1 Introduction
Much of the theory concerning financial investment assumes that investors behave in a rational manner and that all market information is embedded in the investment process (Bloomfield 2006). However, human beings are not always fully rational when making investment decisions. The investment decisions of individual investors are often driven by passion rather than by logic (Gibson 2006).
In particular, the relevance of personality traits, such as anger and anxiety has been illustrated to influence financial decision-making (Gambetti and Giusberti 2009, 2012, 2014). A conceptual study by ul Hassan et al. (2013) showed that wrong decisions are made by angry investors. Such angry decisions create grim consequences because the decisions have been done without proper analysis and no rational judgement. Gambetti and Giusberti (2019) showed that anxious people tend to save money and avoid investments, perceiving high risks and low control and returns. This contrasted with extroverted, independent individuals with self-control who were more likely to make investments. This depth of research on personality traits has been prevalent in a European context. However, it does not inform the disparate nature and different environment of individuals in an emerging economy. Research within an emerging market is important, not only because of the substantial heterogeneity in the economic, cultural and social environments, but also because of the rapid change in these characteristics (Sudhir et al 2015).
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This study aims to add to the body of literature on the effect of two personality traits from psychology, anger and anxiety, and the way they relate to investment decisions that involve risk. This is done by not only assessing individuals in an emerging economy, but also, by assessing unique demographic characteristics such as race and gender. Specifically, the study is performed in South Africa, one of the BRICS nations, making a valuable contribution to emerging market findings. Sudhir et al (2015) encourage research that is country-specific to exploit unique characteristics of a specific emerging market, rather than diverse cross-country insights. The incorporation of gender and race as explanatory variables is also important, as research has illustrated both gender and race to influence financial knowledge (Willows 2019a), which is a notable indicator of financial behaviour and decision-making (Atkinson and Messy 2012).
This study does not aim to be exhaustive. Pertinently, a student experimental cohort is used with hypothetical scenarios to allow for the assessment of any prima facie evidence of the merits of future research using more experienced participants and more realistic scenarios. A questionnaire was completed by 288 University of Cape Town (UCT) finance students. Trait anxiety was measured using the T-Anxiety subscale of the State-Trait Anxiety Inventory (STAI-Y2), which evaluates relatively stable aspects of anxiety proneness, such as general states of calmness, confidence and security. Trait anger was measured using the T-Anger subscale of the State-Trait Anger Expression Inventory-2 (STAXI-2), which measures how often angry feelings are experienced over time. The questionnaire was further supplemented with questions about investment decisions and demographics.
Trait anger was found to be a significant predictor of an investment in equity. White participants (rather than Black, Indian or mixed-race participants) were also more inclined to invest in equity. Given that equity is considered a high-risk investment, the finding supports riskier investment decision-making by White individuals and individuals displaying higher levels of anger. Furthermore, women were more likely to select lower risk asset classes as investment than males (Montford and Goldsmith 2015). These findings are of importance, as literature has shown the impact of risk aversion on investment performance (Willows and West 2015). Given the uniqueness of this study’s inclusion of race as an explanatory demographic variable, this provides valuable information to financial advisors and investment managers, particularly those tasked with tailoring investment products for clients. It also highlights the necessity of including demographic variables when assessing the effects of personality traits.
This study continues by reviewing literature on the influence of trait anger and trait anxiety on investment decision-making. Following that, the research method will be presented. To end, the results of both binary logistic regressions and multinomial logistic regressions will be discussed.
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2 Literature review
This literature review begins with some general views on affect and how it is believed to influence decision-making. Risk decision-making refers to a “process of making choices with potential for either positive or negative outcomes” (Maner et al. 2007: 665). Following that, trait anger and trait anxiety and its role in risk decision-making specifically, is discussed.
2.1 Affect and its influence in decision-making
Peters et al. (2006) distinguished between two kinds of affect: integral and incidental affect. Integral affect relates to emotions that are experienced in relation to a specific stimulus, while incidental affect relates to general mood states that are not attributable to a specific stimulus. Peters et al. (2006) highlighted several ways in which affect could influence judgment and decision-making. For instance, affect could act as information to be used in decision-making in that those having to make a decision could ask themselves why they are experiencing a particular feeling. That initial feeling thus serves to signal a potential threat, which influences the decision to be made. Affect could also influence judgement as part of a two-step process. The first step refers to the decision-maker becoming aware of alternative information to be taken into consideration by reflecting on the nature of the affect experienced. In the second step, the new information generated by the feeling, and not the feeling itself, then guides the decision to be made.
Anticipatory emotions, in contrast, refers to emotions that are experienced at the time of decision-making due to something that could happen in the future which could have either positive or negative implications (Zampetakis et al. 2017). Historically, these emotions were addressed by researchers in social and clinical psychology and neuropsychology and not by those in the field of decision-making who adhered to the cognitively orientated consequentialist perspective (Bechara et al. 1997). Research into anticipatory emotions suggests that ignoring these emotions as information inputs into the decision-making process is ill-advised (Zampetakis et al. 2017). The potential also exists that anticipatory emotions could conflict with sound reasoning and, by doing so, diverge from the cognitive appraisal of choice alternatives and risks.
