Neurotransmitters, Emotional Intelligence, Personality Traits, and the Behavior and Decisions of Individual Investors
Subject Areas : Finance
Mohammad Nazaripour
1
,
Babak Zakizadeh
2
1 -
2 -
Keywords: Neurotransmitters, Emotional Intelligence, Personality Traits, Individual Investors, Tehran Stock Exchange ,
Abstract :
Purpose: Neurotransmitters, emotional intelligence, and personality traits are the key factors that affect the behavior and decisions of individual investors. This study aimed to investigate the effects of neurotransmitters, emotional intelligence, and personality traits on the behavior and decisions of individual investors simultaneously, in the Tehran stock exchange.
Design/methodology/approach: In terms of purpose, the current research was practical. In terms of method, it was a descriptive survey research. In terms of time, it was a cross-sectional research. The research data were collected through a questionnaire and convenience sampling method. The sample size was 212 individual investors. Composite reliability, average variance extracted, convergent validity and divergent validity were used to measure the validity and reliability of the questionnaire. Data were analyzed using structural equation modeling.
Findings: This study includes 19 constructs (variables). These constructs include dopamine, serotonin, norepinephrine, and epinephrine (neurotransmitters); Self-appraisal of emotions, other’s emotion appraisal, use of emotion and regulation of emotion (emotional intelligence); extraversion, neuroticism, openness, conscientiousness, and agreeableness (personality traits); investment horizon, risk attitude, personalization of loss, confidence, and control (investors' behavior); and investors' decisions. The current study includes six main hypotheses and 78 sub-hypotheses. The research findings showed that neurotransmitters, emotional intelligence, and personality traits affect the investors' behavior and decisions.
Originality/value: The findings of this study showed that the neurofinance and behavioral finance have a key role in decision making of policymakers and investors. Finally, the findings of this study would provide new horizons in neurofinance and behavioral finance.
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Received: 13/04/2024 Accepted: 26/08/2024 |
Journal of System Management (JSM)Online ISSN: 2538-1571, Print ISSN: 2322-2301
11(1), 2025, pp. 39-54
RESEARCH ARTICLE Open Access
Neurotransmitters, Emotional Intelligence, Personality Traits, and the Behavior and Decisions of Individual Investors
Mohammad Nazaripour 1*, Babak Zakizadeh 2
Abstract
Purpose: Neurotransmitters, emotional intelligence, and personality traits are the key factors that affect the behavior and decisions of individual investors. This study aimed to investigate the effects of neurotransmitters, emotional intelligence, and personality traits on the behavior and decisions of individual investors simultaneously, in the Tehran stock exchange.
Design/methodology/approach: In terms of purpose, the current research was practical. In terms of method, it was a descriptive survey research. In terms of time, it was a cross-sectional research. The research data were collected through a questionnaire and convenience sampling method. The sample size was 212 individual investors. Composite reliability, average variance extracted, convergent validity and divergent validity were used to measure the validity and reliability of the questionnaire. Data were analyzed using structural equation modeling.
Findings: This study includes 19 constructs (variables). These constructs include dopamine, serotonin, norepinephrine, and epinephrine (neurotransmitters); Self-appraisal of emotions, other’s emotion appraisal, use of emotion and regulation of emotion (emotional intelligence); extraversion, neuroticism, openness, conscientiousness, and agreeableness (personality traits); investment horizon, risk attitude, personalization of loss, confidence, and control (investors' behavior); and investors' decisions. The current study includes six main hypotheses and 78 sub-hypotheses. The research findings showed that neurotransmitters, emotional intelligence, and personality traits affect the investors' behavior and decisions.
Originality/value: The findings of this study showed that the neurofinance and behavioral finance have a key role in decision making of policymakers and investors. Finally, the findings of this study would provide new horizons in neurofinance and behavioral finance.
Keywords: Neurotransmitters, Emotional Intelligence, Personality Traits, Individual Investors, Tehran Stock Exchange
[1] 1*. Associate Professor, Department of Accounting, Hazrat-e Masoumeh University, Qom, Iran (Corresponding Author: m.nazaripour@hmu.ac.ir)
2. MA in Business Management, Department of Management, Sanandaj Branch, Islamic Azad University, Sanandaj, Iran
Introduction
Investment is an integral part of a person's life. People always try to use investment opportunities according to their interests (Dhiman & Raheja, 2018). Today's capital markets are complex and competitive, thus the direct entry of those who have little financial literacy into such markets may have many negative consequences for them. Nowadays, investment in capital markets is generally based on traditional financial models. These models do not include the neuro-financial (e.g., neurotransmitters), behavioral psychology (e.g., emotional intelligence), and the personality of investors (Ahmad, 2018). Therefore, these models mislead individual investors and hinder them for making a correct decision.
