory, investors abandon the information they possess and follow others’
decisions, thereby producing herding behaviors (Banerjee 1992; Bikhchandani, Hirsheifer, and
Welch 1992; Demier, Kutan, and Chen 2010). Choi and Skiba (2015) indicated that information
asymmetry causes information cascades. Hence, reducing information asymmetry can prevent information cascades, thereby reducing herding behavior. Besides, Chung, Judge, and Li (2015) emphasized that increasing transparency can reduce information asymmetry. Based on the above, this study
infers that increasing transparency can discourage investors’ herding behavior. This result is echoed by
the empirical results of this study, and H1 and H2 are supported.
The empirical results show that herding behavior universal exists in the Taiwan stock market,
and is consistent with the ones of Chang, Cheng, and Khorana (2000), Demier, Kutan, and Chen
(2010), Chiang and Zheng (2010). Moreover, this study finds that the herding behavior of
investors are more severe during the financial crisis period of 2007–2008 than the normal periods.
However, this study does not find that herding behavior of investors are more severe during the
Asia financial crisis of 1997–1998 than the normal periods. This empirical result is not exactly
consistent with the ones of Jlassi and Bensaïda (2014), Huang, Lin, and Yang (2015), Economou,
Katsikas, and Vickers (2016). The possible reason is that the 1997–1998 Asia financial crisis did
not make too large attack to the Taiwan stock market. Besides, Kaminsky and Schmukler (1999)
indicated that the Asian stock markets were soaring in the early 1990s with a daily average of
0.04%. However, in the period of January 1997 to May 1998, in spite of the positive daily average
of 0.01% in the Taiwan stock market, the daily averages of other Asia stock market are all
negative. Therefore, the attack of 1997–1998 Asia financial crisis is relatively small to the Taiwan
stock market.
According to the empirical results, this study finds that the promotion of IDTRs of the financial
supervisory institution has the positive influence on financial market. Besides, the management could
mitigate the irrational shock of stock prices by enhancing the transparency of the corporations. Finally, this
study suggests that the future research might take the styles of investors into consideration to explore.
EFFECTS OF TRANSPARENCY ON HERDING BEHAVIOR
19
Acknowledgments
We acknowledge the helpful comments and suggestions from the anonymous reviewers.
Funding
This work was supported by the Ministry of Science and Technology [104-2815-C-468-036-H].
Notes
1. According to the information cascade theory, herding occurs when observant investors ignore the information they possess and follow others’ decisions (Banerjee 1992; Bikhchandani, Hirsheifer, and Welch 1992;
Demier, Kutan, and Chen 2010).
2. Taking Equation 3 for example, if β2,f is statistically significantly negative, then the more (Rm,t)2, the
less CASDf,t. From the financial intuition viewpoint, it means that when market returns are getting higher or
lower, investors might abandon their own experimental judgments, and make their investment decisions by
macro-economic environment. Then, the more of (Rm,t)2 might make the deviations of the stock returns
smaller, and produce the nonlinearly incrementally decreasing phenomenon (Chang, Cheng, and Khorena,
2000). This phenomenon is regarded as a result of herding behavior of investors.
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