Van Winden et al. (2011) focused on the role of anticipatory emotions (defined as emotions that are being experienced while awaiting the outcome of an uncertain event) during the time that passes between the initial decision to take a risk and the resolution of that risk. The experimental design incorporated insights from both experimental economics and psychology, where 127 Dutch students had to invest their own money under different conditions of risk and resolution timing. While the results illustrated a significant negative correlation between trait anxiety and investment behaviour, Van Winden et al. (2011) emphasised that a single type of emotion, like anxiety, is insufficient to explain investment behaviour fully. A range of other emotions, both positive and negative, experienced during the waiting period were potentially relevant. Furthermore, it was found that the role of affect weakens with a delayed resolution.
2.2 Trait anger
Trait anger is a personality trait that involves a tendency to react with angry feelings across a variety of situations (Gambetti and Giusberti 2009). Individuals who are prone to experience anger tend to perceive their environment in a particular manner and, for that reason, respond to risky situations in a certain way. This argument was tested by Gambetti and Giusberti (2009) in a study of 158 Italian college students. Specifically, they argue that perceptions of familiarity (how familiar the situation is) and saliency (how important it is to achieve a favourable outcome) could mediate the relationship between trait anger and decision-making involving risk. Both familiarity and saliency were found to partially mediate the relationship between trait anger and risky decision-making. A general tendency to feel angry in different contexts also increases the perception of familiarity while decreasing the perception of saliency in risky situations. As a result, people with an anger-prone temperament interpret high familiarity and low saliency as being a relatively safe environment and so take greater risks.
A second study by Gambetti and Giusberti (2012) provided clearer insight into the relationship between financial decision-making and trait anger. It was theorised that anger would activate a sense of high predictability of shares, given the link between anger and the perception that situations are predictable and under individual control. People with dispositional anger, when faced with an investment that suddenly had decreased or increased in value, would delay selling that investment. Given a tolerance for risk and an inflated sense of control, such individuals would delay selling in order to recover any further losses first (in instances of declining investments) or to maximise the eventual gain (in instances of growing investments). Gambetti and Giusberti (2012) applied these insights in a study of 214 working adults where they explored the effect of anger in the context of real-life investment decisions, stock price trend predictability and preferences toward risky investments. They also used three hypothetical scenarios to measure the participants’ risk attitudes in the area of finance. They found trait anger to be positively correlated with a willingness to diversify the investment portfolio, a preference for medium- to long-term investments, and the belief that trends concerning the value of investments are highly predictable. In another study of 107 working adults, Gambetti and Giusberti (2014) found trait anger to be positively related to both mortgage risk and a preference for adjustable interest rates.
2.3 Trait anxiety
Anxiety can be divided into trait anxiety and state anxiety (Wilt et al. 2011). Trait anxiety is a general tendency of people to see events and circumstances as threatening. It refers to both a person’s general disposition to be anxious as well as her/his typical level of anxiety. State anxiety, on the other hand, is experienced in relation to specific situations and is thus temporary. It can be defined as a person’s level of anxiety within a limited time interval. The two types of anxiety are related in that trait anxiety represents a personality disposition where any situation is perceived as a potential threat, which could be expressed as state anxiety in a specific stressful situation.
Trait anxiety appears to be associated with strong avoidance goals, i.e. goals that are focused on avoiding or eliminating undesirable outcomes (Wilt et al. 2011) and with risk avoidance behaviour after controlling for levels of depression (Maner and Schmidt 2006). As a result, anxious individuals tend to settle for low-risk/low-reward options. Highly anxious individuals prefer safe alternatives, often because of the subjective costs of the risky option, which is that they will feel worse should the negative outcome materialise (Mitte 2007). Peng et al. (2014) found that individuals with high levels of trait anxiety were more likely to select conservative solutions in a hypothetical exercise that involved risk. Individuals with high levels of trait anxiety also have a strong desire to reduce uncertainty, even if it means making incorrect assumptions (Bensi and Giusberti 2007).
Maner et al. (2007) hypothesised that trait anxiety could relate to risk-avoidant decision-making in one of two ways. Firstly, for individuals with trait anxiety, the emotion that is experienced typically signals the presence of a potential threat (Schwarz and Clore 1983). Given that threat avoidance is a core element of risk-avoidant decision-making, the logical conclusion would be for individuals experiencing high levels of trait anxiety to avoid risks. Secondly, individuals displaying high levels of trait anxiety are often also more inclined to pessimistic appraisals of future events across a range of situations (Shepperd et al. 2005). Thus, given a general inclination to anticipate distress or a negative outcome in the event of uncertainty, highly anxious people will be more prone to risk-avoidant decision-making.
Maner et al. (2007) found a significant correlation between trait anxiety and risk-avoidant decision-making in a study of 97 undergraduates at an American university. Participants who experienced a greater degree of anxiety exhibited greater risk avoidance. Risk avoidance is measured as a score on the computer-based Balloon Analogue Risk Task (BART) (Lejuez et al. 2002). To rule out the possibility that the relationship between trait anxiety and risk avoidance is facilitated by a third factor (a general negative mood), the researchers also included a measure of general negative affect in the analysis. When controlling for the influence of this general negative affect, the correlation between anxiety and risk avoidance remained unchanged. Thus, Maner et al. (2007) concluded that the relationship between trait anxiety and risk avoidance cannot simply be explained by the general negative mood often experienced by anxious individuals.
