Abstract
Purpose:
In an era marked by heightened market volatility and growing investor responsibility, understanding the psychological determinants of sound investment behaviour has become increasingly important. This study examines how emotional intelligence (EI) shapes investment performance (IP), with overconfidence bias (OB) and self-regulation (SR) acting as mediators and financial literacy (FL) serving as a moderator.
Design:
A quantitative, cross-sectional research design was employed. A structured questionnaire was administered to 448 active investors in India, predominantly working professionals aged 30–60 years who actively managed their own investment portfolios. Participants were selected through purposive sampling. The proposed conceptual model was tested using confirmatory factor analysis and structural equation modelling (SEM) in AMOS 26. Common method bias was assessed using Harman's single-factor test (which showed that a single factor accounted for 45.86% of the variance), and convergent and discriminant validity were established (CR > 0.87; AVE > 0.57).
Findings:
The structural model demonstrated a satisfactory fit (CFI = 0.970; RMSEA = 0.055). EI exerted a significant positive association with IP (β = 0.465, p < 0.001) and a strong negative association with OB (β = −0.871, p < 0.001), while OB negatively influenced IP (β = −0.194, p < 0.01). Both OB and SR significantly mediated the the relationship between EI and IP, and FL significantly moderated this relationship (β = 0.128, p < 0.001), strengthening it under conditions of high financial literacy.
Practical implications:
The findings suggest that integrating EI training into financial literacy programmes can enhance investor decision-making, improve advisory effectiveness, and improve long-term portfolio outcomes.
Originality:
The study contributes a unified, theory-driven model that simultaneously tests dual mediation and moderation pathways within the behavioural finance literature.
1 Introduction
In an era of increasingly complex and volatile financial markets, the psychological dimensions of investor decision-making have emerged as a critical area of inquiry. In the contemporary financial landscape, every investor aspires to be a responsible investor, seeking sustainable investment strategies that generate long-term, stable returns. Emotional intelligence (EI) has been shown to exert a meaningful influence on individual decision-making (Santos et al., 2018), yet its direct effect on financial decision-making and investment performance remains insufficiently explored (Pérez-Díaz et al., 2021).
The motivation for investigating this topic stems from the increasing sophistication of financial markets and the growing need for investors to make informed and rational decisions. Since financial markets are inherently volatile, the ability to regulate one's emotions and cognitive biases is critical for improving investment returns (Zhou and Gao, 2017). Overconfidence bias (OB) is a common cognitive distortion in which individuals overestimate their knowledge or abilities, frequently leading to suboptimal investment decisions (). Emotional intelligence fosters self-awareness, self-control, motivation, social skills, and empathy, all of which may play a vital role in reducing such biases (Mikolajczak et al., 2020). Financial literacy (FL), encompassing knowledge of financial concepts and risk assessment, is essential for making informed investment decisions (). Self-regulation, a core component of emotional intelligence, helps individuals manage their emotions and impulses, thereby facilitating more rational investment choices (Xiao and Porto, 2017).
The existing literature has examined the individual roles of self-regulation, financial literacy, and emotional intelligence in financial decision-making (Barber and Odean, 2001; Lusardi and Mitchell, 2011). However, a notable gap persists in understanding how these factors interact collectively. There is limited research on how self-regulation and financial literacy jointly operate with emotional intelligence to influence investment performance (). One study indicates that investors with higher emotional intelligence exhibit reduced emotional bias and enhanced self-regulation (). In addition, financial literacy has been proposed to amplify the effect of EI on investment outcomes (Murendo and Mutsonziwa, 2017).
Against this backdrop, the present study presents an integrated model to examine how emotional intelligence influences investment choices, whether EI can mitigate overconfidence bias, and what roles self-regulation and financial literacy play in the relationship between EI and investment performance. The aim of this study is therefore to develop and empirically test a unified framework that explains the psychological and behavioural mechanisms by which emotional intelligence shapes responsible investment behaviour.
The remainder of this paper is structured as follows. Section 2 reviews the relevant literature, develops the theoretical framework, and presents the study hypotheses and study importance. Section 3 outlines the research methodology. Section 4 presents the empirical findings. Section 5 discusses the findings and their implications. Section 6 concludes the paper and identifies directions for future research.
2 Theoretical framework and development of hypotheses
A robust theoretical foundation is necessary to comprehend the relationship between EI and investment performance. Several well-established theoretical perspectives illuminate how behavioural tendencies, emotions, and self-regulation influence financial decision-making. The present study is grounded in four complementary theoretical perspectives, each of which contributes distinctively to the structural model and provides a specific theoretical rationale for the proposed hypotheses.
First, Emotional Intelligence Framework provides the primary theoretical basis for Hypothesis 1 (H1: EI → IP). This framework posits that emotional intelligence comprises five interrelated competencies: self-awareness, self-regulation, motivation, empathy, and social skills. In the investment context, these competencies collectively enable investors to appraise risk with greater objectivity, maintain composure during market turbulence, and make deliberate, goal-aligned decisions. Goleman's framework uniquely contributes to this model by explaining the direct pathway from EI to investment performance: emotionally intelligent investors are less susceptible to impulsive, affect-laden decision-making and more likely to apply systematic, evidence-based reasoning when managing their portfolios. This theory thus underpins the direct EI–IP relationship tested in H1.
