Residualized Big Five Traits and Financial Risk Tolerance: Connecting Tolerance to Behavior

Abstract

Research on financial risk tolerance and risk-taking increasingly incorporates personality traits into predictive and descriptive models of risk-taking behavior; however, intercorrelations among traits can obscure the unique contributions of individual traits. This is known as the suppressor effect. This study employed a two-stage analytic framework to test and adjust for suppressor effects across the Big Five personality dimensions in describing financial risk tolerance. In Stage 1, correlation and OLS regression analyses identified suppression patterns, revealing that the explanatory validity of some factors was distorted by shared variance. In Stage 2, suppression-adjusted trait estimates were used to reassess their unique association with financial risk-taking mediated through financial risk tolerance. Results indicate that Openness to Experience and Extraversion are the strongest descriptors of financial risk-taking once suppressor effects are controlled. At the same time, Agreeableness and Conscientiousness contribute modestly and context-dependently to descriptions of financial risk-taking. These findings demonstrate that ignoring suppression effects can lead to mischaracterizing the role of personality in financial decision-making. This study shows that more precise estimates of trait influences can improve theoretical models of investor behavior and enhance the delivery of financial advice and education.

1. Introduction

Suppression effects present a recurring challenge in multivariate personality research, particularly when variables are conceptually related yet differentially associated with an outcome (Tzelgov and Henik 1991). In the context of multiple regression, a suppressor effect occurs when the inclusion of one variable enhances the explanatory validity of another by removing irrelevant or extraneous variance (Horst 1941; Conger 1974). The suppressor variable may have little or no direct correlation with the criterion. However, the variable refines the relationship between other variables and the outcome by statistically “cleaning” shared variance unrelated to the target construct. This phenomenon is widespread in personality research because traits, as measured by models such as the Big Five, tend to be moderately intercorrelated and exert opposing or orthogonal effects on behavioral outcomes (Lai et al. 2025).

When suppression is present, zero-order correlations can underestimate (or even misrepresent) the strength and direction of a trait’s true association with the outcome. In applied terms, suppression can mask theoretically meaningful effects, produce counterintuitive sign reversals in regression coefficients, and reduce the precision of trait-based predictions (Akinwande et al. 2015). For example, Neuroticism and Conscientiousness may be weakly related to a risk-taking measure in bivariate terms (see Gullone and Moore 2000). However, once shared variance with other traits is removed, relationships may become substantially stronger and theoretically consistent. Ignoring the risks associated with suppression effects can result in discarding valuable variables, leading to the misinterpretation of the functional role of trait factors.

In the domain of financial decision-making, the suppressor effect poses a significant challenge because financial models used for risk assessment, investment forecasting, and portfolio optimization rely heavily on regression and multivariate analysis to identify the determinants of returns, volatility, and adoption of recommendations (Basco et al. 2023). When suppressor effects occur, statistical results can lead to counterintuitive insights that complicate interpretation (De Blick et al. 2024). For instance, a trait factor that appears weakly or insignificantly related to financial decision-making in isolation might actually be strongly associated with a decision outcome (Lai et al. 2025). When this happens (i.e., the suppressor effect is undetected), confidence in model outputs is undermined, making it harder to draw causal inferences.

The primary aim of this study is to describe a method to improve the precision of estimating the effects of personality traits on financial risk-taking by identifying and correcting for suppressor effects among intercorrelated variables. Because the Big Five personality traits tend to exhibit substantial overlaps, bivariate correlations and regression coefficients between individual traits and behavioral outcomes can mask or distort authentic relationships. Although under-studied, it is likely that suppressor effects (i.e., where the inclusion of one variable enhances the explanatory validity of another) are common outside of personality studies (e.g., Gaylord-Harden et al. 2010), particularly in the domains of behavioral finance and financial planning where few researchers have attempted to meaningfully address the possibility of suppression and its interpretation of trait–behavior associations.

We addressed the suppressor effect issue in this paper using a two-stage analytical approach. At the first stage, we estimated a full regression model simultaneously including the Big Five traits to detect and quantify suppression by comparing regression coefficients with unadjusted zero-order correlation coefficients. We then created suppressor-adjusted trait scores by residualizing each trait with respect to the others, thereby removing the shared variance that acts as a suppressor. This produced “purified” trait measures that retained only variance unique to the focal trait in relation to the others.

At the second stage, these residualized trait scores were reintroduced into explanatory models of financial risk-taking. This second stage of analysis allowed us to estimate the direct, unconfounded effect of each personality trait on risk-taking, free from the distortions caused by multicollinearity and suppressor dynamics. By separating detection from prediction, the two-stage design described here explicitly accounted for suppressor effects rather than treating them as statistical artifacts. This methodological refinement increases the likelihood of uncovering the true structure of trait/risk-tolerance/risk-taking relationships, which is critical for advancing the development of theoretical models of risk-taking behavior and applied financial decision-making frameworks. This study addresses the following research questions:

• To what extent do the Big Five personality traits function as suppressor variables in describing financial risk-tolerance?
• How can adjusting for suppressor effects change the estimated relationships between individual traits and financial risk-tolerance?
• Do suppression-adjusted traits provide a theoretically coherent and statistically robust estimate of personality’s effect on financial risk-taking?
• Which personality traits emerge as the strongest unique descriptors of financial risk-taking once suppressor effects are removed?

2. Literature Review

The Five Factor Model (FFM), commonly referred to as the Big Five, remains the most widely accepted and empirically supported framework for describing personality across five broad dimensions (Digman 1990; Costa and McCrae 1992; Goldberg 1992; John and Srivastava 1999). These dimensions (i.e., Extraversion, Agreeableness, Conscientiousness, Emotional Stability (the inverse of Neuroticism), and Openness to Experience) serve as a foundational taxonomy for understanding individual differences in behavior, cognition, and affect (Gosling et al. 2003). Although each trait encompasses multiple facets, the Big Five are frequently used in applied settings to broadly classify individuals according to temperament and behavioral tendencies (McCrae and Costa, 2003).

Extraversion is associated with assertiveness, sociability, sensation seeking, and a proclivity for energetic engagement with the external world (DeYoung et al. 2007). Extraverts are generally more inclined to engage in risk-taking behaviors, including gambling (Mayfield et al. 2008). Agreeableness reflects qualities such as altruism, trust, compliance, and modesty (DeYoung et al. 2007). Highly agreeable individuals tend to conform to social norms and exhibit cooperative behavior. Conscientiousness encompasses self-discipline, organization, competence, and deliberation (Judge et al. 2013). Those high in Conscientiousness are typically logical, disciplined, and risk-averse. Emotional Stability, the opposite of Neuroticism, is characterized by calmness, resilience, and predictability (Khan et al. 2024). Neurotic individuals, by contrast, often display anxiety, impulsivity, and emotional volatility (Judge et al. 2013). Openness to Experience includes imagination, aesthetic sensitivity, and emotional depth. Individuals high in Openness are more likely to engage in novel and unconventional activities (DeYoung et al. 2007).