The behavior of investors is an integral part of a financial system and plays a crucial role in its prosperity. If feelings and emotions are quantifiable, then factors such as neurotransmitters, emotional intelligence, personality traits, and self-control methods is considered effective. Therefore, the current research investigated the effect of neurotransmitters, emotional intelligence, and personality traits on the behavior and decisions of individual investors.
Issues such as modern financial systems and income-oriented investments make neurofinance (e.g., neurotransmitters) to become more important. Neurotransmitters are chemical messengers in the human brain that transmit signals from one neuron to another (Rizo, 2018). Neurotransmitters are essential in working and daily life (Snyder, 2017). Neurotransmitters include dopamine, serotonin, epinephrine, and norepinephrine, which may be related to investors' behavior. Dopamine, serotonin, and norepinephrine play a role in signaling and include the investors' behavior (Harlow & Brown, 1990).
Dopamine affects the behavioral aspects of investors (e.g., optimism, overconfidence, and risk aversion). Low serotonin can cause this situation (Pompian, 2006). People have different reactions to risk. Dopamine is also related to risk return (Preuschoff et al. 2006). According to Kuhnen & Chiao (2009), dopamine and serotonin affect the investors' risk-taking. Furthermore, these neurotransmitters are effective for data processing associated with the rewards and risks.
Emotional intelligence indicates attitude, thought, and logic (Mayer, et al. 2000). Emotional intelligence plays an essential role in making decisions (Cherniss, 2000). According to Ameriks et al. (2009), there is a relationship between emotional intelligence and investment behavior. Emotional intelligence could influence investors' behavior by playing a role in predicting investment motives (Rubaltelli et al. 2015). According to Salovey (2013), emotional intelligence includes the behavioral characteristics of investors, such as loss aversion, endowment effect1, and status quo bias. In general, Emotional intelligence and personality traits are defense mechanisms that affect investors' decisions (Lubis et al. 2015).
Psychological variables (e.g., personality traits and behavior of investors) influence the stock market performance. Investors could make efficient decisions due to easy access to information and integrated financial systems. There are different views about personality (John et al. 1991). According to Durand et al. (2013), there is a relationship between personality traits with trust and the overreaction of individual investors. Mallick (2015) showed that personality traits have a direct and significant relationship with various behavioral aspects of individual investors. According to Rizvi & Fatima (2015), there is a substantial relationship between the five-factor model of personality2 and the behavior of individual investors.
The behavior of investors is one of the crucial elements of the capital market, which plays a vital role in the development of society. The investors' behavioral traits may include risk attitude, loss aversion, investment horizon, trust, and control (Wood & Zaichkowsky, 2004). According to Ghun & Mimg (2009), the behavioral traits of investors include overconfidence, trust in historical information, loss aversion, and control. Factors such as regret, self-confidence, belief, fear, and anxiety affect the behavior of investors (Chin, 2012). According to Thapa (2014), factors influencing investors' behavior involve overconfidence, optimism, risk attitude, and participation. According to previous studies [e.g. Tedongap (2015) and Alaoui et al. (2015)] investment horizon is affected by the investors' expected dividends. According to Dangel et al. (2015), loss-averse investors usually consider low-return and guaranteed investments. Sheikh and Riaz (2012) demonstrated that overconfidence correlates with dividends, volatility, and trading volume.
Emotional intelligence and psychological aspects of investors' behavior impact the capital market (Ameriks et al. 2009). According to Akhtar et al. (2015), Personality traits affect people's risk-taking and investment plans. Kuhnen et al. (2013) could not find a significant relationship between neurotransmitters and financial choices. Therefore, they suggested that future studies investigate the effect of nerves or hormones on investors' decisions in a larger sample size. According to Mosher & Rudebeck (2015), capital market researchers should conduct more research on rewarding neurons related to cognitive functions.
