Testing The Existence Of Herd Instinct: An Empirical Study On Bse Sectoral Indices


Testing the Existence of Herd Instinct: An Empirical Study on BSE Sectoral Indices

Abstract
Being a social creature, the herd instinct pushes individuals to imitate and ape the mental actions of surroundings. Similarly, the investors gravitate themselves with the behavioral pattern of environs which instigates the herding behavior. The research piece of work is trying to explore the prominent herding behavior amongst the 13 sectoral indices of Bombay Stock Exchange (BSE), starting from the period of January 2006 to December 2013. The abnormal returns of BSE sector indices have been calculated and the methodology of CH (1995) and CHK (2000) is being used for evidencing the existence or non-existence of herding behavior in BSE sectoral indices. The paper will also try to attempt the herding behavior in bull and bear phases. To test the presence of herd instinct a linear regression model has been applied. The result of the paper will endorse the fact whether BSE sectoral indices are efficient or inefficient.

Keywords: Herd instinct, Sectoral indices, Bull and bear, Efficient markets

Introduction
Behavioral finance is an evolving theme of finance which incorporates the psychology of individuals into the traditional finance. Efficient Market Hypothesis (EMH), propounded by Eugene Fama (1960s) states that markets are efficient and every piece of information is being reflected in the prices of securities, but the theories of behavioral finance evidences that in general markets are quasi-rational or irrational. The notion behind this framework is the existence of human minds which gets affected by the happenings in the surroundings. The investment decision revolves around the behavioral aspects of investors, which gives birth to biases and distorts the meticulous investment decisions.
Herding behavior is one of the prominent biases in the field of behavioral finance which affects the market efficiency. The rational investor always wants to buy low and sell high, but the instincts of herding push investor to buy what others are buying and sell what others are selling irrespective of their own analysis and information what they possess with them. Many of the observers cite that the bubbles and crashes are also the end result of herding behavior as investors follow the acts of the crowd (Abhijeet V. Banerjee, 1992)
The theory of selective auditory attention also postulates the herding bias in the manner that individual listens to what they want to listen and give their ear to that sound and noise. This niche is connected with intentional herding rather than spurious herding where the piece of information is taken rationally on the grounds and investors take similar decisions. On the other side of the coin, this intentional herding finds its own ways by imitating the action of others by putting aside their own analysis and knowledge and rushes with the crowd. The bandwagon effect is to be seen by the acts and assessments of investors and the only thing is the decisions of early investors are to be followed by others keeping in view the snowballing effect (Sushil Bikhchandani and Sushil Sharma, 2001).
The underlying reasons for herding in human are that individuals feel secure to be a part of the group and chases the tag of being called labeled rather than a dissenter (Robert R. Prechter Jr.and Wayne D. Parker, 2007). Other than that, individuals think that they will get benefited with the piece of available information remained with the group which they might cannot access. The ultimate idea for herding instinct in individuals is driven by obtaining resources and minimizing the overall risks, but they want to ignore the fact that it can escort them to terrible crashes (Michael E. Price, 2013)
The Bombay Stock Exchange (BSE) is one of the oldest stock exchanges in Asia and fifth most active exchange in terms of number of transactions handled electronically. It has 13 Sectoral Indices namely, Automobile, Banking, Capital goods, FMCG, IT, Teck, Oil & Gas, Power, Metal, Consumer durables, Realty, Healthcare and PSU. This research paper is an attempt to explore the herding instinct in the BSE Sectoral Indices.