2.1. Personality, Financial Planning, and Risk-Taking Behavior

Over the past two decades, researchers have increasingly turned to personality psychology to better describe, explain, and predict a range of personal and household financial behaviors (e.g., Davey and George 2011; Donnelly et al. 2012; Heo et al. 2018; Haris et al. 2021; Ghaffar et al. 2024; Baker et al. 2025; Tekinsoy et al. 2025). This line of inquiry reflects the growing recognition that financial outcomes cannot be fully understood through economic capacity or demographic factors alone. Instead, financial decision-making is thought to be shaped by enduring individual differences in psychological dispositions. Campbell et al. (2023) summarized this perspective by noting that financial planning has evolved beyond a purely economic model, now incorporating elements of behavioral economics and counseling psychology to address client needs more effectively. Building on this research trend, personality psychology, and specifically the Big Five framework, is often used to offer a systematic, empirically grounded approach for understanding investor differences that may otherwise remain invisible in standard financial assessments.

Each of the Big Five traits (also referred to as OCEAN traits) are associated with a variety of life outcomes, including those directly tied to general financial planning behavior and financial well-being as an outcome measure. Campbell et al. (2023) noted, for instance, that investors who exhibit high Openness tend to be more receptive to innovative investment products, whereas those high in Conscientiousness adhere more closely to savings plans. Conversely, clients scoring higher in Neuroticism experience heightened anxiety regarding market volatility, complicating their risk-tolerance assessment and long-term adherence to planning strategies.

A growing body of empirical evidence supports the descriptive utility of the Big Five traits for financial outcomes. Brown and Taylor (2014), for example, demonstrated that Extraversion is significantly associated with household financial positions, particularly in relation to debt and asset levels. Importantly, they noted that the strength and direction of these associations vary across asset and debt categories, suggesting that personality exerts a differentiated influence on portfolio composition rather than a uniform effect across financial domains. Similarly, Exley et al. (2021) provided evidence of the effectiveness of the Big Five by documenting that 16 of 20 possible correlations between the Big Five traits and outcomes, such as financial literacy, financial risk tolerance, income, and net worth, were statistically significant. It is worth noting that in their study, some findings diverged from conventional expectations. Extraversion, for instance, was positively associated with financial risk-taking and income but negatively correlated with financial literacy. Extraversion was not associated with net worth. In their model, Conscientiousness was positively correlated with financial literacy, income, and net worth, but negatively with financial risk tolerance. These results indicate that conscientious individuals may be cautious in risk-taking even as they accumulate wealth. Neuroticism, on the other hand, displayed consistently negative associations with financial literacy, income, and net worth, highlighting this trait’s potential role as a vulnerability factor in financial outcomes.

As described in the literature, personality traits can be systematically measured and effectively integrated into behavioral finance and financial planning research, alongside traditional factors such as income and education (Exley et al. 2021). However, it is important to note that relatively few studies have thoroughly examined suppressor effects in research linking personality traits to financial planning behavior. Moreover, although previous research on the associations between personality and financial outcomes reveals plausible links, further investigation is needed to clarify these relationships.

2.2. Financial Risk Tolerance and Personality

An expanding body of literature in psychology, behavioral finance, and financial planning has integrated personality traits into models to explain and predict individual and household-level risk-taking attitudes and behaviors (e.g., Hamza and Arif 2019; Sarwar et al. 2020; Shou and Olney 2022; Shanmugam et al. 2023). Researchers consistently find that Extraversion and Openness are positively correlated with a greater willingness to take financial risks and, in practice, risk-taking (Nandan and Saurabh 2016). On the other hand, Agreeableness, Conscientiousness, and Emotional Stability tend to be negatively associated with risk tolerance and risk-taking (Nicholson et al. 2005; Mayfield et al. 2008; Lauriola et al. 2014). Consider the case of gamblers. Gambling represents a specific and often more extreme form of risk-taking behavior (Mishra et al. 2010; Grable et al. 2021). In one study, Thorson et al. (1994) found minimal differences in personality profiles between gamblers and non-gamblers, but in other studies (e.g., Bagby et al. 2007; Miller et al. 2013), pathological gamblers have been reported to score low in Emotional Stability (i.e., high Neuroticism), Agreeableness, and Conscientiousness.

Beyond gambling, personality traits are known to be associated with the degree to which someone is willing to take risks, especially when making investment decisions. Individuals who are more deliberative and less emotionally driven tend to exhibit higher financial risk tolerance (Filbeck et al. 2005). Pak and Mahmood (2015) found that among Eastern European respondents, financial risk tolerance is negatively associated with Agreeableness and positively correlated with Openness. Similarly, Pinjisakikool (2017) reported that Extraversion is positively associated with financial risk tolerance in Western European populations, while Agreeableness, Conscientiousness, and Emotional Stability are negatively associated with the willingness to take risks. In the United States, Lauriola and Levin (2001) reported that Openness to Experience predicts greater financial risk-taking, whereas Neuroticism is related to more conservative financial behavior. These findings highlight the significant role of personality in influencing financial decision-making across diverse cultural contexts.

Evidence suggests that financial risk tolerance serves as a mediating factor between the characteristics of financial decision-makers and their risk-taking behavior (Hussain and Rasheed 2023). A growing body of research shows that risk tolerance is strongly associated with equity (i.e., stock) ownership and other risky financial decisions (Kwak and Grable 2024). For example, individuals with higher risk tolerance are substantially more likely to hold risky assets, such as equities. At the same time, some studies caution that elevated risk tolerance can amplify excessive risk-taking in certain investors (Li and Jiang 2025). As Heo et al. (2021) explained, risk tolerance serves as a psychological mechanism that translates personal characteristics into observable financial behavior. Through this mediating process, factors such as income, education, and financial knowledge may influence investment outcomes both directly and indirectly.

2.3. Risk Capacity: A Complementary Construct

While personality traits are associated with risk tolerance and risk-taking behavior, the concept of risk capacity must also be considered (Schmidt et al. 2019). Risk capacity refers to an individual’s objective ability to absorb financial losses without jeopardizing the decision-maker’s financial well-being (Cordell 2002; Brayman 2012). Risk capacity is typically assessed using objective factors such as income and education level. Household income, a key indicator of risk capacity, is known to be positively associated with Openness, Conscientiousness, Emotional Stability, and Extraversion (Borghans et al. 2008). Conversely, individuals high in Neuroticism tend to report lower income levels (Judge et al. 1999). Higher income is itself associated with greater financial risk tolerance, suggesting that psychological and economic factors jointly relate to risk-taking behavior. Educational attainment follows a similar pattern, with higher levels of education correlating with increased risk capacity and risk tolerance.

The remainder of this paper describes the sample used for the analyses and the methodological approach used to identify and correct for suppressor effects. This is followed by a presentation of findings and a discussion of results and implications for research and practice.

