herding behaviour in the chinese and indian market
TRANSCRIPT
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
Market SHA BSE500 Market SHA BSE500
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
Market SHA BSE500 Market SHA BSE500
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
Market SHA BSE500 Market SHA BSE500 Market SHA BSE500
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
Market SHA BSE500 Market SHA BSE500 Market SHA BSE500
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
Market SHA BSE500 Market SHA BSE500
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.