Second, prospect theory () provides the theoretical mechanism underlying Hypotheses 2, 3, and 4 (H2: EI → OB; H3: OB → IP; H4: EI → OB → IP). Prospect theory demonstrates that individuals systematically deviate from rational economic behaviour due to cognitive biases rooted in how gains and losses are perceived and evaluated. Overconfidence bias—the tendency to overestimate one's predictive accuracy and underestimate investment risk—is one of the most pervasive of such biases. Prospect theory contributes uniquely to the model by explaining why overconfidence leads to suboptimal investment outcomes (H3): overconfident investors overweight their own judgments, underreact to negative signals, and take on excessive risk, all of which reduce portfolio performance. Crucially, prospect theory also creates theoretical space for EI as a corrective mechanism (H2 and H4): investors with higher EI, possessing greater self-awareness and capacity for emotional regulation, are better able to recognise and counteract the cognitive distortions that prospect theory identifies. The EI–OB–IP-mediated pathway (H4) is therefore theoretically grounded in the interplay between affective regulation and cognitive bias correction.
Third, self-regulation theory () provides the theoretical grounding for Hypotheses 4 and 5 (H4: EI → OB → IP; H5: EI → SR → IP). Self-regulation theory conceptualises the capacity to govern one's emotions, impulses, and behaviours in the service of longer-term goals as a finite, learnable resource that varies systematically across individuals. Within the investment domain, self-regulation enables investors to override emotionally driven reactions—such as panic selling or speculative trading—in favour of considered, plan-consistent action. This theory contributes distinctively to the structural model by explaining how EI translates into better investment performance through the mediating role of self-regulation (H5): emotional intelligence enhances an individual's self-regulatory capacity, which, in turn, promotes disciplined investment conduct and superior financial outcomes. Self-regulation theory thus explains the internal psychological mechanism through which EI exerts its beneficial effects on investment performance, beyond the direct pathway captured by H1.
Fourth, social learning theory () provides the theoretical justification for Hypothesis 6 (H6: FL moderates EI → IP). Social learning theory posits that behaviour is shaped not only by individual dispositions but also by acquired knowledge, observational learning, and cognitive appraisals of one's environment. Applied to investment behaviour, this theory suggests that financial literacy—encompassing knowledge of financial products, risk-return trade-offs, and market mechanisms—is a critical learned resource that shapes how investors interpret and respond to market information. Social learning theory contributes uniquely to the model by explaining why financial literacy amplifies the effect of EI on investment performance (H6): EI equips investors with the affective and motivational capacity to act wisely, while financial literacy provides the cognitive content and analytical tools needed to translate that capacity into well-informed decisions. The moderating role of financial literacy thus reflects the interaction between dispositional emotional competence and acquired knowledge—a theoretically distinct and empirically testable contribution. Collectively, these four theoretical frameworks do not merely describe the study's constructs but provide precise mechanistic explanations for each proposed relationship in the structural model, rendering the framework theory-driven rather than construct-assembled.
The specific theoretical contribution of this study lies in the simultaneous and integrated application of these four frameworks within a single structural model. Prior research has examined the roles of EI, overconfidence bias, self-regulation, and financial literacy in investment behaviour in a largely piecemeal fashion—investigating one or two constructs at a time, or failing to test mediating and moderating pathways concurrently. The present study addresses this gap by constructing a unified model in which (a) EI exerts a direct effect on investment performance (grounded in Goleman's EI framework); (b) this effect is partially mediated through the suppression of overconfidence bias (grounded in prospect theory); (c) it is further mediated through the enhancement of self-regulation (grounded in self-regulation theory); and (d) the direct relationship between EI and IP is further conditioned by the level of financial literacy (grounded in social learning theory). This configuration—testing dual mediation pathways and a moderation effect simultaneously within a structural equation model—has not been empirically examined in the behavioural finance literature in this form. The novelty of this study therefore lies not in the individual constructs but in their theoretically grounded integration, which yields a more complete and nuanced account of the psychological mechanisms underlying responsible investment behaviour.
2.1 Emotional intelligence and investment performance
Emotional intelligence is a multidimensional construct that has attracted considerable scholarly attention in organizational and psychological research (, p. 483). Salovey and Mayer (1990) defined emotional intelligence as the capacity to perceive, appraise, and regulate emotions in oneself and others. subsequently popularized this concept by demonstrating its critical relevance to personal and professional outcomes. The five core components of emotional intelligence include self-awareness, self-regulation, motivation, empathy, and social skills. Collectively, these competencies enhance an the capacity of an individual to regulate emotions, thereby facilitating more rational investment choices (Pirsoul et al., 2023).
Emotional intelligence significantly influences financial decision-making by shaping how investors assess risk, manage cognitive biases, and refine portfolio strategies. Individuals with strong EI demonstrate superior self-discipline, reducing impulsive actions driven by market anxiety or excessive confidence. EI enables investors to navigate volatile market conditions with greater composure, fostering disciplined and rational investment behaviour (). Studies indicate that EI enhances the ability of an investor to gauge market sentiment, recognize investment patterns, and adjust asset allocation accordingly (Mancini et al., 2024). Emotionally intelligent investors are also more adept at evaluating risk tolerance, acknowledging overconfidence, and mitigating psychological biases such as herd behaviour (Loewenstein et al., 2001). Their capacity to maintain emotional stability during market fluctuations contributes to sustained wealth accumulation, distinguishing them from investors who react impulsively under pressure (Barber and Odean, 2001). Mayer et al. (2004) suggested that individuals with high EI are better equipped to manage stress and make sound, logical decisions, which is particularly important in financial markets where emotional stability significantly affects investment outcomes (Rode et al., 2017). Moreover, recognizing and understanding one's emotions can improve decision-making, and EI can reduce conflict and hesitation during complex financial decisions (Sharma, 2024).