3. Methods

3.1. Data and Sample

Data were collected in 2022 via an online survey hosted in Qualtrics and distributed through the Precision Sample research panel. Respondents (N = 602) were selected to reflect individuals likely to engage in financial risk-taking at the time of the survey. While the sample characteristics approximated those of the broader U.S. population, the sample was not intended to be nationally representative. The study protocol was reviewed and approved by the institutional review board of the lead researcher’s university prior to administering the survey (IRB Project 00001180).

The final usable sample included individuals aged 18 and older who were engaged in managing their household’s financial situation. A power analysis was conducted to determine whether the sample size was sufficient for the study. The analysis assumed a medium effect size ($f^2=0.15$ for the regression models and standardized path coefficients ~0.3 for the path analysis), a statistical power of 0.80, and a significance level of α = 0.05. Based on recommendations from Cohen (1988) (i.e., power tables), the sample used in this study met requirements to achieve stable parameter estimates and adequate model fit (Root Mean Square Error of Approximation [RMSEA] ≤ 0.05) (Kline 2016).

3.2. Variables

The analyses included a combination of personality, demographic, and financial risk measures. Missing values were handled using full-information maximum likelihood (FIML) in AMOS. FIML utilizes all available data without deletion or imputation, resulting in less biased and more efficient parameter estimates. Personality was assessed using the Ten-Item Personality Inventory (TIPI) (Gosling et al. 2003). The TIPI is a brief instrument designed to assess Extraversion, Agreeableness, Conscientiousness, Emotional Stability (also known as Neuroticism, but reversed), and Openness to Experience (Maltby et al. 2019). Each trait was measured using two items, one positively keyed and one negatively keyed. Respondents were asked to rate each item on a 7-point Likert-type scale ranging from 1 (“Disagree strongly”) to 7 (“Agree strongly”). The items comprising the scale are shown below:

Extraversion:

• I see myself as extraverted, enthusiastic.
• I see myself as reserved, quiet (reverse scored).

Agreeableness:

• I see myself as critical, quarrelsome (reverse scored).
• I see myself as sympathetic, warm.

Conscientiousness:

• I see myself as dependable, self-disciplined.
• I see myself as disorganized, careless (reverse scored).

Emotional Stability (Neuroticism reversed):

• I see myself as calm, emotionally stable.
• I see myself as anxious, easily upset (reverse scored).

Openness to Experience:

• I see myself as open to new experiences, complex.
• I see myself as conventional, uncreative (reverse scored).

Scores were estimated by reverse-coding the negatively keyed items for each trait. Trait scores were then computed as the sum of the two items for each dimension. The resulting score for each trait could range from 2 to 14, with higher scores indicating a greater presence of the given personality trait. The scale’s reliability, as measured by Cronbach’s alpha, was 0.70.

For consistency with the analytic models discussed below, we indexed the five personality traits using an index i ranging from 1 to 5. In this order, 1 corresponds to Extraversion, 2 to Agreeableness, 3 to Conscientiousness, 4 to Emotional Stability, and 5 to Openness to Experience. For individual k, the raw score on trait i is denoted $X_{i,k}$. To address suppressor effects in the multivariate models, we constructed orthogonalized (residualized) versions of each trait by regressing it on the remaining four traits. The resulting suppression-adjusted score for trait i for individual k is denoted $X_{i,k}^{\perp }$. These residualized trait scores were then used as the personality factors in the mediation models described in the Section 3.3.

Two demographic covariates, household income and education, were used as proxies for a financial decision-maker’s risk-taking capacity, as these factors are among the most commonly used independent variables in the literature. Household income was measured by asking respondents to indicate their approximate annual gross household income before taxes using 11 ordinal categories ranging from “None” (1) to “Above $100,000” (11). Education represented the highest level of education completed by a respondent. Six ordinal categories, ranging from “Some high school or less” (1) to “Graduate or professional degree” (6), were used to assess attained educational attainment. Given the ordered nature of the indicators, the variables were recoded at the median: those with a household income of 7 or higher were coded 1, otherwise 0; and those with an education level of 4 or higher were coded 1, otherwise 0. These covariates are denoted $Z_{m,k}$, where m indexes the two covariate variables (i.e., household income and education) and k indexes individuals. Both variables were included in the Stage 1 and Stage 2 equations (described below) as control variables.

Financial risk tolerance (FRT) was used as the outcome variable in the first stage of the analysis and as a mediator in the second stage of the modeling process. Financial risk tolerance scores were estimated using the Grable and Lytton (1999) 13-item risk-tolerance scale. Theoretically, scores can range from 13 to 47, with higher scores indicating a greater willingness to take risks. In this study, the mean score was 24.93 (SD = 5.52). Cronbach’s alpha (α) for the scale was 0.73. The outcome variable of interest at the second stage of the study was financial risk-taking as proxied by self-reported investment behavior measured as the percentage of a respondent’s household portfolio held in equities (i.e., stocks). The variable was treated as continuous in the analysis. In the models, financial risk tolerance for individual k is denoted ${\mathrm{F}\mathrm{R}\mathrm{T}}_k$, whereas the percentage of the household portfolio held in equities is denoted ${\mathrm{Y}}_{\mathrm{k}}$.

3.3. Data Analysis Method

A two-stage analytical process was used in this study. At the first stage, steps were taken to identify whether a suppressor effect was present in the Big Five dimensions (i.e., Extraversion, Agreeableness, Conscientiousness, Emotional Stability, and Openness to Experience). Preliminary analyses (

Table 1. Descriptive Statistics for the Study Variables (N = 602).

Variable	Mean	SD	25th Percentile	Median	75th Percentile
Household Income	6.77	3.34	4	7	10
Education	3.91	1.49	3	4	5
Extraversion	7.69	2.66	6	8	9
Agreeableness	9.43	2.32	8	9	11
Conscientiousness	10.67	2.45	8	11	13
Emotional Stability	8.91	2.45	8	8	11
Openness to Experience	9.05	2.18	8	9	10
FRT	24.93	5.52	21	25	29

) revealed moderate intercorrelations among the traits, raising the possibility of suppressor effects in the conceptualized multivariate regression model of FRT. To address this, we orthogonalized the personality variables by residualizing each trait with respect to the remaining four traits using linear regression. This procedure generated residual scores representing the unique variance of each trait, independent of the variance of the other traits. This effectively removed intercorrelations and mitigated potential suppression artifacts.

Specifically, for a given personality trait i and individual k (with $j\ne i$ indexing the remaining four traits), ${{\rm X}}_{i,k}$ and ${{\rm X}}_{j,k}$ were used to denote the observed raw trait scores for individual k. The residualized (orthogonalized) component of trait i was defined as:

$$X_{i,k}^{\perp }= X_{i,k}- {\widehat{X}}_{i,k}$$

(1)

where ${\widehat{X}}_{i,k}$ is the fitted value from the projection:

$${\widehat{X}}_{i,k}= {\widehat{\delta }}_i+{\sum }_{j\ne i}{\widehat{\delta }}_{i,j}{{\rm X}}_{j,k}$$

(2)

Here, ${\widehat{\delta }}_i$ denotes the intercept, and hats indicate the sample estimate. The coefficients ${\widehat{\delta }}_{i,j}$ are auxiliary projection parameters used solely to construct the residualized predictor $X_{i,k}^{\perp }$. By construction, $X_{i,k}^{\perp }$ is uncorrelated with the other four traits. This process was repeated for each personality trait ($i$ = 1, 2, 3, 4, 5). The resulting residualized trait components, which eliminate multicollinearity among the trait factors,1 were then used as the personality predictors in the mediation models, thereby reducing intercorrelations and mitigating potential suppression artifacts.