Neurofinance is an interdisciplinary field that aims to establish a connection between neuroscience and financial markets. On the other hand, behavioral finance is a field that includes behavioral psychology and capital market players. Therefore, neurofinance is a relatively new field whose purpose is to use the capabilities of neuroscience and psychology in finance (Miendlarzewska et al. 2019). According to Kumar & Sireesha (2017) neurofinance makes a relationship between the human mind and investment decisions. According to Diacogiannis & Bratis (2013), neuroscience uses the capacity of brain science and behavioral finance. Moreover, the findings of these researchers showed that neurofinance affects financial and investment choices.
Emotional intelligence is one's ability to recognize and understand emotions and use them in interaction with others (Pizzani, 2017). According to Akhtar et al. (2015), personality traits affect people's risk-taking and investment plans. Investment decisions are influenced by trust and performing financial obligations. Overall, success in the stock market is affected by knowledge and wisdom (Qureshi, 2018).
Nowadays, financial markets are mainly knowledge-based, and thus, understanding the role of neurotransmitters in finance and investment is essential. Neurotransmitters affect the behavioral aspects significantly (Harden & Klump, 2015). According to Shao et al. (2015), neural networks play a crucial role in investment and expected return. Dornelles et al. (2007) showed that epinephrine (as a neurotransmitter) influences the recall process through psychological incentives. According to Conway & Slavich (2017) dopamine and serotonin affect the investors' behavior. Efremidze et al. (2017) explored that dopamine has different effects on the human mind and body and may increase focus on the latest news with positive consequences.
Since the behavior and decisions of individual investors have been paid little attention from the perspective of neuroscience, the current research investigated this issue along with two other components (i.e., emotional intelligence and personality traits). Therefore, this study aimed to examine the effect of neurotransmitters, emotional intelligence, and personality traits on the behavior of investors and their investment decisions. The findings of this study could strengthen the research literature and may also be used by individual investors, financial institutions, and decision-makers. Finally, the main goal of the current research is to help investors make more rational and informed decisions through conscious control over their emotions and feelings.
Literature Review
After introducing the neurofinance branch (Kuhnen & Knutson, 2005), the capital market was one of the sectors that paid special attention to this new finance branch. On the other hand, emotional intelligence and personality traits are among the factors influencing the behavior of investors. Therefore, knowing and understanding better the effects of neurofinance on the investors' decisions would be important in the capital market development. Investors are the central pillar of a capital market.
The studies related to neurotransmitters and behavioral factors are mainly done in the developed countries. Therefore, these issues require further investigation in developing countries like Iran. Moreover, there is a little literature about the relationship between neurotransmitters (e.g., dopamine and serotonin) and stock trading, thus this topic needs more investigation. Furthermore, little research has been done on the relationship between emotional intelligence and investors' behavior. Therefore, this issue also needs further investigation, especially in developing countries like Iran.
After introducing the theory of investment behavior by Klein (1951), a number of studies have been done about the behavior of investors. According to Wood & Zaichkowsky (2004), investors' behavior comprises investment horizon, risk attitude, confidence, control, and personalization of loss. Chun and Ming (2009) showed that investors' behavior contains overconfidence, trust in historical information, control, and loss aversion. Chin (2012) exhibited that investors' behavior consists of belief, investment decisions, and psychological concepts such as regret and self-confidence. According to Thapa (2014), investors' behavior is influenced by overconfidence, optimism, risk attitude, and participation.
After Kuhnen & Knutson's research (2005) that showed a relationship between neurotransmitters and investors' behavior, this topic is investigated by some researchers. According to Ahmad (2018) neurotransmitters make a communication between the innermost structures of the body, through sending signals. Emotional intelligence is another factor influencing the behavior of investors. According to Mayer et al. (2004), emotional intelligence is a person's ability to use feelings and emotions to calm a situation. In other words, emotional intelligence is the ability to recognize and understand emotions. Moreover, emotional intelligence may strengthen your logical thinking skills and the ability of regulating emotions. In general, the emotional intelligence aims to improve individual skills (Carolyn et al. 2014).
Personality is a trait that make human beings unique (Jafari et al., 2023). This trait includes qualities, desires, intentions, and social behaviors (Storm & De-Vries, 2006). Since the introduction of Allport & Allport's work (1921) much research has been done on personality traits. Personality is a dynamic and inherent trait, showing the relationship between human mind and body. Moreover, personality affects the performance, decisions, and thought of people (McCrae & Jr, 1997). Parashar (2010) explored that financial institutions may advise the clients effectively by understanding their personality traits. Personality traits could help understand the self-confidence, preferences, and risk-taking of investors. According to Kourtidis et al. (2011), there is a positive relationship between personality traits (e.g., openness to experience and extroversion) and behavioral traits (e.g., hindsight, neuroticism, and overconfidence). On the other hand, there is a negative relationship between openness to experience (as a personality trait) and availability (as a behavioral trait). According to Kourtidis et al. (2016) personality traits (e.g., overconfidence and loss aversion) affect the behavior of investors.
