Political Instability and Herding Behaviour: Evidence from Egypt’s Stock Market

Mertzanis, Charilaos; Allam, Noha

doi:10.1177/0972652717748087

Cited by 35 publications

(42 citation statements)

References 53 publications

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“…where CSAD t is a proxy that indicates the distance from the market average return, how much of the stock returns are dispersed around the average return, N is the total number of stocks in industry index, R i,t is the return of stock i on day t and the variable R m,t is the cross-sectional average market return at day t . Since herding would increase the correlation of stock returns, the presence of herding in the market would transform the linear relationship between individual stock return and market return based on the capital asset pricing model into a non-linear relation (Mertzanis & Allam, 2018). Following the Lee et al (2013) study, we examine the herding behaviour using the modified regression model as per the following equation: …”

Section: Empirical Methods For Herding Detectionmentioning

confidence: 99%

“…In normal circumstances, people would have sufficient time to collect adequate information, think rationally, analyse the market and make informed decisions (Mertzanis & Allam, 2018). Therefore, we took more than 5 years of data before the COVID-19 pandemic to detect the herding behaviour, and we assumed that the market was in normal trading phase before the COVID-19 outbreak.…”

Section: Datamentioning

confidence: 99%

“…However, to the best of our knowledge, this study is the pioneering research work to examine the herding behaviour in the financial market under the present conditions of the COVID-19 pandemic. In normal circumstances, people would have sufficient time to collect adequate information, think rationally, analyse the market and make informed decisions (Mertzanis & Allam, 2018). However, in periods of market distress, for instance, the COVID-19 outbreak, investor is more inclined to follow the crowd decision with suppressing their private information.…”

Section: Introductionmentioning

confidence: 99%

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The COVID-19 Pandemic and Herding Behaviour: Evidence from India’s Stock Market

Dhall

Singh

2020

Millennial Asia

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This article examines the herding behaviour at the industry level from national stock exchange (NSE). The novel contribution of this article is to examine the herding behaviour during the whole, pre- and post-coronavirus disease 2019 (COVID-19) pandemic outbreak period. We deployed the popular model proposed by Chang et al. (2000) to examine herd formation. Using daily stock closing prices of 191 firms, which constitute the 12 industry indices for the period from 1 January 2015 to 1 June 2020, the results for the full sample period (1 January 2015 to 1 June 2020) and before COVID-19 outbreak period (1 January 2015 to 29 January 2020) indicate the non-existence of herding formation at the industry level, but they do suggest a strong evidence of anti-herding behaviour. In addition, during the bull and bear market conditions, we found evidence of herding behaviour during the post-COVID-19 outbreak period (1 January 2020 to 1 June 2020). Further, the findings suggest that COVID-19 pandemic caused the formation of herding behaviour at the industry level. The study facilitates investors to devise their trading strategies in the regime of the COVID-19 pandemic.

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Section: Empirical Methods For Herding Detectionmentioning

confidence: 99%

Section: Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The COVID-19 Pandemic and Herding Behaviour: Evidence from India’s Stock Market

Dhall

Singh

2020

Millennial Asia

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“…Hence when investors herd, they tend to restrain their decisions and follow others. During times of market distress such as market anomalies, price bubbles, rumors, presence of herding effect is more profound (Mertzanis & Allam, 2018). Herding has seen as collective imitation leading to a confluence of movements (Philippas, Economou, Babalos, & Kostakis, 2013).…”

Section: Herding Behaviormentioning

confidence: 99%

An Analysis of Behavioral Biases in Investment Decision-Making

Madaan¹,

Singh²

2019

IJFR

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Individual investor’s behavior is extensively influenced by various biases that highlighted in the growing discipline of behavior finance. Therefore, this study is also one of another effort to assess the impact of behavioral biases in investment decision-making in National Stock Exchange. A questionnaire is designed and through survey responses collected from 243 investors. The present research has applied inferential statistics and descriptive statistics. In the existing study, four behavioral biases have been reviewed namely, overconfidence, anchoring, disposition effect and herding behavior. The results show that overconfidence and herding bias have significant positive impact on investment decision. Overall results conclude that individual investors have limited knowledge and more prone towards making psychological errors. The findings of the study also indicate the existence of these four behavioral biases on individual investment decisions. This study will be helpful to financial intermediaries to advice their clients. Further, study can be elaborated to study other behavioral biases on investment decisions.

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“…Based on this, it's important for investors to know if herding exists in the market to consider that in their investment decisions and to exploit the profitable opportunities that may be caused by the behavior (Demirer & Zhang, 2018). Herd behavior in financial markets was studied in many areas including its existence (Curto, Falcã o & Braga, 2017;Hammami & Boujelbene, 2015;Mertzanis & Allam, 2018), the differences between individual and institutional herding (Hsieh, 2013;Li, Rhee, & Wang, 2017;Trenca, Pece & Mihut, 2015), the causes of herding (Chang & Su, 2017;Fang, Lu, Yau & Lee, 2017;Shusha & Touny, 2016), and the impact of herding on investment decisions (Akbar, Salman, Mughal, Mehmood & Makarevic, 2016;Bakar & Yi, 2016;Kengatharan & Kengatharan, 2014).…”

Section: Introductionmentioning

confidence: 99%

Testing Sectoral Herding in the Jordanian Stock Market

Elshqirat¹

2019

IBR

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The main purposes of this quantitative study were to examine the existence of herding behavior among investors in Amman stock exchange (ASE) at market and sector level in addition to testing the behavior during the market rising and falling and examining whether the behavior existence is different before and after the global financial crisis of 2008. The theoretical base of the study was the behavioral finance which assumes that investors are not completely rational and they may follow others when taking investment decisions. The main enquires of the study were about the existence of herding in the Jordanian market, whether it's affected by conditions of market rising and falling, and whether it's affected by the financial crisis. A quantitative design was employed to achieve the purposes of this study which covers the period 2000 - 2018. Data were obtained from ASE website and analyzed using ordinary least squares method. The results indicated that herding is absent in the Jordanian market if tested at market level while it exists in services and industrial sectors if tested at sectors level. The financial crisis did not affect the presence of herding at market level while it did affect the behavior in services and industrial sectors. Moreover, the results revealed that market condition of rising and falling affected herding at market level but not at sectors level. It is also concluded that the global financial crisis changed the presence of herding behavior during conditions of rising and falling in market and in each sector.

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Political Instability and Herding Behaviour: Evidence from Egypt’s Stock Market

Cited by 35 publications

References 53 publications

The COVID-19 Pandemic and Herding Behaviour: Evidence from India’s Stock Market

The COVID-19 Pandemic and Herding Behaviour: Evidence from India’s Stock Market

An Analysis of Behavioral Biases in Investment Decision-Making

Testing Sectoral Herding in the Jordanian Stock Market

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