A systematic review and research agenda on the causes and consequences of financial overconfidence

Singh, Dharmendra; Malik, Garima; Jain, Prateek; Abouraia, Mahmoud Article A systematic review and research agenda on the causes and consequences of financial overconfidence Cogent Economics & Finance Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Singh, Dharmendra; Malik, Garima; Jain, Prateek; Abouraia, Mahmoud (2024) : A systematic review and research agenda on the causes and consequences of financial overconfidence, Cogent Economics & Finance, ISSN 2332-2039, Taylor & Francis, Abingdon, Vol. 12, Iss. 1, pp. 1-19, https://doi.org/10.1080/23322039.2024.2348543 This Version is available at: https://hdl.handle.net/10419/321487 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Cogent Economics & Finance ISSN: 2332-2039 (Online) Journal homepage: www.tandfonline.com/journals/oaef20 A systematic review and research agenda on the causes and consequences of financial overconfidence Dharmendra Singh, Garima Malik, Prateek Jain & Mahmoud Abouraia To cite this article: Dharmendra Singh, Garima Malik, Prateek Jain & Mahmoud Abouraia (2024) A systematic review and research agenda on the causes and consequences of financial overconfidence, Cogent Economics & Finance, 12:1, 2348543, DOI: 10.1080/23322039.2024.2348543 To link to this article: https://doi.org/10.1080/23322039.2024.2348543 © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group Published online: 03 Jun 2024. Submit your article to this journal Article views: 3141 View related articles View Crossmark data Citing articles: 5 View citing articles Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oaef20 FINANCIAL ECONOMICS | REVIEW ARTICLE A systematic review and research agenda on the causes and consequences of financial overconfidence Dharmendra Singh a , Garima Malik b , Prateek Jain c and Mahmoud Abouraia a a Department of Business and Economics, Modern College of Business and Science, Bawshar, Oman; b Department of Marketing, Birla Institute of Management Technology, Noida, India; c Department of Strategy Management, Birla Institute of Management Technology, Noida, India ABSTRACT The literature on overconfidence has witnessed prolific growth since the beginning of the century. This context underscores the necessity to comprehend and categorize an increasingly diverse body of overconfidence research within the financial domain. This study reviews existing literature on financial overconfidence from its inception to the present with a detailed review of 132 articles from 84 journals by examining theories, context, and methods (TCM) used in overconfidence research. Our review unpacks significant themes (i.e. determinants of overconfidence, overconfidence and risk-taking, overconfidence measures and type of investors, overconfidence in a volatile market, overconfidence, and personal financial behavior). We propose a pertinent research framework to investigate the less investigated aspects of financial overconfidence and suggest future research direction. IMPACT STATEMENT The significance of this paper lies in its potential to deepen our understanding of how psychological biases and significant overconfidence influence investment behavior and market outcomes. By synthesizing findings from multiple studies, this literature review highlights common themes, identifies gaps in knowledge, and suggests avenues for future research. Ultimately, insights gained from such a review can inform investors, financial professionals, and policymakers about the importance of recognizing and addressing overconfidence in investment decision-making processes. This understanding can lead to more informed and rational investment strategies, potentially mitigating the adverse effects of overconfidence on individual investors and market efficiency. ARTICLE HISTORY Received 10 November 2023 Revised 19 March 2024 Accepted 11 April 2024 KEYWORDS Overconfidence; bibliometric; decision-making; clusters; investor behavior REVIEWING EDITOR Dr David McMillan, University of Stirling, United Kingdom of Great Britain and Northern Ireland SUBJECTS Finance; Business, Management and Accounting; Cognitive Psychology 1. Introduction Overconfidence bias is a cognitive bias that refers to the tendency of investors to overestimate their knowledge, skills, and abilities when making financial decisions (Kahneman & Riepe, 1998). Overconfidence bias has been studied extensively in psychology and was introduced to finance in the late 20th century; overconfidence among investors is a crucial notion in behavioral finance (Michailova & Schmidt, 2016; Statman et al., 2006). Pioneering research in behavioral economics and finance, such as the work of Barber and Odean (2001) and Kahneman & Riepe (1998), highlighted the role of overconfidence in shaping economic and financial decisions. Recognizing and dealing with overconfidence bias is critical for investors; overconfidence bias can hurt long-term investing performance. Investors who assume they are more informed or skilled than they are may take undue risks or make poor decisions (Du & Budescu, 2007; Pikulina et al., 2017). Overconfidence bias can result in various behavioral outcomes, including excessive trading (Fellner-R€ ohling & Kr€ ugel, 2014; Wilaiporn et al., 2021), failure to seek professional advice (Hsu, 2022) that may result in inadequate portfolio diversification (Pak & Chatterjee, 2016), increasing exposure to individual risk, and reduced value of overconfident traders (Odean, 1998). Financial overconfidence can result from various antecedents or underlying variables contributing to people’s tendency to overestimate their skills. Understanding the causes of overconfidence bias is critical CONTACT Garima Malik [email protected] Department of Marketing, Birla Institute of Management Technology, Noida, India ß2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. COGENT ECONOMICS & FINANCE 2024, VOL. 12, NO. 1, 2348543 https://doi.org/10.1080/23322039.2024.2348543 for individuals and organizations aiming to reduce its harmful consequences. The overconfidence of a trader changes considerably with his successes and losses (Gervais & Odean, 2001). The dominant factors affecting overconfidence are social comparison, experience (Pak & Chatterjee, 2016), and demographic factors, especially gender (Baker et al., 2019; Jiang et al., 2020), income (Ansari et al., 2023). Overconfidence affects the financial market; overconfidence among investors can contribute to market booms, crashes, and inefficiencies (Bouteska et al., 2023). Their excessive trading and risk-taking might aggravate market volatility (Filbeck et al., 2017). The synthesis of the diverse literature on financial overconfidence is essential to understanding its causes, effects, and potential treatments. We have conducted a systematic literature review (SLR) on overconfidence. We employed the Theory Context-Methodology (TCM) framework proposed by Paul and Rosado-Serrano in 2019 to comprehend overconfidence’s theoretical and empirical aspects. In light of this framework, our review article aims to investigate the following research questions. RQ1. How has the literature on overconfidence evolved? RQ2. What different theoretical perspectives are applied in the overconfidence literature? RQ3: Which research contexts have been explored in the study of overconfidence in behavioral finance? RQ4. What are the various methods utilized in overconfidence research in behavioral finance? RQ5. What are the future research agendas for financial overconfidence based on the TCM framework? This review will help promote a greater understanding of the topic by proposing a research framework and future research directions. The rest of the paper is structured as follows: it begins with a discussion of the background and definitions of overconfidence, followed by a discussion of the methodology and bibliometric analysis. In conclusion, the paper presents the proposed framework and discusses prospective research directions in overconfidence. 2. Background of financial overconfidence: definition and measures Overconfidence is multidimensional and dynamic (Deaves et al., 2009). Overconfidence is one of the most common biases among individual investors (Jain et al., 2019; Kansal & Singh, 2018). ‘Three definitions of overconfidence are used in the psychological literature: overestimation, over-placement, and calibration of subjective probabilities’(Olsson, 2014, p.1). Using ‘overestimation’to describe overconfidence means comparing a person’s actual performance to how they perceive they performed (Moore & Healy, 2008). The second measure of overconfidence is ‘overplacement’, which is assessed by comparing an individual’s performance with that of others. Both overestimation and over-placement denote a tendency to excessively estimate one’s ability (Pikulina et al., 2017). Overprecision or miscalibration is the third sign of overconfidence that investors most often show. Traders overestimate how precise a private signal is and underestimate how volatile an asset is (Merkle, 2017). Table 1 showcases the most cited and impactful articles in the financial overconfidence field based on the citations. Among the top influential articles, one noteworthy study was conducted by Barber and Odean (2001) that posits that overconfident investors engage in excessive trading and men are more Table 1. List of seminal papers in the field. Seminal papers in the field of overconfidence Reference ‘Judgment Under Uncertainty: Heuristics and Biases’Tversky et al. (1982) ‘Advances In Prospect Theory: Cumulative Representation of Uncertainty’Tversky and Kahneman (1992) ‘Volume, Volatility, Price, And Profit When All Traders Are Above Average’Odean (1998) ‘Are Investors Reluctant to Realize Their Losses?’Odean (1998) ‘Aspects Of Investor Psychology’Kahneman and Riepe (1998) ‘Do Investors Trade Too Much?’Odean (1999) ‘Trading Is Hazardous to Your Wealth: The Common Stock Investment Performance of Individual Investors’Barber & Odean (2001) ‘Boys Will Be Boys: Gender, Overconfidence, And Common Stock Investment’Barber and Odean (2001) ‘Learning to be Overconfident’Gervais and Odean (2001) ‘Investor Overconfidence and Trading Volume’Statman et al. (2006) ‘Overconfidence And Trading Volume’Glaser and Weber (2007) ‘The Trouble with Overconfidence’Moore and Healy (2008) ‘Sensation Seeking, Overconfidence, And Trading Activity’Grinblatt and Keloharju (2009) ‘Financial Literacy and Stock Market Participation’Van Rooij et al. (2011) ‘Prospect Theory: An Analysis of Decision Under Risk’Kahneman & Tversky, (2013) Source: Authors compilation with the help of Scopus Database. 2 D. SINGH ET AL. prone to overconfidence. An influential work by Gervais and Odean (2001) formulated a multiperiod market model and explained the development of overconfidence bias. Furthermore, Glaser and Weber (2007) suggested that overconfident investors trade more than rational investors. In a related vein, the study by Barber and Odean (2001) explains that high trading levels result in poor performance for overconfident retail investors and enhance market depth (Odean, 1998). The research papers listed comprehensively explore behavioral economics and investor psychology. Kahneman and Tversky’s seminal work on prospect theory laid the foundation for understanding how individuals make decisions in uncertain situations. Odean’s(1998) delves into the psychological reluctance of investors to cut their losses. The study by Statman et al. (2006) sheds light on the impact of overconfidence on trading behavior. Meanwhile, Tversky et al. (1982) discuss cognitive shortcuts and biases that influence decision-making. Tversky and Kahneman (1992) expand on their original prospect theory, while Grinblatt and Keloharju (2009) investigate how sensation-seeking tendencies affect trading. Moore and Healy (2008) offer insights into the challenges overconfidence poses. Odean (1999) examines the phenomenon of excessive trading. Lastly, Kahneman and Riepe (1998) delve into the various facets of investor psychology. Together, these papers provide a comprehensive understanding of the complexities of human decisionmaking and its impact on financial markets. The central message here underscores that excessive trading due to overconfidence can harm one’s wealth. Van Rooij et al. (2011) examined financial literacy’s relationship to the stock market and certified that financial literacy affects financial decisionmaking. In conclusion, the seminal research undertaken on overconfidence has provided valuable insights into finance and decision-making. 