In an investing environment, Gambetti and Giusberti (2012) anticipated that anxiety would activate a sense of low predictability of the share price, as anxiety is associated with the avoidance of risky situations and perceived threats (Bensi and Giusberti 2007; Maner and Schmidt 2006). Anxious people, therefore, would sell an investment immediately should it either increase or decrease in value, as a way of reducing uncertainty and eliminating the threat of either missing out on a gain (in instances of growing investments) or losing even further (in instances of declining investments). In their study of working adults, Gambetti and Giusberti (2012) found trait anxiety to be correlated with having interest-bearing accounts, and the belief that investment trends cannot really be predicted. Trait anxiety therefore predicted a preference for a low-risk portfolio and the decision to immediately sell investments that started to increase or decrease in value.
3 Methodology
This study follows on the work by Gambetti and Giusberti (2009, 2012, 2014) by investigating the relationship between financial decision-making and trait anger and trait anxiety in a South African context. The study thus aims to assess whether anger or anxiety are associated with more risky investment decision-making, in an emerging market.
Gender and race were included as additional explanatory variables in the relationship between the two personality traits and investment decision-making. The reasons for including gender and race were threefold. Firstly, there is evidence in the literature that males have a higher preference for risk in investment decision-making and that females are more risk averse for investment decisions (Willows and West 2015). Thirdly, the influence of race on investment decision-making is relatively unresearched, yet highly relevant in South Africa. The inclusion of these variables thus provide a unique contribution to this area of research.
3.1 Research method
The study will use survey methodology to answer its research question. Participants were 288s-year finance students from the University of Cape Town (UCT), South Africa. Questionnaires were completed in class, following a brief explanation of the study. Requisite ethical clearance was obtained to perform the survey. An investment of ZAR1000 (i.e. US$70) in a collective investment scheme (i.e. unit trust) was offered as an incentive to encourage participation.
The questionnaire consisted of three sections: measures of the two personality traits (Sect. 1), questions about investment decisions (Sect. 2) and demographics (Sect. 3). All questions, except for Sect. 3, were taken from Gambetti and Giusberti (2012).
Of the 284 participants who provided demographic details, 48% were women. Of the 267 participants who provided details pertaining to their race, 35% were Black African, 8% were Coloured (i.e. of mixed-race descent), 11% were Indian, 41% were White, and 5% were other. In order to allow sufficient respondents in each racial category for statistical analysis, a binary variable for race needed to be created. This was done using the broad-based black economic empowerment (BEE) classification within South Africa, whereby Black people consist of African, Coloured and Indian people (von Steiger 2018). The historical past of South Africa created disparate opportunities amongst individuals within this BEE category. Thus, differentiation of those respondents from White respondents is the most meaningful means of comparison.
3.1.1 Measurement of personality traits
Trait anxiety was measured using the T-Anxiety subscale of the State-Trait Anxiety Inventory (STAI-Y2) (Forgays et al. 1997). The T-Anxiety subscale evaluates relatively stable aspects of anxiety proneness, such as general states of calmness, confidence and security. The measure comprises 20 items which respondents need to rate on a Likert-scale, ranging from ‘almost never’ (1) to ‘almost always’ (4). Seven of the items are positively worded (e.g. ‘I am a steady person’) and the other 13 items negatively worded (e.g. ‘I feel like crying’). To calculate a total score for the T-Anxiety subscale, all positively worded items first had to be reverse-scored. Total scores range from 20 to 80, with higher scores suggesting greater levels of anxiety and lower scores suggesting lower levels of anxiety. The measure illustrated good internal consistency reliability, as evidenced by a Cronbach’s alpha coefficient of 0.90.
The T-Anger subscale of the State-Trait Anger Expression Inventory-2 (STAXI-2) (Forgays et al. 1997) was used to measure trait anger. The scale measures how often angry feelings are experienced over time. It consists of 10 items which are rated on a Likert-scale, also ranging from ‘almost never’ (1) to ‘almost always’ (4). The wording of all 10 items are such that the ‘almost always’ response indicates an individual who is prone to expressing anger. Total scores range from 10 to 40, with higher scores indicating greater levels of anger and lower scores suggesting lower levels of anger. This measure also illustrated good internal consistency reliability, as evidenced by a Cronbach’s alpha coefficient of 0.80.
3.1.2 Measurement of investment decisions
Section 2 in the questionnaire comprised five questions, which corresponded to two aspects of investment decision-making. The first aspect concerned a preference for investment in six asset classes: alternatives (commodities), cash, equity (shares), fixed income (bonds), insurance products and real estate. The first question required the participants to indicate all asset classes in which they would choose to invest. For the second question, they rated each asset class in terms of the predictability of the trend of the investment, by using a five-point scale (1 = ‘highly unpredictable’ and 5 = ‘highly predictable’).
The second aspect of investment decision-making concerned a preference for risky investments. It comprised three questions. The first question asked participants to indicate which investment would suit them best. The interest rates used by Gambetti and Giusberti (2012) was adjusted for the South African macroeconomic environment. The second question was “You are offered different investment portfolios with the following characteristics: ‘low gain/no loss’, ‘medium gain/medium loss’ and ‘high gain/high loss’. Which portfolio do you choose?” The third question was “You have shares, what do you do if the share declines in price?” Participants had to choose one of three options: “sell at a loss”, “wait some days with the possibility to either lose or advance” and “wait some weeks with the possibility to either lose or advance even further”. The above three questions represented hypothetical scenarios to measure the participants’ risk attitudes in financial decision-making.
