Impact of neurotransmitters, emotional intelligence and personality on investor's behavior and investment decisions

Ahmad, Mumtaz Article Impact of neurotransmitters, emotional intelligence and personality on investor's behavior and investment decisions Pakistan Journal of Commerce and Social Sciences (PJCSS) Provided in Cooperation with: Johar Education Society, Pakistan (JESPK) Suggested Citation: Ahmad, Mumtaz (2018) : Impact of neurotransmitters, emotional intelligence and personality on investor's behavior and investment decisions, Pakistan Journal of Commerce and Social Sciences (PJCSS), ISSN 2309-8619, Johar Education Society, Pakistan (JESPK), Lahore, Vol. 12, Iss. 1, pp. 330-362 This Version is available at: https://hdl.handle.net/10419/188348 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by-nc/4.0/ Pakistan Journal of Commerce and Social Sciences 2018, Vol. 12 (1), 330-362 Pak J Commer Soc Sci Impact of Neurotransmitters, Emotional Intelligence and Personality on Investor’s Behavior and Investment Decisions Mumtaz Ahmad Capital University of Sciences and Technology (CUST) Islamabad, Pakistan Email: mumtazka[email protected]m Abstract Mainstream of the investors and investment advisory consultants suggest and focus on standard finance models and do not take into account the behavioral and neurological dimensions of finance as neurotransmitters, emotional intelligence and personality of investors. These aspects of individual investors can cause of several mistakes while investing in stock market. The primary data of 455 investors from Pakistan Stock Exchanges is used for analysis. The data analysis performed with the help of Hierarchical Latent Variable Models in PLS-SEM by using the reflective-formative type constructs as guided by Becker et al., (2012). The empirical evidence of the study reveals that personality dimensions especially openness and consciousness as well as emotional intelligence dimensions especially self emotions apraisal and regulation of emotions have significant relations with the behavioral features of investor especially investment horizon, personalization of loss and control level. Similarly, neurotransmitter’s dimensions dopamine and epinephrine have significant relation with investment decisions of indiviual investors. In view of this, emotional intelligence, neurotransmitters and personality collectively have 13.2% impact on ivestor behavior and these dimensions collectively have 4.1% impact on investment decisions of individual investor. The study opens new horizon by providing supplemented inner view of investor’s behavior and their decision’s in the stock market of Pakistan and demands more effort to determine universal latent constructs for combine model of neurofinance and behavioral finance. However, limitation of study is that it does not analyze the current model beyond the current sample size for stock market of other regions of the world. Keywords: neuro-finance, behavioral finance, neurotransmitters, emotional intelligence and personality. 1. Introduction In current circumstances, investment is extremely imperative for every person because individuals constantly favor the investment opportunity according to their behavioral elements of investment (Dhiman & Raheja, 2018). Similarly, the stock market’s environment is becoming very competitive in the world integrated economy, where as in Pakistan the investors progressively more worried on the subject of humanizing their acts to meet up the modern challenges. Mainstream of the investors and investment advisory Ahmad 331 consultants both suggest and focus on standard finance models and do not take into account the neurological facets of finance as neurotransmitters and behavioral psychological aspects of finance as emotional intelligence and personality of investors and their investment decisions. These aspects of individual investors can cause several mistakes while investing in stock market as to make unprofitable decisions. Normally, investors behavior takes part an indispensable job in sustainability, efficiency as well as prosperity of the investing environment in liberated financial system. neurotransmitters, emotional intelligence (EI) and personality are being documented, at the same time as a system in support of scheming along with implementation of a selfregulated checking and remedial method, where feeling or sentiment or emotion as statistics or figures. This study would be center of attention on shaping the impact of neurotransmitters, EI and personality measures on investor’s behavior and its eventual rearender on investment decisions in stock market. The transformation in the financial system as well as scenery of equity investment sector from investing to profit/loss concentrated actions has activated the worth of neurofinance concept as neurotransmitters. The neurotransmitters are chemical messenger in human brain which generates the signals from one neuron to another neuron (Lodish, 2000). In individuals neurotransmitters enter into a most important responsibility in daily life and working (Cherry, 2015). Neurotransmitters composed of dopamine, serotonin, epinephrine and norepinephrine which may have association