https://dinastipub.
org/DIJEFA Vol.
No.
May 2024 DOI: https://doi.
org/10.
38035/dijefa.
Received: 8 June 2024.
Revised: 22 June 2024.
Publish: 24 June 2024 https://creativecommons.
org/licenses/by/4.
Generation Z Investment Decision: An Analysis Using Behavioral Factors Iqbal Fadhiil1.
Putri Fariska2 School of Economics and Business.
Telkom University.
Indonesia, iqbalfadhiil@student.
School of Economics and Business.
Telkom University.
Indonesia, fariskaputri@telkomuniversity.
Corresponding Author: iqbalfadhiil@student.
Abstract: Investment has gotten more known and popular since a few years back, particularly during the pandemic.
The growth itself was majorly contributed by people within the age group of Generation Z.
One of the most contributing factors of their participation is their fear of missing out, especially with the exposure of social media investment content.
Their behavioral biases oftentimes result in loss instead of return, due to the unwise investment decisions.
This research investigates the behavioral bias within the investment decision-making of Generation Z in the area of Greater Bandung, with the sample of 489 The collected data from the sample through questionnaires analyzed using SPSS software with Multiple Linear Regression method.
The results show that trait anger does not partially influence investment decision significantly, while trait anxiety, overconfidence, herding behavior, and self-monitoring partially influence investment decision significantly.
All the independent variables simultaneously influence investment decision.
Keyword: Behavioral Finance.
Investment Decision.
Trait Anger.
Trait Anxiety.
Overconfidence.
Herding Behavior.
Self-Monitoring.
INTRODUCTION
Globalization era, catalyzed by the digitalization of information media, individuals nowadays are encouraged to evolve themselves with new knowledge and understanding of finance, with the ability to effectively manage their personal financial resources is highly Despite the conservative approach of personal financial management, many individuals are not able to fulfil their daily needs, as the daily needs themselves get more demanding with each time.
This condition triggers the tendency of consumptive lifestyle, as the individuals are not able to fulfil their cravings.
Financial issues are not solely for those who are already living by their significant others, but also for those Generation Z with modern-consumptive lifestyle with their inability to fulfil their satisfaction (Dewi & Apriyati, 2.
, despite the fact that Gen ZAos lifestyle and worldview being realistic and competitive (Renaldo et al.
, 2.
Combined with the 886 | P a g e https://dinastipub.
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According to clinical psychologist of teenagers.
Tara de Thouars, in Liputan 6 .
, stated by referring to the report of The Deloitte Global 2023, that Generation Zs are the Generation with the tendency of choosing work-life balance instead of live to work.
Therefore, they set the Aiwork smart instead of work hardAn state of mind.
In addition to the stated issue of Generation Z above.
Generation Z are also being overwhelmed by FOMO, which stands for The Fear of Missing Out (Detik Finance, 2.
The FOMO behavior is a bias where the Generation Z prefer to do the exact same thing as what the crowd, or the other investors, are doing, without taking their own judgment into account when they are making an investment decision.
Although on the other hand, a representative of Bursa Efek Indonesia Ae BEI .
, stated that there is a decline in the FOMO trend.
The decline itself occurred due to several factors, including the realization of investors to educate themselves and the declining trend of investment content in various social media platform where the creators themselves actually do not have the expertise within the investing field (Kumparan, 2.
The social dynamic changes is also imminent amongst the Generation Z individual (Tamara & Agustina, 2.
Generation Z, compared to the forming generations, are more resilient to changes and technological advancement in their daily life.
Their high adaptability to the rapid technological and social changes is due to the fact that they were born in the technological age.
They are also more innovative and creative when it comes to managing the challenges that they are facing in their daily lives, including financial difficulties (Liputan 6.
In connection with their financial behavior, there are high number of Generation Z investors that are classified as aggressive investor due to their preference of high-risk highreturn investment (Tirto.
id, 2.
Investment can be defined as a form of commitment of fund or other resources that are being done in the present time, sacrificing the current time asset in order to get the bigger return in the time coming.
There are various investing activity with different kind of investment instrument, such as stock investment, mutual fund, government bonds, and foreign currency exchange (Riana & Royda, 2.
Kustodian Sentral Efek Indonesia, or the Indonesian Central Securities Custodian, stated that there has been a surge of newcoming investors in the capital market, which are dominated from the younger generation under the age of 30 years old, reached 59,91% from the total of new investors.
The behavioral changes of the Gen Z concerning of investments proof that this new generation put their mind to invest part of their retained funds (KSEI, 2.
According to the data of KSEI .