3.2 Research approach
The analyses entailed reliability analyses for the two personality measures (Cronbach’s alpha), basic descriptive analyses (frequencies and cross-tabulations), statistical significance testing of bivariate relationships (correlation coefficient, chi-square and independent t-tests) and logistic regressions. Two kinds of logistic regressions were performed. A binary logistic regression was used to predict the likelihood of an event occurring on the basis of a set of predictors, where the event, called “outcome”, comprised only two levels (e.g. “yes” or “no”). Multinomial logistic regressions were then used to predict an outcome that comprised more than two levels (e.g. “low gain”, “medium gain” or “high gain”).
4 Results
4.1 Predictor variables and interactions
All logistic regression procedures used, irrespective of the nature of the outcome, included four predictor variables (gender, race, trait anxiety and trait anger). One regression included a fifth predictor, namely the participants’ perception of how predictable they believed the trend of a particular asset class to be. Before presenting the findings of the logistic regressions, the interactions among predictor variables are discussed.
Table 1 reports the mean trait anxiety and anger scores for males and females. Two independent t-tests were conducted to test for significant differences.
Table 1
Descriptive statistics of personality traits by gender
Personality traits | Gender | Mean | Median | SD | Lowest score | Highest score | N |
|---|---|---|---|---|---|---|---|
Trait anxiety | Female | 45.10 | 46.00 | 10.713 | 27 | 69 | 118 |
Male | 39.55 | 39.00 | 7.315 | 23 | 62 | 133 | |
Trait anger | Female | 19.48 | 19.00 | 5.051 | 13 | 38 | 133 |
Male | 19.22 | 19.00 | 4.598 | 12 | 34 | 137 |
The two genders did not differ significantly regarding anger [t (268) = 0.446, p = 0.656], but a statistically significant difference was observed for anxiety [t (203) = 4.736, p < 0.05]. Females (mean score 45.10) were more prone to anxiety than males (mean score 39.55). This same pattern of significance was observed for the comparison between participants of different races in Table 2.
Table 2
Descriptive statistics of personality traits by race
Personality traits | Race | Mean | Median | SD | Lowest score | Highest score | N |
|---|---|---|---|---|---|---|---|
Trait anxiety | Black | 45.09 | 44.00 | 9.593 | 28 | 66 | 117 |
White | 37.75 | 38.00 | 7.034 | 23 | 54 | 104 | |
Trait anger | Black | 19.32 | 18.00 | 5.126 | 12 | 38 | 137 |
White | 19.14 | 19.00 | 3.925 | 13 | 28 | 102 |
Black and white participants did not differ significantly in terms of anger [t (237) = 0.34, p = 0.754]. The statistically significant difference found for anxiety [t (212) = 6.537, p < 0.05] revealed Black participants (mean score 45.09) to be more prone to this personality trait compared to White participants (mean score 37.75).
Finally, the two measures of trait anxiety and trait anger were found to be significantly correlated, based on the Pearson correlation coefficient (r = 0.304, N = 245, p < 0.05). This positive correlation implies a tendency for relatively high levels of trait anxiety to be associated with relatively high levels of trait anger.
The incorporation of gender and race as explanatory variables is unique to this study and in these preliminary results already shows differences in the prevalence of trait anxiety. Research has illustrated both gender and race to influence financial knowledge and risk aversion (Willows 2019a; Willows and West 2015), which are notable indicators of financial behaviour and decision-making (Atkinson and Messy 2012).
Despite the sample of participants being similar in respect of their year of study and course of study at a tertiary institution, South Africa’s historical apartheid policy has caused many divides among the population (Willows 2019b). The racially exclusionist education policies implemented resulted in many Black South Africans experiencing substandard schooling or having grown up in households with parents who might never have had a formal qualification or exposure to financial instruments (Draper and Spaull 2015). Financial literacy is acquired over time; thus, this might have been a disadvantage to Black participants. Willows (2019a), when assessing actual and self-assessed financial literacy, also noted that White participants tend to rate their own perceived level of financial knowledge on a higher scale than African participants. This might explain the preference of White South Africans to select more aggressive investments (Willows 2019b) and the racial differences noted in the measurement of trait anxiety.
Pertaining to the gender differences noted; women are more risk averse than men (Gambetti and Giusberti 2019; Willows and West 2015) and gender differences in financial knowledge scores are seen in research performed around the world (Lusardi and Mitchell 2011; Willows 2019b; Xu and Zia 2012). Like the findings with race, Willows (2019a) noted that men tend to rate their own perceived level of financial knowledge on a higher scale than women. Kannadhasan et al. (2016) add that even when women possess higher self-esteem than men (which is positively related to risk tolerance), men are still more risk tolerant as they are overconfident in their ability to achieve positive investment gains. Furthermore, Grable and Roszkowski (2007) found that women tend to underestimate their risk tolerance whereas men overestimate. As trait anxiety appears to be associated with strong avoidance goals, i.e. goals that are focused on avoiding or eliminating undesirable outcomes, these differences can be expected amongst the different genders.
4.2 Preference for investment in six asset classes
For the first investment decision, the participants were asked in which of six asset classes they would choose to invest. They could choose more than one. They were also asked to indicate how much they believed the trend of the investment for each asset class to be predictable. Table 3 summarises the responses.