with investor behavior of individuals. Harlow & Brown (1990) explored that dopamine, serotonin and norepinphrine as the neurotransmitters are involve in signaling and have relation with investor’s behavior. Pompian (2006) explored that dopamine has contribution towards the investor’s behavioral aspects for instance optimism, overconfidence and loss aversion possibly will be a straight forward outcome of low level of serotonin. Individuals have different presences towards the risk as Preuschoff et al. (2006) illustrated that dopamine is associated with risk and reward. Kuhnen & Chiao (2009) studied and found that neurotransmitters dopamine and serotonin are important factors of risk taking in decisions of investment and these mentioned neurotransmitters have consequence towards the method a human being process the facts and figures related to the financial incentive as well as the loss avoidance. Mayer, et al. (2000) describe emotional intelligence as sentiments of mind-set that someone have whereas cleverness as the capability of reasoning with something. Cherniss (2000) describes that EI shows the approaches wherein one makes fastidious large support in the coming time period. Ameriks et al. (2009) studied and found the clue of significant associations between emotional intelligence and investor’s behavior in numerous, although not the entire areas which were investigated. Rubaltelli et al. (2015) studied that emotional intelligence estimate the motivation for the investment as well as EI has a distinctive consequence on investor’s behavior by manipulating the additional extent that was explored. Salovey (2006) described that emotional intelligence has relationship with behavioral features of investor as loss aversion, endowment effect and status quo bias. Lubis et al. (2015) studied that emotional Neurotransmitters, Emotional Intelligence and Personality - Investor’s Behavior 332 intelligence and personality are defense mechanism and have relationship with individual investor decisions. Psychological variables in finance as personality and individual investor behavior are efficient and helpful in stock market operation and then it is impossible for the investors not be successful in information base world and integrated equity market system. As we know that personality is the concept which has been derived from the diverse theoretical corner as well as different phases of ideas (John et al., 1991). Durand et al. (2013) explored that personality traits have correlation with investor’s overconfidence and overreaction while investing in the stock market. Similarly, Mallick (2015) investigated that different personality traits have straight and unambiguous relation with different behavior aspects of individual investor. Meanwhile, Rizvi & Fatima (2015) investigated and found that investor’s personality traits such as extraversion, agreeableness, conscientiousness, neuroticism, and openness have relationship with the individual investor behavior. Investor’s behavior has been identified as a most key element in the capital market which acts as a decisive operator towards investment program that give astonishing economic benefits. Wood & Zaichkowsky (2004) mentioned that behavior constructs of investors are as risk attitude, personalization of loss, investment horizon, confidence and control. Ghun & Mimg (2009) performed the research in the Malaysian perspective and exposed that constructs of investor’s behavior are overconfidence, anchoring, loss aversion and representativeness. Chin (2012) also studied the investor behavior in Malaysian stock market and mentioned in his work that regret, self-confidence, belief and snake and bite effect shapes the investor’s behavior. Thapa (2014) studied the individual investor behavior in the Stock Market of Nepal and said that Overconfidence, optimism, risk attitude and involvement are constructs which shapes the investors behaviors. Tedongap (2015) exposed that different investment horizon have different relation with cross sectional expected gain from stock. Alaoui et al. (2015) performed investigation and found that investment horizon have association with gain of stock. Dangl et al. (2015) revealed that loss-averse investors come out to utilize a standard for assessment to estimate the profit and loss of investment of bunch of stock of different companies. Sheikh & Riaz (2012) found that overconfidence has association with stock market gain and other things as volatility and trading volume. Further the investigation expands and pursues the upcoming direction related to research recommended by Ameriks et al. (2009) emotional intelligence and different psychological aspects of investor behavior. Kuhnen et al. (2013) during the investigation of the different neurotransmitters and financial choice, found unlear relation and recommended that futher studies of neural or hormonal influence on the investment decisions of investors with large sample size. Mosher & Rudebeck (2015) recommended the futher studies on reward related planning signals association with cognitive functions. Neurofinance is inter-disciplinary field for probing which engage neurobiology with over and above financial market