ArticlePDF Available

Determinants of Herding Behavior in The Time Of COVID-19: The Case of Egyptian Stock Market Sectors

October 2020
SSRN Electronic Journal

DOI:10.2139/ssrn.3717995

Authors:

Mansour Abdelrhim

Ain Shams University

Samira Allam

Ain Shams University

This research consists of two parts, the first part aims to study the Herding Behavior in the sectors of the Egyptian Stock Exchange, and the second part aims to study the factors that affect Herding behavior in accordance with the identification of those factors based on presentation of the previous literature on the Herding behavior, and those factors are represented in the exchange rate Stock trading volumes, stock returns, indicators of the spread of the corona virus, represented by the ratio of the total number of infections and deaths according to the population in Egypt. During the period from 1/3/2020 till 31/7/2020.

Content uploaded by Mansour Abdelrhim

Content may be subject to copyright.

Determinants of Herding Behavior in The Time Of COVID-19:

The Case of Egyptian Stock Market Sectors

Abstract

This research consists of two parts, the first part aims to study the Herding Behavior in

the sectors of the Egyptian Stock Exchange, and the second part aims to study the factors that

affect Herding behavior in accordance with the identification of those factors based on

presentation of the previous literature on the Herding behavior, and those factors are

represented in the exchange rate Stock trading volumes, stock returns, indicators of the

spread of the corona virus, represented by the ratio of the total number of infections and

deaths according to the population in Egypt. During the period from 1/3/2020 till 31/7/2020.

Key Words: COVID- 19, Herding Behavior, Stock market return, Exchange Rate, Sector, Trading

Volume as an indicator of Liquidity, mimic.

1. Introduction

This Section Divided Into four Parts, It begins with an Introduction of

Behavioral Finance, Which followed by a through presentation of Herd

Behavior, and rational and irrational herding. The last part concerns the study’s

specific approach Measuring Herding Behavior.

1.1. Behavioral Finance

The Field of Behavioral Finance was introduced which ( Schiller ,2003 )

describes as a mix of Finance, Social , science and psychology , it criticizes

traditional Finance Theory, and it is a direct opposition to The Efficient Market

Hypothesis (EMH ) , The Cornerstone of many financial models Ohlson

(2010).And this Behavioral Finance has grown toward the end of 20th Century

as reaction of (EMH) Özsu(2015) , Barber & Odean (1999) claim that EMH

condition rarely reflect the reality and evidence of real investor behavior and

view Behavioral Finance as a new set of theories that allow investors to be

irrational and markets to be inefficient , Thus help to attain deeper knowledge

about financial markets .

According to Shusha & Touny (2016) many behavioral finance studies

have addressed it as “Phenomenon of Deviations in the investors’ decisions

Samira Allam

Lecturer of Business Administration Faculty of Commerce, Ain Shams University

Mansour Abdelrhim

Ph.D. Researcher, Faculty of Commerce, Ain Shams University

Mahmoud Mohamed

Ph.D. Researcher, Faculty of Commerce, Ain Shams University

Electronic copy available at: https://ssrn.com/abstract=3717995

from rational track which couldn’t be explained by Classical Theory”. and

illustrate that as irrational behavior, taking into consideration that “Behavioral

Finance not only about human actions, But also an understanding the reasoning

patterns of investors including emotional factors involved to the extent of its

influence in decision making. When we are speaking about investors’ decision

making we must concern not only on investors’ decision making but also on

financial markets which must affected by these decisions. As some market

results may be different from that anticipated by traditional finance theories

including EMH , These results can be explained by the behavioral biases

affecting investors’ decisions Baker & Ricciardi (2015) these explanations are

provided for many inefficiencies and anomalies exhibited in financial markets

and can’t explained by theories of traditional finance , As this paper aims to

Determine the “ Determinants of Herding Behavior “ , so we have to investigate

the investment behavior of Market participants in Egyptian Stock Exchange ,

Specially with regard to their tendency to mimic the action of others or engage

in Herd Behavior and study whether“ Sector , Stock return , Exchange Rate,

Trading Volume and Covid -19 “ are Determinants of Herding .

1.2. The concept of herding behavior

The word Herding is basically from the word “Herd” describes the animal

spirit to explain the naïve optimism and confidence in the capital market. Herd

defined as “The behavior of investors who tend to imitate or follow the behavior

of other investors (Armansyah, 2018).

According to Ricciardi & simon (2000) herding behavior is associated

with people who (Blindly) following the decisions of others. Lakonishok ,

shleifer & vishny (1992) define the herding in the stock market as “ The

tendency of a group of many managers to buy(sell) stock , Specially at the same

time , relative to what can be expected if the money managers itself doing trades

and for herding .

So, when individuals imitate others in most decisions by passing their own

judgment or decision, they how can all individuals claim to be rational. Herding

is one of the important behaviors of human being which explain the deviation of

human being from the rational decision making by following others Yousaf , ali

& shah (2018) .

In the existence of herding, the stock price will deviate from its intrinsic

value, resulting in the inappropriate price Dang & Lin (2016) as a consequence,

This intentional imitation will cause the market fragility, Excess volatility and

systematic rise Bikhchandani & Sharma (2001).

Electronic copy available at: https://ssrn.com/abstract=3717995

From the above it can be said, there are a multiple definitions of herding,

the following quote capture the essence of herding as will be discussed in this

paper :

“In financial markets investors are influenced by others when deciding

whether to participate in the market, which securities to trade, and whether to

buy or sell. Such influences may cause investor behavior to converge…. To

explain these phenomena various theoretical models have been proposed in the

rational herding literature. For example cascade models show that investors

optimally decide to ignore their own information and imitate previous investor

actions.” (Bikhchandani et al., 1992) .