Previous studies such as Dhochak & Sharma (2016), Salmani Danglani et al. (2019), and Goshtasbi Maharlooi et al. (2022) demonstrated that personality traits affect investors' decisions. Tauni et al. (2017) found that people with openness to experience, neuroticism, extroversion, and conscientiousness have poor concentration on buying and selling stocks. According to Raheja & Dhiman (2020) there is a significant relationship between personality traits and investment decisions. These researchers believe that investors should ask themselves what, where, why, how, and when while facing investment opportunities. According to Kaur (2017), personality traits influence the behavioral aspects of investment decisions.
According to Dhiman & Raheja (2018), the behavioral characteristics of investors are affected by their emotional intelligence and personality traits. Lazer et al. (2017) investigated the connection between Cloninger’s model of personality and neuropsychological aspects of individuals. They found a significant relationship between neurotransmitters and risk attitude. Furthermore, this research exhibited a significant association between personality dimensions and investors' decisions.
Previous studies, such as Lang et al. (2017) and Mamula & Blazanin (2017), investigated the relationship between neurotransmitters and investment decisions. According to Singh et al. (2017), there is a connection between neurotransmitters (e.g., dopamine, serotonin, and norepinephrine) and investors' decisions. Fineberg et al. (2017) revealed that there is a relationship between neurotransmitters and investment decisions. Wang et al. (2017) found that dopamine has a significant effect on investment behavior by examining the impact of dopamine on income-oriented decisions. Neurotransmitters could benefit society by supporting financial decisions (Ty et al. 2017). According to Pertl et al. (2017), there is a relationship between neurotransmitters and saving-based investment decisions.
Ingram et al. (2017) displayed a significant relationship between different aspects of emotional intelligence and investors' decisions. According to Nakamura et al. (2017), there is an association between investment decisions and various aspects of emotional intelligence. Vakola et al. (2017) found a relationship between long-term investment decisions and different aspects of emotional intelligence. Reid (2017) showed that non-natural intelligence of emotions may improve investment decisions. Emotional intelligence would influence investment decisions as a kind of wisdom and talent (Corea, 2017).
The Model and Hypotheses of the Research
Based on the existing literature, especially Ahmad (2018), the conceptual model and research hypotheses are as follows.
[1] . The endowment effect causes investors to undervalue their assets because of ownership
[2] . extraversion, neuroticism, openness to experience, agreeableness, and conscientiousness
Figure 1. The Conceptual Model
Hypotheses:
H1: Neurotransmitters have a significant influence on investor behavior
H2: Neurotransmitters have a significant influence on investment decisions
H3: Emotional Intelligence has a significant influence on investor behavior
H4: Emotional Intelligence has a significant influence on investment decisions
H5: Personality has a significant influence on investor behavior
H6: Personality has a significant influence on investment decisions
Methodology
In terms of purpose, the current research was practical. In terms of method, it was a descriptive survey research. In terms of time, it was a cross-sectional research. The purpose of the present study was to investigate the effects of neurotransmitters on the personality traits and emotional intelligence of the people involved in the capital market participants, thus, the individual investors were selected as statistical population. The population of this research was the individual investors of the Tehran Stock Exchange. Since the researchers are not directly involved in the data collection process, the possibility of the attraction effect1 increases. Therefore, using the convenience sampling method would be useful. The research objectives were explained well to the respondents. This was done because of encouraging them to participate actively. The research population was large and unknown. Therefore, the following formula was used to determine the sample size.
