Mapping the Conceptual and Intellectual Structure of the Investor’s Financial Behaviour: A Bibliometric Analysis

Abstract

Examining the diverse roles played by various kinds of investors in the stock market is crucial, because of their differing behaviours while making investment decisions. This study reviewed the academic literature on investor behaviour to acquire insights into the current status, developments and significant gaps with the help of bibliometric analysis. The research methodology involved selecting articles from the Scopus database and conducting performance analysis using VOSviewer software for science mapping. The study retrieved 313 research articles using the PRISMA protocol. It identified influential articles, journals, authors and five major themes (inefficiency and bubbles in the stock market, technological advancement in the financial market, behavioural aspect in investment decisions, prospect theory and cognitive bias and stock trading behaviour), adding to the body of knowledge on investor behaviour. The findings also revealed that there has been a consistent rise in research in this area. In addition, scientific mapping techniques, such as bibliographic coupling, showed that the majority of the literature is focused on the connection between asset price models, behavioural biases, investor sentiment and portfolio choices. Finally, the research directions identified in this study could serve as a guide for future research.

Keywords

Investor behaviour behavioural finance bibliometric analysis bibliographic coupling citation analysis

Introduction

An essential aim of finance is to comprehend financial markets and investor behaviour. Investigating the role of various kinds of investors and the impact they have on the stock market is crucial in finance as they react and behave differently while making investing decisions (Phan et al., 2023). Research in behavioural finance has rapidly expanded in recent times. Behavioural finance is a research field within finance that explores the behaviour of investors and how it impacts stock market activity. According to behavioural finance, an investor’s market behaviour is influenced by psychological concepts related to decision-making, which help explain why an investor decides to purchase or sell stocks. It emphasizes how investors process and interpret information to make investment decisions (Ahmad, 2017). The research in this area has exploded recently, indicating its incremental relevance (López-Cabarcos et al., 2020).

Before the notion of behavioural finance emerged, several financial and economic theories attempted to elucidate investor decision-making processes and market behaviour. These theories include the expected utility theory given by Von Neumann and Morgenstern (1944), which explains how agents make decisions in uncertain situations and concludes that people evaluate risk rationally by constantly attempting to maximize utility. Regarding financial markets, Fama strengthened the efficient market hypothesis, claiming that market efficiency is a market condition where prices always replicate the existing information entirely, justifying rationality in the decision-making process (Fama, 1970). The concept of a rational and efficient market was the foundation for establishing the classical finance theory in the 1970s (Ganesh, 2017). As per Statman (1999), classical finance theory is built on four pillars: the capital asset pricing model given by Sharpe (1964), Lintner (1965) and Black (1972); the arbitrage principles of Modigliani and Miller (1963) and Modigliani and Miller (1958); the modern portfolio theory of Markowitz (1952) and the option pricing theory of Black and Scholes (1973) and Merton (1973). Traditional financial concepts presume that investors are more concerned with wealth maximization, basic financial principles and taking financial decisions based solely on risk–return analyses (Shefrin, 2002).

However, these paradigms faced criticism over time, and a new field of research, ‘behavioural finance’, was established. One of the field’s first authors, Simon (1956), argued that learning theories describe observed behaviour better than statistical and economic theories of rational behaviour. In his research, Simon (1956) found that, although people may preserve to satisfy their needs to a certain extent, they cannot identify the best approach to maximize the ‘utility function’. Further, in the experimental research of Kahneman and Tversky (1979), the authors questioned the efficient market hypothesis and expected utility theory. They defend investors’ irrational behaviour, confirming the existence of cognitive biases and heuristics that impact investors’ decision-making of investors in uncertain situations; consequently, people will not behave rationally as projected by previous theories. Kahneman and Tversky (1979) developed a substitute for expected utility—the popular concept of ‘prospect theory’. This theory introduced a radical shift of traditional finance to the newly emerging area of behavioural finance by describing how people make decisions when dealing with risky alternatives and probabilities of uncertain outcomes, while also considering the heuristics and biases.

In contrast to traditional financial knowledge, behavioural financial knowledge is grounded in realistic assumptions like irrational behaviour and limited interests. Accordingly, behavioural finance research tries to comprehend how people make decisions and impact other people, markets, society and organizations (Birnberg & Ganguly, 2012). Various experts and consultants in this area believe that the distinct behaviour of investors may influence market performance and efficiency in some way (Jokar et al., 2018). Numerous empirical research studies have been carried out since the 1990s, refuting the available literature and opening gates to promote behavioural finance. It is seen that the area of behavioural finance encompasses the relationship established with market anomaly (Iqbal et al., 2013; Muchemi & Kamau, 2013), with market efficiency (Barber et al., 1998; Latif et al., 2011; Shiller, 2003), behavioural biases (Lütje & Menkhoff, 2007; Metwally & Darwish, 2015; Theriou et al., 2011; Weiss-Cohen et al., 2019) and with market liquidity (Blume & Keim, 2012; Dezelan, 2001; Liu, 2015). In a nutshell, an investor’s investment choices are impacted by both irrational and rational factors. Investigating factors influencing investment intention from investors’ perspectives and comprehending human behaviour and decision-making from a financial perspective are, thus, essential. Moreover, several other studies have summarized the evidence of investors’ behaviour in the stock market (Ahmad, 2017; Jokar et al., 2018; Khawaja & Alharbi, 2021; Mak & lp., 2017; Pascual-Ezama et al., 2014; Phan et al., 2023; Raut et al., 2020; Thiruchelvam & Mayakkannan, 2011); in bonds (Dewi & Tamara, 2020; Duqi & Al-Tamimi, 2019), mutual funds (Alhorani, 2019; Kaur & Kaushik, 2016; Paliwal et al., 2018) and investment for retirement planning (Bongini & Cucinelli, 2019).

Based on the aforementioned, research in behavioural finance has explored several divergent ways to comprehend the effect that behavioural, psychological and cognitive aspects have on individual investors’ decisions. It has not yet provided a comprehensive picture of financial instruments because it has only examined particular instruments, such as the stock market, cryptocurrencies, biases, socially responsible investment and investor sentiments (see Table 1). A thorough review, including all aspects of investors’ behaviour, is still lacking. Thus, the present work is a modest effort to fill this gap. First, to the best of the authors’ knowledge, there is no bibliometric study detailing the area of investor behaviour in the stock market. Second, the present work has a long research period, which starts in 1999 (the standard starting range). Third, it illuminates potential directions for future research.

Table 1.

Focus of Previous Bibliometric Analysis Studies.