Several ideas mentioned in the quote above are important to

understanding herding behavior in financial markets :

• Investors are influenced by others in making investment decisions (or

decisions not to invest) .

• Herding causes investors to imitate previous investor actions .

• As a result, investor behavior may converge .

• When herding behaviors occur investors may ignore available

information .

According to the definitions that has been exposed a growing body of

literature analysis herding in the stock market using measures of dispersion

around the market return during periods of significant changes in stock prices

(Christie & Huang ,1995), (Chang et al,2000), (Caparrelli et al,2004), (Tan

et al,2008) .

The rationale is that if during these periods of market pressure movements

of stock returns have the tendency to be more clustered , This is the evidence

that there is co-movement of stock prices which is independent of their

fundamental characteristics , according to (Christie & Huang ,1995), These

periods are particularly informative because “ a herd “ is more likely to form

under conditions of market stress, when individual investors tend to suppress

their own beliefs and follow the market consensus . Cross sectional dispersions

of return are predicted to be low when herd behavior is present .

Herd behavior can be a positive thing but it also have a negative impacts on

the development of capital markets, Be a positive thing if such behavior by

investors who have the precise information of the investment will make the

market growth positive, Otherwise, will be negative or bad if wrong decisions

Electronic copy available at: https://ssrn.com/abstract=3717995

by investors lead to the destruction or financial crisis as happened in the capital

market Argentina in 2000 to 2006 and also the financial crisis Asia in 1997 to

1998. Investors basically have a rational behavior in determining investment

decisions. Those behavior would be seen if the stock price fell, the stock will be

purchased and vise versa. However, in the presence of certain conditions such as

a crisis, investors tend to behave irrationally (Armansyah, 2018).

1.3. Rational and Irrational Herding

Many of the definitions are proposed to detect herding behavior in

literature. According to these definitions, there are two different forms of

herding: first is rational herding and second is irrational herding. According to

rational herd behavior perspective, herding behavior is associated with the

situation in which investors are tried to restore their returns by ignoring

voluntarily their own analysis; and replicate or follow another manager’s

decision who possesses a more reliable source of information or who has high

level of analysis competencies of investment decisions (Bikhchandani &

Sharma ,2000). Sometimes, it’s more difficult to distinguish between irrational

herding behavior and rational herding behavior. Most of the studies in literature

have focused upon rational herding behavior. According to irrational herding

perspective, herding behavior is associated with collective actions of individuals

under uncertain conditions. The investors show herd behavior to reduce

uncertainty and to increase their confidence in investment returns (Devenow &

Welch, 1996), (Yousaf et al, 2018(.

During periods of market stress that are usually characterized by high

volatility flow of information (Gleason et al., 2004) and significant market

changes, investors are willing to ignore their own beliefs and knowledge in

order to follow the market consensus i.e. the herd (Christie & Huang,

1995),(Lao & Singh, 2011).

Under such extreme market situations investors are seeking the

psychological safety of the herd and prefer collective action that will protect

them from the painful feeling of regret coming from individual failure. Herding

behavior is usually defined as" imitation that leads to correlated behavior

patterns (Bikhchandani et al, 1992); (Devenow & Welch, 1996);(Welch,

2000); (Hirshleifer & Teoh, 2003); (Gleason et al, 2004) and it has been

widely analyzed for several market participants (individual investors,

institutional investors, fund managers, financial analysts) and financial markets

(stock market, bond market, real estate market, commodities market, Exchange

Traded Funds, Foreign Exchange market, futures market etc ." (

Electronic copy available at: https://ssrn.com/abstract=3717995

Even though imitating might seem rational on the individual level,

collectively this leads to herd behavior which is definitely irrational (Shiller,

2000). Interestingly, Posner (2009) argues that herding might be risky but not

irrational, since the existing information asymmetry justifies the notion that

someone else might have better information set. Herding can also be

characterized as irrational (Devenow & Welch, 1996), coming from investors’

psychology and several behavioral biases. In both cases, empirical evidence of

herding in financial markets seriously questions market efficiency, having

important implications for both individual and institutional investors. Market

participants are exposed to the unpredictable herd behavior that may lead to

significant stock price fluctuations and deviations from their fundamental values

(Christie & Huang, 1995); (Tan et al., 2008); (Chiang & Zheng, 2010);

(Tseng, 2010(.

In that sense herding can cause or intensify existing crises, and finally lead

to the formation of stock market bubbles (positive or negative) (Caparrelli et

al, 2004); (Gleason et al, 2004), creating at the same time profitable

opportunities (Tan et al, 2008). Profitable momentum strategies have also been

attributed to herding (Kang et al, 2002). Market efficiency hypothesis is also

violated since decision making may be seriously distorted when it is based on

herding, causing a subsequent information loss (Welch, 1992); (Bannerjee,

1992). Herding behavior implies investors’ irrationality, which is reflected in

asset pricing, with a potential destabilizing effect for the market and the

examination of herding can provide investors a better understanding of asset

prices formation (Lao & Singh, 2011); (Dasgupta et al, 2011). Moreover, the

significant asset returns co-movement that occurs in the presence of herd

behavior clearly reduces the benefits of diversification (both domestic and

international) and makes it necessary to hold a portfolio with a larger number of

assets in order to achieve the desired diversification level, than in a market with

lower asset returns’ correlations (Chang et al, 2000); (Chiang & Zheng, 2010);