Table 3
Predictability of trend of investment associated with six asset classes and percentages of participants selecting each class
Asset classes | % who selected class (N = 288) | Rating of predictability of trend of investment | |||
|---|---|---|---|---|---|
(1 = highly unpredictable; 5 = highly predictable) | N | ||||
Scores of 1 and 2 (%) | Score of 3 (%) | Scores of 4 and 5 (%) | |||
Alternatives (commodities) | 22 | 40 | 32 | 29 | 278 |
Cash | 37 | 12 | 24 | 64 | 284 |
Equity (shares) | 72 | 56 | 23 | 21 | 282 |
Fixed income (bonds) | 38 | 4 | 13 | 82 | 284 |
Insurance products | 9 | 14 | 31 | 55 | 278 |
Real estate | 40 | 14 | 19 | 67 | 284 |
Investment in equity was the most popular choice (72%), with real estate (40%), fixed income (38%) and cash (37%) trailing relatively far behind. Only 9% of participants chose insurance products as an investment. Table 3 further illustrates that equity, the most preferred asset class, was perceived as the most unpredictable asset class (56%). This means that equity was perceived as the riskiest asset class. Contrasting to this, a large percentage of participants (82%) considered fixed income to be the most predictable and therefore the least risky choice.
In order to determine the relative importance of trait anxiety and trait anger in choosing a particular asset class, a series of binary logistic regressions was performed. In each regression, the outcome variable was one of the six asset classes. In addition to trait anxiety and trait anger, gender and race were also included as predictors to determine their relative importance in relation to the two personality traits. The rating of the predictability of the trend of investment was added as a fifth predictor. This was done on the assumption that the choice for a particular investment was influenced by the investor’s perception of how predictable the investment is. The results of the binary logistic regression is summarised in Table 4.
Table 4
Logistic regression predicting likelihood of investing in six asset classes
Variables | b | SE | Wald | p | OR | 95% confidence interval for odds ratio | |
|---|---|---|---|---|---|---|---|
Lower | Upper | ||||||
Alternatives (commodities) (N = 211) | |||||||
Predictability | 0.330 | 0.204 | 2.621 | 0.105 | 1.391 | 0.922 | 2.076 |
Gender | 1.788 | 0.467 | 14.666 | 0.000 | 5.979 | 2.394 | 14.931 |
Race | − 0.263 | 0.402 | 0.430 | 0.512 | 0.769 | 0.350 | 1.688 |
Trait anxiety | 0.006 | 0.026 | 0.046 | 0.829 | 1.006 | 0.956 | 1.058 |
Trait anger | − 0.059 | 0.047 | 1.550 | 0.213 | 0.943 | 0.860 | 1.034 |
Constant | − 2.510 | 1.386 | 3.278 | 0.070 | 0.081 | – | – |
Cash (N = 211) | |||||||
Predictability | − 0.111 | 0.136 | 0.662 | 0.416 | 0.895 | 0.685 | 1.169 |
Gender | 0.802 | 0.313 | 6.577 | 0.010 | 2.230 | 1.208 | 4.115 |
Race | 0.123 | 0.322 | 0.146 | 0.703 | 1.131 | 0.601 | 2.127 |
Trait anxiety | 0.240 | 0.190 | 1.565 | 0.211 | 1.024 | 0.986 | 1.064 |
Trait anger | − 0.090 | 0.033 | 0.079 | 0.778 | 0.991 | 0.929 | 1.057 |
Constant | − 1.383 | 1.098 | 1.584 | 0.208 | 0.251 | – | – |
Equities (shares) (N = 211) | |||||||
Predictability | 0.038 | 0.172 | 0.049 | 0.826 | 1.039 | 0.741 | 1.455 |
Gender | 0.054 | 0.357 | 0.023 | 0.879 | 1.056 | 0.524 | 2.126 |
Race | 0.995 | 0.380 | 6.843 | 0.009 | 2.704 | 1.283 | 5.698 |
Trait anxiety | − 0.032 | 0.022 | 2.070 | 0.150 | 0.969 | 0.928 | 1.012 |
Trait anger | 0.091 | 0.040 | 5.132 | 0.023 | 1.095 | 1.012 | 1.184 |
Constant | 0.189 | 1.128 | 0.028 | 0.867 | 1.208 | – | – |
Fixed income (bonds) (N = 211) | |||||||
Predictability | 0.058 | 0.176 | 0.111 | 0.739 | 1.060 | 0.752 | 1.495 |
Gender | 0.183 | 0.306 | 0.358 | 0.550 | 1.201 | 0.659 | 2.190 |
Race | − 0.429 | 0.323 | 1.768 | 0.184 | 0.651 | 0.346 | 1.225 |
Trait anxiety | 0.007 | 0.019 | 0.161 | 0.689 | 1.008 | 0.971 | 1.045 |
Trait anger | − 0.011 | 0.032 | 0.112 | 0.738 | 0.989 | 0.929 | 1.053 |
Constant | − 0.796 | 1.221 | 0.425 | 0.514 | 0.451 | – | – |
Insurance products (N = 209) | |||||||
Predictability | 0.386 | 0.281 | 1.896 | 0.169 | 1.472 | 0.849 | 2.550 |
Gender | − 1.491 | 0.616 | 5.852 | 0.016 | 0.225 | 0.067 | 0.754 |
Race | − 2.001 | 0.794 | 6.350 | 0.012 | 0.135 | 0.029 | 0.641 |
Trait anxiety | 0.009 | 0.029 | 0.092 | 0.762 | 1.009 | 0.954 | 1.067 |
Trait anger | − 0.310 | 0.500 | 0.383 | 0.536 | 0.970 | 0.880 | 1.069 |
Constant | − 2.398 | 1.767 | 1.843 | 0.175 | 0.091 | – | – |
Real estate (N = 211) | |||||||
Predictability | 0.343 | 0.167 | 4.240 | 0.039 | 1.410 | 1.017 | 1.954 |
Gender | − 0.357 | 0.309 | 1.335 | 0.248 | 0.700 | 0.382 | 1.282 |
Race | − 0.720 | 0.331 | 4.727 | 0.030 | 0.487 | 0.254 | 0.931 |
Trait anxiety | − 0.027 | 0.019 | 2.005 | 0.157 | 0.973 | 0.938 | 1.010 |
Trait anger | − 0.018 | 0.033 | 0.308 | 0.579 | 0.982 | 0.921 | 1.047 |
Constant | 0.231 | 1.201 | 0.037 | 0.848 | 1.260 | – | – |
In Table 4, the b coefficient represents the change in the logit of the outcome variable that can be attributed to a one-unit change in the predictor variable. The logit of the outcome variable is the natural logarithm of the odds of that outcome occurring (Field 2013). The Wald statistic indicates whether the b coefficient for a predictor is significantly different from 0, in other words, whether it significantly predicts the outcome. This is the case when the associated p value is less than 0.05. Since the b coefficient involves a logarithmic transformation, the odds ratio presents an easier way to interpret the relative contribution of predictors to the outcome.