Review of related Literature
The varied available literature threw light on the existence and non-existence of herd behavior in the stock market.
(Christie, W.G., & Huang, R.D, 1995), used Cross Sectional Standard Deviation (CSSD) for finding the herd behavior in the markets. They evaluated that the mimic and following the flock behavior alternates in ordinary and intense phases of the market. They opened the layer of the mindset in the manner that if investors follow the direction of the market by not following the crowd the asset return will not deviate much from the average market return. That gives the reason of a reduction in CSSD.
(Andrea Devenow and Ivo Welch, 1996), explained the reasons for herding through available literature such as extreme negativity in bank runs, extreme positivity in liquidity aspects, considering the warning signals to reputation and learning through information. These factors give inputs to exacerbate the level of herding cognitive to the irrational minds of the investors. It is also found that effective communication channel may reduce the herd instinct, but the dilemma is to get that traffic filled data.
(Eric C. Chang , Joseph W. Cheng and Ajay Khorana, 2000), swayed the work of Christie and Huang (1995) on different international markets such as the U.S, Taiwan, South-Korea, Japan and Hongkong. The robust manner of Cross Sectional Absolute Deviation (CSAD) was being used which evidenced that in emerging markets like Taiwan and South-Korea herding exists significantly. Countries like the U.S. and Hongkong are affected by herd instinct only to a smaller extent. The level of information access and macroeconomic factors plays an important role which directly hits the herding gut feeling.
(Sushil Bikhchandani and Sunil Sharma, 2001), beautifully extended the work by focusing upon the reasons for herding, such as imperfect information, compensation structure and concern for reputation. The developed economies show signs of non-herding and the pattern is keenly correlates with the performance of manager for working upon the momentum strategies. In contrast, the emerging economy's tendency of herding is higher as reporting practices are not meticulous. Due to the reason of information asymmetry, there is always a possibility of informational drops and of reputation and reward foot herding.
(Guglielmo Maria Caporalea, Fotini Economou and Nikolaos Philippas, 2008), explored the instincts of herding in the Athens stock exchange in extreme market conditions. This evidenced that at the time of the crisis of the year 1999, herding was prevailing in the market conditions, but after the establishment of Greek equity markets in 2002 the behavior of investors skewed towards rationality. This paves the way for understanding that regulatory and other reform matters for the benign presence of balanced and lucid state of mind so that investors may apply their own logics and does not follow the irrational ways of the crowd.
(Tzewei Fu and Monli Lin, 2010), apparently explored the turnover effect on herding. Rather than using CSSD and CSAD, turnover effect is being studied during the period Jan 2004 to June 2009. As it is assumed that low turnover will result in higher dimensions of herding. In the Chinese equity market, the herding was found in the declining side in place of the advancing horizon of the markets.
(Jaya M. Prosad, Sujata Kapoor and Jhumur Sengupta, 2012), explored that for the period 2006 to 2011 Indian equity markets were informational efficient, due to the fact of non-existence of herding behavior. In continuation with this verity the herding was evidenced in gravitation with bull phase, whereas it contradicts with the proverb that crowd can never be wrong in bear phase of the market.
(Sardjoe, 2012), comprehensively studied that herd behavior is likely to be present at the time of extreme market returns. The period of volatility is characterized by panic situation for investors, which persuade them to hear the flocks. The approach employed in this paper is to ascertain the aspects of CSAD which assumes that if herding exists, the return of security will not deviate from the range of overall market returns.
(Safi Ullah Khan, 2013) , investigated the existence of herding instinct in emerging markets. The work in this series was tested on 18 sectors in the markets of Pakistan, which out rightly evidenced no herding behavior. Only 2 sectors were statistically observed as non linear herding behavior, even considering the ups and downs of the market.
(A. F. M. Mainul Ahsan and Ahasan H Sarkar, 2013), also evidenced in their research work that herding was not statistically significant in the period 2005 to 2011 in Dhaka stock exchange. It is just reverse of the common consensus of existence of herding instinct in Bangladesh stock exchange. Investors now realizing that following the others direction might hit their portfolio. (Dalia El-Shiaty and Ahmed Abdelmoteli Badawi, 2014), also explored that although the emerging markets are full of information asymmetry, herding does not exist in Egyptian stock exchange.
The profound insight on research objectives and methodology which is the heart of any research is being provided in the next section.
Research Objective
The notion of this research lies in the framework of finding out the herd instinct in the market. In this series, whether the bull and bear phase are touched with this herding behavior horizon will also be explored. This work will also examine whether the herding pattern is linear or non-linear by following the methodology of Christie and huang (1995) and Chang et.al. (2000).
Data, Hypothesis and Methodology
The study employs the sample of BSE sectoral indices which travels around 13 indices, including Automobile, Banking, Capital goods, FMCG, IT, Teck, Oil & Gas, Power, Metal, Consumer durables, Realty, Health Care and PSU. The available literature in context of Indian markets evidenced the presence and absence of herding behavior, but this paper is an attempt to explore the herd instinct in Bombay Stock Exchange sectoral indices. The idea is to see the sights, whether which of the indices are full of rational minds of investors and which one are lacking and equipped with quasi-rationality or irrationality.
Hypothesis
H1: Herding does not exists in each of the BSE Sectoral indices
Methodology
The methodology of Chang et.al (2000) is being adopted for giving the shape to this research work. This is an extended version of Christie & Huang (1995). Both the models for return dispersion uses cross sectional returns which tends that the return of security will not deviate from the overall market returns if there is a pattern of herding. This also measure the existence of herding in extreme market situations, both positive and negative sides. This cross sectional methods narrows the gap between the return of security and overall market return.
Christie & Huang (1995) gave the way for calculating the result, which is known as CSSD (Cross Sectional Standard Deviation) for calculating the returns with respect to market returns. This is as underneath:

Here,
Rit is the observed return of sectoral index i at time t,
Rmt is the cross sectional average return of the market at time t,
N is the number of sectoral indices
This is a rational asset pricing model which has its root in CAPM model. In contrast to this later on Christie & Huang (1995) used the CSAD (Cross Sectional Absolute Deviation) model which is more keen and sensitive to individual security return. This is expressed as follows:

Here,
N is the number of firms in the aggregate market portfolio,
ri,t, is the observed stock return on firm i for day t, and
rm,t, is the cross-sectional average return on day t.

The regression equation is as follows:-

CSADt = a + b1DtL + b2DtU + et

The CSSD and CSAD of return were regressed against one constant and two dummies. In this series for identifying the extreme market phases DL is given 1 if it is in the extreme phase of 1% and 5% lower side of the dispersion and rest is equal to zero. Similarly, this will be applied for DU for the upper side of the distribution.

In place of this approach, Chang et.al (2000), proposed an approach for entire distribution of market returns which is expressed as follows:-

CSADt = ?? + ??1|rm,t| + ??2r 2 m,t + et

Here, the relationship is between CSAD and market returns for detecting the presence of herd behavior. Likewise the CAPM model, herding does not exist if there is a positive coefficient ??, whereas the presence of herding postulates the negative coefficients. The squared market returns are also introduced as an additional term.

Herding behavior in bull and bear phases of the market

The movement of markets has strong impact on CSAD and on overall returns which paves the chances for the existence of herd behavior either in bull or bear phases. For this, the whole data points from January 2006 to December 2013 were segregated in decreasing and increasing modes.

CSADt Down = ?? + ??1D|rm,tD| + ??2D(rm,tD)2 + et

CSADt Up = ?? + ??1 U|rm,tU| + ??2U(rm,tU)2 + et

Where: ??2D (??2U) is the coefficient of the value-weighted market portfolio return at time t when the market declines (increases), and rm,t Down (rm,t Up) is the value weighted market portfolio return at time t when the market decreases (increases).

Results for overall regression for BSE Sectoral Indices
Table 1
CSADt = ?? + ??1|rm,t| + ??2r2m,t + et
Indices Intercept Standard Error Return of Market Return of Market Squared Adjusted R Square
Capital Goods 0.056384406 0.054108256 0.206909236 11.0016659 0.04000894
Auto 0.052553543 0.048494416 -0.005963157 19.14100214 0.116350144
FMCG 0.059368753 0.059955374 -0.063236235 34.06616708 0.213935921
Realty 0.108658571 0.108041783 -0.190796514 34.07938601 0.076141717
Oil & Gas 0.05072251 0.04670294 -0.12136611 0.062571749 0.02920127
IT 0.066279248 0.070789724 0.000411767 19.02769718 0.057155909
Consumer Durables 0.078455319 0.077253622 -0.008702363 16.72120531 0.037399031
Metals 0.072972611 0.070726536 -0.145055266 12.1311148 0.023263532
Health Care 0.054673379 0.051598936 0.162728618 34.16930988 0.277753419
Power 0.049609569 0.047198613 0.001464892 7.677851161 0.021127661
PSU 0.044487186 0.043439731 0.036220891 9.779528639 0.041413602
Teck 0.048753417 0.047630683 0.09435148 8.25155978 0.026426673
Bankex 0.054843763 0.053191827 0.082451171 10.16741988 0.031081592