(Economou et al., 2011). From a regulatory point of view, such coordinated

investor behavior as well as cross-market herding could increase market

volatility and finally pose a threat for market stability in general. Literature

identifies the relationship between herding and market volatility (Chang et al.,

2000); (Tan et al., 2008); (Blasco et al., 2012) further increasing financial

system’s fragility. Recently, (Economou et al., 2011) documented the existence

of cross market herding in four stock 4 markets, the Greek, Italian, Portuguese

and Spanish, that could have a potential destabilizing effect and could finally

cause a regional financial crisis. In the same spirit, cross-market herding is also

Electronic copy available at: https://ssrn.com/abstract=3717995

closely associated with the concept of crisis transmission and financial

contagion across international markets (Karolyi & Stulz, 1996); (Bae et al.,

2003); (Boyer et al., 2006). Special focus has been placed on the continuously

increasing role of institutional investors since their capital flows driven by

herding behavior (Hsieh et al., 2011) may also considerably increase market

volatility and pose a threat for the financial systems’ stability (Tsionas, 2013) .

“Even completely rational people can participate in herd behavior when

they take into account the judgments of others, and even if they know that

everyone else is behaving in a herd-like manner. The behavior, though

individually rational, produces group behavior that is, in a well-defined sense,

irrational ”.

The source of this irrational group behavior is, according to Shiller,

information cascade, a concept discussed in the Bikhchandani, Hirshleifer &

Welch (1992) paper. In a financial investment context information cascade

occurs when investors’ choices are influenced by those made by other investors

rather than by information independently gathered by each investor. The

irrationality suggested by Shiller is based on the fact that information cascades

seem to have a life of their own and once initiated may cascade erroneous

information. Shiller fears that persons who seek the easy path to investment

strategies are likely to not “waste their time and effort in exercising their

judgment about the market, and thus choose not to exert any independent impact

on the market.” Such persons may easily be attracted to the herd and, once

recruited, susceptible to herd behaviors .

While there is general agreement about the emotional nature of investor

herd behavior there is disagreement about the impact of the herds on the pricing

of financial instruments. Analysts identify two basic different types of investors,

noise traders and arbitrageurs .

Noise traders are thought to be irrational. They falsely believe that they have

special information about the future price of an asset and, as a result, they

exhibit the fallacy of excessive subjective certainty (Alpert & Raiffa, 1982).

Noise traders often create herds by communicating their investment strategies

through social interaction, frequently enabled by modern communication

technology. They extend their collective bet against what rational traders

consider to be the inherent value of a security. Noise trader action thus

reinforces itself and the size of the herd increases(Boehner & Gold, 2013) .

Electronic copy available at: https://ssrn.com/abstract=3717995

1.4. Measuring Herding Behavior

Our Measuring method builds on (Christie & Huang, 1995), (Chang et

al., 2000) who proposed the Cross-Sectional Absolute Deviation (CSAD) and

Cross-Sectional Standard Deviation (CSSD) as a herding indicators. These

indicators measure the average distance between an individual stock return and

market stock return, and help to ascertain whether an investors’ decisions feature

herding. If the investor decides to mimic the group’s behavior in the period of

heightened stock market volatility, individual stock return become less dispersed

around the market, which lead to decline in (CSAD) and (CSSD).

The cross sectional standard deviation, CSSD, is measured with :

Where Ri,t is the observed stock return of firm i at time t, Rm,t is the cross-

sectional average return of the N returns in the market portfolio at time t, and N

is the number of stocks in the portfolio.

To determine the presence of herd behavior a dummy variable technique is

used. The CSSD returns are regressed against a constant and two dummy

variables to identify the ex-treme market phases with the following formula:

𝑪𝑺𝑺𝑫𝒕= 𝜶 + 𝒃𝟏𝑫𝒕

𝑳+ 𝒃𝟐𝑫𝟏

𝑼+ 𝒆𝒕

Where DL is market with a “1” if the market return on day t lies in the

extreme 1% and 5% lower or upper tails of the distribution of market returns,

and marked “0” otherwise., where the boundaries of each dummy variable are

marked. The dummy variables’ function is to capture differences in herd

behavior in ex-treme up or down periods versus relatively normal market

periods. The α coefficient represent the average dispersion of the sample

excluding the regions corresponding to the two dummy variables. Presence of

herd behavior is determined by statistically significant negative values for b1 or

b2. The rational is that DL and DU represents the dummy variables indicating

extreme phases of the market return. If CSSD values are lower during these

phases CSSD and Rm,t move in opposite direction indicated by a negative value

of the Coefficient. For example, if b1 or b2 has a negative relation to the CSSD

estimate, herd behavior is implied to be present. In that case it means that in the

Electronic copy available at: https://ssrn.com/abstract=3717995

most extreme market days the CSSD measure actually decreases (Henker et al.,

2006).

Therefore, this research clarifies the determinants of herding behavior for

the spread of the Corona virus in the Egyptian stock market sectors.

Figures (1) and (2) illustrate the developments of Coronavirus spread

during the research period, as follows:

Figures (1): Total Corona virus Cases in Egypt

https://www.worldometers.info/coronavirus/country/egypt/

Figures (2): Total Corona virus Deaths in Egypt

https://www.worldometers.info/coronavirus/country/egypt/

Electronic copy available at: https://ssrn.com/abstract=3717995

- Selecting a research sample

The Egyptian Stock Exchange inaugurated the sectoral indices on January

2, 2020, in order to complete the usual comprehensive structure of the corporate

sectors whose papers are restricted on the Egyptian Stock Exchange, with the

purpose of developing the money market system by increasing the depth and

raising the efficiency of the market in the market. The Egyptian Stock Exchange

is committed to applying the best international practices in the field of managing

stock market indices, starting with the criteria for selecting the companies that

are eligible to join the index, the periodic review mechanisms and the

mechanisms for calculating the index and publishing its data.The index is

calculated in 1000 index. Sectoral indicators provide investors with the ability to

measure the performance of the constituent companies for each sector according

to the market capital weighted by free circulation, by no more than 35% for one

company (Elsayed & Elrhim, 2020).