Table 4 illustrates that trait anger is only statistically significant in predicting whether to invest in equity. The odds of choosing equity as an asset class increased by about 1.1, by moving from one score in the anger measure to the next higher score.
The gender variable was predictive across three asset classes, with it being statistically significant in choosing alternative investments, cash and insurance products. The highest odds ratio was for alternative investments (5.979). This means that the odds of choosing alternative investments were about six times higher for males than for females. The odds of choosing cash products were also 2.2 times higher for males. Showing opposite preferences, the odds of choosing insurance products were 4.4 times (1/0.225) higher for females. This is explained by the higher risk aversion in women (Willows and West 2015). Reviewing experimental evidence of risk aversion, Eckel and Grossman (2008) find evidence of women purchasing insurance more often than men and purchasing more extensive insurance than men.
The race variable was also predictable, being statistically significant in choosing equity, insurance products and real estate. The odds of White participants choosing to invest in equity were almost three times higher than those of a Black participant. Alternatively, the odds of a Black respondent choosing insurance products or real estate were 7.4 times (1/0.135) and 2.1 times (1/0.487) higher than for a White participant. These findings suggest similar risk aversion differences amongst participants of different race, as was found with gender. It also confirms findings by Willows (2019b) who looked at the investment choices for retirement funds among a sample of South Africans and noted an increased preference for higher risk (higher return) retirement products by White South Africans.
Regarding perceived predictability of the asset class, the odds of choosing real estate increased by about 1.4 as the perception of predictability increased. The predictability of this variable for real estate only suggests a possible relationship that is a recommended area for future research.
4.3 Preference for risky investments
Three scenarios measured the participants’ preference for risky investments. These are reported in Table 5.
Table 5
Responses to three scenarios that measured preference for risky investments
Scenarios | Count | Percentage |
|---|---|---|
What is the best investment for you? (N = 286) | ||
12-month deposit with an interest rate of 7.5% | 170 | 59 |
6-month bond with an interest rate of 6.9% | 90 | 32 |
Interest-bearing account with an interest rate of 3.5% | 26 | 9 |
You are offered different investment portfolios with the following characteristics: ‘low gain/no loss’, ‘medium gain/medium loss’ and ‘high gain/high loss’. Which portfolio do you choose? (N = 288) | ||
Low gain/no loss | 28 | 10 |
Medium gain/medium loss | 211 | 73 |
High gain/high loss | 49 | 17 |
You have shares, what do you do if the share declines in price? (N = 286) | ||
Sell at a loss | 18 | 6 |
Wait some days with the possibility to either lose or advance | 152 | 53 |
Wait some weeks with the possibility to either lose or advance even further | 116 | 41 |
The first scenario in Table 5 measured the participants’ willingness to take investment risk. Most participants expressed a low willingness to accept risk, as indicated by the 59% who chose the 12-month deposit with an interest rate of 7.5%. The second scenario asked the participants to identify with one of three categories of investors, which were indirectly derived from the response options provided: risk-averse (“low gain/no loss”), risk-neutral (“medium gain/medium loss”) and risk-loving (“high gain/high loss”), they would choose. Close to three-quarters (73%) of participants could be classified as risk-neutral investors. The third scenario measured the participants’ behaviour regarding volatility, expressed as a decline in share price. Only 6% of participants indicated that they would sell at a loss. This implies that most of the participants (94%) tended to be risk-loving, albeit to different degrees (waiting days vs. waiting weeks).
For each of the scenarios above, a logistic regression was performed with the scenario variable as outcome, and anxiety, anger, gender and race as predictors. Table 6 reports the results of a multinomial logistic regression for scenario 1, which relates to the participants’ willingness to take investment risk. A multinomial logistic regression was performed since the outcome variable was non-binary. The outcome variable was presented as two comparisons, with the least risky option (12-month deposit with an interest rate of 7.5% as reference category).