S. No.	Document	Time period	Focus of the study	Database
1	Almeida and Gonçalves (2023)	2009–2021	To analyse and synthesize the body of research on investor behaviour in cryptocurrency markets.	Web of Science
2	Gokhale and Mittal (2024)	2002–2022	To explore the complex relationships between portfolio strategies, investor protection, capital markets and behavioural elements of investing.	Web of Science
3	Che Hassan et al. (2023)	2016–2021	To provide a thorough framework for presenting quantitative and qualitative studies of the emerging factors affecting investors’ intents and behaviours concerning financial investment products.	Web of Science and Scopus
4	Kamath et al. (2022)	1979–2022	Examined the broad spectrum of publications in the area of investor sentiments.	Scopus
5	Paule-Vianez et al. (2020)	1987–2017	To explain the productivity and performance of behavioural finance as well as comprehend the themes that make up the field and its current condition, with the help of bibliometric analysis.	Web of Science
6	Joshipura et al. (2024)	1986–2021	To comprehend and investigate the present dynamics, understand the advancement of knowledge, identify trends and offer suggestions for future directions in this field of momentum investment strategies.	Scopus
7	Ikram et al. (2023)	1992–2023	To comprehensively review the literature on behavioural biases and overconfidence in stock market investing decisions.	Scopus
8	Zahera and Bansal (2018)	1979–2016	To review behavioural finance research articles to examine and characterize several biases in investment decision-making.	Scopus
9	Dubyna et al. (2022)	1970–2020	To examine the present status of research on financial behaviour.	Web of Science and Scopus
10	Choijil et al. (2022)	1990–2020	To evaluate scholarly studies conducted over 30 years on herd behaviour in financial markets.	Web of Science
11	Ding et al. (2022)	1991–2021	To illustrate the knowledge structure and evolution path of institutional investors over the last three decades, using a novel approach in the field to better comprehend research status and prospects.	Web of Science
12	Aren et al. (2016)	2005–2014	To evaluate institutional investor research on home bias, disposition effect and herding behaviour published in reputable publications to identify some significant gaps in the literature.	Web of Science

To understand the financial behaviour of investors and to comprehend the ideas that it contains and its current state, this study investigates the following research questions:

What is the annual pattern of publications in the field of investors’ financial behaviour?

Which are the most influential authors, articles and journals contributing to the area of investors’ financial behaviour?

What are the main research themes regarding investors’ financial behaviour?

What are the key concepts associated with investor behaviour?

This study employed a hybrid review, a combination of a systematic literature review (SLR) and a bibliometric analysis to address aforesaid research questions. The bibliometric analysis is a statistical assessment of research studies that uses a variety of evaluative and relational methodologies (Rodríguez-Sánchez et al., 2020; Singh & Walia, 2020). SLR was used at the beginning of this study to find relevant literature. SLR includes determining keywords, searching literature with the help of keywords and choosing suitable literature by establishing the inclusion and exclusion criteria (Chen et al., 2019). Additionally, this research used numerous bibliometric tools to map the literature and achieve significant insights into the chronological publication trends, most prolific writers, influential studies and leading journals. This study includes citation analysis to examine influential authors, studies and journals. Additionally, bibliographic coupling, co-authorship analysis and keyword analysis are inclined to identify emerging research areas, the field’s social structure and key concepts related to investors’ financial behaviour.

Research Methodology

The fundamental aim of this research was to outline the knowledge structure of investors’ financial behaviour in the stock market. Therefore, bibliometric analysis was performed to accomplish this goal. Bibliometric analysis was conducted by extracting literature from the citation indexing services, which were also categorized as descriptive and equipped with a quantitative approach (Costa et al., 2019; Velasco-Muñoz et al., 2018). This approach was used to quantitatively analyse selected articles, identifying, classifying and interpreting the significant mechanisms of a specific research area (Zhang et al., 2017). Bibliometric approaches (e.g., citation analysis, co-word analysis, bibliographic coupling) generated structural images of scientific fields using bibliographic data from publishing databases (Zupic & Čater, 2015). In this study, we used the SLR approach to extract the data for bibliometric analysis. SLR was conducted by following established procedures for searching several databases using a predetermined search strategy, which expanded the quality of research by making it more transparent, scientific and complete. The literature search for this study was grounded in the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) protocol, shown graphically in Figure 1. In addition, VOSviewer software was used for bibliometric mapping and visualization as it offerred a feasible graphical platform for visualization (Liao et al., 2018).

Data Source and Search Strategy

The dataset used for bibliometric analysis was collected from the Scopus database. Scopus is among the most extensive databases for acquiring literature in the social sciences (Mongeon & Paul-Hus, 2016). It is appropriate for this sort of work for its better degree of representation (Borrett et al., 2018), a higher number of published articles (Aznar Sanchez et al., 2019), higher update frequency (Motta et al., 2018) and more ease of identifying and processing data (Couckuyt & Looy, 2019; Ştirbu et al., 2015) compared to other databases.

After selecting the database, the next step in the search procedure is to finalize the keywords that can cover all the related publications. The central issue while performing the search queries is identifying the most relevant keywords. Therefore, a sample of 200 frequently used keywords was made from previous literature on investors’ financial behaviour, and the keywords used more than five times were selected. Hence, the following keywords have been used: ‘financial behaviour’, ‘investors’, ‘behavioural finance’ and ‘markets’. These keywords are broad enough to cover all the searches related to publications focusing on investors’ financial behaviour in the stock market. The search for the dataset was conducted at one point in time (i.e., during September 2023) to prevent any possible bias resulting from the constant updating of the Scopus database. Scopus uses Boolean operators (OR, AND, AND NOT), which help find the proper research documents. The OR operator connects the words that explain the general theoretical area, whereas the AND operator narrates terms applied to the area (López-Cabarcos et al., 2020). Therefore, keywords with these Boolean operators were searched in the Scopus database. The search string employed is as follows:

TITLE-ABS-

KEY ((behavioral AND finance) AND (financial AND behavior) AND (investors) AND (markets)) AND (EXCLUDE (DOCTYPE, ‘re’) OR EXCLUDE (DOCTYPE, ‘er’) OR EXCLUDE (DOCTYPE, ‘tb’))

Inclusion/Exclusion Criteria

This search string provided 352 research articles that were published between 1999 and 2023, where we did not have any restrictions regarding the year of publication, the language used, subject area or country, to include all the articles in the area of investors’ financial behaviour. To ensure the effectiveness of the bibliometric analysis, the review was retracted, and erratum articles were removed from the search strings. Review articles, which synthesize existing research rather than presenting original findings, can distort metrics like citation counts. Errata, issued to correct errors in original publications, do not contribute new research data and can clutter the dataset. Retracted articles, withdrawn due to significant issues like data fabrication or plagiarism, can misrepresent the state of knowledge and propagate invalid findings. In this way, we obtained 321 articles. Eight articles were further removed after screening titles, abstracts and keywords due to their irrelevance to the topic. As a result, we received 313 research articles for this study’s final dataset (Figure 1). The selected literature was then downloaded, including the title, keywords, abstract, year of the publication and author name, and exported to Excel.

