Personality Traits and Investment Behaviour: The Sequential Mediation of Overconfidence Bias and Risk Propensity

Abstract

Unlike previous research that examines solely the direct effects of personality traits on investment behaviour, this study introduces a mechanism to investigate how individual investors’ personality traits, combined with their overconfidence and risk propensity, jointly influence investment behaviour. The data were collected from 392 individual investors registered with the Pakistan Stock Exchange through a survey. The purposive sampling technique was used to select individual investors. Structural equation modelling was employed for data analysis using SmartPLS 3.0 software. The findings showed that conscientiousness and extraversion are the strongest predictors of investors’ overconfidence, followed by openness to experience. Neuroticism was neither supported nor found to be significant regarding investors’ overconfidence. Furthermore, investors’ overconfidence has a positive influence on their risk propensity, which, in turn, exhibits a positive association with their investment behaviour. This study is one of the earliest attempts to apply the sequential mediation approach to investigate how overconfidence and risk propensity influence investment behaviour.

Keywords

Individual investors big five personality traits investment behaviour overconfidence risk propensity

Introduction

Behavioural finance is a relatively new phenomenon in the field of securities markets, emphasising that investors’ decisions are driven by their psychological characteristics (Almansour et al., 2023). Examining behavioural finance is essential for understanding the behaviour of investors, specifically why they make investment decisions differently under varying circumstances (Anwar et al., 2020; Sharma & Kumar, 2020). Investing in stocks and funds is comparatively easy in today’s competitive business landscape, as most individual investors benefit from buying and selling stocks online. However, since the majority of individual investors are ordinary people, in most instances, they tend to make illogical decisions when investing in the stock market, deviating from rationality under conditions of risk and uncertainty. A review of behavioural finance literature indicates that most of these illogical decisions were driven by investors’ behavioural biases resulting from their personality traits and individual characteristics (Almansour et al., 2023; Bashir et al., 2013a). One such behavioural bias commonly observed in investment behaviour is the overconfidence bias (ul Abdin et al., 2017).

Overconfidence is viewed as a mindset in which an individual believes they are superior to others in terms of abilities, knowledge and skills, often disregarding potential risks (Grežo, 2021; Kumar & Goyal, 2015). Overconfidence occurs when an investor believes they are better than others and either ignores the risk perspective or underestimates the risk associated with the investment (Da Costa et al., 2013). When an investor is overconfident, they are indifferent to the risk associated with the investment, demonstrating an increased propensity to take higher risks (Gill et al., 2018; Grežo, 2021). Furthermore, evidence in behavioural finance suggests that an investor’s willingness to take risks is firmly rooted in the investor’s personality and is represented by the big five personality traits (Ul Abdin et al., 2022). Although the notions of personality traits, behavioural biases and investors’ risk propensity are conceptually interlinked, empirical findings are scattered and linkages remain understudied, especially in developing country contexts (Sharma & Kumar, 2020; Valcanover et al., 2020). Therefore, it is worthwhile to investigate how overconfidence and risk propensity influence the association between personality traits and the investment behaviour of individual investors.

Analysis of survey data from 392 individual investors registered with the Pakistan Stock Exchange (PSX), using structural equation modelling (SEM), revealed that extraversion, conscientiousness and openness have a positive influence on investors’ overconfidence. In contrast, agreeableness is negatively associated with overconfidence. Furthermore, overconfidence was found to positively affect risk propensity, which subsequently exerts a significant impact on investment behaviour. Notably, neuroticism emerged as the most robust predictor of investment behaviour, demonstrating both direct effects and indirect effects via sequential mediation.

This study contributes to the behavioural finance literature by proposing investors’ overconfidence and risk propensity as potential mediators in the association between investors’ personality traits and investment behaviour. By doing so, we gain a more thorough and nuanced understanding of the effect of investors’ personality traits on investment behaviour, focusing on the interactions between investors’ stable personality traits and the varying effects of overconfidence and risk propensity. The justification for using overconfidence and risk propensity as mediators stems from the fact that they are dynamic and vary from individual to individual, depending on the socio-economic circumstances from which they originate (Grežo, 2021; Saivasan & Lokhande, 2022). The next significant contribution of this study is to investigate the differential effect of each personality trait on potential mediators and investment behaviour. This study also contributes to the literature by empirically testing the proposed interaction model in a developing country context, which has received scant scholarly attention.

The rest of the article is structured as follows. The next section discusses hypothesis development, followed by a presentation of the methodology adopted. A detailed discussion of the findings is then presented. Finally, the study’s conclusions are presented by highlighting the main limitations and providing future research directions.

Literature Review and Hypotheses Development

Personality Traits

Recent studies on investment behaviour emphasise that, in most circumstances, individual investors often engage in irrational decision-making processes when making investment decisions (Almansour et al., 2023; Bashir et al., 2013b; Sharma & Kumar, 2020). This is primarily because the psychological characteristics of investors influence most investment decisions (Almansour et al., 2023; Valcanover et al., 2020). Bashir et al. (2013b) argued that investors’ psychological characteristics mainly stem from their personality traits and influence the likelihood of exhibiting bounded rationality and behavioural biases during investment decisions.

According to trait theory, individuals’ personalities can be studied through five broad characteristics known as the big five (Hogan & Sherman, 2020). These are openness to experience, consciousness, extraversion, agreeableness and neuroticism (Hogan & Sherman, 2020). Numerous scholars (e.g., Baker et al., 2021, 2022; Jiang et al., 2024) have applied this prominent theory of personality to investigate how individual differences affect investment decisions.

Extraversion and Overconfidence

Extroverts possess characteristics such as being friendly, sociable and warm-hearted and prefer to engage with the outside world (Fazli-Salehi et al., 2022). Overconfident investors believe in their abilities, knowledge and skills. Extroverted investors are often positively associated with overconfidence (Schaefer et al., 2004). Thus, extrovert individuals are overconfident and willing to take risks in their investments. Overconfident investors believe that they can gain an abnormal return on their investment. They can effortlessly indulge in conversations with strangers (Zhang et al., 2014). Therefore, investors who are talkative and easily share their experiences with others can be classified as extroverts. Previous research (e.g., Bowden-Green et al., 2020; Liu & Csikszentmihalyi, 2020) has shown that individuals with high levels of extraversion are more involved in social activities than those with low signs of extraversion. Zhang et al. (2014) explained that extroverted investors exhibit positive behaviour, leading to higher trading activity and potentially earning a higher return. Thus, investors with a high score in extraversion are perceived as overconfident, leading them to take on more risk. Extroverted personalities are prone to overconfidence and risk-taking (Yadav & Narayanan, 2021). In their research, Zhang et al. (2014) showed a positive correlation between willingness to take risks and extraversion. Extraversion is substantially associated with measuring risk tolerance (Gîrla & Jacob, 2017). Extrovert investors earned a higher return on their investment and were satisfied with their investment decisions (Zaidi & Tauni, 2012). Hence, extroverted individuals are often overconfident and risk-takers who seek a higher return on their investment. In a comprehensive study, Zaidi and Tauni (2012) found that extraversion has a positive effect on overconfidence. This study also proposes that extroverts are more likely to become overconfident. Overconfident investors engage in excessive trading to achieve higher returns and are willing to invest in riskier securities. Therefore, this study suggests that personality traits lead to the psychological trait of overconfidence, which makes them risk-takers. The above explanation suggests that extraversion is related to overconfidence, making individuals risk-takers and influencing their investment behaviour. Arguably, the higher the level of extraversion, the higher the overconfidence and risk propensity that affect investment behaviour. Thus, we assume:

H₁: Extraversion has a significant positive effect on investors’ (a) overconfidence and (b) risk propensity.