while behavioral finance involves behavioral psychology and financial market and their participant’s activities. Particularly the research would respond the following main query. Ahmad 333  How do neurotransmitters, EI and personality have effects on the investor behavior and investment decisions in stock market? This research is an effort to show the possible linkages of neurotransmitters, EI and personality with investor behavior and investment decisions in stock market so that individual investors by avoiding the feelings or sentiments or emotions intelligently with reasons that would mold the neurological, psychological issues in favor of profitable investment program. The objective of the investigation to build up unique sculpt showing the relation between the neurotransmitters, EI, personality, investor behavior and investment decisions in stock market with some latest constructs to amplify and inflate the association. Further, specifically the investigation would be focused to: i) Find out the relationship of neurotransmitters, EI, personality, investor behavior and investment decisions in stock market ii) Explore the impact of neurotransmitters, EI and personality on investor behavior iii) Explore the impact of neurotransmitters, EI and personality on investment decisions in stock market Neurofinance is a comparatively latest research area in order to make the struggle for recognizing the monetary verdicts as a result of joining the forthcoming as of neuroscience and psychology with financial hypothesis (Miendlarzewska et al., 2017). In the meantime, Kumar & Sireesha (2017) disclosed that neurofinance act as bridge among the human mind and decisions in the financial market. While studying “collaboration of psychology, neurology and investor behavior” Diacogiannis & Bratis (2013) revealed that neurofinance make addition to the traditional finance with the help of neuroscience as well as psychology. Similarly, author also disclosed the advantages of the advancement in neurofinance as a substitute way of internal best judgment of the selection process while making investment decisions. The capability of investors to carry on and nurture in the 2st century, awareness base market can be controlled, depending upon know-how of effective and efficient neurotransmitters to exploit financial assets of investors. Neurotransmitter’s signals movement in human brain act as hammering force for the behavioral aspects (Harden & Klump, 2015). Similarly, Shao et al. (2015) documented that role of neural bases observed in individuals’ investors when making decisions regarding the total sum of appreciated outlay of funds and percentage of required return. Dornelles et al. (2007) studied and found that neurotransmitter namely epinephrine makes adjustment in the human remembrance process for the psychologically triggering situation. In recent times, Conway & Slavich (2017) revealed that neurotransmitters, dopamine and serotonin involve in different aspects of behavior which are beneficial for individuals of society. For the time being, Efremidze et al. (2017) observed that dopamine has various functions in the human mind as well as physical structure, along with inspiring concentration to latest news in the surroundings as long as the human being with a enjoyable know-how. Neurotransmitters, Emotional Intelligence and Personality - Investor’s Behavior 334 Mayer et al. (2001) studied that individuals in the midst of superior emotional intelligence are best practiced to recognize their personal as well as outsiders sentiment in circumstances, utilize that data to direct their dealings, as well as oppose forces as compare to others. Ameriks et al. (2009) reported that emotional intelligence takes up the person's glee, rage, or attitude on a specific moment and sensations like figures or statistics. Pizzani (2017) revealed that emotional intelligence is your capability to identify and realize our emotion and exploit this understanding to administer yourself as well as your associations among others. Akhtar et al. (2015) documented that investor making decisions, related to the tolerance of threat as well as the investing plans, are highly influenced by the personality features. Investment decisions determine a degree of safety, ability to meet the financial obligations. As we know that investment decisions are critical and tricky particularly in a stock market moreover these kinds of decisions require superior sympathetic and insight (Qureshi, 2012). 