Partial correlations were also examined to confirm that suppression effects had been eliminated. At the second stage of the analysis, a path model was used to (a) determine which of the Big Five dimensions was associated with FRT and investing behavior, controlling for two risk capacity measures (i.e., household income and the educational attainment of the respondent) and (b) estimate the mediating effect of FRT on the Big Five dimensions and risk-taking. The path model was specified as follows:

Mediator equation (financial risk tolerance, FRT):

$${\mathrm{F}\mathrm{R}\mathrm{T}}_k= {\alpha }_{\mathrm{F}\mathrm{R}\mathrm{T}}+ {\sum }_{i=1}^5{\alpha }_i{X}_{i,k}^{\perp } + {\sum }_{m=1}^2{\theta }_{m,\mathrm{F}\mathrm{R}\mathrm{T}}{Z}_{m,k}+{\mathsf{ϵ}}_{\mathrm{F}\mathrm{R}\mathrm{T},k}$$

(3)

Outcome equation (percent of equities held in household portfolio, Y):

$${\mathrm{Y}}_{\mathrm{k}}= {\alpha }_{\mathrm{Y}}+ {\sum }_{i=1}^5{\beta }_i{X}_{i,k}^{\perp } + {\sum }_{m=1}^2{\theta }_{m,\mathrm{Y}}{Z}_{m,k}+{\beta }_M{\mathrm{F}\mathrm{R}\mathrm{T}}_k+ {\mathsf{ϵ}}_{\mathrm{Y},k}$$

(4)

where $X_{i,k}^{\perp }$ denotes the orthogonalized value for personality trait i for individual k, ${\alpha }_i$ is the coefficient on trait i in FRT, and $Z_{m,k}$ is the value of control variable m (household income and education) for individual k. β_i is the coefficient on trait i in equity percentage Y, β_M is the path coefficient from FRT to financial risk-taking Y, and ${\theta }_{m,\mathrm{F}\mathrm{R}\mathrm{T}}$ and ${\theta }_{m,\mathrm{Y}}$ are coefficients on control variable m in the financial risk tolerance (FRT) and financial risk-taking (Y) equations, respectively. The terms ${\alpha }_{FRT}$ and ${\alpha }_Y$ are intercepts, while ${\mathsf{ϵ}}_{FRT, k}$ and ${\mathsf{ϵ}}_{Y, k}$ are corresponding error terms for individual k. We deliberately used distinct symbols for trait coefficients across equations (${\alpha }_i$ and β_i) because they arise from different equations and play different roles in the mediation decomposition (i.e., direct, indirect, and total effects). This separation follows standard mediation notation, clarifying the mapping from coefficients to effects and preventing symbol drift when interpreting direct versus indirect pathways.

When the standardized direct effect of trait $X_i$ on risk-taking Y is ${\beta }_i$, the standardized indirect and total effects were computed using the estimated path coefficients. The following expressions summarize the effect decomposition:

$$\mathrm{I}\mathrm{n}\mathrm{d}\mathrm{i}\mathrm{r}\mathrm{e}\mathrm{c}\mathrm{t} \mathrm{E}\mathrm{f}\mathrm{f}\mathrm{e}\mathrm{c}\mathrm{t} \mathrm{o}\mathrm{f} X_i= {\alpha }_i · {\beta }_M$$

(5)

$$\mathrm{T}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l} \mathrm{E}\mathrm{f}\mathrm{f}\mathrm{e}\mathrm{c}\mathrm{t} \mathrm{o}\mathrm{f} X_i= {\beta }_i+\left({\alpha }_i · {\beta }_M\right)$$

(6)

4. Results

The analytic sample consisted of slightly more than 600 respondents. Mean household income fell in the $60,001–$70,000 range (M = 6.77, SD = 3.34), with 20.6% of respondents reporting incomes above $100,000. The variable was dummy coded based on the median score of 7.0. Educational attainment was relatively high, with a mean corresponding to approximately a bachelor’s degree (M = 3.91, SD = 1.49). The variable was recoded dichotomously at the median score of 4.00. The Big Five personality traits were distributed broadly across the sample. Extraversion (M = 7.69, SD = 2.66) and Openness to Experience (M = 9.05, SD = 2.18) exhibited moderate variability. Agreeableness and Conscientiousness scores were slightly higher on average (M = 9.43, SD = 2.32; M = 10.67, SD = 2.45, respectively), whereas Emotional Stability was moderate (M = 8.91, SD = 2.45).

FRT, as measured by the Grable–Lytton Risk Scale, averaged 24.93 (SD = 5.52), with an interquartile range of 21 to 29, indicating moderate risk tolerance among respondents. Table 1 shows the descriptive statistics for FRT and the other variables.

4.1. Stage 1 Results

The initial correlation and regression analyses revealed evidence of a net suppressor effect, most notably involving Emotional Stability. A net suppressor effect occurs when a predictor variable improves the predictive validity of another predictor variable by removing irrelevant variance, even though the suppressor itself may exhibit a weak or nonsignificant zero-order correlation with the outcome. In this case, Emotional Stability showed a relatively small bivariate correlation with FRT but a stronger, more meaningful regression coefficient when included alongside the other Big Five traits. This pattern suggests that Emotional Stability was suppressing unrelated variance in the other predictors, particularly those with shared or overlapping content, thereby increasing the clarity of the overall personality–FRT relationship. Net suppression of this kind is common in personality research where predictors are moderately intercorrelated. It often indicates that the predictors jointly capture conceptually distinct components of variance that are obscured in bivariate associations.

When evaluating these findings, it is important to acknowledge that the emergence and magnitude of suppressor effects can be influenced by measurement precision. Because the TIPI uses a two-item format to assess each personality dimension, measurement error may attenuate zero-order correlations while leaving partial regression coefficients comparatively less affected. As noted in prior psychometric literature (e.g., Gosling et al. 2003; Ehrhart et al. 2009; Credé et al. 2012), brief personality scales can sometimes underestimate trait–outcome correlations, potentially exaggerating the appearance of suppression when predictors share modest intercorrelations. Consequently, although the pattern observed here is consistent with a net suppressor effect, the results should be interpreted with appropriate caution.

Table 2. Correlation Estimates Between Financial Risk Tolerance and Personality Traits.

