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Archives of Business Research – Vol. 10, No. 2
Publication Date: February 25, 2022
DOI:10.14738/abr.102.11886. Arabi, K. A. M., & Abdelmageed, H. M. (2022). Is Herd Behavior Still Persistent in the Saudi Stock Market? Archives of Business
Research, 10(02). 244-251.
Services for Science and Education – United Kingdom
Is Herd Behavior Still Persistent in the Saudi Stock Market?
Khalafalla Ahmed Mohamed Arabi
College of Business, King Khalid University Abha, KSA
Hemeda Mohamed Abdelmageed
College of Business, King Khalid University, Abha, KSA
Faculty of Commerce, Al-Azhar University, Cairo, Egypt
ABSTRACT
This paper inspects the prevalence of Herd Behavior in the Saudi Stock Exchange,
using the model suggested by (Bhaduri & Mahapatra, 2013) which regresses the
absolute difference between average market returns and median, expecting
positive effect by absolute market returns and negative one by nonlinear squared
market returns. Daily data from January 1, 2007, to December 31, 2020, was
analyzed using Ordinary Least Squares, two Censored Regressions (TOBIT), and
switching regression. The constant and absolute market returns are both positive,
while the nonlinear quadratic term is negative, produced by all estimation
methodologies, which confirm model hypothesis. There were two market
conditions, and two regimes produced by both censored, and switched regression.
The nonlinear coefficient of growing prices is three times that of declining prices,
meaning that venture capitalists are more likely to herd during rising market
returns than during dropping market returns, according to the censored
regression.
Key Words: Herd Behavior, growing prices, declining prices, rising market, dropping
market.
INTRODUCTION
Herd behavior, which is defined as intentionally or unintentionally following the actions of a
group of individuals, has become a popular research topic as in (Ahmed et al., 2015;
Bikhchandani & Sharma, 2000; Mishra & Mishra, 2021; Yousaf et al., 2018). This action disrupts
the stock prices, drives them away from the major value, and results in investing in
overestimated stocks. Herding can be deliberate or not deliberate (Ahmed et al., 2015).
Receiving the same information can lead to unintentional herding. When there is a lot of noise
in the market, investors are more likely to keep an eye on a leader who they believe has a well
understanding of the situation. In the lack of information about the intentions of others, various
investors may take similar activities (Ah Mand et al., 2021).
In the absence of information about the objectives of others, different investors may take the
matching actions. Knowledge of others' objectives is the key determinant of defining herd
behavior. Financial market players' herding destabilizes markets and makes the financial
system more at risk by deviating prices from principal value and affecting risk features of stock
prices, some consider it as an opportunity to have profit (Tan et al., 2008). The following are
some of the reasons for herd behavior: some people may know something about the return and
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Arabi, K. A. M., & Abdelmageed, H. M. (2022). Is Herd Behavior Still Persistent in the Saudi Stock Market? Archives of Business Research, 10(02). 244-
251.
URL: http://dx.doi.org/10.14738/abr.102.11886
their actions expose this information; those who invest on behalf of investors promote
replication because of rewards, reputation, and an intrinsic inclination for conformity
(Bikhchandani & Sharma, 2000). Yousaf et al. (2018) argue that investors may follow the crowd
to lower regret intensity.
In terms of market capitalization, the Saudi stock exchange is the major in the Arab world, and
it ranks highly among emerging markets. The companies on the list are divided into fifteen
categories. To the best of my knowledge, only two empirical studies have investigated the
prevalence of the phenomenon in the Saudi Stock Exchange; the first is represented by Ramady
(2010) study for the Council of the Gulf States, and the second by (Balcilar et al., 2013). Even
though both studies revealed the issue, no real effort was taken to address it, which encourages
us to test for its presence. The literature review, theoretical backdrop, methodology, findings
discussion, and conclusion make up the structure of this paper.
LITERATURE REVIEW
A variety of factors have contributed to the appearance of herd conduct in emerging economies,
whereas the behavior made Bikhchandani and Sharma (2000) to summarize theoretical and
empirical studies. The phenomenon is not limited to individuals in a certain stock exchange, but
among exchanges themselves, as noted in the early 1990s by Conrad et al. (2015) and Hamao
et al. (2015). Measures of high-frequency cross-section data, as well as a variety of
methodologies, including standard deviation CSSD, absolute deviation CSAD, switching regime
(Balcilar et al., 2013), symmetric attributes of return, threshold, and capital asset pricing model,
are frequently used to recognize the phenomenon. Few studies used primary data of which Gul
and Khan (2019) on Pakistan led to the identification of psychological factors behind herd
behavior.