3. Bibliometric research method Our research is limited to peer-reviewed papers retrieved from the Scopus database, frequently utilized for systematic literature review (Singh & Malik, 2022). Initially, 1,566 articles were extracted from the Scopus database using the keywords in the field (Title-Abstract-keywords). Searches for (‘overconfidence’) AND (‘finance’OR ‘credit’OR ‘debt’OR ‘stress’OR ‘invest’OR ‘risk’OR ‘literacy’OR ‘advice’OR ‘behavior’OR ‘financial knowledge’OR ‘well-being’) were made in the database, combined with AND NOT (‘CEO’OR ‘manager’OR ‘CSR’OR ‘corporate social responsibility’OR ‘firm’). The ‘and not’search criteria were included to exclude articles that highlight managerial overconfidence in corporate governance and social responsibility. Managers’overconfidence can affect investment decisions, business strategy, and the overall course of the organization. Meanwhile, an individual’s overconfidence is considerably more comprehensive and relevant in its implications for their risk-taking, investment, and saving habits. Therefore, the decision was taken to restrict the research scope to individual overconfidence in financial decision-making rather than managerial overconfidence. After the exclusion of conference papers, proceedings, and non-English journal articles, the count became 1179. Further, the subject area filter was imposed and limited to Economics, Econometrics, and Finance; Business, Management, and Accounting; Social Sciences, and Psychology; this reduced the paper count to 774. The manual screening was done by reading abstracts, keywords, and, in some cases, full articles to check the relevance of the retrieved articles. After all the filters and screening, the final sample of 132 studies from 2001 to Aug 31, 2023, was used for the analysis. The critical information about the data set is displayed below (Table 2). 4. Analysis The current review study covers productivity and impact of authors, sources, and documents; coverage of theories used; bibliographic coupling to unearth the significant themes; and conceptual coverage through keyword co-occurrence. Table 2. Overview of the sample. Sources: 84 Documents: 132 Annual Growth Rate: 14.04% Authors: 326 International Collaboration: 19.7% Author Keywords:352 References:6242 Average Citations per document:21.73 Source: Scopus Database. COGENT ECONOMICS & FINANCE 3 4.1. Performance analysis Table 2 provides a comprehensive overview of the sample papers included in this systematic literature review. The table presents data on several aspects: the total number of sources, the annual growth rate of the publications, the number of authors, international collaboration, the number of author keywords, and average citations per document in the field of overconfidence bias. Figure 1 shows the progression of the literature on investor overconfidence bias for 2001–2023. Interest in the research on overconfidence bias picked up after 2012. There is an increasing trend in the publications, representing this as the growing field of research; close to 60% of the papers have been published in the last five years. Table 3 presents an overview of the core zone sources in the given domain as per Bradford’s law of scattering (1934). A core zone is a small group of journals with the most relevant articles widely cited. Nine journals represent the research field’s core zone; these are the most productive and highly cited. The journals have been ranked in terms of productivity; the top three journals are Qualitative Research in Financial Markets (n ¼8, 216 citations, h-index 6), Journal of Behavioral and Experimental Finance (n ¼7, 93 citations, h-index 6), and Journal of Economic Behavior and Organization (n ¼5, 189 citations, h-index 5). The following two journals are Finance Research Letters and Review of Behavioural Finance.Table 4 indicates the most influential authors in overconfidence research. Deaves and Luders are the most influential authors, each with 234 citations, followed by Maciejovsky (133 citations). 4.2. Keyword analysis In this part of the study, the most frequently used author keywords in the overconfidence literature have been analyzed to study the conceptual structure of the given research domain (Syed et al., 2023). Figure 2 depicts the map of keyword co-occurrence for the top 30 keywords with a minimum of 3 occurrences. The most co-occurring keywords in this literature are financial literacy, behavioral biases, Figure 1. Growth of publications. Source: Scopus Database. Table 3. Core sources in the field. Core sources in the research domain Rank Freq TC H-Index Qualitative Research in Financial Markets 1 8 216 7 Journal of Behavioral and Experimental Finance 2 7 93 6 Journal of Economic Behavior and Organization 3 5 189 5 Finance Research Letters 4 5 89 3 Review of Behavioral Finance 8 4 122 3 Journal of Behavioral Finance 5 4 72 4 Journal of Economic Psychology 6 4 64 4 Research In International Business and Finance 7 4 61 3 Accounting and Finance 9 3 67 3 Source: Scopus Database. 4 D. SINGH ET AL. investment decisions, demographic factors, especially gender, risk-taking, trading activity, financial advice-seeking, financial behavior, and personality traits of the investors. In Figure 2, the close distance of two terms and the thickness of the linking lines represent how closely they have been used in the literature, while the size of the nodes shows how frequently they co-occurred as keywords. Furthermore, Figure 3 illustrates the keywords utilized most frequently over the past decade with at least two occurrences. Figure 3 emphasizes the most recent patterns in the literature, whereas Figure 2 is a keyword-based map from 2000. Figure 3 shows that trading activity, risk-taking, personality traits, portfolio selection, investment decisions, herding bias, financial literacy, behavioral biases, and behavioral factors have frequently been used in overconfidence research in the decade. However, financial advice, professional overconfidence, and overconfidence in cryptocurrency have received relatively little attention in overconfidence research. Figure 2. Keywords co-occurrence analysis. Source: Authors compilation with the help of Scopus Database. Table 4. Prominent authors in financial overconfidence. Authors TC h_index g_index m_index NP PY_start Deaves R 234 2 2 0.133 2 2009 L€ uders E 234 2 2 0.133 2 2009 Maciejovsky B 133 2 2 0.091 2 2002 Goyal N 130 2 2 0.25 2 2016 Kumar S 130 2 2 0.25 2 2016 Schmidt U 93 2 3 0.182 3 2013 Singh S 42 2 2 0.333 2 2018 Gerrans P 42 2 2 0.4 2 2019 Rahman M 32 2 3 0.5 3 2020 Das N 29 2 2 0.5 2 2020 Source: Scopus Database. COGENT ECONOMICS & FINANCE 5 4.3. Thematic and influence structure analysis through bibliographic coupling Bibliographic coupling is used to get the significant clusters in each literature (Syed et al., 2023). We conducted bibliographic coupling with a minimum of 12 citations per article, resulting in 48 articles having the most significant total link strength grouped into five clusters (Figure 4). 