Agreeableness and Overconfidence

Investors with agreeableness characteristics are cooperative, good-natured and easy-going. Gambetti and Giusberti (2012) emphasise that agreeable investors are socially cooperative, respectful of others and trust one another. As a result, such individuals are highly reliant; thus, deceiving others is extremely difficult for them, and they are forthright (cf. Tajeddini & Mueller, 2009, 2012). They will prioritise the needs of others. Hence, agreeable investors tend to follow their peers and quickly adjust their decisions based on their peers’ advice.

Byrne and Worthy (2015) note that individuals with agreeableness characteristics are influenced by their surroundings and social pressure when making investment decisions. They also discovered that when an investor is under pressure from others, agreeableness negatively affects investment decisions, resulting in a lack of confidence (Lin, 2011). According to behavioural finance literature, agreeableness is negatively correlated with investors’ overconfidence. A higher level of agreeableness is believed to reduce investors’ overconfidence when engaging in investment behaviour (Baker et al., 2021, 2022; Grežo, 2021). At the same time, previous research has shown that individuals with high agreeableness tend to have a low-risk propensity in their investment decisions and are less willing to take risks (Nicholson et al., 2005; Shah et al., 2025). Nicholson et al. (2005) propose an inverse relationship between agreeableness and risk propensity. It is demonstrated that the effect of agreeableness on risk propensity is primarily driven by individual investor preferences rather than beliefs (Baker et al., 2022; Nga & Yien, 2013). Thus, the following hypothesis is proposed.

H₂: Agreeableness has a significant negative effect on investors’ (a) overconfidence and (b) risk propensity.

Conscientiousness and Overconfidence

Conscientious individuals are organised, dependable and responsible, and they can accept challenges (Hogan & Sherman, 2020). Furthermore, this trait gives individuals a direction to make the right decision. Conscientious people are self-disciplined, trusted, goal-oriented, dutiful, highly competent, cautious and actively participate in decision-making (Charles & Kasilingam, 2014; Sadi et al., 2011). Moreover, conscientious individuals tend to favour making decisions that will have a favourable impact on the welfare of the decision-maker. High risk can turn into high returns, but it may also result in high losses and adverse consequences, making conscientious people even more cautious. Prior literature has demonstrated that conscientiousness has a positive effect on overconfidence (Tajeddini & Tajeddini, 2008; Zaidi & Tauni, 2012). Conscious investors are overconfident about their goals and actively participate in making investment decisions. Therefore, they strongly believe in their characteristics and become overconfident in their investment decision (Lin, 2011). Prior research shows that the higher the level of conscientiousness, the higher the investors’ overconfidence in engaging in investment behaviour (Grežo, 2021; Rzeszutek, 2015). Thus, the following hypothesis is suggested.

H₃: Conscientiousness has a significant positive effect on investors’ (a) overconfidence and (b) risk propensity.

Neuroticism and Overconfidence

Neuroticism is a personality trait that refers to individuals with neuroticism traits who are emotionally unstable, depressed and self-centred (Kleine et al., 2016; Wong & Carducci, 2013). Neuroticism is concerned with an individual’s emotional control. Kleine et al. (2016) defined individuals who exhibit little signs of neuroticism as having stable emotional control, whereas those who exhibit substantial signs of neuroticism experience negative emotions when making investment decisions.

Nga and Yien (2013) state that individuals with a high score in neuroticism tend to be risk-averse and are more likely to avoid short-term investments. Because of their self-centred and emotional characteristics, neurotic people are not overconfident. Neuroticism makes individuals anxious, leading to feelings of insecurity (Caliendo et al., 2014). Zaidi and Tauni (2012) examined the relationship between risk propensity and investors’ personality traits, finding that neuroticism has a negative effect on overconfidence. Furthermore, Bashir et al. (2013b) found that neuroticism is associated with a negative relationship to overconfidence. Therefore, it can be concluded that the higher the level of conscientiousness, the lower the level of investors’ overconfidence in investment behaviour (Grežo, 2021; Jiang et al., 2024). Thus, the following hypothesis is anticipated.

H₄: Neuroticism has a significant negative effect on investors’ (a) overconfidence and (b) risk propensity.

Openness to Experience and Overconfidence

Openness to experience is associated with trying different activities and unconventional ideas. Individuals who score high on openness to experience and try new approaches in their fields tend to be more creative (Tan et al., 2019). Previous research (e.g., Bucciol & Zarri, 2017; De Bortoli et al., 2019) suggests that these individuals tend to accept uncertainty and change highly. These types of individuals are known for risk propensity. Therefore, it is proved that individuals with a high score in openness to experience are high-risk takers.

On the other hand, openness to experience has a positive relationship with high-risk propensity because of investors’ overconfidence in their investment decisions. Investors with this trait avoid the herding bias in decision-making and make investment decisions based solely on their own overconfidence (Bashir et al., 2013b). Individuals with a high openness to experience actively participate in the stock market and tend to overestimate their risk-bearing ability due to overconfidence (Bashir et al., 2013b). Investors with an openness to experience are associated with higher risk-taking and risk-seeking abilities than those who are less open to experience. Previous research shows that investment decision-making is strongly influenced by openness to experience (e.g., Niszczota, 2014).

The literature discussed above shows that investors who are open to experience tend to need more confidence in their investment decisions (Nga & Yien, 2013). Notably, the greater the level of openness, the greater the level of investors’ overconfidence concerning investment behaviour (Grežo, 2021; Rzeszutek, 2015). Thus, the following hypothesis is proposed.

H₅: Openness has a significant positive effect on investors’ (a) overconfidence and (b) risk propensity).

Overconfidence Bias and Risk Propensity

Overconfidence bias refers to the tendency to overrate one’s abilities in investment decision-making (Combrink & Lew, 2020). Overconfidence is an over-reliance on an individual’s abilities and knowledge in investment decision-making. Gigerenzer (2018) posits that overconfidence occurs in three ways: individuals’ confidence that their knowledge and abilities are above average, their capacity to deduce information and make accurate judgements and misapprehension of control. Overconfidence can be defined as positive and excessive optimism, as well as a positive self-perception. Overconfidence is the propensity of an individual to overestimate their skills, knowledge, abilities and the accuracy of their predictions (Grežo, 2021; Kannadhasan et al., 2014), as well as their willingness to take risks. Overconfident individuals tend to hold high views of their beliefs and capacities, often exhibiting impractical, wishful thinking. Kannadhasan et al. (2014) argued that overconfident individuals often make risky investment decisions, which in turn influence their investment behaviour. This is the investors’ belief in their ability to make accurate financial decisions, which enables them to select options that maximise their utility and satisfaction. Overconfidence is characterised as an inappropriate belief in an individual’s judgement and cognitive abilities (Sadi et al., 2011) and affects their investment behaviour (Merkle et al., 2021; Sowmyarani & Dayananda, 2018).

Yadav and Narayanan (2021) and Jain et al. (2022) demonstrated that individuals are overconfident due to their personality traits. Jain et al. (2022) define two types of overconfidence: ‘prediction overconfidence’ and ‘certainty overconfidence’. Sometimes, investors have a very narrow confidence interval, known as narrow confidence interval. In contrast, at other times, investors are confident in their judgement and knowledge, identified as certainty overconfidence, which encourages them to participate in the stock market actively and influences their investment behaviour (Jain et al., 2022). Overconfidence causes investors to disregard warning signs, leading to increased trading, a continued lack of portfolio diversification and increased risk-taking (Pompian, 2011). Confidence in one’s abilities as an investor can lead to excessive trading and a willingness to take greater risks to increase their stock substantially (Ali et al., 2016).