Research indicates that individuals with high emotional intelligence tend to trade less frequently and make more effective fund selections, thereby improving investment performance (
).
found that individuals with high emotional intelligence demonstrated superior decision-making ability and were less susceptible to emotional bias. Similarly,
Mahalingam et al. (2021)reported that emotional intelligence education strengthens individuals' capacity to manage investment behaviour more effectively. Although these findings underscore the relevance of EI in investment decision-making, empirical research examining its impact on investment performance remains scarce. Behavioural finance has largely concentrated on cognitive biases, overlooking the distinct contribution of emotional intelligence to financial performance. Accordingly, the present study aims to provide valuable empirical insights into this relationship, with a view to helping investors develop more resilient and effective investment strategies. Based on the foregoing discussion, the following hypothesis is proposed:
H1: A higher level of EI is positively associated with better IP.
2.2 Overconfidence bias
In the context of financial decision-making, overconfidence bias is a well-documented cognitive distortion in which individuals exaggerate their knowledge, skills, or ability to predict outcomes (). This tendency frequently leads to excessive trading, inadequate diversification, and inferior investment outcomes. examined overconfidence within the behavioural finance framework and demonstrated that it consistently leads to suboptimal decision-making and reduced returns. further investigated the interaction between overconfidence bias and emotional intelligence, finding that individuals with high EI exhibit greater self-awareness and self-regulation and are consequently less prone to overconfidence. Their study documented that investors with stronger EI are more effective at recognizing their own limitations and making decisions based on rational analysis rather than emotional impulse. Niu et al. (2019) similarly found that emotional intelligence training helps less experienced investors overcome overconfidence bias, thereby improving investment performance.
Overconfidence bias plays a critical role in investment decision-making as a form of cognitive misjudgment. Investors prone to overconfidence often engage in excessive trading, assume disproportionate risk, and make poor financial decisions, ultimately resulting in portfolio underperformance. This misplaced confidence leads to riskier strategies, including concentrated holdings and leveraged positions, which increase exposure to market volatility and potential financial losses (). Empirical evidence indicates that overconfident investors trade more frequently than their rational counterparts, yet this heightened activity does not yield superior returns. Furthermore, overconfident individuals frequently misjudge market reversals, holding losing positions under the erroneous assumption that prices will eventually recover ().
Differences in overconfidence bias have also been observed across demographic groups. Research suggests that male investors typically exhibit higher levels of overconfidence than female investors, leading to greater trading volume and consequently lower net returns. Cultural factors also shape investment behaviour, as individuals from societies that emphasize individualism tend to display heightened overconfidence, thereby increasing their risk-taking tendencies (
). Variables such as overconfidence, disposition effect, and risk aversion play significant roles in investment decision-making, whereas herd behaviour appears to exert a comparatively weaker influence (
Poudel et al., 2024). Overconfidence bias has been found to exert a moderate but meaningful impact on investment decision-making processes (
Parhi, 2022). Despite extensive research on overconfidence, its interaction with other psychological and cognitive traits, such as emotional intelligence, financial literacy, and investor sentiment, warrants further investigation. As global financial markets become increasingly complex, there is a growing need to develop behavioural strategies that mitigate the adverse effects of overconfidence. Understanding how investors can recognise and manage this bias may lead to more prudent decision-making, thereby improving financial stability and long-term investment success for both individual and institutional investors (
Pompian, 2012). In light of these considerations, the following hypotheses are proposed:
H2: EI is negatively associated with OB.
H3: OB is negatively associated with IP.
H4: EI negatively influences OB, which in turn improves IP.
2.3 Financial literacy
Financial literacy refers to the awareness, knowledge, and comprehension of financial concepts, risks, and the capacity to make informed financial decisions. Lusardi and Mitchell (2011) established that financial literacy plays a significant role in sound investment behaviour and effective financial planning. It encompasses a broad range of competencies, including investing, budgeting, and a comprehensive understanding of financial products and services. Financial literacy is associated with positive attitudes and greater confidence, equipping investors with the knowledge and skills required for effective financial decision-making (Maheshwari et al., 2024). Adequate financial guidance helps reduce irrational behaviour at the point of decision-making (Nadhila et al., 2024). While overconfidence can undermine the quality of investment decisions, financial literacy can enhance decision quality (). The relationship between financial behaviour and financial attitude is further moderated by investment opportunities (). Collectively, these findings suggest that financial literacy builds the confidence necessary to make sound investment decisions ().
Financially literate investors are more likely to adopt prudent investment strategies, such as portfolio diversification and long-term financial planning, which optimize returns while minimizing unnecessary risk exposure (Van Rooij et al., 2011). Studies indicate that financially knowledgeable individuals excel in risk management, making well-reasoned investment choices and avoiding detrimental behaviours such as panic selling or speculative trading (). Moreover, greater financial awareness fosters confidence in investment decisions, reducing susceptibility to emotional biases that often lead to irrational market participation (Yoong, 2011). Conversely, individuals with limited financial knowledge tend to struggle with fundamental investment principles, misallocating resources toward low-return instruments rather than higher-growth investment opportunities (Lusardi and Tufano, 2015).
