Herding Behaviour in the Chinese and Indian Market

Paulo Lao and Harminder Singh

Deakin University, [email protected]

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Herding Behaviour in the Chinese and Indian stock markets

Introduction

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What is herding behavior?Individuals who suppress their own beliefs and

base their investment decisions solely on the collective actions of the market, even when they disagree with its prediction (Christie and Hwang, 1995)

Why China and India?Uprising economic powersGrowing stock market driven by economic growthTarget of fund managers and other investors Abnormal average returns and high risk in these

stock markets may be explained by herdingChang, Cheng and Khorana (2000) indicate higher

level of herding in emerging marketsThere is no such study on Indian market.

Literature review

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Christie and Hwang (1995)- Examined herding behaviour in US market and use return dispersion to estimate herding and found no herding behavior in the U.S. market

Nofsinger and Sias (1999)- measure herding by the relationship between change of institutional ownership and excess return and find herding behaviour in the U.S. market

Iihara, Kato and Tokunaga (2001) use the approach of Nofsinger and Sias (1999) and detect herding behaviour in Japanese market.

Literature review

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Caparrelli, D’Arcangelis and Cassuto (2004) investigate herding behavior in the Italian stock market and found herding exists in extreme market conditions.

Chang, et al. (2000)–herding behaviours are detected in developing countries but not in developed countries

Demirer and Kutan (2005) & Tan, Chiang, Mason and Nelling (2007) – Examining herding behavior in Chinese market. Herding

behaviour is found in the latter.

Research Questions

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1. Does herding behaviour exist in the Chinese and Indian stock market?

2. Is the herding behaviour during extreme market condition higher than that during normal market condition?

3. Are Herding behaviours during up and down market symmetric in China and India?

4. Is herding behaviour more significant in high volume state in China and India?

Methodology

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1. Measure of herding behaviourAs per rational asset pricing model, the

relationship between the absolute value of the market return and equity return dispersion is positive because investors obtain different information and have different expectations about the market

Nevertheless, when herding behaviour is presented in the stock market, the relationship between the absolute value of the market return and equity return dispersion becomes negative and non-linear (Chang et al. 2000)

Thus, by examining the relationship, the herding beahviour in the stock market can be detected

In this study, Cross-sectional absolute deviation (CSAD) is employed to measure the equity return dispersion, the equation of CSAD is shown below

Methodology

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Where is the average return of the equal-weighted market portfolio at time t, which represents the market return, and, Ri,t is the individual stock return of firm i at time t

To examine the relationship between the absolute value of the market return and equity return dispersion, the following regression is used:

Where lamda 2 is the coefficient of Herding behaviour if it comes as significantly negative

Methodology

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2. Measure of the herding behaviour during extreme market condition Extreme market returns are defined as those lie below the cutoff

point in the lower tail and above that in the upper tail of the market return distribution. 1%, 5% and 10% cutoff points are employed in this study

To test the herding behaviour during extreme market condition, the equation below is employed

Where , if the market return on day t lies in the extreme lower tail of the distribution; and equal to zero otherwise, and

= 1, if the market return on day t lies on the extreme upper tail of the distribution; and equal to zero otherwise

Methodology

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3. Measure of the herding behaviour during increasing and decreasing market The following equation is used to test the herding behaviour in up

and down market

, if < 0

, if > 0

Where is the coefficient of the equally weighted portfolio return at time t when the market declines

is equally weighted p/f return at time t when the market decreases

Thus, the variables with superscript “down” refer to the condition in which the market declines, whereas superscript “up” refers to that in which the market goes up.

Methodology

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4. Measure of the herding behaviour in high and low trading volume stateHigh volume state is defined as the trading volume on

day t is greater than that its last 30-day moving average. By contrary, trading volume is low if it is less than the last 30-day moving average.

Where is the coeff of the equally weighted portfolio return at time t when the market is in high

volume state is the equally weighted portfolio return at time t

when the market is in high volume state

Data collection

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The top 300 firms in Shanghai-A shares (SHA) and Bombay stock exchange (BSE500) based on market capitalization

Daily and weekly shares price and trading volume over the last ten years;(1/7/1999~ 30/6/2009) are collected.