T-stat and P-Value for overall Regression
Table-2

Indices t-stat P-Value
Intercept Rm Rm2 Intercept Rm Rm2
Capital Goods 44.3723 2.85418604 8.2930 2.6596E-299 0.004359519 2.01041E-16
Auto 46.1452 -0.09178051 16.0987 0 0.9268817 6.7025E-55
FMCG 42.1644 0.787233958 23.1747 6.0222E-278 6.0222E-278 2.3864E-105
Realty 42.8241 -1.318088675 12.8652 2.5263E-284 0.187626123 1.93558E-36
Oil & Gas 46.2458 -1.939631124 7.8052 0 0.052566105 9.53582E-15
IT 39.8679 0.00434157 10.9631 8.4453E-256 0.996536376 3.40527E-27
Consumer Durables
43.2434
-0.084078374
8.8281
2.2256E-288
0.933002601
2.28792E-18
Metals 43.9333 -1.530795029 6.9957 4.7072E-295 0.12597948 3.59254E-12
Health Care 45.1182 2.353906027 27.0093 1.6107E-306 0.018674344 4.3307E-137
Power 44.7561 0.023165537 6.6348 5.1324E-303 0.981520558 4.1779E-11
PSU 43.6078 0.622354651 9.1822 6.6413E-292 0.53378021 1.02984E-19
Teck 43.5847 1.478522171 7.0659 1.1097E-291 0.139426805 2.20015E-12
Bankex 43.9034 1.156958697 7.7962 9.1534E-295 0.247428513 1.02174E-14

Result at 1%
Table-3

CSADt Down = ?? + ??1D|rm,tD| + ??2D(rm,tD)2 + et

CSADt Up = ?? + ??1 U|rm,tU| + ??2U(rm,tU)2 + et

Indices Phase Intercept Standard Error Return of Market Return of Market Squared Adjusted R Square
Capital Goods Bear 0.10191933 0.056196515 0.197951321 7.134804889 0.058470216
Bull 0.10191933 0.056196515 0.197951321 7.134804889 0.058470216
Auto Bear 0.077327503 0.041454378 0.992428262 7.590205245 0.282325195
Bull 0.094130437 0.047033217 0.429930682 17.18108687 0.115632219
Realty Bear 0.185253938 0.098211186 -0.3154497 59.91698837 0.24298558
Bull 0.192744797 0.109643926 1.442494609 1.343734465 0.037608778
Oil & Gas Bear 0.078655356 0.041726823 0.009653038 24.73391248 0.206056025
Bull 0.094536293 0.04563751 -0.015631086 1.73838409 -0.001477822
IT Bear 0.110283252 0.082526861 1.046975464 5.812212036 0.07012163
Bull 0.119897861 0.070100918 0.168109836 16.22014208 0.05048842
Consumer Durables Bear 0.122966657 0.069969198 1.622157056 -1.445293314 0.102069488
Bull 0.13870592 0.083902329 0.764623754 -3.137394145 0.003950445
Power Bear 0.080577747 0.042994081 0.157012943 15.71422754 0.086481311
Bull 0.081990394 0.048472039 1.013792386 -5.589410516 0.032089658
Health Care Bear 0.069458332 0.048468585 2.218341852 9.959890995 0.517712522
Bull 0.084345574 0.051324074 1.293883741 8.04410257 0.183485573
Teck Bear 0.044332293 0.050643272 0.753943321 1.078888213 0.040594804
Bull 0.044053669 0.043946073 0.651748947 -0.461615399 0.025895004
Bankex Bear 0.090385609 0.046029441 0.788266007 -4.700201007 0.022173327
Bull 0.089879509 0.058074565 1.240833052 -4.616120804 0.046039584
PSU Bear 0.061220358 0.0413613 1.578409137 -6.140059053 0.140914496
Bull 0.074670709 0.043993752 0.789387476 -3.899735103 0.027984502
Metal Bear 0.099485965 0.067498631 2.520835826 -14.64115103 0.117201715
Bull 0.12917142 0.069654687 0.978546121 -8.812179555 0.006338524