Financial literacy also plays a crucial role in facilitating market participation. Empirical research suggests that individuals with limited financial education are significantly less likely to invest in equity markets, thereby forgoing potential long-term gains (
). Furthermore, inadequate financial understanding leaves investors vulnerable to deceptive financial schemes, misinformation, and poor financial decision-making, all of which can result in significant monetary losses (
). Well-informed investors, by contrast, are better equipped to evaluate various investment products, understand risk-return trade-offs, and make efficient use of financial instruments to build sustainable wealth (
). Research by
Stolper and Walter (2017)further demonstrated that financial literacy interventions enhance decision-making capacity and skills. These findings support the inclusion of financial literacy as a moderator in the proposed conceptual model. Based on the foregoing discussion, the following hypothesis is proposed:
H5: FL moderates the relationship between EI and IP, such that this relationship is stronger when financial literacy is high.
2.4 Self-regulation
defined self-regulation as the capacity to control one's emotions, feelings, impulses, and behaviours in ways that facilitate goal attainment. Self-regulation plays a crucial role in financial decision-making by maintaining discipline, preventing emotionally driven decisions, and sustaining commitment to long-term investment objectives. Tangney et al. (2004) posited that overconfidence bias can be effectively controlled through self-regulation, which promotes careful, systematic decision-making. Investors with strong self-regulatory abilities demonstrate greater discipline, enabling them to resist short-term market temptations and make well-informed, rational financial decisions ().
Research suggests that self-regulated investors exhibit enhanced risk management skills, enabling them to evaluate market trends objectively and avoid common behavioural pitfalls, such as excessive trading, herd behaviour, and loss aversion. By maintaining patience and emotional discipline, such investors are more likely to adhere to structured investment approaches, ultimately optimizing portfolio returns over time (Shiv et al., 2005). Conversely, investors lacking self-regulation frequently alter their investment strategies in response to market fluctuations, often making speculative decisions that hinder long-term financial growth. One of the key benefits of self-regulation in financial decision-making is its ability to mitigate emotional biases, such as fear and greed, which often lead to irrational financial choices. For instance, during economic downturns, self-regulated investors are less likely to engage in panic selling, whereas those with weaker self-discipline may react emotionally by disposing of assets at a loss (Gervais and Odean, 2001).
Moreover, self-regulation is closely linked to goal-setting and long-term financial stability. Investors who establish clear financial objectives and consistently adhere to well-planned strategies tend to achieve more sustainable returns (
Thaler and Benartzi, 2004). These individuals are also more likely to follow fundamental investment principles, such as disciplined saving and gradual wealth accumulation over time (
). A study by
Strömbäck et al. (2017)demonstrated that self-regulation skills are correlated with improved investment performance and sound financial conduct. Research by
Thaler and Benartzi (2017)further confirmed that interventions aimed at enhancing self-regulation improve the quality of financial decision-making and offer practical guidance for financial trainers and educators. Despite its recognized importance, the relationship between self-regulation and investment performance remains underexplored empirically. Behavioural finance has extensively examined cognitive biases, yet further research is needed to understand how self-regulation influences investment discipline and financial success. Based on the foregoing discussion, the following hypothesis is proposed:
H6: SR mediates the relationship between EI and IP.
2.5 Study importance
This study makes several important contributions to the behavioural finance literature. Theoretically, it offers a unified framework that integrates four well-established perspectives, viz., emotional intelligence theory, prospect theory, self-regulation theory, and social learning theory, within a single structural model, thereby moving beyond the piecemeal treatment of these constructs in prior research. Methodologically, the simultaneous testing of dual mediation pathways (through overconfidence bias and self-regulation) and a moderation effect (through financial literacy) provides a more comprehensive empirical account of the relationship between EI and investment performance than has previously been reported. In practice, the findings inform the design of investor education programmes, the calibration of advisory services, and the development of behavioural finance tools by demonstrating that emotional, cognitive, and educational competencies act jointly—rather than independently—to shape investment performance. The study thereby supports a holistic, psychologically grounded approach to investor development with implications for individual investors, financial institutions, and policymakers in emerging markets such as India.
2.6 Study hypotheses
Drawing on the foregoing theoretical and empirical literature, the present study proposes the following six hypotheses:
H1: A higher level of EI is positively associated with better IP.
H2: EI is negatively associated with OB.
H3: OB is negatively associated with IP.
H4: OB mediates the relationship between EI and IP.
H5: FL moderates the relationship between EI and IP, such that the relationship is stronger when financial literacy is high.
H6: SR mediates the relationship between EI and IP.
presents the conceptual framework illustrating the relationships among the constructs examined in this study.
Figure 1
3 Research methodology
This study employed a quantitative, cross-sectional research design grounded in the methodological recommendations of for survey-based behavioural research using structural equation modelling (SEM). The target population comprised financial advisors and individual investors who actively managed their investment portfolios. Respondents were predominantly working professionals aged 30–60 years, with a minimum educational qualification of a university graduate degree. A purposive sampling technique was adopted to ensure that respondents possessed the requisite financial experience and portfolio size to provide meaningful data. The minimum educational qualification for participation was a university graduate degree. Data were collected through a structured online questionnaire administered via Google Forms. Follow-up reminders were sent via email and WhatsApp to increase the response rate. The data collection period ran from 10 October 2025 to 21 January 2026, totaling approximately 4 months. Questionnaires were distributed to more than 1,200 potential respondents, yielding 630 responses. To ensure data quality and relevance, responses from investors with portfolios below INR 5 lakhs and with less than one year of investment experience were excluded. Following this screening process, a final usable sample of 448 responses was retained for analysis.