2. Literature Review The world give attention to neurofinance in 2005 when the first study related to the neurotransmitters role in financial decision making gives awareness to the individuals who keenly occupied positions in the field of business, especially stock market business. The label of initial research was “neural basis of financial risk taking” by the (Kuhnen & Knutson, 2005) in the Stanford University, USA. Emotional Intelligence and complementary psychosomatic attribute as personality has incredible relationship with different aspect of investor’s behavior. Due to the worth mentioning role in stock market for investors show the need of advancement in these neurofinance and behavioral finance aspects. The majority of the existing literature related to the neurotransmitters and behavioral aspects of investors appears in the developed world and proposed that connection stay alive among the stock trading, dopamine, serotonin and buying as well selling of stocks and trading behavior in stock market. No single study has ever tried to combine these four measures of neurotransmitters with investor behavior and investment decisions. Just only single investigation try to explore the relationship between EI and investor behavior however to achieve the concluding remarks as of limited view of investor behavior (Ameriks et al., 2009). Scholarly discussion about the investor behavior of individual openly started when Klein in 1951, wrote a section with the title of “Studies in Investment Behavior” in the book of “Conference on Business Cycles” under the umbrella of National Bureau of Economic Research in Cambridge. Klein (1951) called it as investor behavior theory and financial circumstances that are scene to be occur. Wood & Zaichkowsky (2004) said that stock market investor’s behavior includes investment horizon, risk attitude, confidence, control and personalization of loss. Chun & Ming (2009) discussed the investor behavior of Malaysian stock market investors that includes constructs as overconfidence, representativeness, loss aversion and anchoring. Similarly, Chin (2012) investigated the investor behavior of Malaysian stock market investors that may include their belief, decision making and psychological concepts as regret, self-confidence. Thapa (2014) Ahmad 335 studied the investor behavior of individual in the stock market of Nepal and finalized the constructs as overconfidence, optimism, involvement and risk attitude. First time in scholarly studies few words were exchanged about the relation of neurotransmitters measures and investor behavior openly in scholarly investigation, by the Kuhnen & Knuston (2005) when got in print his manuscript in neuron academic periodical in the discipline of neuroscience, it is believed, at that time the earliest piece of writing on neurotransmitters measures and behavioral aspects of investor (Sahi, 2012). Lodish et al. (2000) documented that neurotransmitters are something like substance/material that makes possible communication with the help of impulses/signals in innermost anxious structure of body. Mayer et al. (2004) defined the EI as the personnel capability to practice the emotional data and utilize it to downbeat the situation. Carolyn et al. (2014) describe that EI as a talent of recognizing the feelings, combine the feelings to assist thinking process, realize feelings as well as adjust feelings to support individuals strengthening. Scholarly work of the personality gives a scientific description about the uniqueness of individuals. It also highlights the determinants of inner behavioral aspects as qualities, desires, intentions, and social facets of person’s uniqueness (Storm, & De-Vries, 2006). Allport & Allport (1921) investigation on personality features started then countinue grow over and over. As explored by the Allport (1961) that personality is a vibrant involvement, in the inner personality of the individual, of psychophysical configuration so as to make the individual’s characteristic prototype of dealings, decisions and frame of mind. Different experts work done on it as McCrae & Jr (1997). Similarly, Parashar (2010) studied that individual’s personality characteristics may be helpful for experts of assets supervisors who can give better advice to their customers and these personality features may be source in favor of assembling the opinion regarding the psychology of investor, investment preferences, adventuresome even as making investment in stock market. Sadi at al. (2011) performed the study in Iranian equity market and fond that personality features as openness and extroversion have positive relation with the behavioral characteristics of investor as hindsight, neuroticism also have relation with overconfidence but negative relationship among the openness and availability. Kourtidis et al. (2011) investigated and documented that personality characteristics have influence on the investor behavior as overconfidence as well as hazard forbearance. The latest litrature revealed the linkages among the different dimensions of study as personality characteristics influence the investment decisions (Dhochak & Sharma, 2016). It is revealed that behavioral features of investors influenced by emotional intelligence and personality features (Dhiman & Raheja, 2018). Similarly, Tauni et al. (2017) revealed the relation among the personality charcteristisics and behavior features of investor and found that individual who have openness and neuroticism qualities make investment more repeatedly at the same time as investors with extraverted and conscientious personality traits buy or sell shares with less concentration. Similarly, Lazer et al. (2017) studied the Cloninger’s model of personality with neuropsychological aspects of individuals and observed the association between the neurotransmitters and attitude of risk. They also revealed the relation between the personality