[1] . The high probability of choosing inappropriate options of the respondents
Describing the above formula
| the value for the selected alpha level (confidence level) |
p | the estimated proportion of an attribute that is present in the population |
q | 1-p |
e | the acceptable margin of error for the proportion being estimated (the confidence interval) |
Based on the above formula, the sample size was 384 people. A questionnaire was used to collect the research data. The period of this study was the first three months of 2024. The questionnaires were distributed online. In this regard, the survey link was sent to many individual investors directly. Moreover, the link was sent to the managers of some virtual networks related to the capital market. These managers were asked to post survey links on the channel and invite their members to cooperate. A total of 225 surveys were collected during three months. Of these, 212 were usable. The overall response rate was 55%, higher than the threshold rate (33%) (Pennings et al. 2002). Before data analysis, the score of the negative items reversed and outliers' data were identified and discarded. The skewness and kurtosis of the items were less than 2, which indicates their normality. Structural equation modeling was used to test hypotheses. For this purpose, SPSS version 26 and Amos version 24 software were used.
Measurements of Variables
In addition to laboratory settings, several methods exist to measure neurotransmitters (Peterson, 2014). For example, we could refer to surveys and personality tests (e.g., NEO1 and psychometric instruments). Song et al. (2010) used the symptom scale of the neurotransmitter deficiency in their research, which included 111 items. Similarly, Ge & Lui (2015) used a 111-item questionnaire to measure five aspects of neurotransmitters in their study.
Therefore, based on the research literature, the questionnaire would be an effective tool to measure neurotransmitters. Recognizing the weaknesses of previous questionnaires may prevent them from happening again (Aupperle et al., 1985). According to Stone (1978), the questionnaire is a frequent method for collecting data in field research. The research constructs are shown in table 1.
[1] . a tool for measuring the five big personality factors
Table 1.
Information on the research constructs
Main construct | Sub-construct | Numbers of questions | Sources |
|---|---|---|---|
Neurotransmitters (NT) | Dopamine (Dop) | 6 |
Khan & Mubarik (2022) |
Serotonin (Ser) | 6 | ||
Norepinephrine (Nepi) | 6 | ||
Epinephrine (Epi) | 6 | ||
Personality traits (PT) | Extraversion (Ext) | 4 |
Goldberg (1990) |
Neuroticism (Neu) | 4 | ||
openness to experience (Ope) | 4 | ||
Conscientiousness (Cons) | 4 | ||
Agreeableness (Agr) | 4 | ||
Emotional Intelligence (EI) | Self-Appraisal of Emotions (SAE) | 4 |
Wong & Law (2002) |
Regulation of Emotion (ROE) | 4 | ||
Use of Emotion (UOE) | 4 | ||
Other’s Emotion Appraisal (OEA) | 4 | ||
Investors Behavior (IB) | Investment Horizon (IH) | 5 | Wood & Zaichkowsky (2004) |
Confidence (Conf) | 5 | ||
Control (Cont) | 5 | ||
Personalization of Loss (PL) | 2 | ||
Risk Attitude (RA) | 2 | ||
Investors Decisions (ID) | - | 14 | Pasewark & Riley (2010) |
Note: Fourteen items removed because their factor loads were less than 0.4. These included neurotransmitters (eight items), investors behavior (two items), and investors decisions (four items).
Findings
Normality is the underlying assumption to analyze data. Based on the central limit theorem, if the sample size is more than 200, it has a normal distribution (Demir, 2022). Since the sample size of this study is 212, the data are assumed to be normal. In this research, composite reliability (CR) and average variance extracted (AVE) indices were used to measure reliability and validity (table 2).
Table 2.
The indices of reliability and validity
variable | CR | AVE | variable | CR | AVE |
Dop | 0.890 | 0.574 | Ope | 0.821 | 0.534 |
Ser | 0.907 | 0.619 | Cons | 0.820 | 0.533 |
Nepi | 0.882 | 0.555 | Agr | 0.824 | 0.542 |
Epi | 0.882 | 0.556 | InH | 0.852 | 0.536 |
SEA | 0.846 | 0.580 | RiA | 0.755 | 0.610 |
UOE | 0.845 | 0.577 | Rerl | 0.727 | 0.572 |
OEA | 0.866 | 0.618 | Conf | 0.885 | 0.608 |
ROE | 0.830 | 0.553 | Cont | 0.856 | 0.544 |
Ext | 0.847 | 0.584 | ID | 0.910 | 0.505 |
Neu | 0.842 | 0.571 |
|
|
|
The CR and AVE of all variables are more than 0.70 and 0.50, respectively. In addition, AVE is smaller than CR. Thus, the questionnaire is reliable and valid.
Findings showed that 70 percent of the respondents were male, and 30 percent were female. According to the findings, the education of most respondents (37 percent) was Bachelor's degree. In addition, the age of most participants (81 percent) was between 25 and 50 years old.