4.3.1. Cluster 1: Determinants of overconfidence This cluster includes seventeen publications on overconfidence and investment decision-making behavior. This cluster demonstrates the crucial factors shaping and developing investor’s overconfidence bias. The determinants of the overconfidence bias can be listed as demographic variables (age, gender, income, occupation), financial literacy (Kawamura et al., 2021), personality traits (Akhtar & Das, 2020; Durand et al., 2013; Kleine et al., 2016), employment status (Rahman & Gan, 2020), profession (Prosad et al., 2015), investment experience (Kansal & Singh, 2018) and past investment performance (Parveen et al., 2020). Gender is the most dominant demographic factor affecting investor overconfidence (Baker et al., 2019; Kansal & Singh, 2018), where males are more prone to overconfidence (Kumar & Goyal, 2016). Financial literacy has proved to be a significant positive factor for overconfidence (Kawamura et al., 2021). Meier and De Mello (2020) confirmed that overconfidence is unstable; with contradictory feedback, it vanishes, and with supportive signals, it returns. Figure 3. Author keywords in the last 10 years. Source: Authors compilation with the help of Scopus Database. 6 D. SINGH ET AL. 4.3.2. Cluster 2: Overconfidence & risk taking The collective findings of this cluster shed light on the complex dynamics between overconfidence, perception of risk, and investment patterns. The cluster comprises eleven publications investigating the interplay of overconfidence and risk-taking and their impact on financial decision-making (Merkle, 2017). The cluster is mainly focused on the two consequences of overconfidence: the risk-taking behavior of the investor and excessive trading. The research examines the impact of financial overconfidence on individuals’risk perception and subsequent behavior (Broihanne et al., 2014). Moreover, the influence of traders’overconfidence on their trading behavior may lead to increased trading activity and unfavorable results (Fellner-R€ ohling & Kr€ ugel, 2014). A fascinating finding states that overconfidence in self-financial skills increases with age and affects riskiness and equity percentage in the retirement portfolio (Pak & Chatterjee, 2016). 4.3.3. Cluster 3: Overconfidence measures and type of investors This cluster with ten articles emphasizes the complexity of measuring overconfidence, its dynamism, the unrelatedness in the different measures of overconfidence, and the degree of overconfidence regarding types of investors. Kirchler and Maciejovsky (2002) compared the two measures of overconfidence: subjective confidence intervals and the difference between objective and subjective certainty in the experimental asset market. The overconfidence was more frequent when subjective overconfidence intervals were used, and this overconfidence was positively correlated with the trader’s experience. Among the various measures of overconfidence, which measure is best and closer to financial behavior needs to be clarified (Deaves et al., 2009). The evidence on the dynamics of overconfidence is mixed and needs interpretation (Deaves et al., 2010). Overconfidence rises with wrong investment decisions and task complexity (Dittrich et al., 2005). The degree of overconfidence varies between the investor groups: Investment advisors were identified as the most overconfident group, followed by retail investors and institutional investors (Menkhoff et al., 2013). 4.3.4. Cluster 4: Overconfidence in a volatile market This cluster comprises five papers focusing on the degree of overconfidence during the financial crisis and very highly volatile markets (Biais et al., 2005). The markets with high overconfidence have witnessed price bubbles and intense trading volumes (Michailova & Schmidt, 2016). Experts have shown better forecasting skills than non-experts during financial crises (Zaleskiewicz, 2011). Additionally, the cluster emphasizes that the overconfidence of venture capitalists leads to wrong investment decisions Figure 4. Clusters identified through bibliographic coupling. Note. Colour theme of clusters: Cluster one: red; Cluster two: green; Cluster three: blue; Cluster four: light green; Cluster five: purple. Source: Authors compilation with the help of Scopus Database. COGENT ECONOMICS & FINANCE 7 8 Implications This study highlights the multidimensional nature of overconfidence in financial decision-making by synthesizing evolving ideas and integrating the bibliometric and TCM frameworks. This improved comprehension is critical for academics and practitioners in the financial industry. The research successfully bridges the gap between psychological theories and the financial decision-making process by incorporating the TCM frameworks. This synthesis of theoretical approaches offers a more in-depth comprehension of the cognitive processes that contribute to the development of overconfidence in individuals. This work presents the new research pathways to be investigated in financial overconfidence and opens up new possibilities in this field. Further, Investors, financial advisors, and legislators must deeply comprehend the complexities of displaying excessive confidence in one’s financial situation. When investing, it is beneficial for investors to be aware of their biases and take action to battle overconfidence in their decision-making. Being aware of their biases and trying to combat overconfidence may be found here. Financial advisers can use this information to help their clients better by giving them more educated counsel, which is made possible using the information. Investing in financial literacy programs can effectively enhance investor awareness and promote more rational decision-making. By equipping investors with the knowledge to recognize and mitigate overconfidence, policymakers can contribute to a more stable and resilient financial system (Cwynar et al., 2020;V € or€ os et al., 2021). Also, insights from behavioral economics into policy-making procedures can yield a more holistic comprehension of how psychological biases, such as overconfidence, impact economic behavior. Policymakers can design interventions that consider investors’cognitive limitations and work towards creating a more resilient financial ecosystem. With the help of this tactic, scholars who specialize in a wide range of fields are encouraged to collaborate to contribute to a more in-depth understanding of the part that behavioral biases play in finance. 