dimensions and decision making while making the investment. Raheja & Dhiman (2017) revealed the constructive connection Neurotransmitters, Emotional Intelligence and Personality - Investor’s Behavior 336 among the personality and investment decisions. He also recommended that investor must be vigilant about what, where, why, when and how to take decision of investment in diverse investment opportunities. Similarly, Kaur (2017) disclosed that personality characteristics have influence on behavioal aspect of investment decisions. Similarly, Lang et al. (2017) examine the associations among the neurotransmitters and investment decisions. At the same time, Mamula & Blazanin (2017) examine the links among the signal of brain and investment decisions. Meanwhile, Singh et al. (2017) observed the association of dopamine, serotonin and norepinephrine with investment decisions in stock market. Fineberg et al. (2017) studied and observed the association among the neurotransmitters and decisions of investment. Wang et al. (2017) showed the role of dopamine in decision making regarding the expenditures and gain and concluded that the level of dopamine will decide the investment. Ty et al. (2017) suggested that neurotransmitters support to financial decisions which gave benefit to society. Pertl et al. (2017) observed the relation of neurotransmitters and decisions related to the investment for saving purpose. Ingram et al. (2017) point out the relationship among the emotional intelligence measures and investment decision measures. Nakamura et al.(2017) exposed the relation of investment decisions and facets of emotional intelligence. Similarly, Vakola et al. (2017) discussed the linkages among the long run investment decisions and measures of emotional intelligence. Reid (2017) disclosed that non-natural intelligence of emotions can improve the decisions about the investment. According to Corea (2017) emotional intelligence is wisdom and talent and this will explore the decisions concern to the investment. However, according the Salehi & Mohammadi (2017) emotional intelligence and investment decisions have no relationship. On the base of above mentioned literature, following hypotheses are developed to test the impact of neurotransmitters, emotional intelligence and personality on investor behavior and investment decisions in stock market:  H1: Neurotransmitters have significant influence on investor behavior  H2: Emotional Intelligence has significant influence on investor behavior  H3: Personality has significant influence on investor behavior  H4: Neurotransmitters have significant influence on investment decisions  H5: Emotional Intelligence has significant influence on investment decisions  H6: Personality has significant influence on investment decisions Ahmad 337 Figure 1: Conceptual Framework of Study The main dimensions of the latent variables and their description is given below: Table1: Higher Order Latent Constructs and Their Description Main Latent Variables Description F1 Neurotransmitters (NT) F2 Emotional Intelligence (EI) F3 Personality (PR) F4 Investor Behavior (IB) F5 Investment Decisions (ID) Each of the latent will be measured with the help of set of questions. The main dimensions, their latent variables and their description given below: Neurotransmitters, Emotional Intelligence and Personality - Investor’s Behavior 344 Table 3 indicates the outer loading, Cronbach’s alpha, average variance extracted (AVE) and composite reliability (CR) of lower order latent constructs of investor behavior (IB) such as investment horizon, confidence, control, personalization of loss and risk attitude with their respective items as well as latent construct investment decisions. Table 3: Assessment of Reflective Measurement Model at Lower Order Constructs Items Loading Cronbach's Alpha CR AVE Investment Horizon (IH) IH1 0.82 0.93 0.95 0.82 IH2 0.94 IH3 0.93 IH4 0.93 Confidence (Conf) Conf1 0.90 0.92 0.95 0.86 Conf2 0.96 Conf3 0.93 Control (Cont) Cont1 0.94 0.97 0.98 0.91 Cont2 0.98 Cont3 0.97 Cont4 0.92 Personalization of Loss (PL) PL1 0.97 0.94 0.97 0.94 PL2 0.97 Risk Attitude (RA) RA1 0.92 0.88 0.91 0.89 RA2 0.91 Investment Decisions (ID) ID1 0.97 0.93 0.93 0.68 ID2 0.92 ID3 0.91 ID4 0.49 ID5 0.63 ID6 0.93 Similarly, Table 4 indicate the constructs of neurotransmitters (NT) such as dopamine, serotonin, epinephrine and norepinephrine with their respective items and lower order latent constructs of EI such as self-appraisal of emotions, regulation of emotion, use of emotion and other’s emotion appraisal. In this study researchers removed those items which do not fulfill the threshold level of reliability, convergent validity of constructs of reflective nature because Hair et al. (2014b) suggested the range of values of Cronbach’s alpha from 0.60 to 0.70 and proposed the deletion of every item having loading less as Ahmad 345 compare to the recommended standard which is 0.40 because deletion of items will improve the average variance extracted (AVE). Table 4: Assessment of Reflective Measurement Model at Lower Order Constructs Items Loading Cronbach's Alpha CR AVE Dopamine D1 0.74 0.84 0.88 0.64 D2 0.81 D3 0.82 D4 0.83 Serotonin S1 0.98 