It is acknowledged that the use of purposive sampling and the exclusion of investors with smaller portfolios introduce a degree of socioeconomic selectivity, which may limit the generalizability of the findings to investors with larger, more established portfolios. The implications of this limitation are discussed in the Conclusion section.
Among the final sample, 65.18% of respondents were men and 34.82% were women. The age profile reflects the working-professional investor segment, with the majority of respondents aged 41–50 years. In terms of portfolio size, 23.33% held portfolios valued between INR 5 and 10 lakhs, 50.44% held portfolios between INR 11 and 20 lakhs, and 26.22% held portfolios exceeding INR 20 lakhs (Table 1).
Table 1
| Parameters | Percentage | Frequency |
|---|---|---|
| Gender | ||
| Male | 65.18 | 292 |
| Female | 34.82 | 156 |
| Portfolio size INR (in lakhs) | ||
| 5–10 | 23.33 | 103 |
| 11–20 | 50.44 | 227 |
| >20 | 26.22 | 118 |
| Age (years) | ||
| 30–40 | 28.57 | 128 |
| 41–50 | 50.22 | 225 |
| 51–60 | 21.21 | 95 |
Demographic profile of respondents.
Source: Authors’ own work.
The following constructs were measured in the study:
i. Emotional intelligence: Emotional intelligence was operationalized as a reflective latent construct capturing the five core dimensions identified by : self-awareness, self-regulation, motivation, empathy, and social skills. Although EI is inherently multidimensional, the present study adopts a composite measurement approach consistent with prior research employing abbreviated EI scales in behavioural finance contexts (; ). Specifically, each of the five items was adapted to correspond to one of the five Goleman dimensions, ensuring that the scale remains theoretically comprehensive despite its brevity. Psychometric evidence supports the adequacy of this operationalization: all five items demonstrated strong standardized factor loadings ranging from 0.747 to 0.873, the composite reliability (CR = 0.911) exceeded the recommended threshold of 0.70, and the average variance extracted (AVE = 0.672) surpassed the minimum criterion of 0.50 (), collectively confirming acceptable convergent validity and internal consistency. The present study, however, acknowledges that future research should employ more comprehensive, psychometrically validated EI instruments to further strengthen construct validity. Five items adapted from and were used to measure this construct.
ii. Overconfidence bias: This variable assesses the extent of overconfidence in financial choices made by investors. Five items adapted from Barber and Odean (2001) were used to measure overconfidence bias.
iii. Financial literacy: This variable assesses understanding of financial concepts and risk. Five items adapted from Lusardi and Mitchell (2011) were used to measure financial literacy.
iv. Self-regulation: This variable assesses impulse control and emotional regulation. Five items adapted from were used to measure self-regulation.
v. Investment performance: This variable measures the perceived investment performance of respondents. Three items adapted from and were used to measure investment performance.
4 Results
Prior to estimating the structural equation model, a confirmatory factor analysis was conducted to verify the correspondence between the observed data and the proposed theoretical measurement model. This step is essential for confirming the reliability and validity of the constructs and for establishing the quality of the proposed causal structure between latent and observed variables (). Model fit was assessed using multiple fit indices computed in AMOS 26 software. As reported in Table 2, the measurement model demonstrated a satisfactory level of fit (χ2 = 369.997, df = 220, p < 0.001, χ2/df = 1.682, GFI = 0.933, SRMR = 0.035, RMSEA = 0.039, CFI = 0.980, TLI = 0.977, PClose = 0.996), consistent with recommended thresholds ().
Table 2
| Fit indices | CMIN/DF | GFI | NFI | TLI | CFI | RMSEA | SRMR |
|---|---|---|---|---|---|---|---|
| Recommended value | ≤3.000 | ≥0.90 | ≥0.950 | ≥0.950 | ≥0.950 | ≤0.06 | <0.08 |
| Measurement value | 1.682 | 0.933 | 0.952 | 0.977 | 0.980 | 0.039 | 0.035 |
Model fit indices.
Items measuring overconfidence bias (OB) were reverse-coded prior to analysis.
Source: Authors’ own calculation.
Composite reliability (CR) was employed to evaluate the internal consistency of the measurement scales. The CR values for all constructs exceeded the recommended threshold of 0.70, supporting scale reliability (; Nunnally, 1970). Convergent validity was assessed using average variance extracted (AVE). As reported in Table 3, the AVE values for all five constructs exceeded the recommended minimum of 0.50, and all standardized factor loadings (reported in Appendix A) surpassed the recommended threshold of 0.50 (). Discriminant validity was confirmed by verifying that the square root of each construct's AVE exceeded its correlations with all other constructs, consistent with the criterion established by .