Table 6
Logistic regression predicting likelihood of choosing less risky or more risky investment options (N = 213)
Variables | B | SE | Wald | p | OR | 95% confidence interval for odds ratio | |
|---|---|---|---|---|---|---|---|
Lower | Upper | ||||||
Comparison 1: 6-month bond with an interest rate of 6.9% versus 12-month deposit with an interest rate of 7.5% | |||||||
Gender | − 1.361 | 0.350 | 15.088 | 0.000 | 0.256 | 0.129 | 0.510 |
Race | 0.284 | 0.349 | 0.659 | 0.417 | 1.328 | 0.670 | 2.634 |
Trait anxiety | 0.010 | 0.021 | 0.203 | 0.652 | 1.010 | 0.969 | 1.052 |
Trait anger | 0.017 | 0.035 | 0.221 | 0.638 | 1.017 | 0.949 | 1.090 |
Constant | − 0.863 | 0.879 | 0.964 | 0.326 | – | – | – |
Comparison 2: interest-bearing account with an interest rate of 3.5% versus 12-month deposit with an interest rate of 7.5% | |||||||
Gender | − 1.620 | 0.583 | 7.728 | 0.005 | 0.198 | 0.063 | 0.620 |
Race | 0.834 | 0.574 | 2.112 | 0.146 | 2.303 | 0.748 | 7.092 |
Trait anxiety | 0.062 | 0.033 | 3.479 | 0.062 | 1.064 | 0.997 | 1.135 |
Trait anger | 0.013 | 0.051 | 0.066 | 0.797 | 1.013 | 0.916 | 1.120 |
Constant | − 4.445 | 1.426 | 9.712 | 0.002 | – | – | – |
According to Table 6, gender was the only significant predictor in both comparisons. The odds ratio indicates that the difference in odds between female to male of choosing the 6-month bond over the 12-month deposit was 0.256. To state it differently, the odds of females choosing the 6-month bond over the 12-month deposit were 3.9 times (1/0.256) higher. For the second comparison, the odds of females choosing the interest-bearing account over the 12-month deposit was 5.1 times (1/0.198) higher. Table 7 illustrates the regression result for the second scenario i.e. choosing between different investment portfolios.
Table 7
Logistic regression predicting likelihood of choosing between investment portfolios with different levels of gain/loss (N = 213)
Variables | B | SE | Wald | p | OR | 95% confidence interval for odds ratio | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Lower | Upper | ||||||||||||
Comparison 1: medium gain/medium loss versus low gain/low loss | |||||||||||||
Gender | − 0.546 | 0.557 | 0.959 | 0.327 | 0.580 | 0.194 | 1.727 | ||||||
Race | 0.289 | 0.576 | 0.253 | 0.615 | 1.336 | 0.432 | 4.127 | ||||||
Trait anxiety | − 0.036 | 0.031 | 1.345 | 0.246 | 0.964 | 0.907 | 1.025 | ||||||
Trait anger | 0.106 | 0.066 | 2.559 | 0.110 | 1.111 | 0.977 | 1.265 | ||||||
Constant | 1.989 | 1.401 | 2.016 | 0.156 | – | – | – | ||||||
Comparison 2: high gain/high loss versus low gain/low loss | |||||||||||||
Gender | − 3.936 | 0.995 | 15.640 | 0.000 | 0.020 | 0.003 | 0.137 | ||||||
Race | 1.347 | 0.715 | 3.549 | 0.060 | 3.846 | 0.947 | 15.618 | ||||||
Trait anxiety | − 0.087 | 0.044 | 3.867 | 0.049 | 0.917 | 0.841 | 1.000 | ||||||
Trait anger | 0.263 | 0.081 | 10.506 | 0.001 | 1.301 | 1.109 | 1.525 | ||||||
Constant | − 0.491 | 1.880 | 0.068 | 0.794 | – | – | – | ||||||
Significance is only observed in the second comparison with three predictors being statistically significant: gender, trait anxiety and trait anger. The odds of a male choosing the high gain/high loss option over the low gain/low loss was 50 times higher (1/0.020). The odds of participants with lower anxiety choosing the high gain/high loss option over low gain/low loss were 1.1 times higher (1/0.917). However, the confidence interval included the value of 1. This means that not much emphasis should be placed on this significant finding because 1 is “the threshold at which the direction of the effect changes” (Field 2013: 786). Lastly, the odds of choosing high gain/high loss (over low gain/low loss) increased by about 1.3 as one moved from any score in the anger measure to the next higher score. These results somewhat mirror those seen in Table 4 when choosing to invest in equity i.e. that a higher anger measure is correlated to higher risk tolerance.
Although 18 participants had selected the ‘sell at a loss’ category for the outcome variable in scenario 3 (Table 5), this category had too few data points to be used in the relevant multinomial logistic regression. Consequently, only the two remaining categories were analysed in a binary logistic regression. Table 8 provides the results.
Table 8
Logistic regression predicting likelihood of waiting some weeks with the possibility to either lose or advance even further (N = 211)
Variables | b | SE | Wald | p | OR | 95% confidence interval for odds ratio | ||
|---|---|---|---|---|---|---|---|---|
Lower | Upper | |||||||
Gender | 1.081 | 0.317 | 11.626 | 0.001 | 2.948 | 1.583 | 5.488 | |
Race | 0.402 | 0.326 | 1.519 | 0.218 | 1.495 | 0.789 | 2.835 | |
Trait anxiety | − 0.051 | 0.021 | 6.046 | 0.014 | 0.950 | 0.912 | 0.990 | |
Trait anger | 0.044 | 0.035 | 1.588 | 0.208 | 1.045 | 0.976 | 1.118 | |
Constant | 0.049 | 1.007 | 0.002 | 0.961 | 1.050 | – | – | |
Two variables are statistically significant: gender and trait anxiety. In the case of gender, the odds ratio of 2.948 indicated that the odds of waiting some weeks with the possibility to either lose or advance even further were almost 3 times higher for males. These results are supported in literature which illustrates the prevalence of gambling being heightened in men (Hing et al. 2016), This is substantiated by men’s heightened ability to withstand risk (Willows and West 2015).
