Time-varying herding spillover for emerging countries: evidence from BRIC countries and Turkey

Yasir, Muhammad; Önder, A. Özlem

doi:10.1108/rbf-10-2021-0218

Cited by 9 publications

(8 citation statements)

References 36 publications

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“…For this reason, we collected the 200 individual stocks data along with market index. We used the approach of CSAD developed by Yasir and € Onder (2023) and augmented by Putra et al (2017) to investigate the time varying nature of herding behavior. In order to investigate the break points in our data set of UK stock market, we used the approach of Christie and Huang (1995), Putra et al (2017).…”

Section: Discussionmentioning

confidence: 99%

“…In the recent literature of herding behavior, Spillover of herd anomaly has gained an increasing interest of researchers. Yasir and € Onder (2023) investigate the herding spillover among BRIC, Turkey and US using the structural break approach and reported significant evidence of herding spillover among the targeted stock markets.…”

Section: Investigation Of Herding Behaviormentioning

confidence: 99%

“…For this very reason, when the conditions of market are in stress and the movement in price is large, the proportionate increase in the CSAD would be less. Hence, CSAD and market return would have a nonlinear relationship (Yasir and € Onder, 2023). In order to obtain this relationship, we use the following equation.…”

Section: Data Acquisitionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

Section: Investigation Of Herding Behaviormentioning

confidence: 99%

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Investigation of herding behavior using machine learning models

Asim,

Khan,

Shafi

2023

RBF

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“…Studies include BRICS countries with similar emerging countries in the analysis. We include T€ urkiye in the analysis due to its high growth potential, young population, geopolitical importance and being an important energy hub (Bacik, 2013;Yildirim et al, 2022;Yasir and € Onder, 2023). GG is a proxy of the dependent variable.…”

Section: Financial Determinants Of Green Growthmentioning

confidence: 99%

Macroeconomic and financial determinants of green growth: an empirical investigation on BRICS-T countries

Arzova,

Şahin

2023

MEQ

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PurposeThe purposes of this study are to contribute to the limited green growth (GG) literature in emerging markets, to analyze GG from a financial economy perspective and to determine the contribution of financial development and innovation to GG in Brazil, Russian Federation, India, China and South Africa and Türkiye (BRICS-T). BRICS-T countries significantly impact the world population, international politics, energy resources and economy. In addition, BRICS-T countries are one of the leading countries in the world with their sustainability efforts. Investigating the GG model in these countries may contribute to structuring emerging economies around the principles of GG and advancing global green transformation efforts.Design/methodology/approachThe authors applied panel data analysis from 2001 to 2019. GG is economic growth free from environmental depletion in the model. National income, personnel expenditure and foreign direct investments are macroeconomic variables. These variables measure economic development and promote economic and social progress, which is essential for GG. Capital accumulation and innovation are essential tools in GG transformation. Therefore, financial development and patent applications represent the moderating variables. The authors estimate the fixed effect model with Parks-Kmenta robust.FindingsEmpirical results show that national income growth and foreign direct investments positively affect GG. Personnel expenditure negatively affects GG. On the contrary, financial development and patent growth have little moderating role.Originality/valueThis study contributes to the literature on creating a GG model in emerging countries. The study is original in its model and sample.

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“…For instance, Fang et al (2021) found that the COVID-19 pandemic strengthens the effect of stock markets on herding behavior and reinforces the spillover effect of regional herding on herding behavior. Meanwhile, Yasir and € Onder (2022) showed that under two different regimes, there is herding behavior in the Chinese stock market. They showed also that in times of crisis, there is a cross-border spillover between Brazil and India.…”

Section: Introductionmentioning

confidence: 99%

The nexus between herding behavior and spillover: evidence from G7 and BRICS

Gouta,

BenMabrouk

2023

RBF

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PurposeThis study aims at exploring the nexus between herding behavior and the spillover effect in G7 and BRICS stock markets.Design/methodology/approachThe authors used the dynamic connectedness approach TVP-VAR model of Antonakakis et al. (2019) to capture the spillovers across different markets. Moreover, to explore herding behavior, the authors used a modified version of the CSAD measure of Chang et al. (2000) including extreme market movements. Finally, to study the link between these two phenomena, the authors estimated a DCC-GARCH model.FindingsThe results show that herding behavior exists in the American market and some BRICS markets. Furthermore, spillover between G7 and BRICS increases in times of crisis. Moreover, the authors find a dynamic conditional correlation between herding behavior and spillovers both in the short and long run. The authors conclude that in times of crisis, the transmission of shocks between markets is more frequent, fuelling uncertainty and pushing investors to suppress their own beliefs and follow the general market trends.Originality/valueThis paper uses the TVP-VAR model to explore the spillover effect and the DCC-GARCH model to explore the connectedness between herding behavior and the spillover effect in G7 and BRICS countries in both the short and long run.

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Time-varying herding spillover for emerging countries: evidence from BRIC countries and Turkey

Cited by 9 publications

References 36 publications

Investigation of herding behavior using machine learning models

Investigation of herding behavior using machine learning models

Macroeconomic and financial determinants of green growth: an empirical investigation on BRICS-T countries

The nexus between herding behavior and spillover: evidence from G7 and BRICS

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