Mahalanobis distance was used to detect outliers' data (Hodge & Austin, 2004). Since sample size was adequate, the exploratory factor analysis was used to assess the reliability of latent constructs (Williams et al. 2010). Then, structural equation modeling was used to test the research model through Amos software version 24 (Becker et al. 2012).
In this research, independent variables included neurotransmitters, emotional intelligence, and personality traits, and dependent variables included investors' behavior and decisions. The results of the model and research hypotheses test are as follows.
Figure 2. The research structural model
Table 3.
The results of testing the research hypotheses
Relationship | (B) | (Beta) | (CR) | P-value | ||
NT |
| IB | 0.291 | 0.602 | 5.003 | 0.000 |
NT |
| ID | 0.336 | 0.329 | 4.813 | 0.000 |
| IB | 0.083 | 0.168 | 2.441 | 0.015 | |
EI |
| ID | 0.219 | 0.208 | 3.133 | 0.002 |
PT |
| IB | 0.399 | 0.760 | 5.331 | 0.000 |
PT |
| ID | 0.259 | 0.233 | 3.518 | 0.001 |
B = Unstandardized coefficients; Beta = Standardized coefficients; CR = Critical ratio
As can be seen from table 3, all hypotheses were confirmed at the confidence level of 95%, because their critical ratio were more than 1.96. The effects of independent variables were tested on dependent variables simultaneously by using structural equation modeling (figure 2). The slope of the line between independent and dependent variables indicates unstandardized coefficients. This coefficient shows a change in the dependent variable because of a one-unit change in the independent variables, assuming that other conditions are constant. Therefore, table 3 indicates that a one-unit change in neurotransmitters causes 0.291 and 0.336 units of change in the investors' behavior and decisions, respectively. Moreover, a one-unit change in emotional intelligence causes 0.083 and 0.219 units of change in the investors' behavior and decisions, respectively. Finally, a one-unit change in personality traits generates 0.399 and 0.259 units of change in the investors' behavior and decisions, respectively. Standard coefficients are the path coefficients (correlation) between independent and dependent variables. For example, the path coefficient between neurotransmitters and investors' behavior is 0.602.
In this research, each independent and dependent variable has several sub-constructs. Tables 4, 5, and 6 show the relationship between the sub-constructs of independent and dependent variables. The number of these relationships is 78. In other words, this research has 78 sub-hypotheses.
Table 4.
Sub-hypotheses related to the first and second main hypotheses
Relationship | β | Sig. | Relationship | β | Sig. | ||||
Dop |
| InH | 0.195 | 0.002 | Nepi |
| InH | 0.119 | 0.138 |
Dop |
| RiA | 0.120 | 0.025 | Nepi |
| RiA | 0.152 | 0.029 |
Dop |
| Rerl | 0.190 | 0.002 | Nepi |
| Rerl | 0.167 | 0.012 |
Dop |
| Conf | 0.176 | 0.005 | Nepi |
| Conf | 0.137 | 0.087 |
Dop |
| Cont | 0.079 | 0.145 | Nepi |
| Cont | 0.145 | 0.042 |
Dop |
| ID | 0.229 | 0.000 | Nepi |
| ID | 0.221 | 0.004 |
Ser |
| InH | 0.149 | 0.034 | Epi |
| InH | 0.143 | 0.120 |
Ser |
| RiA | 0.143 | 0.019 | Epi |
| RiA | 0.435 | 0.000 |
Ser |
| Rerl | 0.076 | 0.119 | Epi |
| Rerl | 0.049 | 0.418 |
Ser |
| Conf | 0.214 | 0.003 | Epi |
| Conf | 0.336 | 0.000 |
Ser |
| Cont | 0.159 | 0.011 | Epi |
| Cont | 0.208 | 0.013 |
Ser |
| ID | 0.142 | 0.032 | Epi |
| ID | 0.150 | 0.083 |
Table 4 includes 24 hypotheses, which 17 hypotheses confirmed and seven hypotheses not confirmed. The significance level was 95%. As shown in Table 4, dopamine (Dop) has a significant effect on some factors such as investment horizon, risk attitude, loss aversion, self-confidence, and investors' decisions. Dopamine represents pleasure and happiness. Serotonin (Ser) has a significant effect on some factors such as investment horizon, risk attitude, self-confidence, control, and investors' decisions. Serotonin represents mood and morale. Norepinephrine (Nepi) has a significant effect on some factors such as risk attitude, loss aversion, self-confidence, control, and investor decisions. Norepinephrine shows stress and concentration. Epinephrine (Epi) has a significant effect on some factors such as investment horizon, risk attitude, self-confidence, and control. Epinephrine shows the body's reaction during fight or flight.
