The role of personality traits in investors trading behaviour: empirical evidence from Greek

Kourtidis, Dimitrios; Chatzoglou, Prodromos D.; Šević, Željko

doi:10.1108/ijse-07-2014-0151

Cited by 12 publications

(7 citation statements)

References 127 publications

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“…The most important finding of this study is, notably, the application of the “proposed model” to predict IP in case of investors who directly participate in the financial markets, without the help of any broker/agent. The results of the present study fall in line with Kourtidis et al (2017), Xu et al (2017) and Oehler et al (2018), implying that while making an investment decision, psychological factors such as RT capacity and OC play a very important role (Charness and Gneezy, 2010); these psychological factors also exert an effect on the valuation of financial returns, i.e. IP of an individual investor.…”

Section: Discussionsupporting

confidence: 83%

“…To do so, the study sets apart from the arena of personality trait, that personality is the main factor in determining the IP of an individual investor. In addition, personality traits lead to certain psychological traits, mainly overconfidence (OC) and risk tolerance (Kourtidis et al , 2017; Merkle, 2017; Lin, 2018; Oehler and Wedlich, 2018b) that might act as mediators. These mediators are more than transmitters of the influence of personality, and they form the basis of the stability of relationship between personality and investment performance.…”

Section: Introductionmentioning

confidence: 99%

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Investor personality and investment performance: from the perspective of psychological traits

Akhtar

Das

2020

QRFM

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Purpose The purpose of this study is to analyse the mediation effect of psychological biases, namely, financial risk tolerance (FRT) and financial overconfidence on the relationship between personality traits of individual investors and their investment performance (perceived) in the context of a developing financial market such as Indian Capital Markets. Design/methodology/approach The study uses both quantitative and cross-sectional approach to collect response from 983 individual investors through a questionnaire. The questionnaire had segments that were designed to assess their personality traits, investment performance and psychological traits. The personality traits were assessed through Big-Five personality inventory (TIPI), the psychological traits, i.e. FRT was measured through FRT scale, whereas financial overconfidence was measured through three basic concepts, namely, over-precision, over-placement and overestimation. Investment performance was assessed through perceived investment performance measures. The collected data was then analysed through AMOS and SPSS to validate the hypothesised relationship. Findings Findings of the study depict that personality traits of individual investors are related with their FRT, financial overconfidence and perceived investment performance. In addition, FRT and financial overconfidence are negatively related to perceived investment performance. Furthermore, mediation analysis showed that the two psychological traits were found to fully mediate the relationship between personality traits and investment performance. Research limitations/implications There are still certain limitations of the present study. First, the questionnaire pre-testing and sampling technique allowed for only for those investors who had an experience of investment in financial markets; however, the quantification of actual investment performance for each investor was impossible, and thus the actual investment performance was not determined. Second, this study focusses on the mediating role of financial overconfidence and financial risk-taking, as such it is known that levels of financial overconfidence and risk-taking depend on many other extraneous factors such as socio-economic status and financial knowledge. Practical implications The findings of the present study is useful for financial companies, policymakers as well as issuers of financial securities, who can keep a watch on these behaviour-related traits before issuing a security in the financial market and for the financial service providers; this study would be beneficial to design a “behavioural portfolio” according to the personality and psychological traits of their clients. Social implications Through this study, the investors can recognise their personality traits and psychological biases and take sound and good investment decisions and can also maximise their level of overconfidence. This increased level of overconfidence will propel them further to actively and frequently participate in financial markets and make financial gains. Originality/value The essence of this paper lies in the identification of personality traits and psychological traits of individual investors, and their relationship with investment performance. Studies such as this are less prevalent in the context of a developing country such as India. Moreover, to the best of the authors’ knowledge, this paper is first of its kind to study the meditating effect of psychological biases in the relationship between personality traits and investment performance.

