Do investors herd? An examination of Pakistan stock exchange

Kashif, Muhammad; Palwishah, Rana Imroze; Ahmed, Rizwan Raheem; Vveinhardt, Jolita; Štreimikienė, Dalia

doi:10.1002/ijfe.1895

Cited by 6 publications

(9 citation statements)

References 56 publications

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“…Investors cannot fully abandon themselves from emotions, even if they want to make decisions based on models of technical analysis, they still have to make certain assumptions to establish a specific value (Bouteska, 2019; Ren et al , 2018). Investment sentiments such as fears, greed, and overreaction to bad news also play a major role in influencing investment decisions in extreme market conditions (Kashif et al , 2020). Thus by better understanding the drivers of herding via the consideration of investor sentiment, market participants are better able to identify the situations that result in herding, and the findings provide a deeper understanding of financial markets (Garcia, 2021).…”

Section: Discussionmentioning

confidence: 99%

“…In support of the above argument, some authors also revealed that investor sentiment may lead to investor overreaction and/or underreaction (Barberis et al , 1998) or that investors who are overconfident suffer from self-attribution bias (Daniel et al , 1998). Kashif et al (2020) depicted in their study that investment decisions in extreme market conditions are influenced by investor sentiments such as fears, greed, and overreaction to bad news, which portrayed the Pakistan stock market as inefficient and indicating misleading asset prices. Garcia (2021) in their study contributes to the behavioral finance literature by establishing a link between firm-level investor sentiment and analyst herding.…”

Section: Review Of Literaturementioning

confidence: 99%

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What make investors herd while investing in the Indian stock market? A hybrid approach

Sachdeva

Lehal

Gupta³

et al. 2021

RBF

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PurposeIn recent years, significant research has focused on the question of whether severe market periods are accompanied by herding behavior. As herding behavior is a considerable cause of the speculative bubble and leads to stock market deviations from their basic values it is necessary to examine the motivators which led to herding behavior among investors. The paper aims to discuss this issue.Design/methodology/approachIn this study, the authors performed a two-phase analysis to address the research questions of the study. In the first phase, for text analysis NVivo software was used to identify the factors driving herding behavior among Indian stock investors. The analysis of a text was performed using word frequency analysis. While in the second phase, the Fuzzy-AHP analysis techniques were employed to examine the relative importance of all the factors determined and assign priorities to the factors extracted.FindingsResults of the study depicted Investor Cognitive Psychology (ICP), Market Information (MI), Stock Characteristics (SC) as the top-ranked factors driving herding behavior, while Socio-Economic Factors (SEF) emerged as the least important factor driving herding behavior.Research limitations/implicationsThe current study was undertaken among stock investors from North India only. Moreover, numerous factors are not part of the study but might significantly influence the investors' herding behaviors.Practical implicationsComprehending the influences of the different factors discussed in the study would enable stock investors to be more aware of their investment choices and not resort to herd behavior. This research enables decision-makers to understand the reasons for herd activity and helps them act accordingly to improve the stock market's performance.Originality/valueThe current study will provide an inclusive overview of herding behavior motivators among Indian stock investors. This study's results can be extremely useful for both academics and policymakers to gain some insight into the functioning of the Indian stock market.

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Section: Discussionmentioning

confidence: 99%

Section: Review Of Literaturementioning

confidence: 99%

What make investors herd while investing in the Indian stock market? A hybrid approach

Sachdeva

Lehal

Gupta³

et al. 2021

RBF

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“…In empirical literature, herding model is estimated using linear approach (see (Arslan and Javid, 2020;Chang et al, 2000;Christie and Huang, 1995;Galariotis et al, 2015;Hwang and Salmon, 2004;Kashif et al, 2020) as well as nonlinear approaches of Markow regime switching (Babalos et al, 2015;Balcilar et al, 2013) and Bai-Perron (Yasir and € Onder, 2021). The UK stock exchange performed good and remained stable till the COVID'19 pandemic, however during the pandemic the stock market volatility increased.…”