, there is a constant growth on the domestic investor population in the Indonesian capital market.
The Director of KSEI.
Uriep Budhi Prasetyo, stated that the growth of the number of investors in the Indonesian capital market is due to the convenience to open an investment account for newcoming investors (CNBC.
The data of KSEI .
also stated that the individual investors in the Indonesian capital market can be divided into five groups.
The largest age group being investors under the age of 30, with the population of 58,71% of the total investors.
The group then followed by the age group of 31-40 .
,46%), 41-50 .
,85%), 51-60 .
,22%), and >60 .
,77%).
Therefore, it can be concluded that the group of age who dominates the Indonesian capital market is the investors under the age of 30, of which part of it can be included into the Generation Z group of age.
According to a survey conducted by Katadata Insight Center, along with Zigi.
id and Sisi (Katadata, 2.
, the majority of Generation Z in Indonesia are involved in the investment activity in order to set themselves up for the future.
To keep up with the evergrowing new investors in the exchange, the Indonesian Stock Exchange has promoted the program of 3P (Paham.
Punya.
Panta.
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This program were also introduced to prepare novice investors the understanding of Indonesian capital market to prevent beginner The Deputy Director for Education Implementation of OJK (The Indonesian Financial Services Authorit.
Halimatus SaAodiyah, stated for Detik Finance, that the tendency of reckless investment which results to losses, are catalyzed by several factors, amongst them are the desire to get instant profit.
Her statement is also supported by the OJKAos internal data, which shown that during the year of 2022, there is a recorded loss of Rp 109,67 T resulted by fraudulent investments (Detik Finance, 2.
The previously mentioned habit is also compounded by the tendency of anxious investors to only take in the negative sentiments when they are making investment decisions during the uncertain market trend.
Despite the decision of taking a riskier investment during the time of uncertainty of crisis, it is not impossible to overcome losses in the investors assets within the portfolio.
While negative information can lead investors to a biased and irrational investment decision, this information should be first studied and considered.
Riskier asset investment does not always lead to losses.
If decently managed, risky assets have the potential to yield positive returns while being compared to moderate-low volatility, less-risky portfolio (Salim et al.
, 2.
Although the massive interest of Generation Z towards the investing activity, investment decision-making has not always been fully rational, as previous studies stated that behavioral biases impact investment decision-making behavior (Kimeu et al.
, 2.
Referring to what traditional finance argues, investors are assumed to interpret the markets as all the investors that are involved to it are Airational economic menAn, where efficient market can be constructed, and every relevant information reflects in market prices as the investor make only perfectly rational economic decisions, judging their decision out of perfect rationality, perfect self-interest, and perfect information (Pompian, 2.
In behavioral finance, investment decision based on the investorAos ability to spend their capital is evaluated and learned in relation to psychological biases.
Many researchers agrees that both traditional and behavioral finance influence individualAos investment decision-making (Adiputra et al.
Kimeu et al.
, 2016.
Rahman & Gan, 2020.
YaAoacob et al.
, 2.
Both rational and irrational individual investors are present in the market all around the world (Davis et al.
The Indonesian Bureau of Statistic (Badan Pusat Statistik Ae BPS).
BPS .
on its release in considered that individuals classifiable as Generation Z are those who were born between the year of 1997 and 2012, and aged between 12-27 years old.
According to DataIndonesia .
, the province in Indonesia with the most Generation Z population is West Java, with the total Generation Z population of 11,886,058.
Within the West Java province, lies the Greater Bandung metropolitan area, which has the total population of 7 million inhabitants (Badan Pusat Statistik, 2.
According to the 2020 census statistics, 27,5% resembles the Gen Z out of the total population of 2,51 million residents in the city of Bandung alone.
As Bandung is the second most populated area of Greater Bandung, this also reflects that Generation Z is currently dominating amongst the population of Greater Bandung, therefore the assumption that Generation Z is the dominant investor age group in the Greater Bandung area.
This research will be investigating further of how the behavioral biases of trait anger, trait anxiety, overconfidence, herding behavior, and self-monitoring could affect the investment decision-making among Generation Z of Greater Bandung area in Indonesia, by investigating the biases influence in both partial and simultaneous effects.
METHOD
Population comprises events, things, or people with distinctive attributes that researchers observe and subsequently choose as their research subject.
(Hatmawan, 2.
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Individuals with the stated characteristics, also needs to have the knowledge of experience of investing in one or several investment This research will use non-probability sampling, as this sampling technique comes with a condition that every member of the population does not have equal chance to be included in the sampling (Paramita et al.
, 2.