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Section: Discussionsupporting

confidence: 83%

Section: Introductionmentioning

confidence: 99%

Investor personality and investment performance: from the perspective of psychological traits

Akhtar

Das

2020

QRFM

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“…Graham et al (2009) used data from UBS/Gallop investor survey and discovered that overconfident investors tend to trade more frequently than those who are not. The same findings were found by Kourtidis et al (2017) who collected 373 survey questionnaires from individual investors in Greece.…”

Section: Introductionsupporting

confidence: 79%

“…Since the past decades, a voluminous literature has been exploring the effect of psychological factors such as overconfidence bias (Gervais & Odean, 2001;Glaser & Weber, 2007;Kourtidis et al, 2017;Odean, 1998), confirmation bias (Duong et al, 2014;Park et al, 2013), self-serving bias (Chin et al, 2018;Moosa & Ramiah, 2017), and behavioural biases such as disposition effect, mental accounting, heuristics bias, herding, etc. on investors' anomaly such as excessive trading volume or trading frequency (Baker et al, 2019;Barber et al, 2009;Barberis & Huang, 2001;Chen et al, 2007;Choe & Eom, 2009).…”

Section: Introductionmentioning

confidence: 99%

What Drives Excess Trading during the COVID-19 Pandemic?

Chin

2021

AJBA

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Manuscript type: Research paperResearch aims: This study aims to examine the underlying psychological and sociological factors that drive excess trading in the Malaysian stock market during a global health crisis such as the COVID-19 pandemic. Design/Methodology/Approach: A self-administered online questionnaire was collected from 271 individual investors to examine the association between big-five personality traits and trading frequency. Demographic information and investment behaviours of investors were also included in the study. The multinomial logit regression model was used to test the research hypotheses. Research findings: Findings show that personality traits such as openness to experience and agreeableness have a significant influence on trading frequency. Demographic factors and investment behaviours such as gender, household income level, years of investment experience and type of investor all have a significant positive relationship with trading frequency. Theoretical contribution/Originality: This study contributes to the current investor behaviour literature in Malaysia, which remains to be very limited, especially during a global health crisis. The study indicates that personality traits, demographic, socio-economic factors, and investment behaviours affect the trading frequency of Malaysian. Practitioner/Policy implication: This study offers insights for financial institutions and individual investors on the type of personality traits, demographic, socio-economic factors, and investment behaviours that drive excess trading during a global health crisis. The findings provide important contributions to avoid serious mistakes in investment analysis and trading profitable investment strategies, thus improving individual and team performance. Research limitation/Implications: Some results are not significant and may be limited due to the small sample size used in this study. Future research could recruit more retail investors to confirm the significance level of those variables. Besides, the study can be conducted after the COVID-19 pandemic to explore whether there is any significant difference in the variables during and after the global health crisis.

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“…These types can be distinguished by structural differences; usually domestic, international, or institutional investors, or some combination of all three (Che 2018;Grinblatt and Keloharju 2000). There also exist behavioral finance approaches to investor types that utilize personality theory (Pompian 2012), but these approaches tend to focus on personality traits associated with investing behavior (Kourtidis et al 2017). In contrast, our research is a data-driven approach for relating client behaviours to KYC information to discover investor types.…”

Section: Trading Behaviormentioning

confidence: 99%

Know Your Clients’ Behaviours: A Cluster Analysis of Financial Transactions

Thompson

Feng

Reesor

et al. 2021

JRFM

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In Canada, financial advisors and dealers are required by provincial securities commissions and self-regulatory organizations—charged with direct regulation over investment dealers and mutual fund dealers—to respectively collect and maintain know your client (KYC) information, such as their age or risk tolerance, for investor accounts. With this information, investors, under their advisor’s guidance, make decisions on their investments that are presumed to be beneficial to their investment goals. Our unique dataset is provided by a financial investment dealer with over 50,000 accounts for over 23,000 clients covering the period from January 1st to August 12th 2019. We use a modified behavioral finance recency, frequency, monetary model for engineering features that quantify investor behaviours, and unsupervised machine learning clustering algorithms to find groups of investors that behave similarly. We show that the KYC information—such as gender, residence region, and marital status—does not explain client behaviours, whereas eight variables for trade and transaction frequency and volume are most informative. Hence, our results should encourage financial regulators and advisors to use more advanced metrics to better understand and predict investor behaviours.

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The role of personality traits in investors trading behaviour: empirical evidence from Greek

Cited by 12 publications

References 127 publications

Investor personality and investment performance: from the perspective of psychological traits

Investor personality and investment performance: from the perspective of psychological traits

What Drives Excess Trading during the COVID-19 Pandemic?

Know Your Clients’ Behaviours: A Cluster Analysis of Financial Transactions

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