9. Conclusions The current hybrid systematic review utilizes bibliometric tools and content analysis, employing the TCM framework to examine and synthesize overconfidence research. This review contributes deeper insights into critical contributors (journals, authors, articles) of financial overconfidence research, serving as a valuable reference for academic scholars and industry professionals seeking expert opinions (Merkle, 2017). The article addresses five research questions through a systematic procedure, where our review evaluates the literature, highlights performance trends, and explains the intellectual structure of overconfidence research. Furthermore, discovering five significant clusters in this research field and identifying keyword co-occurrence add robustness to the clusters’contents. Additionally, we conducted in-depth content analysis using the TCM framework, presenting the most prominent theories, geographical context, and impactful methods used in overconfidence research. 10. Limitations Even though the required protocol was followed and a comprehensive literature analysis was performed, the findings of this study are nevertheless restricted in several ways. First, only the SCOPUS database is used for bibliometric analysis research. In subsequent studies, investigators might look at articles sourced from single or numerous databases, such as Web of Science or EBSCO. Second, there is a possibility that particular research has been omitted due to the use of filters and keywords. Third, this investigation was limited to papers written in English; as a result, we may have overlooked any pertinent publications initially published in languages other than English. Author contributions Dr. Dharmendra Singh: Conceptualizing our thoughts in the manuscript, retrieving data from Scopus, conducting bibliometric analysis, and performing performance analysis. Dr. Garima Malik: Cluster Analysis and Comprehensive TCCM Analysis, Proofreading, and Formatting. Dr. Prateek Jain: Future Research Directions and Implications. Dr. Mahmoud Abouria: Revised the manuscript as per the recommended revisions. 14 D. SINGH ET AL. Disclosure statement No potential conflict of interest was reported by the author(s). About the authors Dr. Dharmendra Singh is an Associate Professor and program head (Finance) at Modern College of Business and Science, Muscat (Oman). He possesses rich professional experience of over 23 years in the field of finance. He holds a Ph.D. (Finance), professional certifications like Certified Financial Planner (CFP), an associate diploma (life insurance) from the Insurance Institute of India, and a CFA. He has published several articles & research papers in ABDC, CABS, and Scopus Q1-ranked international and refereed journals. He has published five edited books with renowned publishers. His research areas include banking, corporate finance, entrepreneurial finance, and financial markets. Dr. Garima Malik is an Assistant Professor in the Marketing area at Birla Institute of Technology Management (BIMTECH), Greater Noida. She is a Fellow of Xavier School of Management, XLRI Jamshedpur, India, and a recipient of the K.V. Raju (Narajuan Award) Gold Medal awarded to an outstanding Ph.D. student. She has more than twenty years of academic experience. Her research interests span various areas, including gamification, marketing analytics, customer engagement, destination marketing, and gaming marketing experience. She authored more than 80þ research papers. In addition to her research contributions, Dr. Malik has written five books, two of which are textbooks, and four are case study books. Dr. Prateek Jain is working as Associate Professor of Strategy & Entrepreneurship at Birla Institute of Management Technology (BIMTECH), Greater Noida. Before joining Academics he had worked in Corporate sector for 23 years in various Industries & sectors. Dr. Prateek had done his PhD from IIT Delhi and MBA from IIM Lucknow. He had done his graduation in Mechanical Engineering. Dr. Prateek had also authored 3 Books in the area of Management & Strategy. Dr. Mahmoud Abouraia is a Professor and head of the Ph.D. program at the Modern College of Business and Science, Muscat (Oman). He has published several papers in reputed journals. His research areas include banking, corporate finance, and financial markets. ORCID Dharmendra Singh http://orcid.org/0000-0003-0966-6530 Garima Malik http://orcid.org/0000-0002-3892-8299 Data availability statement The data for the study were retrieved from SCOPUS within the specified period (as mentioned in the manuscript), following the proper procedure for data retrieval, including keywords. Subsequently, a data filtering process was conducted. Anyone can retrieve the data from SCOPUS by applying the same criteria used in the study. References Abdallah, S., & Hilu, K. (2015). Exploring Determinants to Explain Aspects of Individual Investors’Financial Behaviour. Australasian Accounting, Business and Finance Journal,9(2), 4–22. https://doi.org/10.14453/aabfj.v9i2.2 Abreu, M., & Mendes, V. (2012). Information, overconfidence, and trading: Do the sources of information matter? Journal of Economic Psychology,33(4), 868–881. https://doi.org/10.1016/j.joep.2012.04.003 Adel, B., & Mariem, T. (2013). The impact of overconfidence on investors’decisions. Business and Economic Research, 3(2), 53. https://doi.org/10.5296/ber.v3i2.4200 Ahmad, F. (2020). Personality traits as a predictor of cognitive biases: The moderating role of risk-attitude. Qualitative Research in Financial Markets,12(4), 465–484. https://doi.org/10.1108/QRFM-10-2019-0123 Aini, N. S. N. & Lutfi, L. (2019). The influence of risk perception, risk tolerance, overconfidence, and loss aversion towards investment decision making. Journal of Economics, Business, & Accountancy Ventura,21(3), 401–413. https://doi.org/10.14414/jebav.v21i3.1663 Akhtar, F., & Das, N. (2020). Investor personality and investment performance: From the perspective of psychological traits. Qualitative Research in Financial Markets,12(3), 333–352. https://doi.org/10.1108/QRFM-11-2018-0116 Ang, A., Gorovyy, S., & Van Inwegen, G. B. (2011). Hedge fund leverage. Journal of Financial Economics,102(1), 102– 126. https://doi.org/10.1016/j.jfineco.2011.02.020 COGENT ECONOMICS & FINANCE 15 Ansari, Y., Albarrak, M. S., Sherfudeen, N., & Aman, A. (2023). Examining the relationship between financial literacy and demographic factors and the overconfidence of Saudi investors. Finance Research Letters,52, 103582. https:// doi.org/10.1016/j.frl.2022.103582 Baker, H. K., Kumar, S., Goyal, N., & Gaur, V. (2019). How financial literacy and demographic variables relate to behavioral biases. Managerial Finance,45(1), 124–146. https://doi.org/10.1108/MF-01-2018-0003 Bansal, R., & Singh, D. (2021). Efficiency drivers of insurers in GCC: An analysis incorporating company-specific and external environmental variables. Cogent Economics & Finance,9(1), 1922179. https://doi.org/10.1080/23322039. 