However, in contrast to conventional stocks, investment in Islamic stocks exhibited a bias
toward herding behavior according to Ah Mand et al. (2021), who used absolute deviation daily
returns. Furthermore, herding occurs only when prices are rising and are nonlinearly related
to the market return for the entire market. Under rising and expected rising prices, Mishra and
Mishra (2021) found herding behavior in the Indian banking and financial services industries
using quantile regression. In a cross-sectional study of Chinese, A and B shares, Ju (2020)
showed herding tendency in A-shares of small expanding portfolios versus large portfolios. B- shares responded to non-fundamental information while herding. Gul and Khan (2019) found
herd behavior in Pakistan Stock Exchange using primary data. The Egyptian market is free from
herding behavior according to the research conducted by Abd Alla and Sobh (2019) using cross- sectional monthly data applying the asset pricing model augmented by the herding behavior
factor. Market conditions, minimal trading, and return volatility all contribute to herd behavior,
according to Yousaf et al. (2018). AL-Hariri (2019)found that degrees of financial risk tolerance
for different investor categories are influenced by behavioral elements such as prejudice,
excess confidence, experience, comparable use, loss avoidance, avoidance of remorse, mental
calculations, and market information.
The use of cross-section standard deviations and absolute mean deviations confirmed the
behavior in the Spanish stock exchange observed by Ahmed et al. (2015), which was caused by
incorrect mortgages. Purba and Faradynawati (2012) discovered comparable results in the
Indonesian Stock Exchange during normal market conditions and when the market is up. Özsu
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(2015) detects a herding tendency in Borsa Istanbul. With the use of symmetric aspects of the
cross-sectional return distribution, Bhaduri and Mahapatra (2013) noticed herding behavior
during the 2007 crash in the Indian equity market, during the period January 2003 – Marsh
2008. Applying threshold regression to the Indian stock exchange Bhaduri and Mahapatra
(2013) found slight herd behavior noting the ability of investors to differentiate between
equities even in extreme market conditions. A similar conclusion was reached by Prosad et al.
(2012) that herd behavior is observed only during rising prices in the Indian Equity Market.
Daily data were used to the presence of short-term herd behavior in the Athens Stock Exchange
by Caporale et al. (2008).
THEORETICAL BACKGROUND
Developing countries, including Arab countries, are characterized by a severe lack of data that
exposes their vulnerability to financial crises and fluctuation. Therefore, these crises can be
dealt with by examining the occurrence of herd behavior as a phenomenon affecting trading
situations in the financial market. Also, how the investor can monitor the way successful
investment based on information drops and financial contagion.
Financial information is made available through financial bulletins to provide services to
portfolio owners through investment activity products, and this provides an easy way for stock
market investors to access their portfolio and make inquiries about their portfolio's balance
and stock prices, as well as request, buy and sell orders and other necessary investment
operations (Al-Maadhaeedi & Al-Abbas, 2009).
Information is critical to knowing, explaining, and justifying stock exchange investing decisions;
therefore, it is critical to emphasize the importance of asserting the loss of opportunities owing
to a lack of, or disregard for, information. The stock market's efficiency is dependent on the use
of information, and investor behavior should be tailored to the complexities of life, which are
often connected with uncertainty and risk. Six elements, however, are the most influential,
namely, changes in general prices and interest rates on the one hand, and financial contagion
of investor behavior on the other. Apart from these two, there are four more elements that
influence investor behavior and judgment. The first is conformity, which is defined as a person's
tendency to change his attitude, opinion, belief, or behavior to unite in with his surroundings
or group (Scher et al., 2007). The disregard for current knowledge in favor of following
predecessors is related to investors' belief that ancestors make better decisions. The second
aspect is making decisions hastily without giving them enough thought and attention, which
can lead to additional errors and unreasonable conclusions because relevant facts and analysis
are not taken into account. The third aspect is that one's mood has a substantial impact on stock
prices, risk-taking strategies, investor trading habits, and stock returns, with investor
happiness being positively correlated with their willingness to take bigger risks in financial
decisions. (Grable and Roszkowski 2008). The fourth factor is decision accuracy Shusha and
Touny (2016) refers to the uniformity of a decision with the ideal decision as a consequence of
portfolio analysis and consideration of relevant information in decision making (Hillenbrand &
Schmelzer, 2017). According to Allen and Evans (2005), a bullish individual believes he has
more right information than he actually has. Because they are more certain in their
assessments, overconfident investors are more likely to trade higher than other investors.
bullish investors usually make less money since they trade more frequently.