Result

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1. Descriptive statisticsTable 1: Descriptive statistics of cross-sectional absolute deviations

Result

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Summary of Table 1By using both median and mean, the average CSAD

based on weekly data is higher than that based on daily data in both stock markets.

These results are consistent with the findings of Tan et al. (2008) that herding behaviour is less likely to present based on weekly data.

Across markets, the mean and SD of CSAD of BSE is slightly greater than that of SHA in both daily and weekly data, suggesting that the herding behaviour in BSE500 may be less significant than in SHA

DF-test (Dicker-Fuller test) shows the CSAD series is stationary for both stock markets based on daily and weekly data.

Result

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2. Herding behaviour in the Chinese and Indian market Table 2: Analysis of the level of herding in SHA and BSE500

Market SHA BSE500 Market SHA BSE500

α0.014015 (0.0000)

0.018688(0.0000)

α0.030924(0.0000)

0.041619(0.0000)

0.193658(0.0000)

0.153308(0.0000)

0.162896(0.0011)

0.089579(0.0063)

-2.74485(0.0000)

-0.35911(0.0157)

-0.10177(0.8206)

1.079586(0.0000)

AR(1)0.736854(0.0000)

0.751858(0.0000)

AR(1)0.656329(0.0000)

0.70991(0.0000)

Panel A: regression results

for daily data

Panel B: regression results

for weekly data

Result

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Summary of table 2There is herding behaviour based on daily data in

both stock marketsNo herding behaviour is detected in SHA and BSE

based on weekly CSAD. So, herding behaviour is “a very short-lived phenomenon”

Higher negative coefficients in SHA imply that the herding behaviour is more pronounced in the Chinese market than in the Indian market.

Result

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3. Herding behaviour during extreme market condition Table 3: herding behaviour during extreme return

Panel A: 10% criterion

Market SHA BSE500

Market condition

Downward

UpwardDownwar

dUpward

α0.0151

(0.0000)

0.015637

(0.0000)

0.020246(0.0000)

0.020321

(0.0000)

0.276675(0.0000)

0.024601

(0.1647)

0.072487(0.0000)

0.127966

(0.0000)

-3.3299(0.0000)

-1.76735(0.0000)

0.334147(0.1140)

-0.88783(0.0001

)

AR(1)0.749083(0.0000)

0.761388

(0.0000)

0.766553(0.0000)

0.769316

(0.0000)

Panel B: 5% criterionMarket SHA BSE500Market

conditionsDownwar

dUpward

Downward

Upward

α0.01522(0.0000)

0.015603

(0.0000)

0.020312

(0.0000)

0.020418

(0.0000)

0.307679(0.0000)

0.054724

(0.0163)

0.073996

(0.0000)

0.117183

(0.0000)

-3.89421(0.0000)

-2.17312(0.0000

)

0.294097

(0.1961)

-0.80443(0.0017

)

AR(1)0.742742(0.0000)

0.75921(0.0000

)

0.767462

(0.0000)

0.77071(0.0000

)

Result

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Panel C: 1%

criterionMarket SHA BSE500

Market conditions Downward Upward Downward Upward

α0.015381(0.0000)

0.01558(0.0000)

0.020415(0.0000)

0.020484(0.0000)

0.373408(0.0000)

0.100332(0.0101)

0.07913(0.0000)

0.099801(0.0001)

-4.89832(0.0000)

-2.75334(0.0000)

0.16365(0.5764)

-0.65779(0.0268)

AR(1)0.742727(0.0000)

0.757384(0.0000)

0.771036(0.0000)

0.771616(0.0000)

Results- Table-3

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In the Chinese market, the coeff are significantly negative at 1% level during extreme up or downward market movement. It implies the presence of herding behaviour.

Coefficient is suggesting that herding behaviour is more severe during extreme downward market.

In the Indian market, during extreme upward market, the coefficient is significant negative in all three cut-off criteria, indicating the existence of herding behaviour during extreme positive market in the Indian market.

During extreme downward market, the positive coefficient imply that in BSE herding behaviour do not exist when the market is falling heavily

Thus, in the Indian market, herding behaviour exists in extreme up market condition but not in extreme down market condition.