Table 5.
Sub-hypotheses related to the third and fourth main hypotheses
Relationship | β | Sig. | Relationship | β | Sig. | ||||
SEA |
| InH | 0.021 | 0.783 | OEA |
| InH | 0.167 | 0.018 |
SEA |
| RiA | 0.111 | 0.064 | OEA |
| RiA | 0.223 | 0.000 |
SEA |
| Rerl | 0.145 | 0.037 | OEA |
| Rerl | 0.108 | 0.077 |
SEA |
| Conf | 0.137 | 0.078 | OEA |
| Conf | 0.241 | 0.001 |
SEA |
| Cont | 0.129 | 0.040 | OEA |
| Cont | 0.108 | 0.061 |
SEA |
| ID | 0.096 | 0.164 | OEA |
| ID | 0.214 | 0.001 |
UOE |
| InH | 0.129 | 0.149 | ROE |
| InH | 0.105 | 0.188 |
UOE |
| RiA | 0.006 | 0.933 | ROE |
| RiA | 0.266 | 0.000 |
UOE |
| Rerl | 0.121 | 0.113 | ROE |
| Rerl | 0.105 | 0.125 |
UOE |
| Conf | 0.177 | 0.056 | ROE |
| Conf | 0.083 | 0.309 |
UOE |
| Cont | 0.165 | 0.027 | ROE |
| Cont | 0.176 | 0.009 |
UOE |
| ID | 0.068 | 0.403 | ROE |
| ID | 0.283 | 0.000 |
Table 5 comprises 24 hypotheses, which ten hypotheses confirmed and 14 hypotheses not confirmed. The significance level was 95%. As shown in Table 5, self-appraisal of emotions (SEA) has a significant effect on two factors (i.e., loss aversion and control). Use of emotion (UOE) is having a significant effect only on one factor (i.e., control). Other’s emotion appraisal (OEA) affects some factors such as investment horizon, risk attitude, self-confidence, and investors' decisions. Regulation of emotion (ROE) affects three factors (i.e., risk attitude, control, and investors' decisions).
Table 6.
Sub-hypotheses related to the fifth and sixth main hypotheses
Relationship | β | Sig. | Relationship | β | Sig. | ||||
Ext |
| InH | 0.180 | 0.011 | Ope |
| Conf | 0.079 | 0.229 |
Ext |
| RiA | 0.079 | 0.189 | Ope |
| Cont | 0.124 | 0.031 |
Ext |
| Rerl | 0.156 | 0.011 | Ope |
| ID | 0.213 | 0.001 |
Ext |
| Conf | 0.018 | 0.788 | Cons |
| InH | 0.300 | 0.002 |
Ext |
| Cont | 0.050 | 0.376 | Cons |
| RiA | 0.219 | 0.010 |
Ext |
| ID | 0.190 | 0.773 | Cons |
| Rerl | 0.074 | 0.332 |
Neu |
| InH | 0.007 | 0.935 | Cons |
| Conf | 0.356 | 0.000 |
Neu |
| RiA | 0.232 | 0.003 | Cons |
| Cont | 0.390 | 0.000 |
Neu |
| Rerl | 0.116 | 0.095 | Cons |
| ID | 0.488 | 0.000 |
Neu |
| Conf | 0.172 | 0.031 | Agr |
| InH | 0.236 | 0.019 |
Neu |
| Cont | 0.014 | 0.838 | Agr |
| RiA | 0.245 | 0.008 |
Neu |
| ID | 0.020 | 0.795 | Agr |
| Rerl | 0.288 | 0.002 |
Ope |
| InH | 0.028 | 0.676 | Agr |
| Conf | 0.414 | 0.000 |
Ope |
| RiA | 0.198 | 0.003 | Agr |
| Cont | 0.139 | 0.090 |
Ope |
| Rerl | 0.152 | 0.012 | Agr |
| ID | 0.018 | 0.847 |
Table 6 covers 30 hypotheses, which 17 hypotheses confirmed and 13 hypotheses not confirmed. The significance level was 95%. As shown in table 6, Extraversion (Ext) has a significant effect on two factors (i.e., investment horizon and risk attitude). Neuroticism (Neu) has a significant effect on two factors (i.e., risk attitude and self-confidence). Openness to experience (Ope) has a significant effect on some factors such as risk attitude, loss aversion, control, and investors' decisions. Conscientiousness (Cons) has a significant effect on some factors such as (investment horizon, risk attitude, self-confidence, control, and investors' decisions). Agreeableness (Agr) has a significant effect on some factors such as (i.e., investment horizon, risk attitude, loss aversion, and self-confidence).