Overconfident investors feel in charge of the market and dismiss the risk perspective, as demonstrated by the work of Ben-David et al. (2013). When investors are overconfident about their abilities and forecasting, they trade excessively, overestimate their knowledge, skills and experience and are willing to take risks (Sahi, 2017; Tajeddini, 2015), which can influence their investment performance. When overconfident, investors make overly optimistic assumptions about their market knowledge and engage in excessive trading, thereby influencing their investment behaviour (Merkle et al., 2021; Shefrin, 2000). Gill et al. (2018) stated that the willingness of overconfident investors to take risks is associated with their investment behaviour. Barber and Odean (2001) further argue that investors’ greater risk-taking behaviour is attributed to greater levels of overconfidence. Thus, the following hypotheses are proposed.

H₆: Investors’ overconfidence has a significant positive effect on risk propensity.

H₇: Investors’ overconfidence has a significant positive influence on investment behaviour.

Risk Propensity and Investment Behaviour

Risk assessment of a situation’s inherent risks is referred to as risk propensity (Li & Tang, 2010). Numerous definitions of risk propensity are available to individual investors. The premise of prospect theory is that risk-seeking behaviour occurs when decisions are framed negatively (Azadegan et al., 2019). However, if decisions are presented positively, avoiding uncertainty and safeguarding one’s benefits or gains is prudent. If investors have a history of taking excessive risks, they are likely to continue doing so in the future (Block et al., 2015; Ratten & Tajeddini, 2017). This is a propensity that, regardless of the positive or negative situation, an investor will engage in risky behaviour in the future. Hence, this study uses risk propensity and risk-taking behaviour interchangeably. If investors have a risk-taking attitude, they are likely to continue doing so in the future (Leary & Baumeister, 2017). Different explanations are available in the literature, but the overarching theme is that risk is inevitable when making investment decisions (Sachse et al., 2012). The central theme of asset pricing models also aligns with the notion that risky portfolios have higher returns and vice versa (Kaur & Kaushik, 2016).

Uncertainty is always expected in investment decision-making. The level of knowledge, degree of trust against information, market uncertainty and government financial regulations influence how everyone perceives risk (Pearson, 2016). Every investor has a different level of risk tolerance (Bucher-Koenen et al., 2017). On the one hand, investors’ risk tolerance is influenced by their family background, age and income level. An investor’s risk tolerance, on the other hand, is influenced by beliefs and attitudes about money. Argyle and Furnham (2013) established an arrangement of monetary directions extending from traditional types (misers), value seekers (bargainers), wealth-endowed (tycoons), speculators (gamblers) and impulsive (spendthrifts), showing the pattern of risk aversion/tolerance of investors. Speculators choose highly risky stocks (Chun et al., 2013).

In the United States, unethical behaviour and risk tolerance are often associated with a desire for wealth (Lehnert et al., 2015). Risk aversion moderates the relationship between financial proficiency and financial decision outcomes (Cakarnis & D’Alessandro, 2015). The expected utility theory posits that investors seek to maximise their utility. According to expected utility theory, investors prefer to remain risk-neutral and maximise wealth (Kahneman, 2003). The expected utility assumes that risk is neutral and that there is no influence of emotions or other cognitive biases that can affect investors’ decision-making (Kahneman, 2003). However, prospect theory purports that investors are more risk-averse when dealing with gains than losses (Kahneman, 1979; Rabin & Thaler, 2001). Risk aversion creates a disposition effect, where investors hold onto losers for too long and sell winners too early.

Investors are more willing to take risks after winning and hold onto their security if they lose (Duxbury, 2015). It creates overconfidence or the impression of control over some situations, leading investors to overestimate their knowledge and abilities (DeBondt et al., 2010; Kull et al., 2014). When overconfidence exists, investors tend to overlook uncertainty and adverse outcomes (Steinkühler et al., 2014). The impractical and unrealistic hope leads to excessive trading by ignoring risk and developing herding behaviour (Coleman, 2014; Siddiqui et al., 2025). On the contrary, regret and aversion can cause an investor to hold onto a stock for a long time in the hope of achieving a better return (Earl, 2015).

As discussed, the behavioural finance literature indicates that risk propensity is related to investment behaviour (Anbar & Melek, 2010; Nosić & Weber, 2010). It is believed that the higher the risk propensity level, the higher the level of investment behaviour should be (Broihanne et al., 2014; Figner & Weber, 2011; Tajeddini et al., 2023). Thus, the following hypothesis is proposed.

H₈: Investors’ risk propensity has a significant positive effect on investment behaviour.

Figure 1 depicts the proposed theoretical framework of the study with hypothesised relationships.

Methodology

Research Design

This research employs the quantitative research design to investigate the interrelationships between investors’ per sonality traits, overconfidence, risk propensity and investment behaviour. The quantitative research method has been shown to be effective and useful; therefore, it was decided to follow the quantitative research design to examine the causal relationships. This article uses SEM using SmartPLS to estimate and test the anticipated hypotheses simultaneously.

Sampling Procedure and Data Collection

Since the primary focus of this study is to investigate how the personality traits of individual investors influence their uncertain investment behaviour, this research considers all types of individual investors registered with the PSX as the unit of analysis. This study obtains data from individual investors of PSX. The respondents are selected to fully represent the target population of individual investors on the PSX in the most comprehensive manner possible. The study ensured that the respondents had relevant knowledge by providing a brief introduction to behavioural finance.

The study employs the purposive sampling technique to select respondents from individual investors on the PSX, based on the following criteria: (a) The investors should have at least three years of experience. (b) Selected individual investors should be active investors. (c) Selected investors should only consider risky securities for investment purposes. A record at the PSX showed that millions of individual investors were registered. These investors were invited to participate in the survey through an announcement made by the broker and personal references. In the first wave of data collection, only 80 responses were received, which was insufficient for further analysis. In the successive waves of data collection, more proactive steps were taken to encourage subjects, including extra reminders and assistance from the stock market management to manage the sessions, making them more effective for investors.

Therefore, every successive wave of data collection shows an improved response rate, thanks to the assistance of management, which advises investors on the importance of behavioural finance. Approximately 441 responses were collected over the subsequent five waves of data collection. Forty-nine responses were cancelled from this total due to the high density of missing values. Therefore, 392 usable responses were used to validate the proposed model.

Measurements

This study uses a structured questionnaire to collect data from individual investors. The questionnaire consists of four main sections. The first section comprises questions related to the demographics of the respondents and the screening questions. The second section contains questions related to the predictor: personality traits. Personality traits were measured using a measurement scale adapted from Mayfield et al. (2008). The third section contains questions related to the sequential mediators. Investors’ overconfidence was measured using a scale adapted from Mouna and Jarboui (2015), and risk propensity was measured using a scale developed by Ul Abdin et al. (2022) and Keller and Siegrist (2006). The final section contains questions related to the outcome variable: investment behaviour. It was measured using a measurement scale adapted from Wang and Fan (2023).

Data Analysis

SEM combines confirmatory factor analysis (CFA) and multiple regressions (Schreiber et al., 2006). The measurement of reliability is confirmed with CFA and validity, followed by convergent and discriminant validity (Anderson & Gerbing, 1988). In social science, behavioural factors cannot be measured directly as we measure weight and height, so SEM is widely used to analyse the relationship (Lei & Wu, 2007). The primary goal of this study is to investigate how investor behaviour influences personality traits such as overconfidence and risk propensity. Therefore, SEM is the most suitable technique for answering postulated research questions using SmartPLS.

Data Analysis and Results

Descriptive Statistics

Table 1 presents the descriptive statistics results and the correlation of the research model. It is the first indicator to show the relationships (direct and indirect) among all the constructs.