Results

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4. Herding behaviour during increasing and decreasing market Table 4: Herding behaviour in increasing and decreasing market

Panel A: Regression results for

decreasing market

Panel B: Regression results for increasing

market


α 0.014426(0.0000)

0.020225(0.0000)

α 0.015963(0.0000)

0.019954(0.0000)

0.258397(0.0000)

0.020593(0.1044)

-0.06457(0.0000)

0.092391(0.0000)

-2.83944(0.0000)

0.988016(0.0000)

-0.4664(0.0697)

-0.35206(0.0735)

AR(1) 0.772063(0.0000)

0.76683(0.0000) AR(1) 0.771086

(0.0000)0.772371(0.0000)

Test statistic

Market SHA BSE500

1129.665(0.0000)

1354.176(0.0000)

1000.108(0.0000)

1148.408(0.0000)

Result

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Summary of table 4The significance and statistics indicate that herding

behaviour is asymmetric during up and down market in both stock markets.

The herding behaviours are more severe when the market is falling in the Chinese market

In BSE500, the coefficients are significantly negative when the market is rising, but positive when the market is falling, suggesting the herding behaviour occurs only during up market

Result

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5. Herding behaviour during high and low volume market Table 5: Herding behaviour in high and low trading volume state

High trading Volume

Low trading volume


α0.015162(0.0000)

0.01849(0.0000) α

0.015091(0.0000)

0.018933(0.0000)

0.107611(0.0000)

0.082431(0.0000)

0.087662(0.0000)

0.035008(0.0130)

-1.95272(0.0000)

0.09851(0.5345)

-0.50618(0.2108)

0.200921(0.2941)

AR(1) 0.741409(0.0000)

0.778174(0.0000) AR(1) 0.741433

(0.0000)0.798869(0.0000)

Test statistic

Market SHA BSE500

832.7560(0.000)

850.3367(0.000)

830.9488(0.000)

815.1415(0.000)

Result

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Summary of table 5In the Chinese stock market, herding behaviour

exists only in high volume state.In the Indian market, herding behaviour is not

related to the level of trading volume

Result

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6. Robustness test6.1 The effects of the size of the shares on the

herding behaviour In this study, as the equally-weighted measure is

employed, it is suggested that the results may be affected by the size of the stocks in each market

Also, McQueen et al. (1996) imply that large stocks tend to respond much quicker than small stocks to good news. Such asymmetric effect would affect the accuracy of the measure of herding behaviour.

All the shares in each market are categorized into three groups- Group 1(the smallest 10% shares), Group2 (the middle-sized shares) and Group 3 (the largest 10% shares)

The herding behavior of all three groups during the whole sample period, and up and down market, are examined

Result

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Table 6: The comparison of herding behaviour among Group1, Group2 and Group3 over the same period, during up and down

marketPanel A: regression

results over the sample

period

Panel B: regressions

results during up market

Panel c: regression

results during down

market

Group1 Group1 Group1

Market SHA BSE500 Market SHA BSE500 Market SHA BSE500

α0.019907(0.0000)

0.021364(0.0000)

α0.019649(0.0000)

0.021623(0.0000)

α0.021729(0.0000)

0.021314(0.0000)

0.128783(0.0001)

-3.45E-10(0.8802)

0.301171(0.0000)

-3.36E-10(0.8835)

-0.15777(0.0000)

0.082907(0.0000)

-2.00871(0.0002)

1.53959(0.0000)

-3.63614(0.0000)

1.404611(0.0000)

0.776129(0.2014)

-0.0034(0.9919)

AR(1)0.676732(0.0000)

0.559735(0.0000)

AR(1)0.688954(0.0000)

0.575735(0.0000)

AR(1)0.686032(0.0000)

0.580979(0.0000)

Group2 Group2 Group2


α0.014004(0.0000)

0.018794(0.0000)

α 0.0144650.020174(0.0000)

α0.016006(0.0000)

0.020104(0.0000)

0.200013(0.0000)

0.146871(0.0000)

0.263097(0.0000)

0.031408(0.0307)

-0.06345(0.0000)

0.077716(0.0000)

-2.78637(0.0000)

-0.35089(0.0481)

-2.91426(0.0000)

0.999466(0.0000)

-0.41457(0.1145)