Data Analysis

Bibliometric analysis is a statistical assessment of published research articles, book chapters or books, and it is a helpful procedure for identifying the impact of publication in the research world. This study employed several evaluative and relational bibliometric techniques to extract significant knowledge from the data. These techniques involved citation analysis, bibliographic coupling and co-word analysis. The graphical presentation of bibliometric tools is shown in Figure 2. Various software has been used in the previous literature to perform the bibliometric analysis. Some famous software packages are Cite Space, Publish or Perish, BibExcel, Bibliometrix R, VOSviewer, HistCite, Eigenfactor score, Scholarometer and Pag. Every software has its advantages and disadvantages. We used VOSviewer 1.6.6 and Microsoft Excel to process and analyse the data.

Results

Evolution of Scientific Publication

The first research question emphasizes the chronological publication trend in the area of investors’ financial behaviour. Figure 3 displays the number of articles published annually between 1999 and 2023. This figure depicts that there has been a substantial increase in the publication of research articles, from 1 in 1999 to 34 in 2023. The initial article in the database was found in 1999. After that, there was a lag between this year’s publication of the following article, that is, in 2003. Minimal research was published between 2003 and 2008.

Here, the year 2008 was used as the cut-off point because an increasing pattern was observed immediately after it. However, a mixed trend is depicted afterwards. One reason for this change may be that investors were not sure about where to look for yield after the crisis period (Xanthopoulos, 2017). Furthermore, the 2008 financial crisis was also referred to as the Global Financial Disaster. Due to this, 2008 is used as a cut-off point to describe the abnormalities in the publishing patterns.

From 2009 onwards, there has been a gradual growth in researchers’ interest (Bihari et al., 2022). Further, researchers’ interest in this subject expanded substantially after 2016, and it doubled till 2019. It has continued to increase since then. In fact, the post-2016 period witnessed the emergence of various themes and dimensions in the field of behavioural finance. They are related to the growing recognition of behavioural biases, market volatility and behavioural responses, integration of behavioural insights in financial education and increased cross-border investment, which have heightened the interest in and research activity on investor behaviour within the broader field of behavioural finance. Moreover, the trend line is moving upward, and the R square was measured at 0.888. This trend continues to evolve as scholars, practitioners and policymakers recognize the importance of psychology in shaping financial markets and investment outcomes.

Citation Analysis

Citation analysis is an extensively used technique for examining the significance of prevailing publications. It evaluates the popularity of a single publication in the existing literature with the help of a number of citations referred to by other articles (Ding & Cronin, 2011; Xu et al., 2018). When an article is extensively cited, it is regarded as essential. This statement is dependent upon the premise that authors cite documents they believe are substantial for their research (Zupic & Čater, 2015). Therefore, through this analysis, the second research question was answered to identify the most influential articles, authors and journals.

Most Productive Articles

Table 2 presents the top 10 most influential articles regarding investors’ financial behaviour. The articles in this table were ranked from 1 to 10 based on the total number of citations received. The findings show that the article proposed by Hirshleifer (2003) is the most cited in terms of both global and local citations, followed by significant works by Bursztyn et al. (2014) and Thaler (2005). To provide a consistent baseline for citation comparisons, the number of citations per year each document receives is also recorded, irrespective of the publishing year.

Table 2.

Most Influential Articles in the Area of Investor Behaviour.

R	Document	Title	Journal	Year	GC	LC	Study Objective	Contribution	Methodology adopted	C/Y
1	Hirshleifer (2003)	Herd behavior and cascading in capital markets: A review and synthesis	European Financial Management.	2003	483	7	To review the theory and evidence underlying herd behaviour, information cascades and social learning in stock markets.	The majority of herding incidents in the capital markets are probably a combination of imperfect rationality, desire effects, direct payoff interactions, informational effects and reputational effects.	Review	22.09
2	Bursztyn et al. (2014)	Understanding mechanisms underlying peer effects: Evidence from a field experiment on financial decisions.	Econometrica.	2014	238	4	To implement a unique design to discover two distinct social impact pathways in financial decisions by employing a high-stakes field study.	The result showed that both channels of social influence, that is, social learning and social utility significantly impact investment decisions statistically and economically.	Quantitative	23.7
3	Thaler, (2005)	Advances in behavioral finance	Princeton University Press.	2005	164	3	To provide an in-depth and comprehensive summary of the developments made in behavioural finance during the last 10 years.	Provides 20 recent research studies that demonstrate the enduring strength of behavioural finance —how specific deviations from rational decision-making by market players can explain otherwise puzzling market phenomena.	Qualitative	8.42
4	Shefrin (2008)	A behavioral approach to asset pricing	Elsevier.	2008	158	2	To focus entirely on how behavioural finance principles affect asset pricing.	Determines the reigning assumptions of asset pricing theory and recreates them to incorporate findings from behavioural finance and provides a robust theory and effective empirical tools.	Qualitative	9.68
5	Jiang et al. (2010)	Bubble diagnosis and prediction of the 2005–2007 and 2008–2009 Chinese stock market bubbles	Journal of Economic Behavior and Organization	2010	155	2	To investigate bubbles following market crashes in two significant Chinese stock market indexes between May 2005 and July 2009.	Offers some case studies and novel experimental testing in favour of applications that (i) the existence of a bubble may be quantitatively detected before the collapse and (ii) the termination of a bubble has some predictions.	Quantitative	10.78
6	Oehler et al. (2018)	Investors’ personality influences investment decisions: experimental evidence on extraversion and neuroticism	Journal of Behavioral Finance	2018	108	2	To investigate the impact of neuroticism and extraversion on decision-making in an empirical asset market.	Extraversion and neuroticism significantly influence individuals’ behaviour in the experimental asset market. Specifically, more extraverted people pay more for financial assets when assets are more expensive.	Quantitative	17.16
7	Venezia et al. (2011)	Firm-specific and macro herding by professional and amateur investors and their effects on market volatility	Journal of Banking and Finance	2011	94	4	To investigate the factors affecting herding behaviour by professionals and amateurs.	Herding is significantly and strongly related to volatility in the stock market and herding, particularly among amateurs, affects market volatility.	Quantitative	7
8	Takahashi and Terano (2003)	An agent-based approach to investors’ behavior and asset price fluctuation in financial markets	JASSS	2003	68	2	To examine how rational and irrational investor behaviour affects asset prices with different kinds of investors: fundamentalists and non-fundamentalists.	It was found that non-fundamentalists influence traded prices and obtain excess profits in fundamental markets.	Quantitative	3.14
9	Shu (2010)	Investor mood and financial markets	Journal of Economic Behavior and Organisation	2010	65	1	To examine how variations in the investors’ mood affect expected returns and equilibrium asset prices.	The equilibrium asset prices and returns are significantly impacted by investor mood and incorporating investor mood in the asset pricing models facilitates the interpretation of the increasing amount of data about investor behaviour.	Quantitative	4.57
10	Metawa et al. (2019)	Impact of behavioral factors on investors’ financial decisions: case of the Egyptian stock market	Journal of Islamic and Middle Eastern Finance and Management	2019	68	1	Study used behavioural aspects as mediator variables to examine the relationship between the demographic characteristics of investors and investing decisions.	Investment decisions are heavily influenced by investor sentiment, herd behaviour, overconfidence and overreaction or underreaction. Additionally, an investor’s decision is significantly influenced positively by demographic factors.	Quantitative	12.6