T-stat and P-Value at 1%
Table-4

Indices Phase t-stat P-Value
Intercept Rm Rm2 Intercept Rm Rm2

Capital Goods Bear 24.5696 0.6373 2.49764 1.80654E-83 0.524226246 0.012880993
Bull 24.5696 0.6373 2.49764 1.80654E-83 0.524226246 0.012880993

Auto Bear 25.1281 4.7710 3.65428 3.8719E-89 2.4457E-06 0.00028676
Bull 21.2904 0.8926 1.90188 5.42E-68 0.372591 0.057898

Realty Bear 21.8676 -0.4291 5.50250 7.28E-72 0.668058 6.44E-08
Bull 24.1657 2.4224 0.23907 9.9135E-83 0.01582099 0.81116155

Oil & Gas Bear 23.7275 0.0308 5.18505 9.36743E-82 0.975413951 3.24694E-07
Bull 27.7251 -0.0621 0.73963 2.86256E-98 0.950447039 0.45992255

IT Bear 17.0506 2.2681 1.36522 4.79613E-50 0.023820805 0.172905078
Bull 19.5849 0.3160 1.99531 1.48406E-61 0.75209368 0.04663723

Consumer Durables Bear 22.3011 4.4964 -0.40871 6.41255E-74 8.88548E-06 0.68294754
Bull 20.7843 1.1753 -0.31337 5.15222E-67 0.240521614 0.754148748

Power Bear 20.9983 0.4165 2.35932 3.16254E-69 0.677235254 0.018723151
Bull 22.9226 3.7422 -2.22016 8.70582E-76 0.000207965 0.026945883

Health Care Bear 16.1767 8.6038 4.00574 5.4523E-46 1.6052E-16 7.3262E-05
Bull 19.0307 3.4191 1.35662 5.1233E-60 0.00068386 0.17556488

Teck Bear 17.3858 3.6112 0.45966 5.30854E-59 0.000320623 0.645861177
Bull 18.4115 2.6156 -0.10584 3.23278E-65 0.00904227 0.915730254

Bankex Bear 22.5469 2.2650 -0.89759 1.69926E-74 0.024015005 0.369911417
Bull 20.1656 3.9696 -1.55906 2.48021E-64 8.41885E-05 0.119708273

PSU Bear 19.2811 7.3143 -2.94528 2.45286E-61 1.13775E-12 0.003388029
Bull 20.4560 2.3534 -0.74648 1.45298E-64 0.019070269 0.455803018

Metal Bear 19.2633 7.2629 -4.37882 2.60725E-60 1.75096E-12 1.49492E-05
Bull 21.8087 1.5245 -0.76007 7.77423E-72 0.128087108 0.447619415