2. Literature review

The subject of the research has dealt with many studies and scientific

messages that can be considered in the same context, and previous studies have

been classified in terms of factors related to their impact on the behavior of the

herd, studies related to each field are addressed as follows:

2.1. Herding behavior.

2.2. Herding behavior and Exchange rate.

2.3. Herding behavior and Crisis.

2.4. Herding behavior and Stock returns.

2.5. Herding behavior and Market sectors.

2.6. Herding behavior and trading Volume (Liquidity).

- We review those studies as follows:

2.1. Herding behavior

People tend to follow others to make identical investment decision when

there is less publicly available information. This well-known phenomenon is

“Herding “. Both investors and academic researchers have paid more attentions

on herding in financial market over the recent past. Investors are interested in

whether they can make profit by relying on collective information. Academic

researchers also care about herding since it causes prices to deviate from

fundamental values. Existing literature has two kinds of views about herding,

either rational or irrational.(Devenow & Welch,1996) demonstrate that

investors ignore their prior beliefs and follow others without any rational reason.

On the other hand, according to (Scharfstein & Stein, 1990) managers do the

Electronic copy available at: https://ssrn.com/abstract=3717995

same investment actions as others rationally completely ignoring their own

private information , in order to maintain reputation within the same evaluated

peer group .

Researchers have proved herding from both theoretical models and empirical

Studies. For theoretical models,(Scharfstein & Stein,1990) provide reputational

herding behavior model , Bikhchandani et al .(1992) give information casades

model , while Banerjee (1992) has sequential model . For empirical studies ,

Lakonishok, Shleifer and Vishny (LSV)Model of Lakonishok et al.(1992 ) and

the Cross-Sectional Absolute deviation of returns ( CCAD) Chang , Cheng and

Khorana (2000) . are most 4 Commenly referred . Lakonishok et al.(1992 )

proposed the first methodology that has been sequentially widely used for

empirical testing . they use (LSV) model to prove the potential herding effect of

their trading on stock prices . Christie & Huang, (1995) examine the herding

behavior by utilizing the Cross-Sectional Absolute deviation of returns (CCAD)

as ameasure of the average proximity of individual asset returns to the realized

market average in the US equity market .Chang , Cheng & Khorana (2000)

extend the work of Christie & Huang, (1995) by proposing a new and more

powerful approach to detect herding based on equity return behavior , which is

the CSAD.(Lan and Lai, 2011).

In This Paper we use an emprical study like Christie & Huang, (1995),

Chang et al, (2000) using The Cross- Sectional Standard Deviation (CSSD) as

ameasure of the average proximity of individual asset returns to the realized

market average in The general index of the Egyptian Stock Exchange EGX30,

depending on Behavior the sectors of the Egyptian Stock Exchange

Filip, Pochea, & Pece (2015) this paper analyzed the existence of herding

behavior of investors from emerging markets at industry level by using firm

level information. The herding behavior of investors represents a major cause of

speculative bubbles and implies that investors are taking similar trading

decisions which may lead to deviations of the stocks’ prices from their

fundamental value. They have examined the presence of herding behavior on

the CEE capital markets by using the CSAD statistical method proposed by

Chang et al. (2000). Moreover, this paper highlighted the implications of

different market conditions on the existence of herding behavior and finally,

investigates the impact of the subprime financial crisis on the behavior of

investors from CEE capital markets.

Chang et al, (2000) They examined the investment behavior of market

participants within different international markets (i.e., US, Hong Kong, Japan,

Electronic copy available at: https://ssrn.com/abstract=3717995

South Korea, and Taiwan), specifically with regard to their tendency to exhibit

herd behavior. They found no evidence of herding on the part of market

participants in the US and Hong Kong and partial evidence of herding in Japan.

However, for South Korea and Taiwan, the two emerging markets in our

sample, documented significant evidence of herding. The results are robust

across various size-based portfolios and over time. Furthermore, macroeconomic

information rather than firm-specific information tends to have a more

significant impact on investor behavior in markets which exhibit herding. In all

five markets, the rate of increase in security return dispersion as a function of

the aggregate market return is higher in up market, relative to down market

days.

Chiang et al, (2010) this paper examined herding behavior in global

markets. By applying daily data for 18 countries from May 25, 1988, through

April 24, 2009, They found an evidence of herding in advanced stock markets

(except the US) and in Asian markets. No evidence of herding is found in Latin

American markets. Evidence suggests that stock return dispersions in the US

play a significant role in explaining the non-US market’s herding activity. With

the exceptions of the US and Latin American markets, herding is present in both

up and down markets, although herding asymmetry is more profound in Asian

markets during rising markets. Evidence suggested that crisis triggers herding

activity in the crisis country of origin and then produces a contagion effect,

which spreads the crisis to neighboring countries. During crisis periods, They

found supportive evidence for herding formation in the US and Latin American

markets.

Bansal (2020)The COVID-19 pandemic has resulted in dramatic economic

effects, characterized by excessive stock price volatility and a market crash.

Some of the phenomena in effect during the crisis, such as the excessive

volatility and the unshaken confidence of financial institutions, are insufficiently

explained by the traditional finance paradigm. In this paper, they explore such

phenomena from a behavioral finance lens and discuss some cognitive errors

and biases relevant during and after the crisis - overconfidence (miscalibration,

better-than-average effect, illusion of control, optimism bias), representation

bias, risk aversion, herding behavior, and availability bias. They explore each of

these phenomena from the perspective of psychology, and evaluate their

relevance to financial institutions and markets and the COVID-19 induced

global crisis.