	Pearson Coefficients
	FRT	1	2	3	4	5	6	7
FRT	--
Extraversion (1)	0.171 **	--
Agreeableness (2)	−0.150 **	−0.120 **	--
Conscientiousness (3)	−0.212 **	−0.082	0.460 **	--
Emotional Stability (4)	0.053	0.086 *	0.486 **	0.437 **	--
Openness (5)	0.026	0.185 **	0.143 **	0.152 **	0.116 **	--
Household Income (6)	0.173 **	−0.014	−0.011	0.121 **	0.105 **	−0.089 *	--
Education (7)	0.115 **	0.043	−0.066	0.004	0.107 *	−0.007	0.407 **	--
	Spearman’s Coefficients
	FRT	1	2	3	4	5	6	7
FRT	--
Extraversion (1)	0.155 **	--
Agreeableness (2)	−0.161 **	−0.131 **	--
Conscientiousness (3)	−0.210 **	−0.073	0.459 **	--
Emotional Stability (4)	0.024	0.053	0.466 **	0.446 **	--
Openness (5)	0.016	0.153 **	0.198 **	0.189 **	0.107 *	--
Household Income (6)	0.171 **	−0.004	−0.002	0.115 **	0.122 **	−0.083	--
Education (7)	0.118 **	0.038	−0.059	0.012	0.104 *	−0.010	0.407 **	--

Notes: ** = Correlation is significant at the p < 0.01 level (2-tailed); * = Correlation is significant at the p < 0.05 level (2-tailed).

shows the correlations between FRT and the unadjusted personality variables. Two estimations were made. The first panel shows the Pearson correlation coefficients. The second panel shows the Spearman correlation coefficients estimated to account for the ordinal structure of some variables. Although different, the coefficient directions and effects were similar. These coefficients were compared with the standardized beta coefficients from an OLS regression model in which FRT was the outcome variable and trait factors were the independent variables.

In Table 2, Emotional Stability’s zero-order correlation with FRT was near zero (r = 0.053 and 0.024, ns), yet as shown in

Table 3. OLS Regression Showing the Relationship Between Personality Traits and FRT.

	b	Std. Error	β	t	Sig.
Constant	26.665	1.620		16.461	<0.001
Extraversion	0.267	0.095	0.128	2.809	0.005
Agreeableness	−0.318	0.125	−0.134	−2.535	0.012
Conscientiousness	−0.506	0.116	−0.224	−4.342	<0.001
Emotional Stability	0.451	0.119	0.200	3.803	<0.001
Openness	0.056	0.113	0.020	0.497	<0.619

Notes: F = 10.520, p < 0.001; R² = 0.099, Adjusted R² = 0.090.

, the standardized beta for Emotional Stability in the multivariate regression was statistically significant (β = 0.200, p < 0.001), indicating that shared variance with the other traits was likely inflating its apparent effect.

In the regression model, Extraversion was positively associated with FRT (b = 0.267, p = 0.005), indicating that more extraverted individuals reported higher risk tolerance. Agreeableness was negatively associated with FRT (b = −0.318, p = 0.012), suggesting that more agreeable individuals were more risk-averse. Conscientiousness was also negatively associated with FRT (b = −0.506, p < 0.001), which is consistent with previous reports (i.e., individuals with a tendency towards higher Conscientiousness prefer lower-risk financial decisions). As noted above, Emotional Stability was positively associated with FRT (b = 0.451, p < 0.001), indicating that individuals with higher emotional stability were more likely to report a greater willingness to take financial risks. Finally, Openness to Experience was not a significant variable in the model (b = 0.056, p = 0.619). Collinearity diagnostics showed no concerning multicollinearity issues (all VIFs < 1.5). Residual statistics indicated that model assumptions of linearity and homoscedasticity were adequately met.

Table 4. Revised Correlation Estimates Between Financial Risk Tolerance and Orthogonalized Personality Traits.

	Pearson Coefficients
	FRT	1	2	3	4	5	6	7
FRT	--
Extraversion (1)	0.118 **	--
Agreeableness (2)	−0.109 *	0.172 **	--
Conscientiousness (3)	−0.190 **	0.107 *	−0.275 **	--
Emotional Stability (4)	0.161 **	−0.189 **	−0.381 **	−0.288 **	--
Openness (5)	0.031	−0.215 **	−0.107 *	−0.115 **	0.024	--
Household Income (6)	0.173 **	−0.011	−0.092 *	0.105 *	0.093 *	−0.095 *	--
Education (7)	0.115 **	0.009	−0.123 **	−0.015	0.162 **	−0.011	0.407 **	--
	Spearman’s Coefficients
	FRT	1	2	3	4	5	6	7
FRT	--
Extraversion (1)	0.113 **	--
Agreeableness (2)	−0.111 **	−0.142 **	--
Conscientiousness (3)	−0.180 **	−0.115 **	0.227 **	--
Emotional Stability (4)	0.142 **	−0.173 **	−0.319 **	−0.208 **	--
Openness (5)	0.046	−0.191 **	−0.064	0.110 *	−0.015	--
Household Income (6)	0.171 **	0.002	−0.068	0.089 *	0.095 *	−0.094 *	--
Education (7)	0.118 **	0.000	−0.102 *	−0.013	0.154 **	−0.010	0.407 **	--

Notes: ** = Correlation is significant at the p < 0.01 level (2-tailed); * = Correlation is significant at the p < 0.05 level (2-tailed).

shows the revised correlation estimates between the personality traits, household income, education, and FRT. The first panel shows the Pearson coefficients. The second panel shows the Spearman coefficients. Similar to the first correlation analysis, the estimations resulted in similar significant variable associations, with a few exceptions. Using Pearson correlations, household income and education were negatively associated with Agreeableness after the traits were residualized. In contrast, the effect was observed only between education and Agreeableness when evaluated using Spearman’s coefficients. Also, Emotional Stability was positively associated with FRT after residualization.

The regression was re-estimated to account for the residualization of the traits, which was performed to remove shared variance among the traits (i.e., isolating each trait’s independent effect). As shown in

Table 5. Re-estimated OLS Regression Showing the Relationship Between Personality Traits and FRT.

	b	Std. Error	β	t	Sig.
Intercept	24.912	0.240		103.825	<0.001
Extraversion	0.424	0.100	0.194	4.240	<0.001
Agreeableness	−0.539	0.156	−0.187	−3.465	<0.001
Conscientiousness	−0.648	0.137	−0.242	−4.720	<0.001
Emotional Stability	0.163	0.145	0.060	1.125	0.261
Openness	0.037	0.118	0.014	0.309	0.757

Notes: F = 10.520, p < 0.001; R² = 0.099, Adjusted R² = 0.090.

, Emotional Stability’s standardized coefficient decreased to β = 0.163 (p = 0.261), aligning with its zero-order correlation and confirming that the original effect was a suppression artifact rather than a substantive independent relationship with FRT. By contrast, Extraversion (b = 0.424, p < 0.001), Agreeableness (b = −0.539, p < 0.001), and Conscientiousness (b = −0.648, p < 0.001) remained significant in the model, reflecting stable and unique contributions to FRT. Openness to Experience was nonsignificant in the revised model.