Section: Investigation Of Herding Behaviormentioning

confidence: 99%

“…In empirical literature, herding model is estimated using linear approach (see (Arslan and Javid, 2020; Chang et al ., 2000; Christie and Huang, 1995; Galariotis et al ., 2015; Hwang and Salmon, 2004; Kashif et al ., 2020) as well as nonlinear approaches of Markow regime switching (Babalos et al ., 2015; Balcilar et al ., 2013) and Bai-Perron (Yasir and Önder, 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

Investigation of herding behavior using machine learning models

Asim,

Khan,

Shafi

2023

RBF

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PurposeThe study aims to investigate the presence of herding behavior in the stock market of UK with a special emphasis on news sentiment regarding the economy. The authors focus on the news sentiment because in the current digital era, investors take their decision making on the basis of current trends projected by news and media platforms.Design/methodology/approachFor empirical modeling, the authors use machine learning models to investigate the presence of herding behavior in UK stock market for the period starting from 2006 to 2021. The authors use support vector regression, single layer neural network and multilayer neural network models to predict the herding behavior in the stock market of the UK. The authors estimate the herding coefficients using all the models and compare the findings with the linear regression model.FindingsThe results show a strong evidence of herding behavior in the stock market of the UK during different time regimes. Furthermore, when the authors incorporate the economic uncertainty news sentiment in the model, the results show a significant improvement. The results of support vector regression, single layer perceptron and multilayer perceptron model show the evidence of herding behavior in UK stock market during global financial crises of 2007–08 and COVID’19 period. In addition, the authors compare the findings with the linear regression which provides no evidence of herding behavior in all the regimes except COVID’19. The results also provide deep insights for both individual investors and policy makers to construct efficient portfolios and avoid market crashes, respectively.Originality/valueIn the existing literature of herding behavior, news sentiment regarding economic uncertainty has not been used before. However, in the present era this parameter is quite critical in context of market anomalies hence and needs to be investigated. In addition, the literature exhibits varying results about the existence of herding behavior when different methodologies are used. In this context, the use of machine learning models is quite rare in the herding literature. The machine learning models are quite robust and provide accurate results. Therefore, this research study uses three different models, i.e. single layer perceptron model, multilayer perceptron model and support vector regression model to investigate the herding behavior in the stock market of the UK. A comparative analysis is also presented among the results of all the models. The study sheds light on the importance of economic uncertainty news sentiment to predict the herding behavior.

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“…The performance of Pakistan stock exchange fluctuated by the political instability (Khan et al 2022;Sulehri and Ali 2020;Mehmood et al 2020) and financial events effect (Kashif et al 2021;Ghouse et al 2019). A total of 19 out of 21 political and financial events have significantly generated high volatility and only 2 events are insignificant.…”

Section: Testsmentioning

confidence: 99%

Impact of COVID-19, Political, and Financial Events on the Performance of Commercial Banking Sector

Ghouse

Bhatti

Shahid

2022

JRFM

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This paper employs a structural empirical model to gauge the possible effects of COVID-19, political and financial events on the returns and volatility of commercial banks. It observes that insured and run-prone uninsured depositors choose between differentiated commercial banks, which appears to be significantly impacted from the present pandemic, especially for the case of Pakistan’s commercial banking sector. The estimated volatility series for commercial banks is measured through the GARCH model, which explains the current financial and political distress for the case of shocks from COVID-19. We calibrate by Impulse Indicator Saturation (IIS) to detect the structural breaks formed by these events in the returns and volatility series of commercial banks. It is observed that the calibrated model possesses almost all financial events that have had a prominent impact on the returns and volatility series whereas two out of eighteen political events are unimpacted.

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Do investors herd? An examination of Pakistan stock exchange

Cited by 6 publications

References 56 publications

What make investors herd while investing in the Indian stock market? A hybrid approach

What make investors herd while investing in the Indian stock market? A hybrid approach

Investigation of herding behavior using machine learning models

Impact of COVID-19, Political, and Financial Events on the Performance of Commercial Banking Sector

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