Table 3
| Construct | CR | AVE | EI | SR | FL | OB | IP | MSV |
|---|---|---|---|---|---|---|---|---|
| EI | 0.911 | 0.672 | 0.820 | 0.598 | ||||
| SR | 0.872 | 0.578 | 0.583* | 0.760 | 0.420 | |||
| FL | 0.899 | 0.641 | 0.732* | 0.580* | 0.801 | 0.535 | ||
| OB | 0.925 | 0.713 | −0.774* | −0.649* | −0.655* | 0.844 | 0.599 | |
| IP | 0.892 | 0.735 | 0.714* | 0.591* | 0.645* | −0.673* | 0.857 | 0.453 |
Validity analysis.
Bold diagonal values represent the square root of AVE. CR, composite reliability; AVE, average variance extracted; EI, emotional intelligence; SR, self-regulation; FL, financial literacy; OB, overconfidence bias; IP, investment performance; MSV, maximum shared variance.
p < 0.001.
Source: AMOS output (prepared by authors).
To assess common method bias (CMB), Harman's single-factor test was conducted via an exploratory factor analysis, with simultaneous extraction of all factors. The results indicated that no single dominant factor emerged; the single factor accounted for 45.862% of the total variance, which is below the prescribed threshold of 50% recommended by Podsakoff et al. (2003). This finding suggests that common method bias is unlikely to pose a significant threat to the validity of the study results.
4.1 Structural model
Following confirmation of measurement model adequacy, SEM was performed using maximum likelihood estimation in AMOS 26, as illustrated in Figure 2. In this model, EI was specified as a first-order exogenous variable. SR and OB were treated as mediating variables. FL served as a moderating variable. IP served as the endogenous (outcome) variable.
Figure 2
The structural model demonstrated a satisfactory level of fit (χ2 = 305.685, df = 130, χ2/df = 2.351, CFI = 0.970, GFI = 0.929, TLI = 0.965, RMSEA = 0.055, SRMR = 0.039). The examination of path estimates, reported in Table 4, reveals that EI exerts a significant positive effect on IP (β = 0.465, SE = 0.084, p < 0.001), thereby supporting Hypothesis H1. Furthermore, EI negatively and significantly influences OB (β = −0.871, SE = 0.057, p < 0.001), and overconfidence bias, in turn, negatively and significantly influences IP (β = −0.194, SE = 0.065, p = 0.003). These results support Hypotheses H2 and H3.
Table 4
| Dependent variable | Path | Independent variable | Estimate (β) | S.E. | C.R. (t) | p | Result |
|---|---|---|---|---|---|---|---|
| IP | ← | EI | 0.465 | 0.084 | 5.563 | * | Supported (H1) |
| OB | ← | EI | −0.871 | 0.057 | −15.227 | * | Supported (H2) |
| IP | ← | OB | −0.194 | 0.065 | −2.995 | 0.003 | Supported (H3) |
Path estimates.
EI, emotional intelligence; SR, self-regulation; OB, overconfidence bias; IP, investment performance.
Source: Prepared by authors.
p < 0.001.
4.2 Mediation effects
In addition to the direct path estimates, the study examined the mediation effects using a bootstrap procedure (n = 5,000 resamples), and the results are reported in Table 5. The indirect effect of OB on the relationship between EI and IP was statistically significant (EI → OB → IP: β = 0.169, 95% CI [0.049, 0.296], p = 0.005). The indirect effect of SR on the relationship between EI and IP was also statistically significant (EI → SR → IP: β = 0.124, 95% CI [0.026, 0.234], p = 0.011). The non-overlapping confidence intervals confirm the robustness of these mediation effects. These results indicate that while EI reduces overconfidence bias, this reduced bias also contributes to improved investment performance, thus confirming the mediating role of OB. These findings support Hypotheses H4 and H5.
Table 5
| Parameter | Estimate | Lower CI | Upper CI | p |
|---|---|---|---|---|
| EI→OB→IP | 0.169 | 0.049 | 0.296 | 0.005 |
| EI→SR→IP | 0.124 | 0.026 | 0.234 | 0.011 |
Path estimates.
EI, emotional intelligence; SR, self-regulation; OB, overconfidence bias; IP, investment performance. Bootstrap samples = 5,000.
Source: Authors’ own calculation.
4.3 Moderation effect
To examine the moderating effect of FL on the relationship between EI and IP, factor scores for all variables were estimated using the data imputation method in AMOS 26. To construct the interaction term between EI and FL, all variables were standardized in SPSS 25 using the mean-centering procedure recommended by to address potential multicollinearity concerns. The interaction term (EI × FL) was then introduced into the structural model and re-estimated in AMOS 26. Results are presented in Table 6 and support Hypothesis H6, indicating that financial literacy significantly moderates the relationship between EI and investment performance (β = 0.128, SE = 0.022, p < 0.001), such that this relationship is stronger when financial literacy is high.
Table 6
| Dependent | Moderation term | Estimate (β) | S.E. | C.R. | p |
|---|---|---|---|---|---|
| IP | EI | 0.394 | 0.058 | 6.746 | * |
| IP | FL | 0.229 | 0.055 | 4.146 | * |
| IP | EI × FL | 0.128 | 0.022 | 5.800 | * |
Moderation effects.
EI, emotional intelligence; FL, financial literacy; IP, investment performance.
p < 0.001.
To complement the mean-centering procedure and address potential multicollinearity directly, variance inflation factor (VIF) values were computed for all predictors in the structural model. As reported in Table 7, the VIF values for emotional intelligence (1.235), overconfidence bias (3.124), self-regulation (2.635), financial literacy (2.263), and the EI × FL interaction term (2.795) are all well below the conservative threshold of 5 recommended by . These results confirm that multicollinearity does not pose a concern in the present model and that the regression coefficients reported in the structural and moderation analyses can be interpreted with confidence (Figure 3).