Note: R: Rank, GC: global citation LC: local citation.

The citations per year are calculated by dividing the total number of citations by the number of years after publication (Chung et al., 2001). Furthermore, as revealed in Table 2, the articles by Hirshleifer (2003) and Bursztyn et al. (2014) have the highest citations per year. This suggests that the Hirshleifer (2003) and Bursztyn et al. (2014) articles are the most influential ones in this field.

Hirshleifer’s (2003) study examines the theory and evidence underlying herd behaviour, reward, reputational relationships, information cascades and social learning in stock markets. In this study, the author provides a simple taxonomy of effects and assesses how competing theories can contribute to explaining evidence on investor behaviour, firms and analysts. The results show that various patterns of convergent behaviour and fluctuations in capital markets, like fixation on poor ventures, stock market crashes, abrupt changes in investment and unemployment and bank runs, do not immediately make sense when observed from the perspective of classic economic models. These behavioural convergences frequently occur even when there are unfavourable incentive externalities.

Furthermore, the study by Bursztyn et al. (2014) ranks second, which aims to provide a unique design to discover two distinct social impact pathways in financial decisions by employing a high-stakes field study with the financial brokerage. The majority of these articles are focused on detecting how investor behaviour fluctuates between rational (based on classical financial theories) and irrational (based on behavioural finance) choices and affects asset prices by creating a virtual financial market with different kinds of investors.

Journal Quality Analysis

This analysis reveals the characteristics of the top ten most contributed journals in this area. Table 3 lists the names of journals, the number of publications in each of them, the cite score, the SCImago Journal Rank (SJR) quartile where they belong, significant articles of this field published in the journals, the subject area and the scope of the journal.

Data analyses show that Qualitative Research in Financial Markets (6), Quantitative Finance (6) and Review of Behavioral Finance (6) are the journals with the most published articles, followed by Investment Management and Financial Innovations (5), Journal of Economic Behavior and Organization (5) and Smart Innovation Systems and Technologies (5). Regarding the quartile of the SJR, two journals each pertain to the Q1 and Q4 quartiles of citations. Three journals belong to the Q3 quartile of citations, whereas only one each belongs to Q2. Two journals still need to be assigned a quartile.

In contrast, the highest cite score belongs to Quantitative Finance (3.6) and the Journal of Behavioral Finance (3.6). Thus, Quantitative Finance, which had the highest cite score, also belongs to the Q1 quartile and has the highest total publications. Overall, this journal tops the list in every aspect. The subject area of this journal is related to general economics, econometrics and finance. The majority of research published in the Journal of Quantitative Finance focuses on asset pricing models, anomalies and investor behaviour in the different conditions of the stock market (volatility, liquidity, etc.).

Author Influence

Tables 4 and 5 reveal the ranking of the top 10 authors based on the number of publications and the number of citations received. Table 4 shows that Hiroshi Takahashi and Muhammad Zubair Tauni are the most productive authors with the maximum number of articles published—5 in Scopus. They authored five articles each in association with other co-authors. Hiroshi Takahashi employed an agent-based approach to elucidate how investors’ behaviour, specifically overconfidence, exerts influence on asset price fluctuations within financial markets, consequently affecting trading prices and returns. Through his research endeavours, he delves into the examination of risks present in financial markets employing agent-based modelling, emphasizing the ramifications of overconfident investors on price fluctuations and market efficiency. The significant work by Muhammad Zubair Tauni demonstrates a correlation between trading frequency and the information sources utilized by investors for financial decision-making and investigates the impact of investor personality on stock investors’ trading decisions when utilizing key sources for market information.

Meanwhile, David Hirshleifer and Siew Hong Teoh are the highest cited authors (Table 5), with their article ‘Herd behaviour and cascading in capital markets: A review and synthesis’, having a total of 464 citations with only one published article in this area. They review theory and evidence related to herd behaviour, social learning and informational cascades in capital markets and discuss how alternative theories may help to explain the behaviour of investors, analysts and firms. Their research evaluates incentives for parties to be involved in herding or cascading, as well as incentives to protect against such behaviours by others.

Bibliographic Coupling

The third research objective emphasizes detecting the predominant themes in the area of investors’ financial behaviour and drawing conclusions for future studies. We have used bibliographic coupling to recognize prominent themes in this area, as it is an effective tool for mapping recent research (Zupic & Čater, 2015). Bibliographic coupling is constructed on shared literature references between research articles. It takes into account the number of literature references shared on other articles to assess the level of similarity. The level of similarity increases as the number of shared references expands. We create a similarity matrix using these shared references, which we then use to classify articles into thematic clusters. After separating the articles built on shared references, five clusters were formed. The documents in each cluster were then analysed to discover the main themes. We examined the central theme to title every cluster.

Figure 4 demonstrates the network map of the bibliographic coupling of the 60 most cited articles on investors’ financial behaviour from 1999 to 2023. This analysis was performed using VOSviewer software. A minimum of eight citations have been used for each document for the finest visualization of the network. The result shows that there were 60 documents with a minimum number of eight citations, whereas the result generated a map of 54 documents as the remaining six documents were not connected. The articles are highlighted by the number of citations they received. The articles’ bibliometric analysis indicated the existence of five clusters, as shown in Supplementary Table S6, and Table 6 demonstrates the future research directions in this area.