Table 1.

Descriptive Statistics and Correlation.

Variables	1	2	3	4	5	6	7	8	Mean	SD
EXTRA	1								4.96	1.39
NEURO	0.070	1							5.88	1.11
OPENNESS	0.207*	0.339**	1						6.00	0.91
CON	0.246*	0.644***	0.557	1					5.91	0.89
AGREE	0.043	−0.096	−0.191	−0.191	1				3.20	1.73
OC	0.356**	0.343**	0.464***	0.514***	−0.198	1			5.51	1.22
RISK	0.402***	0.295*	0.484***	0.423***	−0.194	0.594***	1		5.36	1.15
INVESTMENT	0.130	0.335**	0.677***	0.455***	−0.296*	0.456***	0.455***	1	5.89	1.09

Source: Authors’ own calculation (statistical analysis of field research data; n = 392, survey based).

Notes: *p < 0.05; **p < 0.01; ***p < 0.001.

Since the respondents were selected from the stock market, there were significant variations in their demographic factors. It was revealed that in the PSX, older investors and males are the dominant group, as 207 investors were aged between 36 and 55. Almost 308 were males, and only 84 were females among the 392 effective responses received. All investors had more than a year of experience in stock trading (see Table 2).

Table 2.

Demographic Profile of the Respondents.

	Gender		Years of Experience
	Male	Female	1–5	6–10	11–15	>15
Sample	308	84	87	98	110	97
Per cent	79	21	22	25	28	25

Source: Authors’ own calculation (statistical analysis of field research data; n = 392, survey based).

Analytical Strategy

The hypothesised research model is tested through a three-step approach. In the first step, the quality of the measurement model was tested by assessing the outer loadings of individual items, composite reliability and convergent and discriminant validity. In the second step, a structural model is used to test the causal effect of each path. Post hoc analysis was applied in the final step to test the un-hypothesised paths.

Path Analysis

The measurement model was tested in three phases. The first phase confirms the reliability of each construct, followed by composite reliability that should be greater than 0.7. In this research, all the constructs achieve the minimum threshold (Table 3). The second phase of the measurement model involves outer loadings, which confirm that the item is related to the same construct. The criteria for outer loadings of reflective constructs are 0.6. In this study, the outer loadings of each item achieved the minimum criteria. In the third phase, the convergent and discriminant validity were confirmed. The average variance extracted (AVE) value confirms the convergent validity. If the AVE value is greater than 0.5, particular constructs converged above 50% of stated items. In this study, the convergent value for each construct is achieved as per the threshold. The discriminant validity shows that each item explains variance in relation to its own construct rather than others. To confirm the discriminant validity, the square root of the AVE value should be greater than the correlation of each construct in comparison (Table 4).

Table 3.

Outer Loading, Reliability and Convergent Validity.

Variable	Items	Loadings	AVE	Composite Reliability (CR)
Neuro	Neuro1	0.830	0.730	0.915
	Neuro2	0.784
	Neuro3 Neuro4	0.864 0.818
	*Neuro5	0.363
Extra	Extra1	0.849	0.671	0.891
	Extra2	0.816
	Extra3	0.828
	Extra4	0.784
Openn	Openn1	0.736	0.587	0.849
	Openn2	0.818
	Openn3 Openn4 *Openn5	0.792 0.616 0.571
Agree	Agree1	0.907	0.731	0.915
	Agree2	0.698
	Agree3	0.863
	Agree4	0.930
Con	Con1	0.877	0.596	0.879
	Con2	0.846
	Con3	0.819
	Con4	0.661
	Con5	0.625
OC	OC1	0.746	0.712	0.908
	OC2	0.882
	OC3	0.888
	OC4	0.854
RP	*RP1	0.324	0.605	0.884
	RP2	0.756
	RP3	0.778
	RP4	0.716
	RP5	0.843
	RP6	0.784
IB	IB1	0.908	0.838	0.940
	IB2	0.910
	IB3 *IB4	0.929 −0.034

Source: Authors’ own calculation (statistical analysis of field research data; n = 392, SmartPLS).

Note: All items in each variable have a factor loading > 0.6, AVE > 0.5 and CR > 0.7.

Table 4.

Discriminant Validity.

Variables	Agree	Con	Extra	Neuro	Openn	OC	RP	IB
Agree	0.855
Con	−0.209	0.772
Extra	0.032	0.273	0.819
Neuro	−0.110	0.571	0.071	0.854
Openn	−0.223	0.575	0.220	0.345	0.766
OC	−0.228	0.528	0.357	0.347	0.489	0.844
RP	−0.221	0.449	0.410	0.306	0.522	0.618	0.778
IB	−0.300	0.459	0.138	0.334	0.715	0.468	0.474	0.916

Source: Authors’ own calculation (statistical analysis of field research data; n = 392, SmartPLS).

Hypothesis Testing

As shown in Table 5, the findings from the structural model affirm that three personality traits have a significant positive effect on investors’ overconfidence. However, it was found that neuroticism and agreeableness significantly negatively affect investors’ overconfidence. Further, it was found that investors’ overconfidence has a significant positive effect on risk propensity, and risk propensity has a significant positive effect on investment behaviour. Table 6 summarises the findings related to hypothesis testing in brief.

Table 5.

Hypotheses Testing Results.

Predictors	Dependent Variables
Predictors	Overconfidence	Risk Propensity	Investment Behaviour
Extraversion	0.234***	0.220^***
Agreeableness	−0.121**	−0.08^**
Conscientiousness	0.251***	−0.016^NS
Neuroticism	0.093^NS	0.071^NS
Openness to experience	0.234***	0.254^***
Overconfidence		0.617***	0.284***
Risk propensity			0.475***

Source: Authors’ own calculation (statistical analysis of field research data; n = 392, SmartPLS).

Note: ***Significant at 99%; **Significant at 95%; NS: Not significant.

Table 6.

Hypothesis Results.

	Hypothesis	Decision
H_1a	Agreeableness has a significant negative effect on investors’ overconfidence	Supported
H_1b	Agreeableness has a significant negative effect on risk propensity	Supported
H_2a	Neuroticism has a significant negative effect on investors’ overconfidence	Not supported
H_2b	Neuroticism has a significant negative effect on risk propensity	Not supported
H_3a	Openness has a significant positive relationship with investors’ overconfidence	Supported
H_3b	Openness has a significant positive relationship with risk propensity	Supported
H_4a	Extraversion has a significant positive direct effect on investors’ overconfidence	Supported
H_4b	Extraversion has a significant positive direct effect on risk propensity	Supported
H_5a	Consciousness has a significant positive effect on investors’ overconfidence	Supported
H_5b	Consciousness has a significant positive effect on risk propensity	Not supported
H₆	Investors’ overconfidence has a significant positive effect on risk propensity	Supported
H₇	Risk propensity has a significant positive effect on Investment behaviour	Supported
H₈	Investors’ overconfidence has a significant positive effect on investment behaviour	Supported

Source: Authors’ own calculation (statistical analysis of field research data; n = 392, SmartPLS).

Discussion

This study examines the impact of individual investors’ personality traits on their investment behaviour—an area that has been underexplored within the behavioural finance literature. Given that behavioural finance is still an emerging field, the nuanced impact of personality on investment decision-making is not yet fully understood. To address this gap, the study proposes a theoretical model that elucidates the pathways through which personality traits shape investment behaviour, incorporating overconfidence and risk propensity as mediating variables.