Özsu (2015) Behavioral finance is a field that has grown toward the end of

20th century as a reaction to the efficient market hypothesis. This new field

studies the effect of investor psychology on financial decisions and explains

stock market anomalies in financial markets. Herding is such an anomaly that is

defined as mimicking others’ decisions or market trend. This study aimed to

detect whether there is herding or not in Borsa Istanbul. To test the existence of

Electronic copy available at: https://ssrn.com/abstract=3717995

herding, stock returns traded on Borsa Istanbul and BIST 100 Index as market

indicator are used. Data covers daily returns from 1988 to 2014 and intraday

returns from 1995 to 2014. Firstly, herding is analyzed based on the

methodology of cross-sectional dispersion of the stocks developed by Christie

& Huang (1995), Chang, Cheng & Khorana (2000). The results indicated that

there is no herding for both up and down markets for daily and intraday intervals

in Borsa Istanbul. However, tendency of herding is higher in up markets.

To enhance and compare the results, the methodology based on the cross-

sectional volatility of beta coefficients suggested by Hwang and Salmon (2004)

is used. This methodology has provided evidence of herding in Borsa Istanbul. It

is also observed that investors follow the market trend more in session two

markets rather than session one markets. Thus, it is concluded that investors

imitate the others more under normal market conditions rather than noisy market

conditions. These results are consistent with the assumptions of Hwang &

Salmon (2004).

Christie & Huang (1995) this study asked do equity returns indicate the

presence of herd behavior on the part of investors during periods of market

stress. To test this proposition, the cross-sectional standard deviation of returns,

or dispersion, is used to capture herd behavior. When individual returns herd

around the market consensus, dispersions are predicted to be relatively low. In

contrast, rational asset pricing models predict an increase in dispersion because

individual returns are repelled away from the market return when stocks differ in

their sensitivity to market movements. The results for both daily and monthly

returns are inconsistent with the presence of herding during periods of large

price movements. For example, during extreme down markets, when herding is

expected to be most prevalent, the magnitude of the increase in the dispersion of

actual returns is mirrored by the increase in the dispersion of predicted returns

that are estimated from a rational asset pricing model.

kizys, Tzouvanas & Donadelli (2020) They studied if government

response to the novel corona virus COVID-19 pandemic can mitigate investor

herding behavior in international stock markets. Their empirical analysis is

informed by daily stock market data from 72 countries from both developed and

emerging economies in the first quarter of 2020. The government response to

the COVID-19 outbreak is measured by means of the Oxford COVID-19

Government Response Tracker, where higher scores are associated with greater

stringency. Three main findings are in order. First, results show evidence of

investor herding in international stock markets. Second, they documented that

the Oxford Government Response Stringency Index mitigates investor herding

behavior, by way of reducing multidimensional uncertainty. Third, short-selling

restrictions, temporarily imposed by the national and supranational regulatory

authorities of the European Union, appear to exert a mitigating effect on

herding. Finally, results are robust to a range of model specifications.

Electronic copy available at: https://ssrn.com/abstract=3717995

Ohlson (2010) in this study the Stockholm Stock Exchange in Sweden is

examined for herd behavior with a market wide approach. Three models, one

created by Christie & Huang (1995) and the others created by Chang, Cheng

& Khorana (1999), are applied to detect herd behavior from 1998 to 2009.

Herd behavior is found in up-going market days, measuring on daily bases over

the entire time frame. When breaking down the test period into annual sub

periods, herd behavior is evident in the bullish markets of 2005 and 2007. In

days with the most extreme market movements herd behavior is found in large

cap stocks but not in the small cap. The result indicates a tendency of an

increasing level of herd behavior over the measured period, which can be

attributed to the increased influence of institutional ownership. Moreover, the

data was adjusted for thinly traded stocks and the result is contradictive to

previous studies. The reduction of thinly traded stocks seems to have an

increasing effect on the herd-measure, implying that the presence of thinly

traded stocks puts a negative bias on the herd-measures.

Kim, Chay & Lee (2020) this study explored the herding behavior of

different types of investors (individual investors and both domestic and foreign

institutional investors) and its impact on the volatility of individual stock

returns. Intraday volatility and daily herding intensity of each investor type are

measured using high-frequency transaction data containing detailed information

on all executed orders in the Korea Exchange. This study realized regresses

volatility on the herding intensity of each investor type and other control

variables and found that herding of domestic and foreign institutions decreases

realized volatility, whereas herding of individual investors increases it. This

study also found that the destabilizing effect of individual investors’ herding

behavior is exacerbated on days of high market uncertainty, and the stabilizing

effect of domestic institutions’ herding is weakened on those days, whereas the

stabilizing effect of foreign institutions’ herding is not affected by the level of

market uncertainty.

Decamps & Lovo (2002) showed that differences in market participants

risk aversion can generate herd behavior in stock markets where assets are

traded sequentially. This in turn prevents learning of market's fundamentals.

These results are obtained without introducing multidimensional uncertainty or

transaction cost.

Shusha & Touny (2016) recently, herd behavior earned the attention of

researchers in the interpretation of the investment decision-making process in

the financial markets. This study aimed to explore the attitudinal determinants of

herd behavior of individual investors in the Egyptian Exchange. Examined four

attitudinal determinants which include decision accuracy, hasty decision,

overconfidence, and investor mood, and tested to what extent the effects of these

determinants differ according to demographic characteristics of individual

Electronic copy available at: https://ssrn.com/abstract=3717995

investors such as gender, educational level, age, experience, and income. The

results indicated that decision accuracy, hasty decision, and investor mood were

the main attitudinal determinants that explain why individual investors follow

herding behavior, but the effect of these factors may differ according to the

investor's demographic characteristics.