-0.51074(0.0263)

AR(1)0.737401(0.0000)

0.666336(0.0000)

AR(1)0.772271(0.0000)

0.694875(0.0000)

AR(1)0.768909(0.0000)

0.703363(0.0000)

Result

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Group 3 Group3 Group3


α0.011402(0.0000)

0.017594(0.0000)

α0.013035(0.0000)

0.017594(0.0000)

α0.013494(0.0000)

0.017362(0.0000)

0.266858(0.0000)

0.037212(0.5544)

0.118184(0.0002)

0.037212(0.0397)

0.089214(0.0020)

0.091121(0.0000)

-2.95931(0.0697)

0.883444(0.0000)

0.196971(0.7602)

0.883444(0.0019)

-2.00181(0.0001)

-0.03285(0.9091)

AR(1)0.368499(0.0000)

0.637852(0.0000)

AR(1)0.400677(0.0000)

0.637852(0.0000)

AR(1)0.419117(0.0000)

0.648958(0.0000)

Table 6 (continue)

Result

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Summary of table 6 In the Chinese stock market, during the whole sample period, strong

herding behaviour shown in all three groups as coefficients are significantly negative

However, during the increasing market, herding behaviour appears in Group 1 (the smallest 10% shares) and Group2 (the middle-sized shares) only. By contrary, herding behaviour exists only in Group3 (the largest 10% shares) during the decreasing market

The results suggest that the level of herding behaviour in different sized-groups vary with the direction of market return.

In the Indian market, during the whole sample period, herding behaviour is shown in Group2 only.

During the increasing market, again, herding behaviour is shown in Group2 only. During the decreasing market, no herding behaviour is detected in all three groups

The findings suggest that herding behaviour in India is as a result of the herding on middle-sized stocks. The findings are also in line with those above that herding behaviour only exists when the market is climbing up

Result

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6.2 The effects of GFC on the herding behaviour The negative impact of the global financial crisis brought the

investors’ confidence level to a very low level and made the market highly volatile and uncertain

This may induce more significant level of herding behaviour in the sampled stock market

Table 7: the level of herding behaviour before and during the global financial crisis

Panel A: regression results before the global financial crisis

Panel B: regression results during the global financial crisis


α0.013258

(0.0000)

0.01801

(0.0000)α

0.01788

(0.0000)

0.02226

(0.0000)

0.16664(0.0000)

0.14864(0.0000)

0.27131(0.0000)

0.15131(0.0000)

-2.56793(0.0000)

-0.1613(0.4581)

-3.38672(0.0000)

-0.43847(0.0854)

AR(1)0.76627(0.0000)

0.73683(0.0000)

AR(1)0.38422(0.0000)

0.74258(0.0000)

Result

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Summary of table 7Herding behaviour is more significant during the

period of global financial crisis in both marketsIn the Chinese market, the herding is more

significant after the GFC and suggests that the higher herding behaviour during this period may lead to more significant herding behaviour during the whole sample period.

In contrast, the insignificant herding behaviour in the Indian market in panel A implies that herding behaviour did not exist before the global financial crisis. The significant herding behaviour over the whole sample period may be the result of the high level of herding behaviour after the global financial crisis.

Conclusion

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The result suggests that herding behaviour exists in both Chinese and Indian stock market

Herding in the Chinese stock market is more pronounced than that in the Indian stock market

The herding behaviour is more significant during extreme market conditions in both the markets.

Higher herding behaviour is found when the market is falling in the Chinese stock market. In the Indian market, there is the presence of the herding behaviour only during the up market.

Conclusion

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The level of herding is greater when the trading volume is high in the Chinese market. In contrast, the level of herding behaviour in the Indian market is unrelated to the size of trading volume.

The magnitude of the herding behaviour in each stock market seems to be affected by the size of the stocks in each stock market and negative effects of the GFC.

More open market and higher ratio of institutional investors may contribute to the less significant herding behaviour in the Indian market.

Foreign and institutional investors are more rational and educated and less likely to herd.

Future research should separate the herding behaviour between individual and institutional investors

Conclusion

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ThanksThanks for not sleeping and snoring.

Documents

Herding Behaviour in the Chinese and Indian Market