Extraversion and Overconfidence

The study findings align with those of Jain et al. (2022), who highlighted the significant role of personality traits in shaping investors’ overconfidence. Specifically, this study provides empirical evidence of a positive correlation between extraversion and overconfidence among individual investors. Extroverted investors, characterised by their sociability, talkativeness and innovative nature, often exhibit heightened overconfidence in their financial decision-making. This trait leads them to overestimate their abilities and underestimate risks, resulting in excessive trading and a preference for risky investments (Piehlmaier, 2022; Zheng et al., 2022). Prior studies support the positive association between extraversion and overconfidence (Baker et al., 2022), as well as its influence on risk propensity (Highhouse et al., 2022). These findings suggest that extroverted individuals are more likely to exhibit overconfident behaviours, which can significantly impact their investment decisions.

Agreeableness and Overconfidence

The second hypothesis posits a negative relationship between agreeableness and both overconfidence and risk propensity among individual investors. Agreeable individuals, characterised by traits such as cooperativeness, empathy and openness to others’ viewpoints, are more likely to engage in cautious decision-making. Their tendency to seek diverse perspectives and consider potential risks and uncertainties may lead to more balanced and accurate investment evaluations, reducing the likelihood of overconfidence and excessive risk-taking. The empirical findings support this hypothesis, indicating a significant negative association. These results are further corroborated by Rao and Lakkol (2022), who noted that agreeable investors are less prone to overestimating their abilities due to their collaborative nature and lower inclination towards risk-taking behaviour.

Conscientiousness and Overconfidence

The findings indicate that conscientiousness is positively associated with overconfidence but not significantly related to risk propensity, partially supporting the proposed hypothesis. Conscientious investors, known for their diligence, discipline and goal-oriented behaviour, tend to believe strongly in the accuracy of their decisions and often invest considerable time in researching alternatives. This confidence in their capabilities contributes to heightened overconfidence, as they assume their thoroughness translates into superior investment judgements (Vuković & Pivac, 2023; Zheng et al., 2022). This study’s results align with prior research by Jain et al. (2022) and Kumar et al. (2023), which also found a positive link between conscientiousness and overconfidence. However, despite their self-assurance, conscientious investors are typically risk-averse. Their structured, methodical approach to decision-making emphasises caution, risk evaluation and alignment with long-term goals—traits that limit unnecessary risk-taking (Sadiq & Khan, 2019). This explains the non-significant relationship between conscientiousness and risk propensity in the present study.

Recent literature (Baker et al., 2021, 2022; Singh et al., 2022) further supports these findings, suggesting that conscientious investors may exhibit overconfidence in their ability to identify successful investments while still adhering to a conservative, research-driven investment strategy. Thus, although conscientiousness contributes to overconfidence, it does not necessarily translate into greater risk-taking, highlighting the complexity of its role in shaping investment behaviour.

Neuroticism and Overconfidence

The results indicate that neuroticism does not have a significant effect on investors’ overconfidence or risk propensity, thereby not supporting the proposed hypothesis. Investors high in neuroticism are generally characterised by emotional instability, heightened anxiety and a strong aversion to uncertainty, which collectively contribute to a more cautious and risk-averse investment approach. Their fear of future losses and susceptibility to negative emotions discourage them from engaging in risky financial decisions (Bashir et al., 2013a; Oehler et al., 2018).

While traits such as extraversion and openness are more commonly associated with overconfidence and risk-taking behaviours, neuroticism is typically linked to loss aversion and conservative investment choices (Johnsi & Sunitha, 2019). Neurotic investors often overanalyse situations, struggle with decision-making and prefer safe and stable investments due to their discomfort with uncertainty. Their emotional reactivity and tendency to avoid potential losses further inhibit their willingness to engage in high-risk investments. Overall, the behavioural tendencies associated with neuroticism—such as indecisiveness, emotional volatility and heightened sensitivity to loss—explain their limited inclination towards overconfidence and risk-taking in investment decision-making.

Openness to Experience and Overconfidence

The results indicate that openness to experience is significantly and positively associated with both investors’ overconfidence and risk propensity. Investors scoring high in openness are characterised by traits such as curiosity, creativity, a willingness to explore new ideas and a high tolerance for uncertainty (Grežo, 2021). These attributes foster greater self-assurance, intuitive decision-making and a proclivity for seeking novel and potentially high-risk investment opportunities driven by the anticipation of future gains (Bashir et al., 2013a; Sarwar et al., 2020).

Consistent with prior findings by Haran et al. (2013) and Bashir et al. (2013), open-minded investors tend to rely more on internal judgement and intuition rather than external information. In contrast, individuals lower in openness—those who are more sceptical of unconventional ideas—tend to exhibit more analytical and conservative decision-making styles. The present study’s findings align with this pattern, reinforcing the notion that openness contributes to overconfidence and a greater willingness to accept investment risks.

Furthermore, the study confirms that overconfidence significantly enhances risk propensity. Overconfident investors often overestimate their knowledge, underestimate risks and engage in excessive trading, which may ultimately lead to suboptimal returns (Sahi, 2017). Their confidence in their decision-making drives a more aggressive investment approach, including a preference for riskier assets, a long-term investment outlook and increased influence on market behaviour. Therefore, the proposed hypothesis is supported, indicating that openness to experience fosters overconfidence, which in turn amplifies risk-taking behaviour in individual investors.

Mediating Effects of Overconfidence Bias and Risk Propensity

The findings of this study reveal that the proposed mediating variables—overconfidence and risk propensity—play a significant and robust role in shaping the relationship between individual investors’ personality traits and their investment behaviour, thereby contributing to the advancement of behavioural finance literature. Specifically, personality traits were found to explain 48% of the variance in risk propensity and 38% of the variance in overconfidence, underscoring the relevance of these mediators in understanding individual investment decision-making. These results align with prior studies by Combrink and Lew (2020) and Ul Abdin et al. (2022), which highlight the crucial role of risk propensity in influencing investment decisions.

Overconfidence emerged as a key mediator influencing investment behaviour. Consistent with the findings of Adiputra (2021) and Pompian (2012), overconfident investors often exhibit assertiveness and self-assurance, encouraging more proactive investment strategies and a higher tolerance for calculated risks. However, such overconfidence may lead to excessive trading, underestimation of risk, neglect of diversification and disregard for external advice—behaviours also noted by Riaz and Iqbal (2015) and Altman (2014). These tendencies can distort rational investment decisions, leading to riskier portfolios and suboptimal financial outcomes.

Risk propensity was also found to have a significant and positive effect on investment behaviour. Investors with higher risk tolerance are more likely to adopt bold, open-minded strategies, explore a wider array of investment opportunities and construct more diversified portfolios in pursuit of enhanced returns. This risk orientation promotes not only broader asset consideration but also greater responsiveness to market signals and improved timing of investment decisions. Furthermore, high-risk investors are more likely to maintain long-term holdings, which can lead to compounding gains over time. These findings are consistent with those of Baker et al. (2022) and Jiang et al. (2024), who argue that higher risk propensity is positively associated with investment satisfaction, reinvestment intentions and overall performance.

In summary, the study highlights the critical mediating roles of overconfidence and risk propensity in linking personality traits to investment behaviour. These insights highlight the importance of incorporating psychological constructs into investment models to enhance understanding and prediction of individual investor behaviour in dynamic financial markets.

Implications

This study advances the understanding of how personality traits influence the investment behaviour of individual investors by proposing a sequential mediation model that incorporates overconfidence and risk propensity. By examining the indirect pathways through which psychological traits shape investment outcomes, the study offers valuable insights for financial consultants, individual investors and academic researchers. The findings enable financial advisers to tailor investment strategies according to clients’ personality profiles and risk tolerance, thereby enhancing decision-making confidence and potential returns. Brokerage firms may also utilise these insights to design investor retention strategies that align with individual behavioural tendencies.

