A bibliometric visualization of behavioral biases in investment decision-making

Abstract

Purpose This study aims to conduct a bibliometric analysis to provide a comprehensive picture and identify future research directions to enrich the existing literature on behavioral biases. Design/methodology/approach The data set comprises 518 articles from the Web of Science database. Performance analysis is used to highlight the significant contributors (authors, institutions, countries and journals) and contributions (highly influential articles) in the field of behavioral biases. In addition, network analysis is used to delve into the conceptual and social structure of the research domain. Findings The current review has identified four major themes: “Influence of behavioral biases on investment decisions,” “Determinants of home bias,” “Impact of biases on stock market variables” and “Investors’ decision-making under uncertainty.” These themes reveal that a majority of studies have focused on equity markets, and research on other asset classes remains underexplored. Research limitations/implications This study extracted data from a single database (Web of Science) to ensure standardization of results. Consequently, future research could broaden the scope of the bibliometric review by incorporating multiple databases. Originality/value The novelty of this research is to provide valuable guidance by evaluating the existing literature and advancing the knowledge base on the conceptual and social structure of behavioral biases.

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“…When market players either overreact or underreact to available information that is frequently unrelated, return predictability occurs (Rasheed, Gul, Hashmi, & Mumtaz, 2021;Xue & Zhang, 2017). This view posits that investor behavior is greatly impacted by biassed information due to a variety of biases, prejudices, heuristics, and framing strategies (Dhingra, Yadav, Saini, & Mittal, 2024;Hussain, Sadiq, Rasheed, & Amin, 2022;Rasheed, Gul, Akhtar, & Tariq, 2020;Tversky & Kahneman, 1974;Waweru, Munyoki, & Uliana, 2008).…”

“…The market's predictability is derived and aided by this insufficient or unwarranted reaction by the investors to the underlying informational flow (Bondt & Thaler, 1985;Thaler, 2015). In stock markets, herding behavior-which results from interactions that distribute irrationality throughout the population-increases predictability and can cause market crashes and bubbles (Amini, Gebka, Hudson, & Keasey, 2013;Baur, Dimpfl, & Jung, 2012;Chen, Hsieh, & Huang, 2018;Dhingra et al, 2024;Lewellen, 2002). Therefore, the motivation of this study is to determine how investors behave and how emotions influence stock market autocorrelation.…”

“…This method emphasizes that not all investments are necessarily rational by arguing that autocorrelation originates from psychosomatic components. Based on historical evidence, investors' overreactions or underreactions to easily accessible and frequently irrelevant news might result in significant returns (Dhingra et al, 2024;Rasheed et al, 2021;Xue & Zhang, 2017). This behavioral explanation is used in the current study to investigate whether stock predictability exists in global stock markets based on MSCI categorization, which varies in terms of development and demographic orientation.…”

“…This interactive strategy has been embraced by stock markets, where brokers engage in regular communication with investors and financial specialists use interactions with executives and brokers to influence the market. These social interactions are important because they improve the degree of consistency and fluctuation in stock returns, particularly in collectivist settings where social influence and behavioral characteristics are likely to have a bigger impact (Dhingra et al, 2024;Rasheed et al, 2018Rasheed et al, , 2021.…”

“…In the field of behavioral finance, irrational behavior is attributed to elements that can produce the psychological phenomenon known as social mood, which is a group of illogical emotions. The study conducted by Dhingra et al (2024) summarizes various behavioral factors that can influence investors' emotional states leading to suboptimal financial choices (Gul & Akhtar, 2016;Kirchler, Maciejovsky, & Weber, 2005;Rasheed et al, 2018). There is also a strong connection between different behavioral components, suggesting that when one bias is present, an investor is more likely to apply additional biases (Kudryavtsev, Cohen, & Hon-Snir, 2013).…”