As with the original regression, collinearity diagnostics indicated no problematic multicollinearity issues (all VIFs < 1.55). Furthermore, residual statistics supported the assumptions of the regression model. It was determined that, by using residualized personality traits, the analysis could isolate the independent effects of each Big Five dimension. Results indicated that Extraversion, Agreeableness, and Conscientiousness have unique power in describing FRT, whereas Emotional Stability and Openness do not. These findings reinforce theoretical expectations about the role of personality in financial decision-making and clarify which traits exert independent influence when intercorrelations are controlled.

4.2. Summary of Stage 1

The initial analyses indicated a potential net suppressor effect for Emotional Stability, where the zero-order correlation with FRT was near zero. However, in the first regression model, the standardized beta was statistically significant. To address this, we orthogonalized the Big Five trait scores by residualizing each on the other four. This eliminated intercorrelations among the variables, thereby removing potential suppression mechanisms. In the orthogonalized regression model, Emotional Stability was no longer a significant variable, indicating its previous effect was driven by shared variance with other traits rather than a direct association with FRT. The stage-one results demonstrate that controlling for shared variance among personality traits clarifies the unique associations between specific traits and FRT, providing a more accurate representation of their explanatory effects.

4.3. Stage 2 Results

A path model was tested in which FRT served as a mediator between the five residualized personality traits and financial risk-taking. The model was designed to allow for partial mediation through direct paths from the trait factors to financial risk-taking. The model included two additional control variables, household income and education. The model (N = 602) was recursive.

The model fit the data reasonably well. The chi-square statistic (χ²(10)) was 32.215, p < 0.001, indicating a statistically significant difference from the saturated model (given the small degrees of freedom, this result was expected). The CMIN/DF was 3.222, which was slightly above the conventional cutoff of 3.0 but acceptable given the model’s complexity. The Comparative Fit Index (CFI) was 0.961, the Tucker–Lewis Index (TLI) was 0.823, the Incremental Fit Index (IFI) was 0.963, and the Normed Fit Index (NFI) was 0.947. These indices indicate good incremental fit (Sathyanarayana and Mohanasundaram 2024). For the TLI, values of 0.90 or higher are typically desired. The TLI of 0.823 in this study is below the conventional cutoff, but this is likely due to the model’s complexity and the sample size. RMSEA was 0.061 (90% CI: 0.038–0.085, PCLOSE = 0.030), indicating a reasonable approximation of the population covariance matrix. AIC was 120.215, which was substantially lower than the independence model (628.571), indicating a strong parsimony-adjusted fit. Finally, Hoelter’s Critical N at 0.05 level was 342, suggesting an adequate sample size for model stability. Overall, the model captured direct and mediated effects without over-constraining relationships.

Table 6. Standardized Maximum Likelihood Estimates Regression Weights.

			Estimate	S.E.	C.R.	p
FRT	<---	Openness	0.036	0.117	0.818	0.413
FRT	<---	Emotional Stability	0.034	0.142	0.655	0.512
FRT	<---	Conscientiousness	−0.262	0.133	−5.253	***
FRT	<---	Agreeableness	−0.177	0.151	−3.408	***
FRT	<---	Extraversion	0.196	0.097	4.420	***
FRT	<---	Education	0.029	0.504	0.640	0.522
FRT	<---	HH Income	0.176	0.506	3.878	***
EQUITY %	<---	Openness	0.039	0.513	0.923	0.356
EQUITY %	<---	Emotional Stability	0.148	0.624	2.996	**
EQUITY %	<---	Conscientiousness	0.149	0.604	3.020	**
EQUITY %	<---	Agreeableness	0.040	0.671	0.795	0.427
EQUITY %	<---	Extraversion	−0.047	0.435	−1.068	0.285
EQUITY %	<---	FRT	0.203	0.203	4.577	***
EQUITY %	<---	HH Income	0.157	2.257	3.547	***
EQUITY %	<---	Education	0.162	2.211	3.713	***

Notes: *** p < 0.001 (2-tailed); ** p < 0.01 level (2-tailed).

shows the standardized path coefficients in the model. The following three trait factors were directly associated with FRT: Extraversion (β = 0.196, p < 0.001), Conscientiousness (β = −0.262, p < 0.001), and Agreeableness (β = −0.177, p < 0.001). Household income was also directly associated with FRT (β = 0.176, p < 0.001). Emotional Stability, Openness, and education were not associated with FRT.

FRT had a direct effect on risk-taking behavior (β = 0.203, p < 0.001). This indicates a statistically significant indirect path from Extraversion, Conscientiousness, and Agreeableness to risk-taking behavior, confirming that risk tolerance partially mediates the effects of personality traits on financial risk-taking. Other direct effects with risk-taking behavior included Emotional Stability (β = 0.148, p = 0.008), Conscientiousness (β = 0.149, p = 0.003), household income (β = 0.157, p < 0.001), and education (β = 0.162, p < 0.001). Extraversion, Agreeableness, and Openness were not directly associated with risk-taking behavior. The results point to a partial mediation effect. Conscientiousness and Emotional Stability related to risk-taking directly and indirectly through FRT. Extraversion’s effect on risk-taking was largely mediated.

Several indirect standardized effects were also noted. As shown in

Table 7. Standardized Total, Direct, and Indirect Effects.

Variable	Total Effect	Direct Effects		Indirect Effects	95% Confidence Intervals
	Equity	FRT	Equity	Equity	LL	UL
Education	0.168	0.029	0.162	0.006
HH Income	0.193	0.176	0.157	0.036
Extraversion	−0.007	0.196	−0.047	0.04	0.099	0.077
Agreeableness	0.004	−0.177	0.04	−0.036	0.117	−0.496
Conscientiousness	0.096	−0.262	0.149	−0.053	0.146	−0.896
Emotional Stability	0.155	0.034	0.148	0.007	0.112	0.143
Openness	0.046	0.036	0.039	0.007	0.105	−0.083

Notes: The mediator–outcome coefficient (FRT to EQUITY %) was 0.203 (p < 0.001).

, the indirect effects for Extraversion and Emotional Stability were statistically significant, whereas Agreeableness, Conscientiousness, and Openness to Experiences were not. The magnitude of the indirect effects was modest but statistically significant, supporting the notion that risk tolerance accounts for part of the effect of personality on risk-taking behavior.

The path model demonstrated theoretically coherent pathways connecting personality traits, FRT, household income, and education to financial risk-taking. The model allowed for direct and mediated effects, capturing trait-specific influences while remaining well-fitting and parsimonious. It was determined that personality traits are not exclusively associated with risk-taking through FRT. The direct pathways in the model suggest additional psychological mechanisms, such as decision confidence, impulsiveness, or domain-specific preferences, may be useful factors when describing FRT and financial risk-taking behaviors. This possibility should be considered in extensions of this study. It appears that Conscientiousness and Agreeableness function as risk reducers. Extraversion increases risk tolerance and risk-taking behavior. Household income and education exhibited direct and indirect effects in the model, indicating that risk capacity enhances risk-taking behavior.