Furthermore, the study offers practical implications for financial policymakers and regulatory bodies in Pakistan by highlighting the need for behaviourally informed strategies to attract and support individual investors in the capital market. At a broader level, the study contributes to the growing body of behavioural finance literature by highlighting that differences in personality traits and risk propensity can meaningfully explain variations in investment behaviour. Ultimately, institutional investors can leverage these findings to gain a deeper understanding of retail investor trends and preferences, enabling more informed and targeted investment recommendations.

Limitations and Future Research Directions

Several limitations of this study are worth mentioning, which provide directions for future researchers. First, this study used the pre-identified dimensions of personality traits. The study results showed that these constructs explain only a portion of the variance in the outcome variable. Further, the sample size is one of the most significant limitations. This study was unable to obtain an adequate sample size due to a lack of responses and delayed responses, which may have influenced the results by targeting a smaller number of individual investors. Third, this study did not consider the moderating effect that should be added, such as financial literacy, in examining the relationship between individual investors’ personality traits and investment behaviour. Next, this study used a shortened version of the standard questionnaire to assess the personality trait. This study employed a cross-sectional research design to test the proposed model empirically.

Hence, in the future, researchers can use alternative measurement scales and longitudinal studies to verify the robustness of the proposed model. Further, to strengthen the validity of the proposed interrelationships among the theoretical constructs, future studies should consider collecting data from different types of investors from various research settings. Additionally, future researchers could strengthen the robustness of the proposed framework by empirically testing potential moderating variables that might influence the proposed relationships.

Conclusion

This article introduces sequential mediation, broadening our understanding of the relationship between investors’ personality traits and investment behaviour. Through the lens of behavioural finance and trait theory, this study examines how investors’ personality traits lead to behavioural biases that influence their investment decisions, with overconfidence and risk propensity serving as mediators. Findings derived from data collected from 392 individual investors registered with the PSX indicate that different aspects of personality traits have varying effects on investment behaviour, resulting in diverse processes. Both individual investors and securities firms may benefit from the valuable insights this study provides, which will help them better understand investor behaviour and give the proper guidance. Furthermore, by applying behavioural finance to emerging stock markets, this study provides new insights. It highlights the applicability of this strategy in securities markets across both developing and developed country contexts.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

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

References

Adiputra

I. G.

(2021). The influence of overconfidence, representative bias, and risk tolerance in investment decision making: Evidence on stock investors in Indonesia. Journal of Hunan University Natural Sciences, 48(4), 195–205.

Ali

, Al-Sabaan

, & Mandurah

(2016). Determinants of the risk-taking attitude among small equity investors. International Journal of Applied Behavioral Economics (IJABE), 5(3), 1–13.

Almansour

B. Y.

, Elkrghli

, & Almansour

A. Y.

(2023). Behavioral finance factors and investment decisions: A mediating role of risk perception. Cogent Economics and Finance, 11(2), 2239032.

Altman

(2014). Behavioral economics, thinking processes, decision making, and investment behavior. In Baker

H. K.

& Ricciardi

(Eds.), Investor behavior: The psychology of financial planning and investing (pp. 43–61). John Wiley & Sons, Inc.

Anbar

, & Melek

E. J. E.

A. B.

. (2010). An empirical investigation for determining of the relation between personal financial risk tolerance and demographic characteristic. Ege Academic Review, 10(2), 503–522.

Anderson

J. C.

, & Gerbing

D. W.

(1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411.

Anwar

, Tajeddini

, & Ullah

(2020). Entrepreneurial finance and new venture success: The moderating role of government support. Business Strategy and Development, 3(4), 408–421. https://doi.org/10.1002/bsd2.105

Argyle

, & Furnham

(2013). The psychology of money. Routledge.

Azadegan

, Srinivasan

, Blome

, & Tajeddini

(2019). Learning from anomalies: Near miss and disruptions in developing supply chain disruption response strategies. International Journal of Production Economics, 216 (October) 215–226.

10.

Baker

H. K.

, Kapoor

, & Khare

(2022). Personality traits and behavioral biases of Indian financial professionals. Review of Behavioral Finance, 15(6), 846–864.

11.

Baker

H. K.

, Kumar

, & Goyal

(2021). Personality traits and investor sentiment. Review of Behavioral Finance, 13(4), 354–369.

12.

Barber

B. M.

, & Odean

(2001). Boys will be boys: Gender, overconfidence, and common stock investment. The Quarterly Journal of Economics, 116(1), 261–292.

13.

Bashir

, Azam

, Butt

A. A.

, Javed

, & Tanvir

(2013a). Are behavioral biases influenced by demographic characteristics & personality traits? Evidence from Pakistan. European Scientific Journal, 9(29), 277–293.

14.

Bashir

, Safia

, Irum

, Waqas

, & Ghullam

(2013b). Impact of demographics and personality traits on confidence level: Determinants of overconfidence (evidence from employees and students). IOSR Journal of Business and Management, 10(1), 58–67.

15.

Ben-David

, Graham

J. R.

, & Harvey

C. R.

(2013). Managerial miscalibration. The Quarterly Journal of Economics, 128(4), 1547–1584.

16.

Block

, Sandner

, & Spiegel

(2015). How do risk attitudes differ within the group of entrepreneurs? The role of motivation and procedural utility. Journal of Small Business Management, 53(1), 183–206.

17.

Bowden-Green

, Hinds

, & Joinson

(2020). How is extraversion related to social media use? A literature review. Personality and Individual Differences, 164, 110040.

18.

Broihanne

M.-H.

, Merli

, & Roger

P. J. F.

. (2014). Overconfidence, risk perception and the risk-taking behavior of finance professionals. Finance Research Letters, 11(2), 64–73.

19.

Bucciol

, & Zarri

(2017). Do personality traits influence investors’ portfolios? Journal of Behavioral and Experimental Economics, 68, 1–12.

20.

Bucher-Koenen

, Lusardi

, Alessie

, & Van Rooij

(2017). How financially literate are women? An overview and new insights. Journal of Consumer Affairs, 51(2), 255–283.

21.

Byrne

K. A.

, & Worthy

D. A.

(2015). Gender differences in reward sensitivity and information processing during decision-making. Journal of Risk and Uncertainty, 50(1), 55–71.

22.

Cakarnis

, & D’Alessandro

S. P.

(2015). Does knowing overcome wanting? The impact of consumer knowledge and materialism upon credit card selection with young consumers. Young Consumers, 16(1), 50–70.

23.

Caliendo

, Fossen

, & Kritikos

A. S.

(2014). Personality characteristics and the decisions to become and stay self-employed. Small Business Economics, 42(4), 787–814.

24.

Charles

, & Kasilingam

(2014). Do investors’ emotions determine their investment personality? Parikalpana: KIIT Journal of Management, 10(2), 45.

25.

Chun

J. S.

, Shin

, Choi

J. N.

, & Kim

M. S.

(2013). How does corporate ethics contribute to firm financial performance? The mediating role of collective organizational commitment and organizational citizenship behavior. Journal of Management, 39(4), 853–877.

26.

Coleman

(2014). Why finance theory fails to survive contact with the real world: A fund manager perspective. Critical Perspectives on Accounting, 25(3), 226–236.

27.

Combrink

, & Lew

(2020). Potential underdog bias, overconfidence and risk propensity in investor decision-making behavior. Journal of Behavioral Finance, 21(4), 337–351.

28.

Da Costa

Jr , Goulart

, Cupertino

, Macedo

, & Sergio

D. S.

(2013). The disposition effect and investor experience. Journal of Banking & Finance, 37(5), 1669–1675.

29.

De Bortoli

, da Costa Jr

, Goulart

, & Campara

(2019). Personality traits and investor profile analysis: A behavioral finance study. PLoS One, 14(3), e0214062.