0.97 0.98 0.93 S2 0.97 S3 0.92 S4 0.98 Norepinephrine N1 0.83 0.90 0.93 0.83 N2 0.92 N3 0.97 Epinephrine E1 0.98 0.99 0.99 0.96 E2 0.99 E3 0.99 E4 0.96 Self Appraisal of Emotions (SEA) SEA1 0.76 0.83 0.88 0.65 SEA2 0.91 SEA3 0.81 SEA4 0.74 Regulation Of Emotion (ROE) ROE1 0.96 0.89 0.88 0.66 ROE2 0.93 ROE3 0.78 ROE4 0.49 Use Of Emotion (UOE) UOE1 0.98 0.87 0.88 0.71 UOE2 0.93 UOE3 0.55 Other’s Emotion Appraisal (OEA) OEA1 0.95 0.88 0.95 0.90 OEA2 0.95 Table 5 indicates the outer loading, Cronbach’s alpha, AVE and (CR) of lower order latent constructs of personality (PR) such as openness, neuroticism, extroversion, conscientiousness and agreeableness with their respective items. However, In this study, researchers removed those items which do not fulfill the criteria of reliability, convergent validity of constructs of reflective nature because Hair et al. (2014b) suggested the range of values of Cronbach’s alpha from 0.60 to 0.70 and proposed the deletion of every item Neurotransmitters, Emotional Intelligence and Personality - Investor’s Behavior 346 having loading less as compare to the recommended standard which is 0.40 because deletion of items will improve the average variance extracted (AVE). Table 5: Assessment of Reflective Measurement Model at Lower Order Constructs Items Loading Cronbach's Alpha CR AVE Openness (OPE) OPE1 0.74 0.91 0.94 0.79 OPE2 0.94 OPE3 0.96 OPE4 0.90 Neuroticism (Neu) Neu1 0.96 0.95 0.96 0.83 Neu2 0.95 Neu3 0.95 Neu4 0.93 Neu5 0.76 Extraversion (Ext) Ext1 0.98 0.97 0.98 0.91 Ext2 0.98 Ext3 0.97 Ext4 0.89 Conscientiousness (Cons) Cons1 0.95 0.95 0.97 0.88 Cons2 0.97 Cons3 0.97 Cons4 0.85 Agreeableness (Agr) Agr1 0.98 0.95 0.97 0.91 Agr2 0.94 Agr3 0.95 There are three techniques to assess the discriminant validity of latent constructs such as one of them is Fornell-Lacker (1981) standard which is usually applied to evaluate the discriminant validity constructs. Whereas others two are Cross loading and MultitraitMultimethod Matrix which is called as Heterotrait-Monotrait ratio (HTMT). Table 6 and 7 indicate the Fornell-Larcker Criterion for the assessment of discriminant validity of latent constructs. Ahmad 347 Table 6: Fornell-Larcker Criterion Agr Conf Cons Cont D E Ext IH N Agr 0.955 Conf 0.001 0.929 Cons -0.005 -0.013 0.937 Cont -0.009 0.094 -0.276 0.955 D 0.067 0.003 0.047 -0.040 0.801 E -0.171 -0.174 0.000 -0.020 -0.055 0.980 Ext 0.000 -0.018 0.045 -0.062 0.003 -0.025 0.955 IH -0.109 -0.030 -0.039 0.068 0.060 0.040 0.045 0.906 N 0.008 0.003 -0.178 -0.041 0.002 -0.029 0.031 -0.012 0.910 Table 7: Fornell-Larcker Criterion Neu OEA OPE PL RA ROE S SEA UOE Neu 0.914 OEA -0.018 0.947 OPE 0.007 -0.072 0.889 PL 0.017 0.031 -0.230 0.970 RA -0.042 -0.116 -0.015 0.056 0.999 ROE -0.070 -0.007 -0.058 0.007 -0.043 0.811 S 0.004 -0.282 0.046 0.050 0.037 0.063 0.964 SEA -0.019 0.008 -0.007 0.069 -0.005 0.026 -0.173 0.807 UOE 0.059 0.233 -0.055 -0.032 -0.007 -0.024 -0.164 -0.122 0.845 Table 8: Heterotrait-Monotrait Ratio (HTMT) Agr Conf Cons Cont D E Ext IH N Agr Conf 0.025 Cons 0.022 0.031 Cont 0.020 0.103 0.287 D 0.075 0.019 0.048 0.049 E 0.176 0.180 0.018 0.021 0.048 Ext 0.017 0.027 0.052 0.065 0.026 0.026 IH 0.115 0.047 0.041 0.070 0.051 0.042 0.050 N 0.017 0.036 0.230 0.043 0.043 0.027 0.058 0.036 Neurotransmitters, Emotional Intelligence and Personality - Investor’s Behavior 348 Table 9: Heterotrait-Monotrait Ratio (HTMT) Neu OEA OPE PL RA ROE S SEA UOE Neu - OEA 0.030 - OPE 0.031 0.083 - PL 0.020 0.035 0.249 - RA 0.042 0.124 0.023 0.058 - ROE 0.201 0.027 0.090 0.013 0.047 - S 0.026 0.302 0.050 0.050 0.038 0.061 - SEA 0.062 0.154 0.032 0.066 0.016 0.047 0.186 - UOE 0.101 0.242 0.056 0.033 0.011 0.036 0.187 0.210 - Table 8 and 9 indicates the discriminant validity of latent constructs with the help of correlations of Heterotrait-Monotrait Ratio (HTMT) of indicators across constructs. Henseler et al. (2015) recommended one more gauge to evaluate the discriminant validity which is on the base of Multitrait-Multimethod Matrix which is called as HeterotraitMonotrait Ratio (HTMT) of correlation. Less than 0.90 are standardized values for Heterotrait-Monotrait Ratio (HTMT). 4.1.2 Second Stage: Evaluation of Formative Measurement Model at Higher Order Measurement model for formative variable at higher order not be evaluated statistically like reflective variable at lower order. The single most fundamental criteria for the assessment of measurement model of formative variables are to judge with the help of its outer weight with the significance level. As Hair et al. (2013) reveals that the significance of external weights of the formative items of variables are judged with help of their t-value. Similarly, Hair et al. (2012) said that there is no need to test the convergent and discriminant validity measures for formative variables and items but their outer weight, level of significance with the help of t-values, p-values and should assess the multicollinearity. The table 10 shows the values of outer weights of all the items of higher order construct which are latent constructs at lower order at first stage. Ahmad 349 Table 10: Assessment of Formative Measurement Model at Higher Order Constructs Items VIF Weight