2.2. Herding behavior and Exchange rate

A direct consequence of herd behavior and financial contagion are large

and unpredictable exchange-rate swings, leading to high exchange-rate

volatility. The paper goes on to deal with the adverse macroeconomic

consequences of episodes with high exchange-rate volatility, especially in terms

of market performance.

The literature has identified different kinds of herding, both rational and

irrational. Concerning the former, the most important reasons for herding are

information cascades, fixed costs of acquiring information and reputational

concerns. Irrational herd behavior is usually explained by momentum trading

strategies.

Information cascades are generally considered to be the most common

explanation for herding. The typical setting of this kind of approach is provided

by two crucial assumptions. First, there is private but imperfect information.

However, investors also react to other actions. Second, a selling or buying wave

by investors does not lead to corresponding price changes, essentially meaning

that prices are fixed. This appears to be an implausible assumption for most

assets. However, in the case of exchange rates it seems justified, given the high

share of pegged exchange rates in emerging markets (Belke & Setzer, 2004).

Interest rate and foreign exchange rate are two important macroeconomic

variables in open economics that significantly affect the stock market.

Numerous studies have investigated the effects of interest rate variation or

monetary policy shocks on stock returns Thorbecke (1997); Bjørnland &

Leitemo (2009). Other studies have explored the relationship between exchange

rate and stock returns (Hau & Rey, 2006); (Cho, Choi, & Kim, 2016).

However, to the best of our knowledge(Gong & Dai, 2017), few studies have

considered the effects of variations in interest and exchange rates on investor

behavior at the micro-level. In this paper, we address this gap and examine

whether the exchange rates is one of the Determinants of herding behavior in the

Egyptian stock market.

Jiang & Verardo (2018) they uncover a negative relation between herding

behavior and skill in the mutual fund industry. Their new, dynamic measure of

Electronic copy available at: https://ssrn.com/abstract=3717995

fund-level herding captures the tendency of fund managers to follow the trades

of the institutional crowd. They found that herding funds underperform their ant

herding peers by over 2% per year. Differences in skill drive this performance

gap: ant herding funds make superior investment decisions even on stocks not

heavily traded by institutions, and can anticipate the trades of the crowd;

furthermore, the herding-ant herding performance gap is persistent, wider when

skill is more valuable, and larger among managers with stronger career

concerns.

Kim, Yoon & Kim (2004) The herd behaviors of returns for the won-

dollar exchange rate and the KOSPI are analyzed in Korean financial markets. It

is shown that the probability distribution P(R) of price returns R for three values

of the herding parameter tends to a power-law behavior P(R) ≃ R −β with the

exponents β = 2.2(the won-dollar exchange rate) and 2.4(the KOSPI). The

financial crashes are found to occur at h > 2.33 when the relative increase in the

probability distribution of extremely high price returns is observed. Especially,

the distribution of normalized returns shows a crossover to a Gaussian

distribution for the time step ∆t = 252. Results will be also compared to the other

well-known analyses.

Gong & Dai (2017) Interest rate and exchange rate are two important

macroeconomic variables that exert considerable effects on the stock market. In

this study, they investigated whether variations in interest and exchange rates

induce herding behavior in the Chinese stock market. Empirical results indicate

that interest rate increase and Chinese currency (CNY) depreciation will induce

herding and this phenomenon is mainly manifested in down markets. Moreover,

the herding level of the highest idiosyncratic volatility quintile portfolio is twice

that of the lowest quintile portfolio which considers evidence of intentional

herding. This result is consistent with those of previous studies, which report

that retail investors prefer and overweigh lottery-type stocks. Finally, they

investigated the effects of monetary policy announcements and extreme

exchange rate volatility on herding because these events elicit considerable

public attention and may trigger collective behavior in the aggregate market.

Kohler (2010) Exchange rate movements during the global financial crisis

of 2007–09 were unusual. Unlike in two previous episodes – the Asian crisis of

1997–98 and the crisis following the Russian debt default in 1998 – in 2008

many countries that were not at the center of the crisis saw their currencies

depreciate sharply. Such crisis-related movements reversed strongly for a

number of countries. Two factors are likely to have contributed to these

developments. First, during the latest crisis, safe haven effects went against the

typical pattern of crisis-related flows. Second, interest rate differentials explain

more of the crisis-related exchange rate movements in 2008–09 than in the past.

Electronic copy available at: https://ssrn.com/abstract=3717995

This probably reflected structural changes in the determinants of exchange rate

dynamics such as the increased role of carry trade activity.

Caporale, Economou & Philippas (2008) this paper examined herd

behavior in extreme market conditions using data from the Athens Stock

Exchange. They tested for the presence of herding as suggested by Christie &

Huang (1995), Chang, Cheng & Khorana (2000). Results based on daily,

weekly and monthly data indicate the existence of herd behavior for the years

1998-2007. Evidence of herd behavior over daily time intervals is much

stronger, revealing the short-term nature of the phenomenon. When the testing

period is broken into semi-annual sub-periods, herding is found during the stock

market crisis of 1999. Investor behavior seems to have become more rational

since 2002, owing to the regulatory and institutional reforms of the Greek equity

market and the intense presence of foreign institutional investors.

Demirer & kutan (2006) this paper examined the presence of herd

formation in Chinese markets using both individual firm- and sector-level data.