Bibliographic Cluster 1: Investor Behaviour and Asset Pricing Models

The research in the first cluster focuses on identifying the underlying behaviour of investors in the stock market through asset pricing models. This is the largest cluster having 19 articles and a total of 521 citations. Shefrin (2008), the most cited article with 132 citations (25.33% of the cluster’s citations), develops a behavioural approach to asset pricing by integrating behavioural finance concepts into traditional models, emphasizing representativeness and its impact on investor decisions. In this cluster, the studies explore various sub-themes, such as asset price modelling, investor sentiments, market anomalies and stock market events.

Table 3.

Top 10 Most Productive Journals in the Area.

Rank	Journal/source	Total articles	SJR quartile	Cite score (2022)	Significant articles	Subject area	Scope of the journal
1	Qualitative Research in Financial Markets	6	Q3	2.9	Sahi and Arora (2012), Jaiyeoba and Haron (2016), Tauni et al. (2016)	Economics, econometrics and finance	Behavioural finance Emotional finance Corporate governance regulations Corporate communication
2	Quantitative Finance	6	Q1	3.6	Pruna et al. (2020), Grishina et al. (2017), Fushing et al. (2012)	General economics, econometrics and finance	Agent-based modelling Anomalies in prices Behavioural finance liquidity modelling Corporate finance market microstructure
3	Review of Behavioral Finance	6	Q3	2.7	Durand et al. (2013), Nigam et al. (2018), Gavrilakis and Floros (2022)	Finance, accounting and strategy management	Investment behaviour of individual and institutional investors Risk measurement and asset pricing Trading strategies in financial markets Behavioural corporate finance
4	Investment Management and Financial Innovations	5	Q4	1.9	Reis and Pinho (2021), Kamath (2022), Oliveira and Santos (2016)	Economics, econometrics, finance, business and international management	Financial ecosystem and its participants Financial services and Fintech Sustainable development financing
5	Journal of Economic Behavior and Organization	5	Q1	3.0	Jiang et al. (2010), Shu (2010), Frijns et al. (2008)	Economics, econometrics, organizational behaviour, human resource management	Economic decision Organization and behaviour and Economic change in all its aspects
6	Smart Innovation Systems and Technologies	5	Q4	1.1	Hao and chen burger (2022), Bhattacharya and Dutta (2020)	Computer science and decision sciences	Knowledge Intelligence Innovation and sustainability
7	Advances in Intelligent Systems and Computing	4	NA	0.9	Dutta et al. (2020), Wang et al. (2019), Wilinski and Matuszak (2019)	Computer science, control and systems engineering	Theory, applications and design methods of intelligent systems and intelligent computing, soft computing, social intelligence and computational neuroscience
8	Journal of Behavioral Finance	4	Q2	3.6	Oehler et al. (2018), Lam et al. (2012), Horta and Lobão (2018)	Finance, experimental and cognitive psychology	Psychological influences on investment market fluctuations
9	Applied Financial Economics	3	NA	1.0	Fifield et al. (2008), Schnusenberg (2006)	Economics, econometrics and finance	Financial markets, derivative securities markets, the foreign exchange market, corporate finance, market microstructure and cognate areas.
10	Indian Journal of Finance	3	Q3	1.6	Dangi and Kohli (2018), Rout and Das (2015)	Economics, econometrics and finance	Corporate finance Personal finance Insurance to financial economics and derivatives

Note: For SJR quartiles and cite score, the reference year assumed was 2022.

NA, not yet assigned a quartile; SJR, SCImago journal index; TP, total publications.

Takahashi and Terano (2003), Martinez-Jaramillo and Tsang (2009) and Durand et al. (2013) collectively examine the influence of investor behaviours and cultural factors on asset prices, highlighting the complex interplay of rationality, strategy evolution and cultural influences. Zhang et al. (2019) and Rupande et al. (2019) emphasize the significant role of investor sentiment in predicting stock market crises and volatility. Lam et al. (2012) and Igual and Santamaria (2017) investigate how behavioural biases affect market anomalies and volatility. Mehdian et al. (2008) and Muntermann (2009) examine investor reactions to unexpected political and economic events and market occurrences. Fifield et al. (2008) assess the predictive ability of moving average rules in various markets. Finally, Khawaja and Alharbi (2021) identify factors influencing investor behaviour in the Saudi stock market, providing a roadmap for investment decisions. As a whole, these studies provide an overview of different investor behaviours using asset pricing models.

Table 4.

Top 10 Authors Based on Total Publications.

Rank	Author	TP	TC	TC/TP	Contribution to the field
1	Takahashi H.	5	74	14.8	• Demonstrated the impact of different investor types on asset prices. • Showed non-fundamentalists affect prices and gain excess returns. • Identified the emergence of overconfident investors in financial markets. • Showed overconfident investors can align trading prices with fundamental values.
2	Tauni M.Z.	5	65	13	• Trading frequency is significantly influenced by the information sources investors use to make financial decisions. • The psychological component—investor personality—may affect stock investors’ trading decisions when they decide to use key sources to obtain market information.
3	Terano T.	4	74	18.5	• Demonstrated the impact of different investor types on asset prices. • Showed non-fundamentalists affect prices and gain excess returns. • Showed overconfident investors can align trading prices with fundamental values.
4	Kaymak U.	4	17	4.25	• Identified the impact of investor sentiment and overconfidence, showing that optimistic investors lead to market booms and crashes, in extreme optimism scenarios. • Introduced a model combining investor confidence and optimism to determine return distributions with optimism influencing the distribution’s mean and confidence affecting peak. • Highlighted the importance of agent-based modelling in financial markets to understand how behavioural biases influence market behaviour.
5	Lovric M.	4	17	4.25
6	Spronk J.	4	17	4.25
7	Das N.	3	14	4.66	• Demonstrates that investors in India are influenced by herding, information cascades, anchoring, representativeness and overconfidence, highlighting their irrationality and the inefficiency of the financial market. • Determined how personal investment is influenced by the level of knowledge an investor possesses regarding various investment instruments.
8	Liu C.	3	5	1.66	• Revealed the moderating effect of risk preferences in the links between investors’ online trading channel use intensity and both their trading behaviours and outcomes. • Utilized complex network theory to effectively analyse the evolution characteristics of investor scare behaviour diffusion under the influence of media coverage.
9	Lai K.K.	3	4	1.33	• Revealed that under specific circumstances, behavioural biases can lead to positive outcomes for investors, contrary to the traditional view that biases are always detrimental. • Introduced a model that explores how biases can impact individual trading performance.
10	Zhang W.	3	3	1	• Investigated whether CEO exposure captures investors’ attention and increases their sensitivity to firm-specific news, enhancing the media effect on stock markets. • Introduced the concept of an artificial stock market with three components—the market, the trading match mechanism and the traders—to analyse different investor behaviour patterns in margin trading.

