Thomas C. Chiang
Marshall M. Austin Chair Professor of Finance
LeBow College of Business, Drexel University
For presentation at Feng Chia University, 2:00 pm, 12-29-2011
1



Definition of herding
 Herding is a form of correlated behavior as investors imitate
and follow other investors’ decisions while suppressing
their own private information.
Why investors herd?
 The observation of prior investors' trades can be
better-off by eliminating transactional/search costs.
What are the results of herding?
 Investors’ trading behavior can cause asset prices to
deviate from economic fundamentals. As a result,
assets are not appropriately priced.
 Investors need to search for broader investment
instruments to achieve asset diversification.

Chang et al (2000) - herding presents in South Korea and Taiwan; partial
evidence of herding in Japan; no evidence of herding on the part of market
participants in the US and Hong Kong.

Demirer and Kutan (2006) - no evidence of herding formation in Chinese stock
markets, implying investors make investment choices rationally.

Tan, Chiang, Mason, and Nelling (2008) - herding occurs under both rising and
falling market conditions, especially presenting in Chinese A-share investors.

Chiang and Zheng (2010) – herding displays in both advanced and Asia
markets except Latin American and the US markets.
- Herding is present in the US and Latin American markets in crisis.

Chiang, Li, and Tan (2010) investigate Chinese stock markets and find
supporting evidence of herding behavior in both A-share and B-share investors
conditional on the dispersions of returns in the lower quantile regime.

Does herding behavior consistently present in emerging countries?
 We examine a broader data set in Asian/Pacific Rim markets to test the
existence of herding behavior.

Is herding behavior static?
 Conventional approach to detect herding behavior is based on a constant
coefficient model in regression estimation. The resulting herding coefficient is
static and fails to describe herding dynamics.

What are the factors that determine herding dynamics?
 Can herding movement be explained by recent market performance, or
conditional volatility?

This paper presents a time-varying coefficient model to address the above
issues.

Markets:
Australia (AU), China (CN), Hong Kong (HK), Japan (JP), South Korea (KR),
Taiwan (TW), Indonesia (ID), Malaysia (MY), Singapore (SG), Thailand (TL),
and the United States (US);

Frequency: daily observations of individual firms for each markets;

Sample period: 7/2/1997 to 4/23/2009

Sources:
 Compustat /CRISP files for the US market
 Data stream international for all other markets;

The return is measured by the natural log- difference of industrial stock
index times 100.
Detecting herding behavior
 CCK (2002) and CSAD
CSADt   0  1 Rm,t   2 Rm2 ,t   t
CSADt
1 N

 Ri ,t  Rm,t
N i 1
6
Detect herding behavior
FCU, 2011
RESET Test
7
Estimates of herding in rising market
8
Estimates of herding in declining market
9
Asymmetry of herding behavior


2
CSADt   01  11 Rm
,t   21( Rm,t )   1,t , if Rm,t  0


2
CSADt   02  12 Rm
,t   22 ( Rm,t )   2,t , if Rm,t <0
(3)
10
Time-varying behavior of herding
 Kalman filter-based model
CSADt   0,t  1,t Rm,t   2,t Rm2 ,t   t
(4)
 i ,t   i ,t 1  vi ,t , vi ,t ~ N (0,  v2,i ) ,
(5)
where i =0,1, and 2.
11
Time-varying behavior of herding
 Time series plots of herding coefficients for 11 markets
.2
.0
-.2
-.4
-.6
-.8
1997
1998
1999
2000
2001
AU
JP
TH
2002
2003
CN
KR
TW
2004
HK
MA
US
2005
2006
2007
2008
ID
SG
12
Statistics of herding dynamics
13
Determinants of herding dynamics
 Stock performance hypothesis
 Volatility hypothesis
 The model
14
Determinants of herding dynamics
15
Herding behavior correlation and dynamic factors
16
Granger causality between stock returns (Rm) and herding (HERD)
17
Granger causality between variance and herding
18
Estimates of nonlinear components of herding equation
19

This study shows that herding exists in all the markets under constant
coefficient regression model (the Australia, China, Hong Kong, Japan,
South Korea, Taiwan, Indonesia, Malaysia, Singapore, and Thailand, and
the US). In contrast to the earlier literature that shows no herding in
advanced markets.

Herding presents in both up and down markets. Most markets show more
profound herding in the rising markets.

The state-space model shows that herding behavior is time varying.

In contrast to the constant coefficient model, state-space model finds
no evidence in supporting the existence of herding in the US market.

Dynamic herding behavior is
- negatively correlated with recent stock return performance;
- positively correlated with stock return volatility;
- Herding behavior is positively correlated among Pacific-Basin markets.