Table 7
| Predictor | VIF |
|---|---|
| EI | 1.235 |
| OB | 3.124 |
| SR | 2.635 |
| FL | 2.263 |
| EI × FL | 2.795 |
Multicollinearity diagnostics (variance inflation factor).
Figure 3
5 Discussion
The present study investigates the relationship between emotional intelligence and investment performance, employing structural equation modeling via AMOS to examine interactions among the key constructs. The findings reveal that a higher level of emotional intelligence is positively associated with better investment performance, consistent with prior research (; ). The study further demonstrates that emotional intelligence is negatively associated with overconfidence bias. Research has consistently shown that overconfidence adversely affects investment performance (Parhi, 2022; Pillai and Rajasekar, 2021; Poudel et al., 2024). The present findings extend this body of evidence by demonstrating that emotionally intelligent investors are more likely to engage in reflective, evidence-based decision-making, thereby reducing the deleterious influence of overconfidence. It is important to note, however, that given the cross-sectional nature of the data, these relationships should be interpreted as associations rather than causal effects. Longitudinal research would be required to establish directionality more conclusively.
The findings also underscore the important role of financial literacy in amplifying the benefits of emotional intelligence on investment performance. Investors with higher levels of financial literacy are better equipped to interpret market trends and make well-reasoned investment choices. This result corroborates the conclusions of Lusardi and Mitchell (2014) regarding the necessity of financial education in developing investment competence. Furthermore, a high level of financial literacy strengthens the relationship between emotional intelligence and the quality of investment decision-making. Self-regulation also emerges as a significant mediating mechanism in the relationship between emotional intelligence and investment performance. Investors with strong self-regulatory capacity are better able to manage impulsive tendencies and maintain a long-term investment perspective, even during periods of market volatility. This finding corroborates the conclusions of , who emphasized the critical role of self-regulation in promoting rational decision-making.
A notable contribution of this study lies in its comprehensive examination of how self-regulation and financial literacy jointly moderate and mediate investment performance outcomes. The findings demonstrate that these elements work synergistically to mitigate overconfidence bias and promote a more deliberate and balanced approach to investing. By integrating emotional intelligence with financial knowledge and self-regulatory capacity, investors can enhance their ability to navigate market challenges and achieve improved financial outcomes.
6 Practical implications
This study highlights the importance of a comprehensive strategy that incorporates emotional intelligence, financial literacy, and self-regulation as interconnected pillars of sound investment decision-making. In an increasingly complex and volatile financial environment, these three factors serve as essential foundations for prudent financial behaviour. The following practical implications are proposed for investors, financial advisors, and policymakers.
6.1 Developing investor education programmes
Financial institutions can design investor education initiatives that integrate emotional intelligence training with financial literacy development, helping investors identify and manage cognitive biases, such as overconfidence. Such programmes should focus on cultivating self-awareness and self-regulation, equipping participants with strategies to counteract impulsive financial decisions. Complementary financial literacy workshops can provide practical knowledge in market analysis, risk assessment, and portfolio management under volatile conditions, thereby enhancing the capacity of investors for data-driven decision-making.
6.2 Tailored financial advisory services
Financial advisors can incorporate EI assessments into their client profiling processes to deliver more personalized investment advice. By identifying emotional biases such as impulsivity or overconfidence, advisors can develop strategies aligned with risk tolerance of clients and long-term financial objectives. Techniques such as behavioural coaching and scenario analysis can encourage a more systematic approach to wealth management. In addition, EI-focused training for financial professionals can enhance empathetic communication, strengthen advisor–client relationships, and improve overall financial planning outcomes.
6.3 Community engagement and policy initiatives
Policymakers and financial regulators can advocate for integrating financial literacy and emotional intelligence education into public finance programmes. Wellness and financial resilience initiatives can implemented by governments and financial organizations to equip individuals with critical financial decision-making skills. Community outreach programmes targeting economically vulnerable populations can reduce financial vulnerability and promote long-term financial stability by combining technical financial knowledge with emotional management strategies.
6.4 Integration of behavioural finance tools in fintech platforms
Fintech platforms can enhance user experience by integrating behavioural finance tools that support rational decision-making. Features such as real-time emotional cues or reflective prompts can encourage investors to consider their psychological state before executing significant financial decisions. Educational content embedded within fintech applications can further guide investors in recognizing emotional biases and applying disciplined investment techniques, thereby promoting financial self-regulation and reducing reactionary trading behaviour.
6.5 Strengthening financial resilience
The combination of emotional intelligence and financial literacy fosters resilience in investment decision-making. Investors who cultivate both intellectual and emotional management skills are better equipped to withstand market volatility without succumbing to panic or herd behaviour. This integrated approach promotes long-term financial health by helping individuals avoid common psychological pitfalls, such as speculative investments, and contributes to broader market stability.
6.6 Adopting a holistic advisory model
Financial institutions can enhance the quality of their advisory services by inntegrating behavioural coaching into conventional investment planning frameworks. This approach may include monitoring emotional responses of clients to market events and providing resources that support goal-oriented, long-term investment strategies. By prioritizing sustainable financial planning over short-term speculative gains, advisors can offer more meaningful and comprehensive guidance. Acknowledging the psychological and emotional dimensions of financial decision-making enables advisors to serve clients more effectively and build lasting trust.