t-value P Values Neurotransmitters (NT) D 1.004 0.916 3.496 0.000 S 1.011 -0.071 0.361 0.718 E 1.014 0.455 1.833 0.067 N 1.001 0.075 0.361 0.718 Emotional Intelligence (EI) SEA 1.017 0.564 1.957 0.050 ROE 1.001 0.645 2.412 0.016 OEA 1.059 -0.229 1.011 0.312 UOE 1.076 -0.325 1.365 0.172 Personality (PR) OPE 1.005 0.874 3.731 0.000 EXT 1.003 -0.056 0.484 0.629 NEU 1.004 -0.021 0.187 0.852 CONS 1.005 0.443 1.711 0.087 AGR 1.006 0.061 0.514 0.607 Investor Behavior (IB) IH 1.013 0.636 2.991 0.003 CONT 1.033 0.444 1.630 0.103 CONF 1.017 0.090 0.714 0.475 PL 1.031 0.488 2.474 0.013 RA 1.009 0.014 0.134 0.893 Here, in table 10 the values of outer weight of items of neurotransmitters such as dopamine, serotonin, epinephrine and norepinephrine. Here, only dopamine’s outer weight is significant which can be seen with the help of t-value and p-value and rest of the items are insignificant but their VIF values are less than 5 which are indications of no multicollinearity. Similarly, emotional intelligence’s items self-emotions appraisal, regulation of emotions, other’s emotion appraisal and use of emotion have VIF values with in limit. Outer weights of self-emotions appraisal and regulation of emotions are significant at 95% confidence level because their t-values are 1.96, 2.41 and p-values are 0.05 and 0.016 respectively and outer weight of rest of the items of emotional intelligence are insignificant. So, the VIF values of all the items of mentioned construct are less than 5.00 indicates no problems of multicollinearity. However, outer weight of items of personality such as openness is significant at 100% confidence level but other items are insignificant but VIF of all items within the range. Similarly, outer weight of indicators of investor’s behavior such as investment horizon and personalization of loss are significant at 99% and 95% confidence level and rest of items are insignificant but VIF of all items are as per the threshold. Neurotransmitters, Emotional Intelligence and Personality - Investor’s Behavior 350 4.1.3 Second Stage: Evaluation of Structural Model at Higher Order Subsequent to the evaluation of measurement model in the first and second stage of hierarchical latent variable in reflective and formative type model by using the PLS-SEM, now the outcomes of second stage at higher order level of constructs can be observed in table 11 as it indicates the evaluation of structural model by using the path coefficients with their p and t-values and f2 and comments related to the effect size which is small for each construct. Once, the decision of structural model on the base of values R-Square or R2 has taken then researchers move toward the path coefficients which are deem to be considered for the assessment of structural model of the research. In structural model, the level of significance of path coefficient has indicates the association among the exogenous as well as endogenous constructs related to the study. Table 11: Evaluation of Structural Model Path Coefficients t-value P Values ƒ2 Effect Size EI → IB 0.115 1.9279 0.05 0.02 Small NT →IB -0.012 0.206 0.84 0.02 Small PR → IB -0.339 7.683 0.000 0.041 Small EI → ID 0.039 0.704 0.48 0.012 Small NT →ID 0.192 2.957 0.003 0.024 Small PR → ID -0.045 0.970 0.33 0.01 Small Table 11 and figure 3 indicates that neurotransmitters as independent latent construct at higher order does not explain the investor behavior in this study because investors of Pakistan have different characteristics as compare to investors belongs to the rest of world. However, robustness has been checked and has shown separate from model, when author observed the relationship of neurotransmitters with investor behavior found it significant which is as per the previous studies because Frydman & Camerer (2016) explored and found relationship of neurotransmitters measures and behavioral features of individual investor. In this research neurotransmitters as a latent construct at higher order explain the investment decisions at lower order with 95% level of significance so this is according to the literature. The emotional intelligence as an independent latent construct explain the investor behavior as a latent construct at higher order with 95% level of significance. This is as recommended by previous studies as mentioned chapter of literature review. But it also checked that path coefficient among the emotional intelligence and investment decisions are significant when author study the relation between them separately, which is also according the previous studies. Similarly, Rubaltelli et al. (2015) in their scholarly work establish the relatiosship among the emotiomal itelligence and investor behavior. Ahmad 351 The path coefficient between personality and investor behavior is negatively significant 95% level of significance. This result is literature consistent as Sadi at al. (2011) performed the study in Iranian equity market and fond that personality features positive relation but in some situation negative relation with the behavioral characteristics of investor. Similarly, Zaidi & Tauni (2012) performed the investigation in Lahore Stock Exchange and found positive relation among the perssonality charachteristics