Stock market volatility: a systematic review

IF 1.8 Q3 MANAGEMENT Journal of Modelling in Management Pub Date : 2023-11-14 DOI:10.1108/jm2-04-2023-0080
Barkha Dhingra, Shallu Batra, Vaibhav Aggarwal, Mahender Yadav, Pankaj Kumar
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Abstract

Purpose The increasing globalization and technological advancements have increased the information spillover on stock markets from various variables. However, there is a dearth of a comprehensive review of how stock market volatility is influenced by macro and firm-level factors. Therefore, this study aims to fill this gap by systematically reviewing the major factors impacting stock market volatility. Design/methodology/approach This study uses a combination of bibliometric and systematic literature review techniques. A data set of 54 articles published in quality journals from the Australian Business Deans Council (ABDC) list is gathered from the Scopus database. This data set is used to determine the leading contributors and contributions. The content analysis of these articles sheds light on the factors influencing market volatility and the potential research directions in this subject area. Findings The findings show that researchers in this sector are becoming more interested in studying the association of stock markets with “cryptocurrencies” and “bitcoin” during “COVID-19.” The outcomes of this study indicate that most studies found oil prices, policy uncertainty and investor sentiments have a significant impact on market volatility. However, there were mixed results on the impact of institutional flows and algorithmic trading on stock volatility, and a consensus cannot be established. This study also identifies the gaps and paves the way for future research in this subject area. Originality/value This paper fills the gap in the existing literature by comprehensively reviewing the articles on major factors impacting stock market volatility highlighting the theoretical relationship and empirical results.
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股票市场波动:系统回顾
随着全球化和技术进步的加剧,从各个变量来看,股票市场的信息外溢效应增加。然而,缺乏对股票市场波动如何受到宏观和公司层面因素影响的全面审查。因此,本研究旨在通过系统回顾影响股票市场波动的主要因素来填补这一空白。设计/方法/方法本研究结合了文献计量学和系统文献综述技术。从Scopus数据库中收集了澳大利亚商学院院长委员会(ABDC)名单中发表在高质量期刊上的54篇文章的数据集。此数据集用于确定主要贡献者和贡献。通过对这些文章的内容分析,揭示了影响市场波动的因素和本学科领域潜在的研究方向。调查结果显示,在“COVID-19”期间,该领域的研究人员对研究股票市场与“加密货币”和“比特币”之间的关系越来越感兴趣。本研究结果表明,大多数研究发现油价、政策不确定性和投资者情绪对市场波动有显著影响。然而,关于机构流量和算法交易对股票波动的影响,结果好坏参半,无法达成共识。本研究还确定了差距,并为该主题领域的未来研究铺平了道路。本文通过对影响股市波动主要因素的文章进行综合梳理,突出理论关系和实证结果,填补了现有文献的空白。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
5.50
自引率
12.50%
发文量
52
期刊介绍: Journal of Modelling in Management (JM2) provides a forum for academics and researchers with a strong interest in business and management modelling. The journal analyses the conceptual antecedents and theoretical underpinnings leading to research modelling processes which derive useful consequences in terms of management science, business and management implementation and applications. JM2 is focused on the utilization of management data, which is amenable to research modelling processes, and welcomes academic papers that not only encompass the whole research process (from conceptualization to managerial implications) but also make explicit the individual links between ''antecedents and modelling'' (how to tackle certain problems) and ''modelling and consequences'' (how to apply the models and draw appropriate conclusions). The journal is particularly interested in innovative methodological and statistical modelling processes and those models that result in clear and justified managerial decisions. JM2 specifically promotes and supports research writing, that engages in an academically rigorous manner, in areas related to research modelling such as: A priori theorizing conceptual models, Artificial intelligence, machine learning, Association rule mining, clustering, feature selection, Business analytics: Descriptive, Predictive, and Prescriptive Analytics, Causal analytics: structural equation modeling, partial least squares modeling, Computable general equilibrium models, Computer-based models, Data mining, data analytics with big data, Decision support systems and business intelligence, Econometric models, Fuzzy logic modeling, Generalized linear models, Multi-attribute decision-making models, Non-linear models, Optimization, Simulation models, Statistical decision models, Statistical inference making and probabilistic modeling, Text mining, web mining, and visual analytics, Uncertainty-based reasoning models.
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