Discussion and Conclusion
Until now, the effects of three independent variables of this research (i.e., neurotransmitters, emotional intelligence, and personality traits) on the behavior of individual investors have been investigated independently. In the other hand, previous studies have paid little attention to the effects of these variables on the behavior and decisions of investors simultaneously. Therefore, the current research examined the impacts of these three independent variables on two dependent variables (i.e., investors' behavior and decisions).
Brain signals influence the investors' behavior and decisions significantly. Neurotransmitters may affect the behavior and decisions of investors by stimulating, inhibiting, or modulating neurons (Blobe et al. 2000). According to the research findings, neurotransmitters (i.e., dopamine, serotonin, norepinephrine, and epinephrine) have a significant positive effect on the behavior and decisions of investors (first and second hypotheses). Until now, little studies have investigated the effects of neurotransmitters on the behavior and decisions of investors. Nevertheless, this finding was consistent with the findings of Singh et al. (2017), Long et al. (2017), and Ahmad (2018).
According to the third and fourth hypotheses, emotional intelligence has a significant positive effect on the behavior and decisions of investors. It means that individual investors can identify, understand, and control their emotions and are also able to use them in their decision-making. The findings indicated that emotional intelligence relates to various aspects of investors' behavior, such as investment horizon, risk attitude, loss aversion, self-confidence, and control. Moreover, according to the findings of this research, emotional intelligence affects financial decisions. In general, emotional intelligence is considered a part of good sense and is related to decision-making. It would affect the stock price. This finding was in line with the findings of Ameriks et al. (2009), Chaarani (2016), Mitroi & Oproiu (2014), and Ashari et al. (2022).
Personality traits affect the individual investors' understanding of financial information and make decisions (Rao & Lakkol, 2022). The findings of the fifth and sixth hypotheses showed that personality traits (i.e., extraversion, neuroticism, openness to experience, conscientiousness, and agreeableness) have a significant positive effect on the behavior and decisions of individual investors. This finding was in consistence with the findings of Crysel et al. (2012), Dhochak & Sharma (2016), and Ashari et al. (2022).
According to Olsen (2007) one of the important human traits is solving the problems by managing the complex thoughts. Many researchers believe that financial decisions are strongly influenced by feelings and emotions, then it is useful to understand them completely and correctly (Sjöberg & Engelberg, 2006). In this regard, the present study endeavored to present a suitable model for this issue by combining two fields of neurofinance and behavioral finance. This action can be considered a positive step.
The findings of this research could be helpful for policymakers and investors in developing the effective financial policies, by considering issues such as reward system, fight and flight situations, and behavioral traits. Moreover, the findings showed that a better understanding of the relationship between neurotransmitters and investment decisions can help investors make sound financial decisions. According to the findings, the investment horizon and loss aversion are among the factors that influence the decisions of individual investors. Finally, the findings of this research could help investigate the concepts of neurofinance and behavioral finance with emphasis on the capital market context.
Since this research is based on cross-sectional data, using longitudinal data may increase the generalizability of the findings. Since the population of the present study only includes individual investors, thus considering the institutional investors and brokers as the population of future studies would be helpful. This issue led to collecting more comprehensive data. In the current research, a limited number of sub-constructs and items have been used for each of the primary constructs. Therefore, expanding these sub-constructs and items can increase the comprehensiveness of the results.
The findings of this research could provide new horizons in the fields of neurofinance and behavioral finance. In addition, the latent constructs related to the introduced personality and behavioral traits can be a good reference for future studies. Furthermore, it is necessary to perform more research in the field of neurofinance with emphasis on the prefrontal cortex and anterior cingulate cortex, because it lead to make sound financial decisions. The findings of this research provide a good picture of the behavior and decisions of individual investors with an emphasis on neurofinance and behavioral finance. Another feature of the current study was providing items to measure the sub-constructs of neurotransmitters.
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