They analyzed the behavior of return dispersions during periods of unusually

large upward and downward changes in the market index. They also

distinguished between the Shanghai and Shenzhen stock exchanges at the sector-

level. Their findings indicated that herd formation does not exist in Chinese

markets. They found that equity return dispersions are significantly higher

during periods of large changes in the aggregate market index. However,

comparing return dispersions for upside and downside movements of the

market, we observe that return dispersions during extreme downside movements

of the market are much lower than those for upside movements, indicating that

stock returns behave more similarly during down markets. The findings support

rational asset pricing models and market efficiency. Policy implications of the

results for policymakers are discussed.

Tsionas (2013) examined herding behavior in the US stock market,

employing 30 blue chip companies of the Dow Jones Industrial Average Index,

through 2001-2011. Proposed a novel multivariate stochastic volatility

methodology extended to allow for common factors that detect and measure the

contribution of herding conditional on stylized-fact features of returns. And

documented the existence of herding during the recent global financial crisis and

its aftermath. Results had important policy implications and highlight the

significant changes encountered by the global financial system as well as the

increased systemic risk market participants are exposed to.

Dasgupta, Prat & Verardo (2011) in this paper they developed a simple

theoretical model to analyze the impact of institutional herding on asset prices.

A growing empirical literature has come to the intriguing conclusion that

Electronic copy available at: https://ssrn.com/abstract=3717995

institutional herding positively predicts short-term returns but negatively

predicts long-term returns. They offer a theoretical resolution to this dichotomy.

In their model, career-concerned money managers interact with profit-motivated

proprietary traders and security dealers endowed with market power. The

reputational concerns of fund managers imply an endogenous tendency to

imitate past trades, which impacts the prices of the assets they trade. Showed

that institutional herding positively predicts short-term returns but negatively

predicts long-term returns. In addition, their paper generated several new

testable predictions linking institutional herding, trade volume, and the time-

series properties of stock returns.

2.3. Herding behavior and Crisis

The outbreak of the novel corona virus COVID-19 in January 2020 has

triggered a public health emergency of international concern and has

exacerbated national health systems across the globe. Although the corona virus

crisis has become a major threat to particularly vulnerable members of the

society, governments in both developed and emerging market countries have

responded with a varying degree of stringency to save lives and alleviate

growing pressures on their health sectors. In general, the ‘gold command’,

elaborated by government strategists, has envisaged school and workplace

closures, social distancing measures, and travel restrictions, along with fiscal

stimulus packages and aggressive monetary expansions, to mention just few.

Nevertheless, the flip side of the coin had become an eye-opener for policy

markets, politicians and financial regulators. Namely, the corona virus crisis is

predicted to descend into a business cycle recession and a global financial crisis.

As a result, stock market investors have succumbed to the growing uncertainty

surrounding the economy and the financial system and have instigated massive

sales of risky assets (Baker et al, 2020b); (Ramelli & Wagner, 2020). In

periods of financial market jitters and heightened uncertainty (Schmitt &

Westerhoff, 2017), particularly of multiple dimensions (Avery & Zemsky,

1998), investors have a tendency to mimic decisions of their peers, i.e., follow

the crowd (Kurz & Kim, 2013).

Against this background, this paper (Kizys et al, 2020) seeked to determine

whether is there evidence of investor herding behavior in international stock

markets during the coronavirus crisis? But we concentrate on Egyptian Stock

Exchange

To address these questions, our methodology builds on Christie & Huang

(1995) and Chang et al (2000), who proposed the cross-sectional absolute

Electronic copy available at: https://ssrn.com/abstract=3717995

deviation (CSAD) and the cross-sectional standard deviation (CSSD) as herding

indicators. These indicators measure the average distance between an individual

stock return and the market return and help to ascertain whether an investor’s

decisions feature herding. If the investor decides to mimic the group’s behavior

in periods of heightened stock market volatility, individual stock returns become

less dispersed around the market return, which leads to a decline in the CSAD

and CSSD (Kizys et al, 2020).

Armansyah (2018) Indonesia is one of an emerging country in Asia. As an

emerging country, Indonesian capital markets attract the investor from around

the world to make investment. Investment require good, clear information and

trustworthy to make decision. The information that investor received may vary

to other investor. These differences could lead to herd behavior. Good herd

behavior will lead to economic growth otherwise will lead to crisis. These

researches examined the effect of herd behavior of investors to the financial

crisis of 2008 and 2013 of the Indonesian capital market. Variables used in this

research is financial crisis was measured using Exchange Market Pressure Index

(EMPI) and herd behavior measured with LSV formula. The method used is a

model of Vector Auto Regression (VAR) with a stationary test phase, co-

integration test, VAR estimations, impulse response analysis, analysis of

variance decomposition, and causality test. The findings is indicate that

investors in Indonesia stock market has irrational behavior that leads to herd

behavior, especially during financial crisis furthermore, herding behavior

affecting the occurrence of financial crisis in Indonesia. These findings provide

knowledge about the effect of herding behavior in financial crisis Indonesia and

provide input for academics in the field of behavioral finance management,

especially in the development of capital markets and for investors to give

feedback on the importance of the behavior of investors in the Indonesian capital

market.

Kizys et al (2020) they studied if government response to the novel corona

virus COVID-19 pandemic can mitigate investor herding behavior in

international stock markets. Our empirical analysis is informed by daily stock

market data from 72 countries from both developed and emerging economies in

the first quarter of 2020. The government response to the COVID-19 outbreak is

measured by means of the Oxford COVID-19 Government Response Tracker,

where higher scores are associated with greater stringency. Three main findings

are in order. First, results showed evidence of investor herding in international

stock markets. Second, we document that the Oxford Government Response

Stringency Index mitigates investor herding behavior, by way of reducing

multidimensional uncertainty. Third, short-selling restrictions, temporarily

Electronic copy available at: https://ssrn.com/abstract=3717995

imposed by the national and supranational regulatory authorities of the European

Union, appear to exert a mitigating effect on herding. Finally, results are robust

to a range of model specifications.