and behavioral characteristic of investor but in some situation also have negative relation. The path coefficient between personality and investment decisions is insignificant; this may be due to the combination of neurological and behavioral facets and different background of investors of Pakistan as compare to the investors of other countries of developed world. But, when author perform the analysis separately, between personality and investment decisions, found the results as per the previous studies Whereas, it has seen the values of path coefficients of neurotransmitters is positive significant with investment decisions and negative insignificant with investor behavior, emotional intelligence’s path coefficient with investor behavior positive significant and insignificant with investment decisions. However, path coefficients of among the personality and investor behavior is positive significant but between investment decisions is insignificant in this research. Similarly, it has been seen that path coefficients 0.56 and 0.65 of latent constructs, selfemotion appraisal and regulation of emotion, of latent construct of emotional intelligence (EI) are significant at 95% confidence level whereas path coefficients of latent constructs, use of emotion and other emotions appraisal are insignificant. The path coefficients 0.92 and 0.46 of latent constructs, dopamine and epinephrine, of latent construct of neurotransmitters (NT) are significant at 95% and 90% confidence level whereas path coefficients of latent constructs, serotonin and norepinephrine are insignificant because of dynamics of investors. Neurotransmitters, Emotional Intelligence and Personality - Investor’s Behavior 352 ` Figure 3: PLS-SEM Results of Structural Model (Second Stage) Figure 4: PLS-SEM Results of Bootstrapping of Structural Model (Second Stage) Ahmad 353 The Bootstrapping method is used in PLS-SEM to check the significance level of the values of path coefficients in every category of structural model because; Hair et al. (2013) recommended this technique with sample of 500 to 5000. Here, author employed 5000 as sample size for bootstrapping to engender the outcomes which is pertinent to the real information. Particularly, during bootstrapping significance of path coefficients is gauged through significance level with the help of P-values at 90%, 95% and 99% and an concrete values with of t-statistics with two-tailed test are ± 1.64, ± 1.96 and ± 2.56 respectively. Figure 3 indicates the path coefficients 0.87 and 0.44 of latent constructs openness and consciousness, of latent construct of personality (PR) are significant at 95% and 90% confidence level whereas path coefficients of latent constructs, agreeableness, neuroticism and extroversion are insignificant because of different demographics of investors. The path coefficients 0.64, 0.49 and 0.44 of latent constructs, investment horizon, personalization of loss and control, of latent construct of investment behavior (IB) are significant at 95% and 90% confidence level whereas path coefficients of latent constructs, confidence and risk attitude are insignificant. The figure 3 shows the weight of latent constructs whereas, figure 4 shows the result of bootstrapping. Table 12: Overall Statistics of Structural Model R2 Q2 NT, EI and PR on IB 0.132 0.013 NT, EI and PR on ID 0.041 0.013 In PLS-SEM, the validity of structural model validity is also evaluated with the help of predictive relevance (Q2). Normally, values of Q2 should be more than zero for independent latent constructs in the structural model of PLS-SEM. Besides this, it is bickered that more the Q2 values higher the prognostic relevance of the structural model otherwise vice versa. On the base of procedure recommended by Hair et al. (2013), the investigators depend on a blindfolding technique to get the cross-validated redundancy as a gauge to authenticate the predictive relevance of research model. Above given table 12 indicate the values of Q2 which are as per the threshold. It is the assurance of the model fitness in this research. Table 12 also indicates the overall statistics of structural model of study. 5. Conclusion and Recommendation In previous studies the impact of each dimension of neurotransmitters, emotional intelligence and personality on investor behavior were observed separately. However, literature related to impact of neurotransmitters, emotional intelligence and personality on investor behavior as latent constructs did not present a precise narrative that is why in this study re-examine and establish the H1, H2, H3, H4, H5 and H6. So, hypothesis 2 (H2) foresee the impact of emotional intelligence (IE) on investor behavior (IB) in Pakistan Stock Exchange (PSX) and observed this significant and positive. The result of hypothesis 2 to some extent is consistent with the studies of (Chaarani, 2016; Mitroi, 2016). The hypothesis 3 (H3) foresee the impact of personality (PR) on investor behavior (IB) in Pakistan Stock Neurotransmitters, Emotional Intelligence and Personality - Investor’s Behavior 360 Mallick, L. R. (2015). Biases in Behavioral Finance: A Review of Literature. 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