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

Sachdeva, Muskan; Lehal, Ritu; Gupta, Sanjay; Garg, Aashish

doi:10.1108/rbf-04-2021-0070

Cited by 22 publications

(35 citation statements)

References 86 publications

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“…Jain et al (2020) use fuzzy-AHP to rank behavioural biases in the investment decision-making of individuals. Sachdeva et al (2021) in their research applies the fuzzy-AHP technique to determine and assign priorities to the motivators of herding behaviour among individual investors. Fuzzy-AHP is also applied in the factors in determining environment responsibility (Garg et al , 2021).…”

Section: Research Design and Methodologymentioning

confidence: 99%

Influence of contextual factors on investment decision-making: a fuzzy-AHP approach

Sachdeva

Lehal

Gupta

2022

JABS

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Purpose The behavioural decision-making process of individuals highlights the importance of investors’ sentiment and their correlation with the real economy. This paper aims to contribute to the literature of behavioural finance by examining the influence of contextual factors on investment decision-making. Design/methodology/approach Using a questionnaire, a total of 445 valid responses were collected from March to May 2021 through online sources. The current study uses a technique of Fuzzy-analytical hierarchical process (AHP) to assign relative weights to various contextual factors influencing investment decision-making. Harman’s single factor test was used to check common method bias. Findings Results of the study reveal that accounting information, self-image/firm-image coincidence, and neutral information as the top-ranked factors in influencing investment decisions, whereas advocate recommendation and personal financial needs emerged as less important factors in influencing investment decisions. Research limitations/implications The current study collects data from Indian stock market investors, which may limit the generalization of the study to India only. Moreover, this study is cross-sectional in nature, and there are numerous factors that are not part of the study but might significantly influence the investors’ decision-making process. Practical implications The research has implications for both academicians working in the area of behavioural finance and practitioners’ who are active in stock markets, more specifically dealing with retail investors and in the domain of personal finance. Also, the current study will accommodate different groups, i.e. policy makers, financial advisors, investors, investment professionals, etc. in carrying out their professional work. Originality/value The current study will provide a comprehensive overview of individual investor behaviour. To the best of the authors’ knowledge, the present study is one of its kind to use the Fuzzy-AHP technique for evaluating the relative ranks of contextual factors influencing investment decision-making.

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Section: Research Design and Methodologymentioning

confidence: 99%

Influence of contextual factors on investment decision-making: a fuzzy-AHP approach

Sachdeva

Lehal

Gupta

2022

JABS

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“…, 2000; Hwang and Salmon, 2004). Herding behaviour prevails in the market in uncertain regimes like crises (Sachdeva et al. , 2021; Yousaf et al.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Many studies argue that herding behaviour is more likely to prevail in developing and emerging markets as compared to developed markets (Wermers, 1999;Chang et al, 2000;Hwang and Salmon, 2004). Herding behaviour prevails in the market in uncertain regimes like crises (Sachdeva et al, 2021;Yousaf et al, 2018;Yasir and € Onder, 2021). Chiang and Zheng (2010) show that there is herding behaviour in Shanghai and Shenzen A-share and B-share markets that are dominated by foreign institutional investors.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Yasir

Önder

2022

RBF

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PurposeThis study aims to investigate herding spillover in BRIC (Brazil, Russia, India and China) countries and Turkey under different regimes by using a time-varying approach.Design/methodology/approachThe authors used the structural change model of Bai and Perron (1998).FindingsThe results indicate that there is an evidence of herding behaviour in the Chinese stock market in two different regimes. These regimes cover the recent global financial crisis and the period of Hong Kong protests. We also report the evidence of herding behaviour in the Turkish stock market in the regime covering the COVID-19 period. Findings of herding spillover show that there is a two-way herding among Russia and China during crises and high volatile regimes. Similarly, there exists a cross-country herding among Brazil and India during crisis regimes. Also, there is herding spillover from Turkey to Russia, China and Brazil during the global financial crisis, post-European debt crisis and COVID-19 periods respectively. Furthermore, it is also evident that there is a herding spillover from Russia and China to India during the period covering COVID-19.Originality/valueTo the best of the authors' knowledge, this is the first study that uses structural change approach to identify herding behaviour spillovers from the US stock market to BRIC countries and Turkey and to investigate the cross-country herding behaviour among BRIC countries and Turkey.

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“…However, the limitation of the study is that the 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 (Sachdeva et al, 2021 ).…”

Section: Literature Review and Conceptual Frameworkmentioning

confidence: 99%

Cross Country Determinants of Investors' Sentiments Prediction in Emerging Markets Using ANN

Rao¹,

Kumar²,

Moonesar³

et al. 2022

Front. Artif. Intell.

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The paper models investor sentiments (IS) to attract investments for Health Sector and Growth in emerging markets, viz., India, Mainland China, and the UAE, by asking questions such as: What specific healthcare sector opportunities are available in the three markets? Are the USA-IS key IS predictors in the three economies? How important are macroeconomic and sociocultural factors in predicting IS in these markets? How important are economic crises and pandemic events in predicting IS in these markets? Is there contemporaneous relation in predicting IS across the three countries in terms of USA-IS, and, if yes, is the magnitude of the impact of USA-IS uniform across the three countries' IS? The artificial neural network (ANN) model is applied to weekly time-series data from January 2003 to December 2020 to capture behavioral elements in the investors' decision-making in these emerging economies. The empirical findings confirmed the superiority of the ANN framework over the traditional logistic model in capturing the cognitive behavior of investors. Health predictor—current health expenditure as a percentage of GDP, USA IS predictor—spread, and Macro-factor GDP—annual growth % are the common predictors across the 3 economies that positively impacted the emerging markets' IS behavior. USA (S&P 500) return is the only common predictor across the three economies that negatively impacted the emerging markets' IS behavior. However, the magnitude of both positive and negative impacts varies across the countries, signifying unique, diverse socioeconomic, cultural, and market features in each of the 3 economies. The results have four key implications: Firstly, US market sentiments are an essential factor influencing stock markets in these countries. Secondly, there is a need for developing a robust sentiment proxy on similar lines to the USA in the three countries. Thirdly, investment opportunities in the healthcare sector in these economies have been identified for potential investments by the investors. Fourthly, this study is the first study to investigate investors' sentiments in these three fast-emerging economies to attract investments in the Health Sector and Growth in the backdrop of UN's 2030 SDG 3 and SDG 8 targets to be achieved by these economies.

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

Cited by 22 publications

References 86 publications

Influence of contextual factors on investment decision-making: a fuzzy-AHP approach

Influence of contextual factors on investment decision-making: a fuzzy-AHP approach

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

Cross Country Determinants of Investors' Sentiments Prediction in Emerging Markets Using ANN

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