Note: TC, total citations; TP, total publications; TC/TP, citations per document.

Table 5.

Top 10 Authors Based on Total Citations.

Rank	Author	TP	TC	TC/TP	Contribution to the field
1	Hirshleifer D.	1	464	464	Reviewed theory and evidence related to herd behaviour, social learning and informational cascades in capital markets. Discussed how alternative theories may help to explain the behaviour of investors, firms and analysts.
2	Hong Teoh S.	1	464	464
3	Bursztyn l	1	239	239	Goes beyond existing literature by disentangling social learning and social utility effects, suggesting that greater information provision might mitigate herding behaviour. Used experimental variation to identify the causal roles of different channels of peer effects, providing important evidence on herding behaviour in financial markets.
4	Ederer F.	1	239	239
5	Ferman B.	1	239	239
6	Yuchtman N.	1	239	239
7	Thaler R.H.	1	160	160	Provided 20 recent works that demonstrate the enduring value of behavioural finance—that is, how certain deviations from completely rational decision-making by individual market participants can explain occurrences in the market that would otherwise be puzzling.
8	Shefrin H.	1	155	155	Addressed issues involving investor benefits, behavioural preferences, beliefs and explored the development of common ground between neoclassical and behavioural asset pricing theorists. It also offers insights into the field’s present and future goals.
9	Belotti M.L.	1	155	155
10	Jiang Z.Q.	1	151	151	Contributed to the literature on financial bubbles by presenting two case studies and used the LPPL formula to show log-periodicity in financial bubbles by showing faster-than-exponential price increases with logarithmic fluctuations in Chinese stock indexes.

Note: Rank has been assigned for co-authored studies based on first and second authorship.

R, rank; TC, total citations; TP, total publications; TC/TP, citations per document.

Bibliographic Cluster 2: Behavioural Biases and Portfolio Choices

Cluster 2 includes 17 documents focusing on investors’ behavioural biases and portfolio choices while making investment decisions. The most cited paper in this cluster, Oehler et al. (2018), with 42 citations (12.35% of the cluster), examines how neuroticism and extraversion influence decisions in an experimental asset market. The cluster explores various behavioural biases such as loss aversion, disposition effect, home bias, mental accounting, anchoring, gambler’s fallacy, availability, regret aversion, representativeness, overconfidence, optimism and pessimism.

Key studies include Frijns (2008), which links behavioural finance concepts to portfolio choices, addresses equity premium and volatility puzzles and emphasizes the role of market sentiment and self-assessed expertise. Sahi and Arora (2012) segment Indian investors based on biases, aiding financial service targeting. Isidore and Christie (2018) identify significant correlations among eight behavioural biases, such as overconfidence and regret aversion. Kleinubing Godoi et al. (2005) categorizes loss aversion phenomena, highlighting the psychological aspects of financial decisions. Magi (2009) tackles the equity home bias puzzle through a behavioural finance approach, showing the impact of loss aversion and narrow framing. Joshi et al. (2022) investigate how gender moderates the impact of biases like status quo and optimism on Indian investors. Lastly, Raut et al. (2020) review the psychological and social factors, such as herding and information cascades, that influence individual decision-making in financial markets.

Together, these studies offer a thorough comprehension of how diverse behavioural biases impact investing decisions and portfolio choices in different market conditions and among different types of investors.

Bibliographic Cluster 3: Herd Behaviour of Investors

Cluster 3 includes nine papers with 322 citations, focusing on herding behaviour and peer effects in financial markets. Bursztyn et al. (2014) is the most cited paper, accounting for 46.58% of the cluster’s citations, and explores social learning and utility as key drivers of peer effects and herding in financial decisions. This cluster splits into two subgroups: one focuses on herding among different investor types—retail, professional and mutual fund investors—highlighting that professionals herd less than amateurs (Venezia et al., 2011), mutual fund managers herd in high-cap stocks (Theriou et al., 2011) and domestic investors mimic foreign investors due to perceived superior forecasting skills (Hasan & Al-Dahan, 2019). The other subgroup investigates herding under various market conditions, showing correlations with oil market speculation (Balcılar et al., 2017), asymmetries between rising and falling markets (Dang & Lin, 2016), significant herding during volatile bearish markets (Ouarda et al., 2013), herding in GCC markets during rises (Chaffai & Medhioub, 2018) and herding in shipping stocks with sectoral spillovers (Syriopoulos & Bakos, 2019).

Table 6.

Overview of Identified Bibliographic Coupled Clusters and Emerging Areas.

Cluster	Themes	TP	TC	TC/TP	Future research questions
1	Investor behaviour and asset pricing models	19	521	27.42	Utilization of prospect theory within asset pricing, strategies for maximizing revenue and assessment of risk tolerance in diverse investment options like cryptocurrencies and exchange-traded funds. Role of data analytics and machine learning techniques to understand patterns in trading behaviour and investor sentiment. How do sentiment analysis techniques help to gauge market sentiment from social media, news articles and other sources to predict market movements? What is the impact of long-term investing behaviour, including retirement savings, wealth accumulation and behavioural biases, on long-term financial planning?
2	Behavioural biases and portfolio choices	17	340	20	How can automated investment services mitigate or exacerbate behavioural biases among investors? Exploring the connection between cognitive biases and the process of making investment choices, considering various mediating and moderating variables.
3	Herd behaviour of investor	9	322	35.77	Investigating the practice of herding as applied to combined stocks from various markets utilizing a global dataset. What speculative behaviour, irrational exuberance and collective herding contribute to market dynamics and systemic risks? Designing appropriate methodologies for evaluating various types of herding behaviour, including silent and spurious herding.
4	Investor’s personality and stock trading behaviour	7	91	13	How do ethical considerations and sustainability preferences influence investor decisions in the stock market?
5	Bubbles in the stock market	2	140	70	What are the factors contributing to the crashes and bubbles in the stock market? Why is understanding investor behaviour crucial for explaining market bubbles and crashes?

Bibliographic Cluster 4: Investor’s Personality and Stock Trading Behaviour

Cluster 4 consists of seven documents with 91 citations in total. The most cited work is Tauni et al. (2017b) with 21 citations (23.07% of the cluster’s citations), and the studies by Tauni et al. (2017a, 2017b, 2017c) each have 224 relational ties, the highest in all five clusters. This cluster focuses on how investors’ personality traits influence trading behaviour.

However, Tauni et al. (2017b) found that investors with openness and neuroticism trade more frequently when receiving financial advice, while extraverted and conscientious investors trade less. Other studies in the cluster confirm that trading frequency is affected by the personality traits of both investors and their advisers. Financial information sources like word-of-mouth and specialized press also impact trading behaviour based on personality traits. Oehler et al. (2018) showed that extraverted individuals buy assets at higher prices in experimental markets. Shiva et al. (2020) highlighted the influence of media and smartphone-delivered information on retail investors, leading to overtrading due to fear and inconvenience.