2021.1922179 Barber, B. M., & Odean, T. (2001). Boys will be boys: Gender, overconfidence, and common stock investment. The Quarterly Journal of Economics,116(1), 261–292. https://doi.org/10.1162/003355301556400 Beckmann, D., Menkhoff, L., & Suto, M. (2008). Does culture influence asset managers’views and behavior?. Journal of Economic Behavior & Organization,67(3-4), 624–643. https://doi.org/10.1016/j.jebo.2007.12.001 Biais, B., Hilton, D., Mazurier, K., & Pouget, S. (2005). Judgemental overconfidence, self-monitoring, and trading performance in an experimental financial market. Review of Economic Studies,72(2), 287–312. https://doi.org/10.1111/ j.1467-937X.2005.00333.x Bourova, E., Anderson, ME., Ramsay, I., & Ali, P. (2018). Impacts of financial literacy andconfidence on the severity of financial hardship in Australia. Australasian Accounting, Business and Finance Journal,12(4), 4–23. https://doi.org/ 10.14453/aabfj.v12i4.2 Bouteska, A., Harasheh, M., & Abedin, M. Z. (2023). Revisiting overconfidence in investment decision-making: Further evidence from the US market. Research in International Business and Finance,66, 102028. https://doi.org/10.1016/j. ribaf.2023.102028 Broihanne, M.-H., Merli, M., & Roger, P. (2014). Overconfidence, risk perception and the risk-taking behavior of finance professionals. Finance Research Letters,11(2), 64–73. https://doi.org/10.1016/j.frl.2013.11.002 Chen, Y., Mandler, T., & Meyer-Waarden, L. (2021). Three decades of research on loyalty programs: A literature review and future research agenda. Journal of Business Research,124, 179–197. https://doi.org/10.1016/j.jbusres.2020.11. 057 Combrink, S., & Lew, C. (2020). Potential Underdog Bias, Overconfidence and Risk Propensity in Investor DecisionMaking Behavior. Journal of Behavioral Finance,21(4), 337–351. https://doi.org/10.1080/15427560.2019.1692843 Costa-Cordella, S., Arevalo-Romero, C., Parada, F. J., & Rossi, A. (2021). Social support and cognition: A systematic review. Frontiers in Psychology,12, 637060. https://doi.org/10.3389/fpsyg.2021.637060 Cwynar, A., Cwynar, W., Patena, W., & Sibanda, W. (2020). Young adults’financial literacy and overconfidence bias in debt markets. International Journal of Business Performance Management,21(1/2), 95–113. https://doi.org/10.1504/ IJBPM.2020.106117 Daniel, K., Hirshleifer, D., & Subrahmanyam, A. (1998). Investor psychology and security market underand overreactions. The Journal of Finance,53(6), 1839–1885. https://doi.org/10.1111/0022-1082.00077 Deaves, R., L€ uders, E., & Luo, G. Y. (2009). An experimental test of the impact of overconfidence and gender on trading activity. Review of Finance,13(3), 555–575. https://doi.org/10.1093/rof/rfn023 Deaves, R., L€ uders, E., & Schr€ oder, M. (2010). The dynamics of overconfidence: Evidence from stock market forecasters. Journal of Economic Behavior & Organization,75(3), 402–412. https://doi.org/10.1016/j.jebo.2010.05.001 Dittrich, D. A., G€ uth, W., & Maciejovsky, B. (2005). Overconfidence in investment decisions: An experimental approach. The European Journal of Finance,11(6), 471–491. https://doi.org/10.1080/1351847042000255643 Doukas, J. A., & Petmezas, D. (2007). Acquisitions, overconfident managers and self-attribution bias. European Financial Management,13(3), 531–577. https://doi.org/10.1111/j.1468-036X.2007.00371.x Du, N., & Budescu, D. V. (2007). Does past volatility affect investors’price forecasts and confidence judgements? International Journal of Forecasting,23(3), 497–511. https://doi.org/10.1016/j.ijforecast.2007.03. 003[Mismatch[InsertedFromOnline Durand, R., Newby, R., Tant, K., & Trepongkaruna, S. (2013). Overconfidence, overreaction, and personality. Review of Behavioral Finance,5(2), 104–133. https://doi.org/10.1108/RBF-07-2012-0011 Duxbury, D. (2015). Behavioral finance: insights from experiments I: Theory and financial markets. Review of Behavioral Finance,7(1), 78–96. https://doi.org/10.1108/RBF-03-2015-0011 Duy Bui, L., Chi Le, T., Ngoc Quang, A. H., & Wong, W.-K. (2021). Determinants of the possibilities by investors’risktaking: Empirical evidence from Vietnam. Cogent Economics & Finance,9(1), 1917106. https://doi.org/10.1080/ 23322039.2021.1917106 Ehrlinger, J., Mitchum, A. L., & Dweck, C. S. (2016). Understanding overconfidence: Theories of intelligence, preferential attention, and distorted self-assessment. Journal of Experimental Social Psychology,63,94–100. https://doi.org/ 10.1016/j.jesp.2015.11.001 Fellner-R€ ohling, G., & Kr€ ugel, S. (2014). Judgmental overconfidence and trading activity. Journal of Economic Behavior & Organization,107, 827–842. https://doi.org/10.1016/j.jebo.2014.04.016 Filbeck, G., Hatfield, P., & Horvath, P. (2005). Risk aversion and personality type. Journal of Behavioral Finance,6(4), 170–180. https://doi.org/10.1207/s15427579jpfm0604_1 Filbeck, G., Ricciardi, V., Evensky, H. R., Fan, S. Z., Holzhauer, H. M., & Spieler, A. (2017). Behavioral finance: A panel discussion. Journal of Behavioral and Experimental Finance,15,52–58. https://doi.org/10.1016/j.jbef.2017.07.008 16 D. SINGH ET AL. Gerrans, P., & Heaney, R. (2019). The impact of undergraduate personal finance education on individual financial literacy, attitudes and intentions. Accounting & Finance,59(1), 177–217. https://doi.org/10.1111/acfi.12247 Gervais, S., & Odean, T. (2001). Learning to be overconfident. Review of Financial Studies,14(1), 1–27. https://doi.org/ 10.1093/rfs/14.1.1 Glaser, M., & Weber, M. (2007). Overconfidence and trading volume. The Geneva Risk and Insurance Review,32(1), 1–36. https://doi.org/10.1007/s10713-007-0003-3 Gloede, O., & Menkhoff, L. (2014). Financial professionals’overconfidence: Is it experience, function, or attitude? European Financial Management,20(2), 236–269. https://doi.org/10.1111/j.1468-036X.2011.00636.x Goetzmann, W. N., & Kumar, A. (2008). Equity portfolio diversification. Review of Finance,12(3), 433–463. https://doi. org/10.1093/rof/rfn005 Gre zo, M. (2021). Overconfidence and financial decision-making: A meta-analysis. Review of Behavioral Finance,13(3), 276–296. https://doi.org/10.1108/RBF-01-2020-0020 Grinblatt, M., & Keloharju, M. (2009). Sensation seeking, overconfidence, and trading activity. The Journal of Finance, 64(2), 549–578. https://doi.org/10.1111/j.1540-6261.2009.01443.x Hala, Y., Abdullah, M. W., Andayani, W., Ilyas, G. B., & Akob, M. (2020). The financial behavior of investment decision making between real and financial assets sectors. The Journal of Asian Finance, Economics and Business,7(12), 635–645. https://doi.org/10.13106/jafeb.2020.vol7.no12.635 Hamilton, E. L., & Winchel, J. (2019). Investors’processing of financial communications: A persuasion perspective. Behavioral Research in Accounting,31(1), 133–156. https://doi.org/10.2308/bria-52211 Hlouskova, J., Fortin, I., & Tsigaris, P. (2017). The consumption–investment decision of a prospect theory household: A two-period model. Journal of Mathematical Economics,70,74–89. https://doi.org/10.1016/j.jmateco.2017.02.003 Hsu, Y.