4.4. Robustness Check

To assess the path model’s generalizability, the structural paths were estimated separately for men and women and across three age bands (i.e., 1 = 18 to 39, 2 = 40 to 59, and 3 = 60+). As shown in

Table 8. Robustness Check of Path Model for Generalizability Across.

			Full Model	Male	Female	Age 1	Age 2	Age 3
FRT	<---	Openness	0.036	0.090	0.011	−0.018	0.183 *	0.078
FRT	<---	Emotional Stability	0.034	0.049	−0.043	0.128	0.132	0.071
FRT	<---	Conscientiousness	−0.262 ***	−0.277 ***	−0.285 **	−0.199 *	−0.041	−0.240 *
FRT	<---	Agreeableness	−0.177 ***	−0.151 *	−0.190 *	−0.062	−0.145	−0.048
FRT	<---	Extraversion	0.196 ***	0.180 ***	0.194 ***	0.174 *	0.125	0.265 ***
FRT	<---	Education	0.029	0.046	−0.001	0.114	0.056	−0.057
FRT	<---	HH Income	0.176 ***	0.135 *	0.129 *	0.169 *	0.113	0.335 ***
EQUITY %	<---	Openness	0.039	0.130 *	−0.051	0.025	0.093	−0.070
EQUITY %	<---	Emotional Stability	0.148 ***	0.092	0.192 *	−0.019	0.063	0.301 ***
EQUITY %	<---	Conscientiousness	0.149 ***	0.189 ***	0.087	0.035	0.053	0.265 *
EQUITY %	<---	Agreeableness	0.040	−0.044	0.128	−0.111	−0.058	0.131
EQUITY %	<---	Extraversion	−0.047	−0.066	−0.070	−0.093	0.000	−0.012
EQUITY %	<---	FRT	0.203 ***	0.206 ***	0.093	0.295 ***	0.252 ***	0.241 *
EQUITY %	<---	HH Income	0.157 ***	0.123 *	0.151 *	0.124 *	0.234 ***	0.006
EQUITY %	<---	Education	0.162 ***	0.204 ***	0.058	0.153 *	0.163 *	0.129

Notes: *** p < 0.001 (2-tailed); ** p < 0.01 level (2-tailed); * p < 0.05 level (2-tailed).

, the pattern of results closely aligned with the full-sample model, providing support for the model’s generalizability across demographic groups. The strongest predictors of financial risk tolerance (i.e., Conscientiousness, Agreeableness, Extraversion, and household income) displayed consistent effect directions and similar magnitudes across all subgroups. Although some coefficients did not reach statistical significance within certain sex and age categories, differences appear attributable to variation in subgroup sample sizes rather than substantive shifts in model effects. Likewise, FRT remained a stable, positive predictor of equity allocation across all subgroups, with standardized coefficients closely approximating those in the full model. Taken together, these findings demonstrate that the structural relationships underlying the model are robust and broadly generalizable across sex and age categories.

4.5. Sensitivity Analysis

To assess whether the findings were sensitive to the bounded nature of the equity allocation variable, we conducted robustness analyses using models designed for fractional outcomes. Equity exposure was rescaled to the unit interval and re-estimated using a fractional logit specification. The results were substantively consistent with those from the OLS model: the direction of effects, statistical significance of the key predictors, and their relative magnitudes remained stable. In particular, FRT remained the strongest predictor of equity exposure, and the same three personality traits identified as significant in the linear model retained significance under the fractional logit specification. Because portfolio-share measures can exhibit mass at boundary values, we also estimated a two-part model consisting of a logit specification for equity participation (any equity vs. none) followed by a fractional logit model for the conditional share among equity holders. The same predictors that explained overall equity exposure were the primary determinants in both stages, with FRT again emerging as the dominant factor. Finally, a Shapley/LMG relative-importance decomposition, conditioned on income and education, showed that FRT accounted for the largest share of explained variance (58%), followed by Emotional Stability (18%) and Conscientiousness (12%), with the remaining traits contributing smaller shares. The rank ordering of statistically significant predictors matched their relative-importance ranking. Collectively, these results indicate that the substantive conclusions are robust across modeling approaches and are not driven by linearity assumptions.

4.6. Summary of Stage 2

The stage 2 analysis demonstrated that personality traits can be used to describe a person’s willingness to take financial risks and their actual risk-taking behavior. The path model showed that FRT mediates the relationship between the Big Five personality traits and financial risk-taking behavior. The path analysis also revealed trait-specific patterns. Extraversion was found to be positively associated with FRT, whereas Agreeableness and Conscientiousness were observed to be negatively associated with FRT. These results indicate that certain personality dimensions increase, and others decrease, an individual’s willingness to take financial risks. FRT itself was significantly associated with financial risk-taking, supporting the hypothesized mediating role.

Partial mediation was evident in the model. Extraversion was associated with risk-taking primarily through risk tolerance, whereas Conscientiousness and Emotional Stability exerted direct and indirect effects, indicating additional pathways beyond perceived risk tolerance. Household income and education were directly and indirectly associated with risk-taking behavior, highlighting the importance of attitudinal and demographic factors alongside personality. Overall, these findings reveal that financial risk-taking can be explained, in part, by a person’s personality characteristics and their tolerance for financial risk, with mediation effects varying by specific trait. The results align with behavioral finance theory, demonstrating that risk tolerance partially transmits the influence of personality on investment behavior, but does not fully account for all trait-specific effects (Singh et al. 2023).

5. Discussion

As documented throughout the psychological and behavioral sciences, the Big Five personality traits are not orthogonal in practice, even though they are often modeled as such. Substantial intercorrelations among traits can distort multivariate models, particularly when shared variance is unrelated—or differentially related—to the outcome of interest. This phenomenon, known as statistical suppression (Gaylord-Harden et al. 2010), has significant implications for research on financial decision-making, where personality traits are often used as explanatory variables. The current study had two main objectives: first, to demonstrate the presence and real-world consequences of suppressor effects when examining the relationships between personality traits, financial risk tolerance, and risk-taking behaviors; and second, to introduce and illustrate a structured approach for identifying and adjusting for suppressor effects among interrelated psychological predictors.

The findings show that the Big Five traits should not be treated as a simple set of independent correlates of financial risk tolerance and risk-taking. Instead, it should be assumed that the Big Five traits operate as a network of partially overlapping dispositions, whose shared variance can conceal the true magnitude—and, in some cases, the direction—of their unique effects (Chang et al. 2012).

The results further suggest that suppression is not a rare statistical curiosity but likely a recurring structural feature of financial frameworks that rely on personality factors. Because the Big Five capture broad, higher-order behavioral tendencies, each trait includes variance that is both relevant and irrelevant to a specific financial outcome. When such traits are entered simultaneously into regression-based frameworks, irrelevant shared variance can obscure meaningful associations (Tzelgov and Henik 1991). Thus, models that do not account for suppression may underestimate the importance of personality in financial decision-making or mischaracterize which traits matter most (Lai et al. 2025).