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Arabi, K. A. M., & Abdelmageed, H. M. (2022). Is Herd Behavior Still Persistent in the Saudi Stock Market? Archives of Business Research, 10(02). 244-
251.
URL: http://dx.doi.org/10.14738/abr.102.11886
METHODOLOGY
The work of Christie and Huang (1995), and Chang et al. (2000) has been replicated by the
plethora of papers, although the CSSD and CSAD are the dependent variables in many models
explained by up-market, and down-market, as well as an absolute market return:
The Bhaduri and Mahapatra (2013) model assume that in a symmetrical market, the deviations
between the average and median market returns are expected to approach zero and that
individual investors are drawn to the market's average investment decisions, implying that
dispersion is minimal, indicating the presence of behavior. The symmetry can be seized by the
following formula:
�����(�!) = |��� ! − ���!| (1)
Where, ���! designates the average cross-sectional returns, and ���! the cross-section
median of the market returns. The symmetrical measure is regressed on absolute and squared
cross-section returns, with the predicted sign of parameter setting the squared average return
to a negative value to show the presence of herd behavior i.e. the symmetry will decrease with
an increase in market returns (Bhaduri & Mahapatra, 2013):
�����(�!) = �" + �#|���!| + �$���!
# (2)
The symmetrical measure is regressed on market conditions, i.e. up-market and down-market,
for additional assertion:
�����(�!) = �" + �#
%&4���!
%&4 + �$���!
#'%& (3)
�����(�!) = �" + �#
()*+4�,-.+,!
()*+ 4 + �$�,-.+,!
#()*+ (4)
When returns are greater than or equal to zero, it is called an up-market; when returns are less
than zero, it is called a down-market.
Censored Regression Model (TOBIT)
Latent variable (�!
∗) regression model is similar to OLS except a scale factor is multiplied by an
error term (�!
∗ = �!
1
� + ��!). Then the normal dependent variable will take the value zero if the
latent variable is less than or equal to zero, one otherwise as equation (5) indicates:
�! = ;
0 �� �!
∗ ≤ 0
�!
∗ ���!
∗ > 0 (5).
RESULTS AND DISCUSSION
The dataset is composed of daily observations of 205 companies of the Saudi market across the
period 1/01/2008 – 12/12/2020. Four variables were used for analysis i.e. cross-section
returns average and median, the dependent variable is gamma which is the absolute difference
between the cross-section mean and the cross-section median, to depend on absolute market
return and squared market return.
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Descriptive Statistics
Table (1) Summary Statistics
GAMMA MR |MR| MR2
Mean 0.0019 -8.15e-05 0.0001 0.0067
Median 0.0015 0.00071 0.0000 0.0040
Maximum 0.0152 0.07038 0.007 0.0860
Minimum 0.0000 -.08604 0.0000 0.0000
Std. Dev. 0.0016 0.0109 0.0004 0.0083
Skewness 1.8979 -1.1429 8.3519 3.3814
Kurtosis 9.6865 12.1397 96.0141 19.2268
Jarque-Bera 10278.87 15432.89 1552814 53735.04
Probability 0.0000 0.0000 0.0000 0.0000
Sum 7.9403 0.475905 27.84084
Sum Sq. Dev. 0.01119 0.000638 0.29016
Observations 4173 4173 4173
The Saudi Stock Exchange's swings are established by the negative returns mean and larger
standard deviation. The gamma (�) peaks were on 19/7/2008 and 16/4/2019, and there were
only 13 days out of 4173 when it was zero. Jarque-Bera test for normality indicates the non- normal distribution of the gamma. In addition to the non-normality that it takes, there is a
distinct disparity between the mean and median of the market return. Moreover, gamma is
skewed to the right, while the market returns are skewed to the left.