The odds of participants with lower anxiety waiting some weeks (rather than some days) was 1.1 times higher (1/0.950). In a laboratory-based delay-discounting task in which participants made choices between electric shocks delivered immediately rather than after various time delays, Salters-Pednault and Diller (2013) found that participants with higher levels of anxiety were more likely to make the choice to delay, despite knowing that it would result in a worse outcome. The results in Table 8 show the opposite: that lower anxiety predicts the ability to wait. However, in this paper, the outcome is a gamble, rather than a guaranteed worse outcome. Furthermore, these results should be interpreted with caution given the upper confidence level of 0.990.
5 Discussion and conclusion
This study investigated the relative importance of trait anger and trait anxiety in financial decision-making, together with gender and race as predictors. This was done in South Africa, to assess unique demographic characteristics of a specific emerging market (Sudhir et al 2015). A student experimental cohort using hypothetical scenarios allowed for rapid assessment of prima facie evidence of the merits of future research using more experienced participants and more realistic scenarios.
Trait anger was found to be a significant predictor of an investment in equity and preferring a high gain/high loss investment portfolio over a low gain/low loss portfolio. This provides evidence for the hypothesis that such individuals would have an appetite for higher risk and higher expected returns (Gambetti and Giusberti 2009, 2014).
Gender was found to be a highly predictive variable. Women were more likely to select insurance products which tend to be very low risk by nature. This was confirmed in another finding which showed women choosing less risky investments. Whereas men were more likely to choose cash and alternative investments. Males were also more likely to wait some weeks rather than some days when facing a decline in share price and preferred investing in a high gain/high loss investment, than a low gain/low loss investment. These findings are all supported in literature which illustrates women to be more risk averse in terms of investment decisions (Gambetti and Giusberti 2019; Willows and West 2015).
The inclusion of the race variable was another unique contribution to this study. Participants were divided into two groups using the BEE classification within South Africa, whereby Black people consisted of those individuals who were previously disadvantaged by South Africa’s historical past (von Steiger 2018). The results of this paper showed a preference by Black participants to select insurance products or real estate and a preference by White participants to invest in equity. These differences might be explained by the difference in financial knowledge amongst South Africans of difference races (Willows 2019a) and the preference of White South Africans to select more aggressive investments (Willows 2019b). South Africa’s historical apartheid policy has caused many divides among the population (Willows 2019b). The racially exclusionist education policies implemented resulted in many Black South Africans experiencing substandard schooling or having grown up in households with parents who might never have had a formal qualification or exposure to financial instruments (Draper and Spaull 2015). Financial literacy is acquired over time; thus this might have been a disadvantage to Black participants.
Trait anxiety was less of a significant predictor in the testing performed. However, the effect of trait anxiety is hypothesised to have been proxied by gender and race. Females and Black participants were more prone to anxiety than men and White participants. This statistically significant difference was not noted with trait anger. Thus, the significant findings pertaining to gender and race should be read with this personality trait in mind. This reinforces the importance of considering race and gender as explanatory variables in investment decision-making.
These findings add to the body of literature in behavioural finance that assess the influence of personality traits on investment decision-making. However, the inclusion of race and gender as explanatory variable provides preliminary insight into the impact of these variables on investment decision-making. In this study, the findings are noteworthy. Strong associations are found with trait anger, gender and race. And most of the predictive outcomes relate to risk aversion. As the ability to withstand risk is vital for investment decisions, these findings are useful to financial advisors. Furthermore, the necessity of including demographic variables when assessing personality traits is highlighted. Further research that assesses the effect of gender and race on personality traits, which might be acting as a possible mediator is suggested. Is it heightened anger that predicts riskier investment choices, or is the different culture, society and historical past of people that are of different gender and races that increases anger, which in turn predicts riskier investment choices?
5.1 Limitations
The analysis of the data elicited by the survey depends on responses to hypothetical choices posed in the survey questions. The use of this method is reliant on the assumption that respondents have no desire to mask their true intentions and know how they would act in actual situations as those posed to them hypothetically. The usefulness of the results therefore relies on the presumption that hypothetical choices return the same values that would be returned by choices within an experimental procedure. In other words, the responses to the hypothetical choices should provide information that is statistically useful in drawing conclusions on the real economic commitment that the respondent would make (Blackburn 1994). To mitigate hypothetical bias, no open-ended questions were included (Neill et al. 1994). Amidst this measure, this study does not claim that the responses to the hypothetical choices are the same as they would have been in a real environment. However, important inferences can still be made from the results. This approach is similar to that followed by Girolamo et al. (2013), Gourville (1998), Kahneman and Tversky (1979), Laibson (1997), Samuelson and Zeckhauser (1988), Shapiro and Slemrod (2001, 2003) and Sahm et al. (2010).
The prima facie indicators sought are evident from the results of the study. As such, there ought to be merit in further research using more experienced participants and more realistic scenarios.
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