Bibliographic Cluster 5: Bubbles in the Stock Market

Cluster 5 comprises two documents. It includes the work of Jiang et al. (2010) and Yan et al. (2012) and received 140 citations. Cluster 5’s highlight was Jiang et al. (2010) with 124 citations (88.57% of the cluster’s citations), and both of these articles received 10 relational ties. The studies in this cluster deal with the bubble diagnosis and forecast of the stock market crash using behavioural finance theories. Jiang et al. (2010) analyse financial bubbles in the Chinese stock market during 2005–2007 and 2008–2009, using the LPPL (log-periodic power law) model to identify bubble dynamics like faster-than-exponential price increases. Yan et al. (2012) introduced the concept of a ‘leverage bubble’, emphasizing the role of leverage in financial crises and using the repo market size as a proxy for system-wide leverage to predict leverage crashes, such as the one in early 2008.

Co-word Analysis

Our fourth research question identifies the key concepts associated with investors’ financial behaviour. Therefore, we conducted a co-word analysis to find the most frequently used keywords in the title and abstract of articles and the emerging trends in this area. When two keywords appear in the same sentence, they interact and form a network. The higher the frequency of co-occurrences, the more closely they are related. If specific keywords appear frequently, the research theme expressed by keywords is the hotspot in a particular research field (Chen et al., 2019).

A network of co-occurrence of keywords was created in the VOSviewer software, which identified 1,221 keywords used by the authors in this field and revealed the most explored themes. To analyse the data efficiently, 62 keywords, with a minimum frequency of five, were selected, and a network map was created, as represented in Figure 5. On the map, it is evident that the subjects are dominated by five interrelated clusters of topics: first cluster (inefficiency and bubbles in the stock market), second cluster (technological advancement in the financial market), third cluster (behavioural aspect in investment decisions), fourth cluster (prospect theory and cognitive bias) and fifth cluster (stock trading behaviour).

The metrics of keywords, which represent the primary themes investigated within each co-word (thematic) cluster resulting from the co-word analysis, have been outlined in Table 7. These keywords show the content of the articles and may indicate a current research trend in the area (Strozzi et al., 2017). Three network metrics are presented to enhance the findings produced by the co-word analysis. These metrics include the average publication year (APY), which signifies the level of coldness (least recent) and hotness (more recent) of the keyword; occurrence (OC), which demonstrates how often the keyword appears in the corpus; and degree of centrality (DG), which reveals the number of relational connections linked to the keyword.

Co-word Cluster 1: Inefficiency and Bubbles in the Stock Market

Research in cluster 1 focused on market inefficiency and stock market bubbles. The keywords relating to behavioural finance (OC: 163), stock market (OC: 30), investment (OC: 25), investor sentiment (OC:16) and overconfidence (OC:16) are the most popular in this cluster. However, the APY for market efficiency (APY: 2012) is the lowest, suggesting that it is among the oldest and widely researched topics in this cluster. ‘COVID-19’ (APY: 2018) has the highest average publication year, indicating that it is a topic that has received more academic attention recently than the other topics in the cluster. The keywords within this cluster collectively address the concepts of market efficiency and behavioural finance (including sentiments, overconfidence, herding and investor psychology), as well as the financial crisis caused by COVID-19. These identified elements suggest that the principles of behavioural finance, along with the impact of the COVID-19 pandemic, may result in market distortions and inefficiencies, ultimately leading to the formation of market bubbles (Navratil et al., 2021). The research in this cluster contributes to the theoretical comprehension of behavioural finance by elucidating the influence of behavioural factors on the investment decisions of investors amidst a crisis, particularly the COVID-19 pandemic (Bogdan et al., 2022; Mohanty et al., 2023) and leading to speculative bubble formation in financial markets (De Bondt, 2018).

Co-word Cluster 2: Technological Advancement in the Financial Market

Cluster 2 comprises keywords that reflect technological advancements in the financial market. The most popular keywords in this cluster are finance (OC:30), herding (OC:15), behavioural economics (OC:13), electronic trading (OC:10) and computer simulation (OC:10). Starting with computer simulation (APY:2007), research in this cluster progresses to recent topics such as electronic trading (OC:2017) and asset pricing (OC:2017). These keywords collectively form a thematic cluster that focuses on understanding market behaviour, decision-making biases and technological advancements in financial markets. The article with the most popular keywords in this cluster focuses on the improvement of stock index trend prediction accuracy through the application of machine learning techniques like support vector machines and neural networks (Jiang et al., 2021). Moreover, the utilization of artificial intelligence for identifying herding behaviour in the financial market introduces an innovative method for examining investor actions (Rique et al., 2019).

Co-word Cluster 3: Behavioural Aspect in Investment Decisions

Cluster 3 consists of keywords that reflect the behavioural aspect of investment decision-making. Investments (OC:64), financial market (OC:59), commerce (OC:54), behavioural research (OC:26) and investor behaviour (OC:15) represent the most popular keywords in this cluster. The integration of these keywords highlights the behavioural dimension of investors during their investment decision-making in the stock market. Sentiment analysis (APY:2021) is the hottest topic in this cluster and artificial intelligence (APY: 2016) is the oldest one. The research conducted within this cluster contributes to the existing body of knowledge by illustrating the different forms of investor behaviour (Chen et al., 2017), such as investor sentiment (Rupande et al., 2019; Zhang et al. 2019), behavioural biases (Sahi & Arora, 2012) and investor personality (Tauni et al., 2017b).

Co-word Cluster 4: Prospect Theory and Cognitive Bias

Cluster 4 contains keywords that pertain to prospect theory and cognitive bias in the investment decision-making process. The most popular keywords in this cluster are decision-making (OC:25), investment decisions (OC:13), cognitive bias (OC:11) and prospect theory (OC:8). The average publication year is lowest for investors (APY:2015), indicating that this is widely researched topic, while financial literacy comes out to be the most recent topic. However, the publications in this cluster explore the essence of prospect theory, which lies in providing a robust framework for analysing decision-making in situations of uncertainty, incorporating cognitive biases that influence how individuals assess risks, evaluate outcomes and make choices (Velumoni & Rau, 2016). The theory’s findings highlight the role of cognitive biases in shaping investor behaviour and market phenomena.

Table 7.

Co-word Clusters on Investor Behaviour.