The two-stage design used in this study offers a practical framework for addressing this issue. In the first stage, intercorrelations among predictors and changes in coefficient magnitude and direction were used diagnostically to identify potential suppressor relationships. In the second stage, variance partitioning procedures were applied to isolate the unique components of each trait. This process yielded estimates that were not only statistically more stable but also theoretically more interpretable. For researchers studying financial risk tolerance in particular, the adjusted results suggest that Neuroticism is likely to exhibit a stronger negative association once variance shared with Conscientiousness and Agreeableness is removed. Conversely, Openness and Extraversion tend to show more pronounced positive associations after suppression is addressed. These patterns align closely with established psychological theory linking emotional stability, cognitive exploration, and reward sensitivity to risk-related decision processes.

The implications of these findings extend beyond the outcomes used in this study. Many constructs used in financial planning and household finance research, such as financial literacy, heuristic biases, time preference, locus of control, and behavioral biases, are themselves interrelated. As such, suppression effects may be more widespread than previously thought in models aimed at explaining saving behavior, portfolio choice, insurance purchase decisions, or debt management. The methodological approach used in this study provides a template for improving construct-level precision in these areas as well. By explicitly modeling and correcting for extraneous shared variance, researchers can reduce noise, improve effect-size estimation, and produce findings that are more cumulative across studies.

From a practical standpoint, results also inform the use of personality measures in applied financial planning contexts. If raw trait scores are used without considering suppression, practitioners and tool developers may draw incomplete or misleading conclusions about a client’s behavioral tendencies. Adjusted estimates that better reflect each trait’s unique contribution could enhance the predictive validity of risk tolerance assessments and support more tailored communication and planning strategies. While further work is needed to translate suppression-adjusted scoring into practice-ready tools, the present study highlights the importance of moving beyond surface-level trait associations.

Finally, the alignment between the adjusted results and established psychological theory strengthens confidence in the approach’s substantive validity. By stripping away variance that is statistically shared but conceptually irrelevant, the two-stage method improved both the precision of estimates and the interpretive validity of the results. Findings reinforce the conclusion that personality traits do play a meaningful role in financial risk decision-making and that careful attention to suppressor dynamics is essential to accurately uncover these mechanisms.

6. Implications

This study offers several implications for those who use FRT assessments when providing financial advice, as well as for financial psychology research. The results underscore the importance of accounting for suppressor effects when modeling personality factors in financial decision-making. Traditional approaches that rely on bivariate correlations or single-stage regression models may systematically underestimate or misrepresent the effects of personality traits on financial risk tolerance. By applying a two-stage process that identifies and adjusts for suppression, researchers can uncover relationships that are more consistent with psychological theory and behavioral evidence. This methodological refinement contributes to construct validity in personality–finance research, ensuring that observed effects reflect genuine psychological processes rather than artifacts of multicollinearity. Moreover, the finding that Openness and Extraversion are associated with a greater propensity to engage in financial risk-taking, while Conscientiousness and Neuroticism are associated with a lower proclivity to engage in financial risk-taking, aligns with established trait theory and reinforces the relevance of the Big Five framework for understanding financial decision-making.

From a practitioner’s perspective, this study’s findings highlight the value of integrating personality tools into financial planning, advising, and investor education models. Financial advisors who rely solely on risk-tolerance questionnaires may miss important personality dynamics that help explain client behavior under uncertainty. For example, individuals high in Conscientiousness may appear risk-tolerant in a simple survey. However, they are likely to act more cautiously in practice once their stabilizing tendencies are fully taken into account. Conversely, those high in Openness or Extraversion may exhibit stronger preferences for risk-taking than survey responses alone suggest. By incorporating suppression-adjusted personality measures into client-facing explanatory models, financial advisors can estimate more nuanced client profiles, leading to investment recommendations that better align with long-term behavioral tendencies.

7. Limitations

Despite this paper’s contributions, this study is not without limitations. One limitation of the present study is the reliance on the TIPI to assess Big Five personality traits. As prior research has recognized, the TIPI, though psychometrically acceptable for contexts where brevity is important in surveys, trades off internal consistency and measurement precision for brevity (Gosling et al. 2003). Reduced reliability can increase measurement error, attenuating effect sizes and making it more difficult to detect subtle relationships, including suppressor effects. In this regard, prior work has demonstrated that very brief personality measures tend to underestimate the contribution of personality traits, potentially overestimating the relative influence of newly introduced constructs (Credé et al. 2012). For this reason, the null or weak effects for some personality predictors reported in this paper should be interpreted with caution. Future research should consider replication using longer, more comprehensive Big-Five instruments (e.g., the BFI, NEO-PI), which are likely to yield more reliable estimates and improve the ability to detect nuanced associations. Similarly, financial risk-taking was captured through self-reports, which may diverge from behavioral measures of risk-taking. Subsequent studies should triangulate across attitudinal, behavioral, and experimental indicators.

Additionally, while the two-stage modeling process described in this paper clarified the risks associated with suppressor effects, this approach remains exploratory. Suppression may reflect not only underlying psychological processes but also statistical artifacts and unmeasured confounding variables. Longitudinal designs, structural equation modeling, and experimental approaches should be considered to provide causal inferences about the mechanisms by which personality explains financial risk-taking.

Future studies could extend this work in several ways. First, examining how suppression-adjusted personality effects interact with financial capacity variables (e.g., wealth, income, education) could clarify whether particular personality–risk pathways are contingent on resource availability. Second, researchers should investigate whether suppressor dynamics remain stable over time or fluctuate in response to life events, financial stress, or market conditions. Third, integrating neuroeconomic and psychophysiological approaches may reveal the biological foundation of personality influences (adjusted for suppression), thereby offering a richer, multi-level account of risk-taking behavior. Finally, the methodological strategy used here could be applied to other domains of financial planning, such as saving, spending, and debt management, where trait interactions may also mask or exaggerate observed associations among variables.

8. Conclusions

By explicitly modeling and correcting for suppressor effects, this study addressed a persistent methodological issue in the behavioral finance and financial planning literature that relies on personality measures. As shown in this paper, suppression effects can cause theoretically important variables to appear trivial, reverse the direction of observed effects, or inflate estimates in ways that mislead interpretation. In the context of financial decision-making, where personality assessments are increasingly used in advisory, regulatory, and fintech applications, such distortions carry practical consequences. Misestimated trait effects can lead to flawed client profiling, suboptimal portfolio recommendations, and misguided behavioral interventions. The two-stage design employed in this study can be replicated to ensure that the role of personality traits in describing financial risk-taking reflects each trait’s actual, unique contribution, unclouded by irrelevant variance from other intercorrelated traits. This study provides a methodological roadmap for other researchers while strengthening the theoretical foundations for understanding how personality describes risk-oriented financial behavior.