Result at 5%
Table-5

CSADt Down = ?? + ??1D|rm,tD| + ??2D(rm,tD)2 + et

CSADt Up = ?? + ??1 U|rm,tU| + ??2U(rm,tU)2 + et

Indices Phase Intercept Standard Error Return of Market Return of Market Squared Adjusted R Square
Capital Goods Bear 0.082620238 0.051148059 0.354614653 6.212622205 0.022345085
Bull 0.087269803 0.056031942 0.01630469 8.539498631 0.047193632
Auto Bear 0.062695877 0.042634396 1.115973657 6.968379311 0.250901666
Bull 0.073243088 0.04834163 1.491493254 -6.751029119 0.082496732
Realty Bear 0.158672486 0.098485306 -0.879588423 69.21150603 0.209380568
Bull 0.162232862 0.109010595 1.774106946 0.638246808 0.046724033
Oil & Gas Bear 0.065362989 0.041338392 0.045513128 24.04934095 0.172437146
Bull 0.076623142 0.04648827 0.281518016 0.742891661 0.006394484
IT Bear 0.092011565 0.077993487 1.03068462 6.296618403 0.069451224
Bull 0.098323601 0.068888659 0.517799432 10.64734165 0.046338996
Consumer Durables Bear 0.099584905 0.070078527 1.86611219 -2.086037318 0.113120251
Bull 0.11332922 0.081384783 0.79222743 0.493413986 0.011420705
Power Bear 0.062915449 0.043548416 0.903486827 -0.957168551 0.059033847
Bull 0.06857952 0.048335728 1.01910676 -5.127932212 0.033882262
Health Care Bear 0.05342061 0.047259988 2.416936168 8.917991973 0.49521363
Bull 0.066631437 0.049288366 1.742481823 4.945610087 0.217685986
Teck Bear 0.043834512 0.050625897 0.768250015 1.007207796 0.041324812
Bull 0.04343524 0.043975289 0.68530499 -1.013745726 0.026505315
Bankex Bear 0.070414236 0.046255395 1.013514928 -3.85848601 0.046668336
Bull 0.075362656 0.057343029 1.196877146 -4.64051142 0.043022188
PSU Bear 0.050028782 0.040668661 1.567621006 -5.86427301 0.138276246
Bull 0.061153727 0.043348318 1.02005461 -5.177611167 0.046846307
Metal Bear 0.081631669 0.065997516 2.323239103 -12.86725703 0.101606067
Bull 0.11305726 0.070462818 0.592800055 1.274324789 0.009052873

T-stat and P-Value at 5%
Table-6

Indices Phase t-stat P-Value
Intercept Rm Rm2 Intercept Rm Rm2
Capital Goods Bear 21.01153 0.83988316 0.78213 2.80118E-73 0.401320558 0.434453958
Bull 24.15979 0.05860999 3.12638 2.26971E-89 0.953283257 0.001859736
Auto Bear 23.69284 5.76890801 3.38964 1.29103E-89 1.24205E-08 0.000742772
Bull 20.54562 4.34747290 -1.22407 4.87665E-70 1.64106E-05 0.221448687
Realty Bear 21.86564 -1.3438754 6.75925 3.10276E-78 0.179500882 3.31482E-11
Bull 23.29731 3.23728924 0.11789 9.93766E-85 0.001277175 0.906190264
Oil & Gas Bear 23.83390 0.16563522 5.44804 1.8362E-90 0.868496091 7.26E-08
Bull 25.71090 1.21054848 0.32357 2.86417E-98 0.226554866 0.746375763
IT Bear 17.67881 2.64396694 1.63391 3.83289E-56 0.008418511 0.10282655
Bull 19.30252 1.10633981 1.43422 1.80883E-64 0.269038914 0.152046993
Consumer Durables Bear 21.17999 5.6459522 -0.61003 3.26E-74 2.57E-08 0.54208
Bull 20.62513 1.41293770 0.05400 8.58196E-72 0.158194296 0.956950435
Power Bear 20.353989 3.189746 -0.21137 3.6822E-71 0.00149465 0.8326637
Bull 22.25890 4.17951534 -2.12749 8.8406E-79 3.3971E-05 0.03382244
Health Care Bear 16.15606 10.814716 3.85605 6.52589E-49 5.33095E-25 0.000127854
Bull 19.15697 5.3291732 0.91665 1.05342E-64 1.37933E-07 0.359675675
Teck Bear 17.27084 3.6886969 0.42963 2.15404E-58 0.000238091 0.667555754
Bull 18.27699 2.7597871 -0.23262 1.74155E-64 0.00589121 0.816098335
Bankex Bear 21.08530 3.2564796 -0.78093 2.22241E-74 0.001191194 0.435148187
Bull 20.06770 4.3277236 -1.64687 2.2624E-68 1.7754E-05 0.10012793
PSU Bear 19.18945 8.2463040 -2.97803 4.2898E-65 9.3837E-16 0.00301148
Bull 20.05542 3.4062164 -1.05813 3.3876E-67 0.00070802 0.29046917
Metal Bear 19.30678 7.6444418 -4.10348 2.77107E-65 8.10389E-14 4.61815E-05
Bull 21.11412 1.0022722 0.11484 3.8514E-73 0.31664731 0.90861305

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