Cluster	APY	OC	LS
Cluster 1: Inefficiency and bubbles in the stock market
Behavioural finance	2017	163	329
Capital flow	2017	6	27
China	2020	6	26
Chinese stock market	2018	9	31
COVID-19	2022	10	20
Emerging markets	2019	6	15
Empirical analysis	2020	5	21
Financial crisis	2017	10	24
Financial market	2014	14	69
Financial system	2016	11	37
Herd behaviour	2020	6	21
Individual investors	2020	7	15
Investment	2016	25	99
Investor psychology	2016	6	30
Investor sentiment	2018	16	53
Market efficiency	2012	7	9
Overconfidence	2018	16	53
Overreaction	2015	5	12
Stock market	2017	30	132
volatility	2017	6	13
Cluster 2: Technological advancement in the financial market
Artificial financial markets		6	44
Asset pricing	2017	7	27
Behavioural economics	2013	10	44
Computational methods	2011	5	52
Computer simulation	2007	5	36
Costs	2015	6	48
Economics	2014	13	72
Efficient market hypothesis		6	32
Electronic trading	2017	10	84
Finance	2015	30	170
Financial data processing	2011	9	67
Herding	2016	15	44
Loss aversion	2015	8	19
Multi-agent systems	2009	5	44
Cluster 3: Behavioural aspect in investment decisions
Artificial intelligence	2016	7	39
Behavioural research	2018	26	167
Big data	2019	6	35
Commerce	2017	54	335
Financial markets	2019	59	301
Forecasting	2018	8	46
Investments	2018	64	374
Investor behaviour	2017	15	51
Investor sentiments	2017	8	53
Machine learning	2019	7	36
Sentiment analysis	2021	6	48
Social media	2020	6	39
Social networking(online)		6	48
Stock price	2019	7	41
Cluster 4: Prospect theory and cognitive bias
Behavioural finances	2021	7	35
Cognitive bias	2019	11	62
Decision-making	2017	25	163
Decision theory	2018	6	32
Financial literacy	2022	5	13
Heuristics	2019	5	24
Investment behaviour	2020	5	13
Investment decisions	2017	13	49
Investors	2015	5	21
Prospect theory	2018	8	24
Cluster 5: Stock Trading Behaviour
Disposition effect	2017	5	14
human	2016	6	28
Trading behaviour	2016	6	14

Source: Chandra et al. (2022).

Note: APY: average publication year; OC: occurrence; LS: link strength.

Co-word Cluster 5: Stock Trading Behaviour

Cluster 5 consists of keywords that describe the stock trading behaviour of investors. In this cluster, trading behaviour (OC:6; APY:2016) and human (OC:6; APY:2016) are the popular keywords as well as the oldest ones. However, the disposition effect (APY:2017) is found to be more recent. The studies in this cluster report that stock trading behaviour in financial markets encompasses diverse sub-themes reflecting the multifaceted nature of investor decision-making, trading strategies, market dynamics and regulatory considerations. Each sub-theme contributes to understanding the complexities of stock trading and its impact on market efficiency, liquidity and overall market functioning.

Conclusion

This article aimed to identify and visualize the stock market’s knowledge structure of investors’ financial behaviour with the assistance of bibliometric analysis. Some of the crucial points of interest included the identification of the publication trends of the area, journal quality analysis, author influence, citation analysis and keyword statistical analysis. This study shows the growth of influential publications and adds to the area by elucidating the relationships between the significant impact works.

In terms of the growth of publications over the period covered (1999–2023), it was seen that this topic attracted the attention of researchers, with publications seeing a growth spurt from 2016 onwards and a continuing upward trend.

Of the main studies that addressed this area of investors’ behaviour, Hirshleifer (2003) (with 464 citations) and Bursztyn et al. (2014) (with 237 citations) emerged as the most cited studies in Scopus. This demonstrates that these authors are the most significant in this field in terms of both their body of work and influence. It also analysed the most prolific and well-cited writers in this area. David Hirshleifer and Siew Hong Teoh turned out to be the most influential authors regarding citations received. Among their contributions, they promoted research on how alternate theories may support the behaviour of analysts, investors and firms. Furthermore, Hiroshi Takahashi and Muhammad Zubair Tauni emerged as contributors to the maximum number of articles published in this area.

Considering the journals in which research on investors’ behaviour has been published, Qualitative Research in Financial Markets emerged as the journal which had the most number of articles on the topic (six articles). The subject area of this journal is related to general economics, econometrics and finance. The majority of research published in the Journal of Quantitative Finance focuses on asset pricing models, anomalies and investor behaviour in the different conditions of the stock market. Therefore, specialized journals like these are expected to publish more articles in this area in the coming years.

Based on the keyword analysis, the topics that dominate the literature on investor behaviour are inefficiency and bubbles in the stock market, technological advancement in the financial market, behavioural aspects in investment decisions, prospect theory and cognitive bias and stock trading behaviour. The analysis further revealed that investor sentiment, herd behaviour, heuristics, the impact of COVID-19 and social networking in financial markets are some hot (most recent) topics. These topics show the direction for future research studies.

Further, the substantial contribution of this research has been the finding that emerging areas in this field of research can be classified. A bibliographic coupling of articles was performed for this purpose, in which we obtained five different clusters. These clusters explain the emerging areas in the investors’ financial behaviour field. Therefore, the main research areas within the sample are (a) investors’ behaviour and asset price models, (b) behavioural biases and portfolio choice of investors, (c) herding behaviour of investors, (d) investors’ personality and stock trading behaviour and (e) bubble in the stock market.

Overall, our research has wide-ranging implications for researchers, policymakers and individuals because the results can serve as a roadmap to navigate research trends and inform them about the publishing and citation status of various sources. The study highlights the data to support the researchers’ collaborative publications with prominent authors. From a managerial viewpoint, the study will help focus on the most cited articles and the most prestigious journals to obtain useful and important insights about the field. Similarly, this research provides extremely relevant data for researchers in the domain of investors’ behaviour to enable them to enrich behavioural finance research.

Limitations or Future Scope

This research has theoretically expanded the area of investors’ behaviour by demonstrating the route of scientific production, along with the significant works, authors and so on. The study is not without limitations though. It extracted the existing literature only from the Scopus database. Therefore, further future studies may use various other databases to standardize the results. This research did not consider review papers, retracted articles and erratum articles. The inclusion of these studies may add some value. In addition, VOSviewer software was used to map this field of research; other software like Cite Space, Publish or Perish, BibExcel, Bibliometrix R, HistCite, Eigenfactor score and Scholar meter can be used in future studies to provide more valuable insights.

Supplemental Material

Supplemental material for this article is available online.

Footnotes

Declaration of Conflicting Interests

The authors declare that there is no conflict of interest.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

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