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 their own private information. as investors imitate and follow other investors’ decisions while suppressing 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

CSAD t

  0   1

R m

,

t

  2 2

R m

,

t

 

t CSAD t

 1

N i N

  1

R i

,

t



R m

,

t

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

CSAD t

   01   11

R



m

,

t CSAD t

   02   12

R



m

,

t

  21 (

R



m

,

t

) 2   1 ,

t

, if

R m

,

t

 0   22 (

R



m

,

t

) 2   2 ,

t

, if

R m

,

t

<0 (3) 10

Time-varying behavior of herding

 Kalman filter-based model

CSAD t

  0 ,

t

  1 ,

t R m

,

t

  2 ,

t R

2

m

,

t

 

t

(4)



i

,

t

 

i

,

t

 1 

v i

,

t

,

v i

,

t

~

N

( 0 ,



v

2 ,

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 CN KR TW 2003 2004 HK MA US 2005 ID SG 2006 2007 2008 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 advanced markets. 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  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.