Yousaf et al (2018) this study examined herding behavior in the Pakistani

Stock Market under different market conditions, focusing on the Ramadan effect

and Crisis period by using data from 2004 to 2014. Two regression models of

Christie & Huang (1995), Chang et al (2000) are used for herding estimations.

Results based on daily stock data revealed that there is an absence of herding

behavior during rising (up) and falling (down) market as well as during high and

low volatility in market. While herding behavior is detected during low trading

volume days. Yearly analysis shows that herding existed during 2005, 2006 and

2007, while it is not evident during rest of the period. However, herding

behavior is not detected during Ramadan. Furthermore, during financial crisis of

2007–08, Pakistani Stock Market exhibits herding behavior due to higher

uncertainty and information asymmetry.

Omay & Iren (2016) this study investigated the effects of crises on

domestic and foreign investors’ behaviors by utilizing a nonlinear approach.

Considering the nonlinearity inherent in many financial variables, this study

proposes an appropriate econometric modelling for analyzing the investors’

behavior, particularly during turbulent times. Specifically, STAR-STGARCH

family models and generalized impulse response function analysis (GIRF) are

employed to understand the different reactions of foreign and domestic investors

at the Malaysian Stock Exchange market during the 1997 Asian crisis. The

results of the model and the GIRF analysis have shown that foreign investors

exhibited a herding behavior during the crisis and responded the shock more

quickly than the domestic investors. When the same analysis is applied to

understand the effects of the 2008 Subprime Mortgage Crisis in the Malaysian

market, the behaviors of foreign and domestic investors are found to be very

similar.

Caporale et al (2008) this paper examined herd behavior in extreme

market conditions using data from the Athens Stock Exchange. They test for the

presence of herding as suggested by Christie & Huang (1995), Chang, Cheng,

& Khorana (2000). Results based on daily, weekly and monthly data indicate

the existence of herd behavior for the years 1998-2007. Evidence of herd

behavior over daily time intervals is much stronger, revealing the short-term

nature of the phenomenon. When the testing period is broken into semi-annual

sub-periods, herding is found during the stock market crisis of 1999. Investor

behavior seems to have become more rational since 2002, owing to the

Electronic copy available at: https://ssrn.com/abstract=3717995

regulatory and institutional reforms of the Greek equity market and the intense

presence of foreign institutional investors.

Allam, Abdelrhim, & Mohamed (2020) this paper aimed to study the

effect of coronavirus on the trading behavior of both individual and institutional

investors in the Egyptian Stock Exchange, as the spread of the Coronavirus was

measured by indicators reveal the virus spread in the Arab Republic of Egypt by

using (Daily cases, total cases, daily deaths, total deaths) On a daily basis as

independent Variables, And dependent variable represented in investors' trading

behavior measured by The daily trading volumes of (Egyptians, Arabs& Foreign

investors) for both " individual and Institutions and measured through the

difference between buying and selling transactions in the Egyptian stock market.

Applied daily from March 1, 2020, to June 30, 2020.The results indicate that

the trading behavior of individual and institutional investors for Egyptians,

Arabs, and foreigners appears to be sensitive to the spread of the Coronavirus.

were the most influential and sensitive independent variables in the dependent

variable, the Daily Deaths variable was more effective and sensitive for

individuals and institutions for Egyptian investors, and for Arab investors, the

Daily Cases variable was more sensitive to the trading behavior of Arab

individual investors. , And the (Total Cases) variable is more sensitive to the

behavior of trading of Arab institutions, and for foreign investors, the variable

(Daily Deaths) was more sensitive to the behavior of foreign individual

investors, and the variable (Total Deaths) was more sensitive to the behavior of

foreign institutions. The results also showed that Significant differences were

statistically significant for the volume of investors' trading in the Egyptian Stock

Exchange, due to the high value of the trading averages of Egyptian individuals,

then foreign individuals and finally Arab individuals, as the results show an

increase in the value of the averages of trading in Egyptian institutions, then

Arab institutions, and finally foreign institutions.

2.4. Herding behavior and Stock returns

During periods of abnormally large average price movements, or market

stress, the differential predications of rational asset pricing models and herd

behavior are most pronounced .specifically, because individual securities differ

in their sensitivity to the market return, rational asset pricing models predict that

periods of market stress induce increased levels of dispersion. In contrast , The

herding of individuals around the market translates into a reduced level of

dispersion(Christie & Huang, 1995).

Teh & Bondt (1997) the purpose of this research was to evaluate the cross-

sectional relationship between expected returns, trading practices, volatility, and

standard measures of investment risk (beta, market value, and the market-to-

Electronic copy available at: https://ssrn.com/abstract=3717995

book ratio). Ceteris paribus, does high trading volume raise share prices? Does it

increase price volatility? Does the identity of investors (individual investors vs.

banks, insurance companies, mutual funds, or money management companies)

matter to the level of prices? Do regulatory restrictions qualify conclusions?

They employ price and volume data for individual U.S. firms over twenty years

(1970-1990). In addition, institutional ownership data are available since 1979.

Their analysis is based on monthly returns. This choice is driven by data

requirements and convenience. It allowed them to focus on issues of asset

pricing rather than the financial economics of market micro-structure.

Christie & Huang (1995) their question was Do equity returns indicate the

presence of herd behavior on the part of investors during periods of market

stress? To test this proposition, the cross-sectional standard deviation of returns,