30.

DeBondt

, Forbes

, Hamalainen

, & Gulnur Muradoglu

(2010). What can behavioural finance teach us about finance? Qualitative Research in Financial Markets, 2(1), 29–36.

31.

Duxbury

(2015). Behavioral finance: Insights from experiments II: Biases, moods and emotions. Review of Behavioral Finance, 7(2), 151–175.

32.

Earl

P. E.

(2015). Anchoring in economics: On Frey and Gallus on the aggregation of behavioural anomalies. Economics: The Open-Access, Open-Assessment E-Journal, 9(2015–21), 1–25.

33.

Fazli-Salehi

, Torres

I. M.

, Madadi

, & Zúñiga

M. Á.

(2022). The impact of interpersonal traits (extraversion and agreeableness) on consumers’ self-brand connection and communal-brand connection with anthropomorphized brands. Journal of Brand Management, 29(1), 13–34.

34.

Figner

, & Weber

E. U.

(2011). Who takes risks when and why? Determinants of risk taking. Current Directions in Psychological Science, 20(4), 211–216.

35.

Gambetti

, & Giusberti

(2012). The effect of anger and anxiety traits on investment decisions. Journal of Economic Psychology, 33(6), 1059–1069.

36.

Gigerenzer

(2018). The bias in behavioral economics. Review of Behavioral Economics, 5(3–4), 303–336.

37.

Gill

, Khurshid

M. K.

, Mahmood

, & Ali

(2018). Factors effecting investment decision making behavior: The mediating role of information searches. European Online Journal of Natural and Social Sciences, 7(4), 758–767.

38.

Gîrla

, & Rub

(2017). Correlation between risk-taking and risk-averting behavior during the white-collar crime perpetrating: Empirical findings for Moldova and Israel. Studia Universitatis Moldaviae-Științe Sociale, 8(98), 156–169.

39.

Grežo

(2021). Overconfidence and financial decision-making: A meta-analysis. Review of Behavioral Finance, 13(3), 276–296.

40.

Haran

, Ritov

, & Mellers

B. A.

(2013). The role of actively open-minded thinking in information acquisition, accuracy, and calibration. Judgment and Decision Making, 8(3), 188–201.

41.

Highhouse

, Wang

, & Zhang

D. C.

(2022). Is risk propensity unique from the big five factors of personality? A meta-analytic investigation. Journal of Research in Personality, 98, 104206.

42.

Hogan

, & Sherman

R. A.

(2020). Personality theory and the nature of human nature. Personality and Individual Differences, 152, 109561.

43.

Jain

, Sharma

, Behl

, & Tiwari

A. K.

(2022). Investor personality as a predictor of investment intention: Mediating role of overconfidence bias and financial literacy. International Journal of Emerging Markets, 18(12), 5680–5706.

44.

Jiang

, Peng

, & Yan

(2024). Personality differences and investment decision-making. Journal of Financial Economics, 153, 103776.

45.

Johnsi

, & Sunitha

(2019). Impact of personality and emotional intelligence on investor behaviour. SDMIMD Journal of Management, 10(1), 21–29.

46.

Kahneman

(1979). Prospect theory: An analysis of decisions under risk. Econometrica, 47, 278.

47.

Kahneman

(2003). Maps of bounded rationality: Psychology for behavioral economics. American Economic Review, 93(5), 1449–1475.

48.

Kannadhasan

, Aramvalarthan

, & Kumar

B. P.

(2014). Relationship among cognitive biases, risk perceptions and individual’s decision to start a venture. Decision, 41(1), 87–98.

49.

Kaur

, & Kaushik

(2016). Determinants of investment behaviour of investors towards mutual funds. Journal of Indian Business Research, 8(1), 19–42.

50.

Keller

, & Siegrist

(2006). Investing in stocks: The influence of financial risk attitude and values-related money and stock market attitudes. Journal of Economic Psychology, 27(2), 285–303.

51.

Kleine

, Wagner

, & Weller

(2016). Openness endangers your wealth: Noise trading and the big five. Finance Research Letters, 16, 239–247.

52.

Kull

T. J.

, Oke

, & Dooley

K. J.

(2014). Supplier selection behavior under uncertainty: Contextual and cognitive effects on risk perception and choice. Decision Sciences, 45(3), 467–505.

53.

Kumar

, & Goyal

(2015). Behavioural biases in investment decision making: A systematic literature review. Qualitative Research in Financial Markets, 7(1), 88–108.

54.

Kumar

, Dudani

, & K

(2023). The big five personality traits and psychological biases: An exploratory study. Current Psychology, 42(8), 6587–6597.

55.

Leary

M. R.

, & Baumeister

R. F.

(2017). The need to belong: Desire for interpersonal attachments as a fundamental human motivation. In Interpersonal development (pp. 57–89). Routledge.

56.

Lehnert

, Park

Y.-h.

, & Singh

(2015). Research note and review of the empirical ethical decision-making literature: Boundary conditions and extensions. Journal of Business Ethics, 129(1), 195–219.

57.

Lei

P. W.

, & Wu

(2007). Introduction to structural equation modeling: Issues and practical considerations. Educational Measurement: Issues and Practice, 26(3), 33–43.

58.

, & Tang

(2010). CEO hubris and firm risk taking in China: The moderating role of managerial discretion. Academy of Management Journal, 53(1), 45–68.

59.

Lin

H.-W.

(2011). Elucidating the influence of demographics and psychological traits on investment biases. World Academy of Science, Engineering and Technology, 77, 145–150.

60.

Liu

, & Csikszentmihalyi

(2020). Flow among introverts and extraverts in solitary and social activities. Personality and Individual Differences, 167, 110197.

61.

Mayfield

, Perdue

, & Wooten

(2008). Investment management and personality type. Financial Services Review, 17(3), 219–236.

62.

Merkle

, Tajeddin

, & Wittmer

(2021). Can the Netflix business model actually work for commercial aviation in central Europe? It is complicated! Journal of Hospitality & Tourism Cases, 9(2), 20–22.

63.

Mouna

, & Jarboui

(2015). Financial literacy and portfolio diversification: An observation from the Tunisian stock market. International Journal of Bank Marketing, 33(6), 808–822.

64.

Nga

J. K.

, & Yien

L. K.

(2013). The influence of personality trait and demographics on financial decision making among Generation Y. Young Consumers, 14(3), 230–243.

65.

Nicholson

, Soane

, Fenton-O’Creevy

, & Willman

(2005). Personality and domain-specific risk taking. Journal of Risk Research, 8(2), 157–176.

66.

Niszczota

(2014). Cross-country differences in personality and the foreign bias in international equity portfolios. The European Journal of Finance, 20(10), 934–956.

67.

Nosić

, & Weber

(2010). How riskily do I invest? The role of risk attitudes, risk perceptions, and overconfidence. Decision Analysis, 7(3), 282–301.

68.

Oehler

, Wendt

, Wedlich

, & Horn

(2018). Investors’ personality influences investment decisions: Experimental evidence on extraversion and neuroticism. Journal of Behavioral Finance, 19(1), 30–48.

69.

Pearson

(2016). Financial literacy and the creation of financial citizens. In The future of consumer credit regulation (pp. 21–46). Routledge.

70.

Piehlmaier

D. M.

(2022). Overconfidence and the adoption of robo-advice: Why overconfident investors drive the expansion of automated financial advice. Financial Innovation, 8(1), 1–24.

71.

Pompian

M. M.

(2011). Behavioral finance and wealth management: How to build investment strategies that account for investor biases (Vol. 667). John Wiley & Sons.

72.