Table (2) Results of Regression Analysis
Variable Down-Market Up-Market OLS
Constant 0.002126*** 0.000431*** 0.001372***
Absolute Market Return 0.138532*** 0.145961*** 0.095308***
Squared Market Return -1.18654*** -3.07012*** -0.92015***
Error Distribution SCALE (4) 0.002093*** 0.001568***
To test for herd behavior three models were estimated. The first model was the application of
ordinary least squares to equation (2) whereas the constant is significantly positive indicating
that return dispersion increases as absolute average returns grow. For significantly negative
squared market returns, the opposite is true: gamma drops as squared market return increases,
proving the nonlinear relationship and thus the presence of herd behavior. The second and
third models were the results of the censored (TOBIT) model where the down-market case uses
data when the average market return is less than 1, and up-market case when it is greater than
or equal to zero. The results of the two models show the same pattern as OLS, but the tendency
to herding behavior during rising returns is almost three times that under decreasing returns,
and both are greater than that of the whole market (OLS). Results in Table (3) confirm the
existence of herd behavior, as well as, the two market conditions supporting the finding of
Balcilar and Hammoudeh (2013).
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Arabi, K. A. M., & Abdelmageed, H. M. (2022). Is Herd Behavior Still Persistent in the Saudi Stock Market? Archives of Business Research, 10(02). 244-
251.
URL: http://dx.doi.org/10.14738/abr.102.11886
Table (3) Results of Switching Regression
Regime 1 Regime 2
Constant 0.000581*** 0.001702***
Abs(MR) 0.062785*** 0.161653***
MR2 -0.909*** -0.84134***
LOG(SIGMA) -7.08207*** -6.33885***
DISCUSSION
The findings of applying two censored models to the Bhaduri and Mahapatra (2013) model,
switching regression and ordinary least squares, revealed convincing indication of herd
activities in the Saudi Stock Exchange. These findings are well-matched with many of the
previous research. Information is an important ingredient in understanding, explaining, and
supporting investment decisions in the stock exchange, so it's important to assert the loss of
opportunities due to the absence and ignoring of facts. The efficiency of the stock market
depends on exploiting information, the behavior of investors should be catered to the
complexities of life that tend to be associated with uncertainty and risk. The effects of herding
behavior are asset mispricing, increase market volatility. Many reasons behind the action taken
in the stock market. The herding tendency may be rational or irrational. The rational conduct
could result from reward externalities, in which the number of agents participating in action
has a favorable effect on the outcome, and they subsequently gain from deeper liquidity and
choose to ignore the information. Fear can have a role in the choice to follow the herd when an
incident or a trading imbalance arises. Traders are afraid of losing money if they keep a stock
position despite high sell volume. Investors' greed and fear of missing out on the stock that
could be the huge money-maker of their dreams are exploited by promoters. The inherent
leaning toward orthodoxy, rapid decision, frame of mind, decision correctness, and bullishness
is the main psychological actions taken by investors and managers. In a sample of four
portfolios, Al-Maadhaeedi and Al-Abbas (2009) discovered the possibility of data cascades and
financial contagion on developing herd behavior in the Saudi Stock Exchange. However,
Medhioub and Chaffai (2021) observed that some sectors herd around lowering oil prices.
Ramady (2010) found that investors' technical and financial expertise is below average, that
few investment portfolios rely on technical or financial analyses, and that a quarter of them rely
on the views of others. Younger people perform better than older people since they have had
more training and education.
Mispricing and market fragility are direct outcomes of herd behavior, which was first observed
in 2004 and verified in 2013. No immediate action has been made to address this problem;
mispricing and market fragility are direct consequences of herd behavior.
CONCLUSION
We used daily cross-sectional data for 205 businesses to run three models based on the
symmetry measure model (gamma � ) regressed on absolute average market return and
nonlinear term squared market return in the Saudi Stock Exchange from 2008 to 2020. The
findings confirmed the herd behavior that had been reported in 2004and 2013. Second, the
symmetry model's validity has been established. According to market conditions, investors that
follow the conventional market decision among expanding returns three times dropping
returns. In terms of identifying two switching regression regimes that support the discovered
herd behavior, the censored model generated equal results. It is recommended that the investor
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should use a disciplined investment approach, avoid allowing emotion to influence judgments,
and invest only in money that he or she does not need. Finally, only logic and facts, not media
noise or communal enthusiasm, should influence his or her investment selections (Ramady,
2010).
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