7 Conclusion
The present study demonstrates that a balanced integration of emotional intelligence, financial literacy, and self-regulation is essential for superior investment performance. Investors with higher emotional intelligence are better equipped to regulate their emotions, thereby reducing the likelihood of impulsive financial decisions. Financial literacy provides the analytical foundation necessary to navigate market complexity, while self-regulation promotes disciplined, systematic decision-making, particularly during periods of market uncertainty. The findings of this study support the importance of integrating behavioural finance principles into conventional investment frameworks and advisory models. Financial institutions can leverage these insights to design targeted interventions, including programmes that combine financial education with emotional intelligence training, enabling investors to make more composed and informed decisions. Investment platforms can further support investors by offering resources that address emotional biases and promote data-driven, methodical decision-making. Providing investors with a diverse range of cognitive, emotional, and educational resources can substantially reduce the risks associated with irrational financial behaviour, such as panic selling and excessive risk-taking. Ultimately, cultivating a more informed and emotionally resilient investor base is likely to contribute to a stronger and healthier financial system.
8 Limitations and directions for future research
The present study has several limitations that should be acknowledged. First, the use of purposive sampling and the exclusion of investors with portfolios below INR 5 lakhs may limit the generalizability of the findings to broader investor populations, including retail investors with smaller portfolios. The sample predominantly represents financially experienced investors, potentially introducing socioeconomic selectivity. Second, all data were collected through self-report questionnaires administered at a single point in time, which constrains the ability to draw causal inferences. Although Harman's single-factor test was employed to assess common method bias, the study acknowledges that self-reported data may still be subject to response biases. Third, the measurement of emotional intelligence using five items adapted from Goleman's framework, while consistent with prior studies in this domain, constitutes an abbreviated operationalization of a complex, multidimensional construct. Future research should adopt more comprehensive, psychometrically validated EI instruments to strengthen construct validity.
These findings open several productive directions for future research. Cross-cultural comparative studies examining the role of emotional intelligence in investment decision-making across different economic contexts would provide important insights into region-specific investment behaviour. Future research may also investigate EI and related constructs in specialized investment domains, such as cryptocurrency markets, real estate, and systematic investing. Longitudinal studies tracking changes in emotional intelligence and their effects on investment decision-making over time would provide valuable causal evidence. Additionally, future research could explore how digital financial platforms and artificial intelligence tools interact with emotional and cognitive characteristics of investors, offering a more nuanced understanding of decision-making in technology-mediated financial environments.
Statements
Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material; further inquiries can be directed to the corresponding author/s.
Ethics statement
The studies involving humans were approved by Institutional Ethics Committee (IEC), MIT School of Distance Education. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.
Author contributions
RR: Writing – original draft. SR: Conceptualization, Writing – review & editing. CK: Formal analysis, Data curation, Methodology, Writing – review & editing. AD: Writing – review & editing, Supervision, Methodology. NK: Project administration, Validation, Writing – review & editing.
Funding
The author(s) declared that financial support was not received for this work and/or its publication.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that generative AI was not used in the creation of this manuscript.
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Appendix A Standardized factor loadings
| Estimate | Path | Constructs | Standardized factor loading |
|---|---|---|---|
| EI1 | ← | EI | 0.832 |
| EI4 | ← | EI | 0.783 |
| EI2 | ← | EI | 0.873 |
| EI5 | ← | EI | 0.747 |
| EI3 | ← | EI | 0.858 |
| SR4 | ← | SR | 0.724 |
| SR5 | ← | SR | 0.725 |
| SR2 | ← | SR | 0.772 |
| SR3 | ← | SR | 0.794 |
| SR1 | ← | SR | 0.783 |
| FL1 | ← | FL | 0.776 |
| FL2 | ← | FL | 0.836 |
| FL3 | ← | FL | 0.806 |
| FL5 | ← | FL | 0.764 |
| FL4 | ← | FL | 0.819 |
| OB1 | ← | OB | 0.779 |
| OB2 | ← | OB | 0.854 |
| OB3 | ← | OB | 0.875 |
| OB5 | ← | OB | 0.848 |
| OB4 | ← | OB | 0.862 |
| IP3 | ← | IP | 0.783 |
| IP1 | ← | IP | 0.883 |
| IP2 | ← | IP | 0.900 |
Summary
Keywords
emotional intelligence, financial literacy, investment performance, overconfidence bias, self-regulation
Citation
Raut R, Rao S, Kumar C, Deshpande A and Kaul N (2026) Exploring the determinants of responsible investing: revisiting the role of emotional intelligence in investment behaviour. Front. Behav. Econ. 5:1826273. doi: 10.3389/frbhe.2026.1826273
Received
09 March 2026
Revised
09 May 2026
Accepted
15 May 2026
Published
24 June 2026
Volume
5 - 2026
Edited by
Huanren Zhang, University of Southern Denmark, Denmark
Reviewed by
Mamdouh Helali, King Faisal University, Saudi Arabia
Bustani Bustani, STIE Nasional Banjarmasin, Indonesia
Updates
Copyright
© 2026 Raut, Rao, Kumar, Deshpande and Kaul.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Rajesh Raut rajesh.raut@mitsde.com
Disclaimer
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