Pompian

M. M.

(2012). Behavioral finance and investor types: Managing behavior to make better investment decisions. John Wiley & Sons.

73.

Rabin

, & Thaler

R. H.

(2001). Anomalies: Risk aversion. Journal of Economic Perspectives, 15(1), 219–232.

74.

Rao

A. S.

, & Lakkol

S. G.

(2022). Influence of hexaco personality model on overconfidence among retail investors. Journal of Positive School Psychology, 6(3), 7534–7541.

75.

Ratten

, & Tajeddini

(2017). Innovativeness in family firms: An internationalization approach. Review of International Business and Strategy, 27(2), 217–230.

76.

Riaz

, & Iqbal

(2015). Impact of overconfidence, illusion of control, self-control and optimism bias on investors decision making; Evidence from developing markets. Research Journal of Finance and Accounting, 6(11), 110–116.

77.

Rzeszutek

(2015). Personality traits and susceptibility to behavioral biases among a sample of Polish stock market investors. International Journal of Management and Economics, 47(1), 71–81.

78.

Sachse

, Jungermann

, & Belting

J. M.

(2012). Investment risk: The perspective of individual investors. Journal of Economic Psychology, 33(3), 437–447.

79.

Sadi

, Asl

H. G.

, Rostami

M. R.

, Gholipour

, & Gholipour

(2011). Behavioral finance: The explanation of investors’ personality and perceptual biases effects on financial decisions. International Journal of Economics and Finance, 3(5), 234–241.

80.

Sadiq

M. N.

, & Khan

R. A. A.

(2019). Impact of personality traits on investment intention: The mediating role of risk behaviour and the moderating role of financial literacy. Journal of finance and Economics Research, 4(1), 1–18.

81.

Sahi

S. K.

(2017). Psychological biases of individual investors and financial satisfaction. Journal of Consumer Behaviour, 16(6), 511–535.

82.

Saivasan

, & Lokhande

(2022). Influence of risk propensity, behavioural biases and demographic factors on equity investors’ risk perception. Asian Journal of Economics and Banking, 6(3), 373–403.

83.

Sarwar

, Sarwar

, Raz

M. A.

, Khan

H. H.

, Muhammad

, Azhar

, Zaman

N. U.

, & Kasi

M. K.

(2020). Relationship of the big five personality traits and risk aversion with investment intention of individual investors. The Journal of Asian Finance, Economics and Business (JAFEB), 7(12), 819–829.

84.

Schaefer

P. S.

, Williams

C. C.

, Goodie

A. S.

, & Campbell

W. K.

(2004). Overconfidence and the big five. Journal of Research in Personality, 38(5), 473–480.

85.

Schreiber

J. B.

, Nora

, Stage

F. K.

, Barlow

E. A.

, & King

(2006). Reporting structural equation modeling and confirmatory factor analysis results: A review. The Journal of Educational Research, 99(6), 323–338.

86.

Shah

S. K.

, Yuan

, Tajeddini

, Gamage

T. C.

, Oláh

, & Acevedo-Duque

Á.

(2025). Exploring the intention-behavior Gap in food delivery applications: A digital transformation perspective in smart tourism. British Food Journal, Vol. ahead-of-print.

87.

Sharma

, & Kumar

(2020). A review paper on behavioral finance: Study of emerging trends. Qualitative Research in Financial Markets, 12(2), 137–157.

88.

Shefrin

(2000). Beyond greed and fear. Harvard Business School Press.

89.

Siddiqui

, Yu-Sheng

, Tajeddini

, & Tajeddini

(2025). Gamble or growth: The role of lottery features in bric stock markets amidst varying environmental, social, governance and economic conditions. Asia-Pacific Journal of Management Research and Innovation, doi: 10.1177/2319 510X251362361.

90.

Singh

, Adil

, & Haque

S. I.

(2022). Personality traits and behaviour biases: The moderating role of risk-tolerance. Quality and Quantity, 1–25. https://doi.org/10.1007/s1 1135-022-01516-4

91.

Sowmyarani

C. N.

, & Dayananda

(2018). K-anonymity versus L-diversity: A comparative analysis on data anonymization techniques. International Journal of Engineering and Technology, 7, 24–27.

92.

Steinkühler

, Mahlendorf

M. D.

, & Brettel

(2014). How self-justification indirectly drives escalation of commitment. Schmalenbach Business Review, 66(2), 191–222.

93.

Tajeddini

(2015). Using the integration of disparate antecedents to drive world-class innovation performance: An empirical investigation of Swiss watch manufacturing firms. Tékhne, 13 (1), 34–50.

94.

Tajeddini

, Gamage

T. C.

, Tajeddini

, & Kallmuenzer

(2023). How entrepreneurial bricolage drives sustained competitive advantage of tourism and hospitality SMEs: The mediating role of differentiation and risk management. International Journal of Hospitality Management, 111, 103480

95.

Tajeddini

, & Mueller

S. L.

(2009). Entrepreneurial characteristics in Switzerland and the UK: A comparative study of techno-entrepreneurs. Journal of International Entrepreneurship, 7(1), 1–25.

96.

Tajeddini

, & Mueller

S. L.

(2012). Corporate entrepreneurship in Switzerland: Evidence from a case study of Swiss watch manufacturers. International Entrepreneurship and Management Journal, 8(3), 355–372.

97.

Tajeddini

, & Tajeddini

(2008). Effect of innovativeness, risk taking, self confidence and the need for achievement on venture performance: Evidence from Switzerland. European Journal of International Management, 2(4), 437–453.

98.

Tan

C. S.

, Lau

X. S.

, Kung

Y. T.

, & Kailsan

R. A. L.

(2019). Openness to experience enhances creativity: The mediating role of intrinsic motivation and the creative process engagement. The Journal of Creative Behavior, 53(1), 109–119.

99.

ul Abdin

S. Z.

, Farooq

, Sultana

, & Farooq

(2017). The impact of heuristics on investment decision and performance: Exploring multiple mediation mechanisms. Research in International Business and Finance, 42, 674–688.

100.

Ul Abdin

S. Z.

, Qureshi

, Iqbal

, & Sultana

(2022). Overconfidence bias and investment performance: A mediating effect of risk propensity. Borsa Istanbul Review, 22(4), 780–793.

101.

Valcanover

V. M.

, Sonza

I. B.

, & da Silva

W. V.

(2020). Behavioral finance experiments: A recent systematic literature review. Sage Open, 10(4), 2158244020969672.

102.

Vuković

, & Pivac

(2023). The impact of behavioral factors on investment decisions and investment performance in Croatian stock market. Managerial Finance, 50(2), 349–366.

103.

Wang

, & Fan

(2023). Green finance and investment behavior of renewable energy enterprises: A case study of China. International Review of Financial Analysis, 87, 102564.

104.

Wong

, & Carducci

(2013). Does personality affect personal financial risk tolerance behavior? IUP Journal of Applied Finance, 19(3), 5–18.

105.

Yadav

, & Narayanan

G. B.

(2021). Do personality traits predict biasedness while making investment decisions? International Journal of Accounting and Finance Review, 6(1), 19–33.

106.

Zaidi

F. B.

, & Tauni

M. Z.

(2012). Influence of investor’s personality traits and demographics on overconfidence bias. Institute of Interdisciplinary Business Research, 4(6), 730–746.

107.

Zhang

, Wang

, & Liu

(2014). Is there any overtrading in stock markets? The moderating role of big five personality traits and gender in a unilateral trend stock market. PLoS One, 9(1), e87111.

108.

Zheng

, Zhou

, & Iqbal

(2022). Working capital management of SMEs in COVID-19: Role of managerial personality traits and overconfidence behavior. Economic Analysis and Policy, 76, 439–451.