-L. (2022). Financial advice seeking and behavioral bias. Finance Research Letters,46, 102505. https://doi.org/ 10.1016/j.frl.2021.102505 Jain, J., Walia, N., & Gupta, S. (2019). Evaluation of behavioral biases affecting investment decision-making of individual equity investors by fuzzy analytic hierarchy process. Review of Behavioral Finance,12(3), 297–314. https://doi. org/10.1108/RBF-03-2019-0044 Jiang, J., Liao, L., Wang, Z., & Xiang, H. (2020). Financial literacy and retail investors’financial welfare: Evidence from mutual fund investment outcomes in China. Pacific-Basin Finance Journal,59, 101242. https://doi.org/10.1016/j. pacfin.2019.101242 Kahneman, D., & Riepe, M. W. (1998). Aspects of investor psychology. The Journal of Portfolio Management,24(4), 52–65. https://doi.org/10.3905/jpm.1998.409643 Kahneman, D., & Tversky, A. (1979). Intuitive prediction: Biases and corrective procedures. TIMS Studies in Management Science,1, 2,313–327. Kahneman, D., & Tversky, A. (2013). Prospect theory: An analysis of decision under risk. In Handbook of the fundamentals of financial decision making: Part I (pp. 99–127). Kansal, P., & Singh, S. (2018). Determinants of overconfidence bias in Indian stock market. Qualitative Research in Financial Markets,10(4), 381–394. https://doi.org/10.1108/QRFM-03-2017-0015 Kawamura, T., Mori, T., Motonishi, T., & Ogawa, K. (2021). Is financial literacy dangerous? Financial literacy, behavioral factors, and financial choices of households. Journal of the Japanese and International Economies,60, 101131. https://doi.org/10.1016/j.jjie.2021.101131 Khalil, F., & Pipa, G. (2022). Is deep-learning and natural language processing transcending the financial forecasting? Investigation through lens of news analytic process. Computational Economics,60(1), 147–171. https://doi.org/10. 1007/s10614-021-10145-2 Khan, M. T. I., Tan, S. H., & Chong, L. L. (2017). How past perceived portfolio returns affect financial behaviors—The underlying psychological mechanism. Research in International Business and Finance,42, 1478–1488. https://doi. org/10.1016/j.ribaf.2017.07.088 Kirchler, E., & Maciejovsky, B. (2002). Simultaneous over-and underconfidence: Evidence from experimental asset markets. Journal of Risk and Uncertainty,25(1), 65–85. https://doi.org/10.1023/A:1016319430881 Kleine, J., Wagner, N., & Weller, T. (2016). Openness endangers your wealth: Noise trading and the big five. Finance Research Letters,16, 239–247. https://doi.org/10.1016/j.frl.2015.12.002 Kourtidis, D.,  Sevi c,  Z., & Chatzoglou, P. (2011). Investors’trading activity: A behavioural perspective and empirical results. The Journal of Socio-Economics,40(5), 548–557. https://doi.org/10.1016/j.socec.2011.04.008 Kumar, S., & Goyal, N. (2016). Evidence on rationality and behavioural biases in investment decision making. Qualitative Research in Financial Markets,8(4), 270–287. https://doi.org/10.1108/QRFM-05-2016-0016 Liyanaarachchi, G., Deshpande, S., & Weaven, S. (2021). Online banking and privacy: Redesigning sales strategy through social exchange. International Journal of Bank Marketing,39(6), 955–983. https://doi.org/10.1108/IJBM-052020-0278 Lude, M., & Pr€ ugl, R. (2018). Why the family business brand matters: Brand authenticity and the family firm trust inference. Journal of Business Research,89, 121–134. https://doi.org/10.1016/j.jbusres.2018.03.040 Marjerison, R. K., Han, L., & Chen, J. (2023). Investor behavior during periods of crises: The Chinese funds market during the 2020 pandemic. Review of Integrative Business and Economics Research,12(1), 71–91. COGENT ECONOMICS & FINANCE 17 Meier, C., & De Mello, L. (2020). Investor overconfidence in experimental asset markets across market states. Journal of Behavioral Finance,21(4), 369–384. https://doi.org/10.1080/15427560.2019.1692845 Menkhoff, L., Schmeling, M., & Schmidt, U. (2013). Overconfidence, experience, and professionalism: An experimental study. Journal of Economic Behavior & Organization,86,92–101. https://doi.org/10.1016/j.jebo.2012.12.022 Merkle, C. (2017). Financial overconfidence over time: Foresight, hindsight, and insight of investors. Journal of Banking & Finance,84,68–87. https://doi.org/10.1016/j.jbankfin.2017.07.009 Michailova, J., & Schmidt, U. (2016). Overconfidence and bubbles in experimental asset markets. Journal of Behavioral Finance,17(3), 280–292. https://doi.org/10.1080/15427560.2016.1203325 Mishra, K. C., & Metilda, M. J. (2015). A study on the impact of investment experience, gender, and level of education on overconfidence and self-attribution bias. IIMB Management Review,27(4), 228–239. https://doi.org/10.1016/j. iimb.2015.09.001 Moore, D. A., & Healy, P. J. (2008). The trouble with overconfidence. Psychological Review,115(2), 502–517. https:// doi.org/10.1037/0033-295X.115.2.502 Moulton, S., Loibl, C., Samak, A., & Michael Collins, J. (2013). Borrowing capacity and financial decisions of low-tomoderate income first-time homebuyers. Journal of Consumer Affairs,47(3), 375–403. https://doi.org/10.1111/joca. 12021 Nguyen, D. V., Dang, D. Q., Pham, G. H., & Do, D. K. (2020). Influence of overconfidence and cash flow on investment in Vietnam. The Journal of Asian Finance, Economics and Business,7(2), 99–106. https://doi.org/10.13106/jafeb.2020. vol7.no2.99 Nicolosi, G., Peng, L., & Zhu, N. (2004). Do individual investors learn from their trading experience? Yale ICF Working Paper no. 03–32 Odean, T. (1998). Are investors reluctant to realize their losses? The Journal of Finance,53(5), 1775–1798. https://doi. org/10.1111/0022-1082.00072 Odean, T. (1999). Do investors trade too much? American Economic Review,89(5), 1279–1298. https://doi.org/10. 1257/aer.89.5.1279 Olsson, H. (2014). Measuring overconfidence: Methodological problems and statistical artifacts. Journal of Business Research,67(8), 1766–1770. https://doi.org/10.1016/j.jbusres.2014.03.002 Pak, T.-Y., & Chatterjee, S. (2016). Aging, overconfidence, and portfolio choice. Journal of Behavioral and Experimental Finance,12, 112–122. https://doi.org/10.1016/j.jbef.2016.10.003 Pan, C. H., & Statman, M. (2013). Investor personality in investor questionnaires. Journal of Investment Consulting, 14(1), 48–56. Parveen, S., Satti, Z. W., Subhan, Q. A., & Jamil, S. (2020). Exploring market overreaction, investors’sentiments, and investment decisions in an emerging stock market. Borsa Istanbul Review,20(3), 224–235. https://doi.org/10.1016/j. bir.2020.02.002 Paul, J., & Rosado-Serrano, A. (2019). Gradual Internationalization vs Born-Global/International new venture models: A review and research agenda. International Marketing Review,36(6), 830–858. https://doi.org/10.1108/IMR-102018-0280 Peress, J. (2004). Wealth, information acquisition, and portfolio choice. Review of Financial Studies,17(3), 879–914. https://doi.org/10.1093/rfs/hhg056 Pertiwi, T. K., Yuniningsih, Y., & Anwar, M. (2019). The biased factors of investor’s behavior in stock exchange trading. Management Science Letters, 835–842. https://doi.org/10.5267/j.msl.2019.3.005 Pikulina, E., Renneboog, L., & Tobler, P. N. (2017). Overconfidence and investment: An experimental approach. Journal of Corporate Finance,43, 175–192. https://doi.org/10.1016/j.jcorpfin.2017.01.002 Prosad, J. M., Kapoor, S., & Sengupta, J. (2015). Behavioral biases of Indian investors: A survey of Delhi-NCR region. Qualitative Research in Financial Markets,7(3), 230–263. https://doi.org/10.1108/QRFM-04-2014-0012 Puaschunder, J. M. (2017). Socio-psychological motives of socially responsible investors. In Global corporate governance (pp. 209–247). Emerald Publishing Limited. Rahman, M., & Gan, S. S. (2020). Generation Y investment decision: An analysis using behavioural factors. Managerial Finance,46(8), 1023–1041. https://doi.org/10.1108/MF-10-2018-0534 Robinson, A. T., & Marino, L. D. (2015). Overconfidence and risk perceptions: do they really matter for venture creation decisions?. International Entrepreneurship and Management Journal,11(1), 149–168. https://doi.org/10.1007/ s11365-013-0277-0 Sabir, S. A., Mohammad, H. B., & Shahar, H. B. K. (2019). The role of overconfidence and past investment experience in herding behaviour with a moderating effect of financial literacy: Evidence from Pakistan stock exchange. Asian Economic and Financial Review,9(4), 480–490. https://doi.org/10.18488/journal.aefr.2019.94.480. 490 [InsertedFromOnline Sahi, S. K., & Arora, A. P. (2012). Individual investor biases: A segmentation analysis. Qualitative Research in Financial Markets,4(1), 6–25. Shah, S. Z. A., Ahmad, M., & Mahmood, F. (2018). Heuristic biases in investment decision-making and perceived market efficiency: A survey at the Pakistan stock exchange. Qualitative Research in Financial Markets,10(1), 85–110. https://doi.org/10.1108/QRFM-04-2017-0033 18 D. SINGH ET AL. Sharma, D., Misra, V., & Pathak, J. P. (2021). Emergence of behavioural finance: A study on behavioural biases during investment decision-making. International Journal of Economics and Business Research,21(2), 223–234. https://doi. org/10.1504/IJEBR.2021.113140 Singh, D., & Malik, G. (2018). Technical efficiency and its determinants: A panel data analysis of Indian public and private sector banks. Asian Journal of Accounting Perspectives,11(1), 48–71. https://doi.org/10.22452/AJAP.vol11no1.3 Singh, D., & Malik, G. (2022). A systematic and bibliometric review of the financial well-being: Advancements in the current status and future research agenda. International Journal of Bank Marketing,40(7), 1575–1609. https://doi. org/10.1108/IJBM-06-2021-0238 Statman, M., Thorley, S., & Vorkink, K. (2006). Investor overconfidence and trading volume. Review of Financial Studies,19(4), 1531–1565. https://doi.org/10.1093/rfs/hhj032 Syed, R. T., Singh, D., Agrawal, R., & Spicer, D. P. (2023). Entrepreneurship development in universities across Gulf Cooperation Council countries: A systematic review of the research and way forward. Journal of Enterprising Communities: People and Places in the Global Economy,17(5), 1045–1062. https://doi.org/10.1108/JEC-03-2022-0045 Syed, R. T., Singh, D., & Spicer, D. (2023). Entrepreneurial higher education institutions: Development of the research and future directions. Higher Education Quarterly,77(1), 158–183. https://doi.org/10.1111/hequ.12379 Tekc¸e, B., Yılmaz, N., & Bildik, R. (2016). What factors affect behavioral biases? Evidence from Turkish individual stock investors. Research in International Business and Finance,37, 515–526. https://doi.org/10.1016/j.ribaf.2015.11.017 Tomar, S., Baker, H. K., Kumar, S., & Hoffmann, A. O. (2021). Psychological determinants of retirement financial planning behavior. Journal of Business Research,133, 432–449. https://doi.org/10.1016/j.jbusres.2021.05.007 Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty,5(4), 297–323. https://doi.org/10.1007/BF00122574 Tversky, A., Kahneman, D., & Slovic, P. (1982). Judgment under uncertainty: Heuristics and biases (pp. 3-20). Van Rooij, M., Lusardi, A., & Alessie, R. (2011). Financial literacy and stock market participation. Journal of Financial Economics,101(2), 449–472. https://doi.org/10.1016/j.jfineco.2011.03.006 V€ or€ os, Z., Szab o, Z., Kehl, D., Kov acs, O. B., Papp, T., & Schepp, Z. (2021). The forms of financial literacy overconfidence and their role in financial well-being. International Journal of Consumer Studies,45(6), 1292–1308. https:// doi.org/10.1111/ijcs.12734 Waweru, N. M., Munyoki, E., & Uliana, E. (2008). The effects of behavioural factors in investment decision-making: A survey of institutional investors operating at the Nairobi Stock Exchange. International Journal of Business and Emerging Markets,1(1), 24. https://doi.org/10.1504/IJBEM.2008.019243 Wilaiporn, P., Nongnit, C., & Surachai, C. (2021). Factors Influencing retail investors’trading behaviour in the Thai stock market. Zacharakis, A. L., & Shepherd, D. A. (2001). The nature of information and overconfidence on venture capitalists’ decision making. Journal of Business Venturing,16(4), 311–332. https://doi.org/10.1016/S0883-9026(99)00052-X Zaleskiewicz, T. (2011). Financial forecasts during the crisis: Were experts more accurate than laypeople? Journal of Economic Psychology,32(3), 384–390. https://doi.org/10.1016/j.joep.2011.02.003 COGENT ECONOMICS & FINANCE 19