As for the sample type, the purposive sample will be taken as the population member that will become a sample has to attain a certain attribute (Paramita et al.
, 2.
For this research, the criteria of the sample are .
the respondent has to be of the age of Generation Z, born in the period of 1997-2012, .
the respondent has to have invested, or is now investing in one or several investment instruments, .
the respondent is domiciled in the Greater Bandung Area.
This research will make use of a questionnaire for the data collection process, since the primary data is required for the research.
According to Hatmawan .
, questionnaire is a data collection method by surveying several questions to the respondents.
The questionnaire itself will be in the form of a digital questionnaire using Google Forms, which will be distributed to various digital platforms or forums.
The collected data will therefore be classified with the use of Likert scale which contains five indicators, ranging from Ai1An as the minimum score of Aistrongly disagreeAn, until Ai5An as the maximum value with it represent Aistrongly agreeAn (Sugiyono, 2.
Afterwards, the data analysis sequence from the descriptive analysis, classical assumption test, multiple regression analysis, and hypothesis At the end of the research, conclusions can therefore be drawn from the test that has been previously conducted.
RESULTS AND DISCUSSION
Results Trait Anger Trait Anger by definition is an emotional state consisting of the intensity variance of feelings that can range from being irritated and annoyed to become furious and rage (Spielberger & Sydeman, 1.
In addition to it, anger appears to motivate proactive and energetic reaction in the form of offence.
Anger can be classified as a negative emotion according to the previous research, while there are also results which indicated that it can be correlated with positive emotion with the eventual positive outcomes such as pleasurable expectation of vengeance and pleasurable belief in an achievement of an objective (Rahman & Gan, 2.
Trait Anger persuade an individual to make the investment, and trust their investment It also raise the investorAos confidence level in relation to the accomplishment opportunity of their investment and to their option of investment (Violeta & Linawati, 2.
Within their conducted research.
Violeta & Linawati .
also stated that trait anger persuades an individual to be optimistic with the opportunity of the success of their investment, with them also having the perception of low investment risk.
Anger increase the perception of an individual of their ability to predict the trend of investment price .
Individual investors with trait anger tends to prefer the long-period investment, as they are not in the need of liquid funds in the near future.
The emotional state of an individual could affect their ability to make a decision, as it is involved on how an event being evaluated by an individual, albeit the sole existence of emotional state cannot assign the interpretation of a situation without involving cognitive evaluation or personal interpretation in the process.
Trait anger was found to be substantial in predicting an investment in equity, and individuals with the higher level of anger attracted more for an investment with high risk, high return investment as they prefer the formerly said risk profile portfolio over a less risky investment portfolio (Bernaola et al.
, 2.
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Trait anxiety is a component of the personality dimension of neuroticism versus emotional stability.
Individual with high tendency of trait anxiety, oftentimes experience and express their state of anxiety, in the otherwise people with less tendency of being anxious will rescind themselves from experiencing and responding to the same situation (Gidron, 2.
The research conducted by (Rahman & Gan, 2.
, with the result that shows trait anxiety negatively and significantly impact the investment decision, stated that trait anxiety will bring individuals to a state of doubt in their ability when it comes to evaluating the available investment options.
The results, which also support various previous researches, stated that an anxious individual tend to pull themselves from investing activities during market uncertainty to conservatively manage their capital.
In addition to the above explanation, according to Gambetti et al.
, .
, trait anxiety give influence on the way of how individuals perceive and respond to investment opportunities, resulting on individuals with high trait anxiety have a different investment behavior compared to individuals with low trait anxiety.
It was shown in the conducted research, which providing default options .
ondition where no other alternatives are actively chose.
as a nudge .
hoices of the individualAos best interest in-line with their objective.
, can support the individuals with high trait anxiety level to improve their financial decisions.
Overconfidence Overconfidence can be summarized as unwarranted faith in an individual intuitive reasoning, judgements, and cognitive abilities.
Overconfidence tends to be classified as emotional bias since investors can be an excessive risk taker to invest in a large sum of capital, in spite of overconfidence being derived from psychological experiments and subjects to the cognitive aspect of predictive abilities of information (Pompian, 2.
Also, according to Pompian .
, there are two types of overconfidence bias, which are prediction overconfidence and certainty overconfidence.
Prediction overconfidence regards the individual investorAos bias of the future value of their investment, which results in the investor giving the unreasonable approximation of their investment value and therefore unsatisfactory investment return.
Certainty overconfidence relates to the overconfident accuracy judgement of investors.
People with overconfidence bias have the tendency of increasing confidence despite their judgement accuracy most likely to stay stagnant or even.
Both of the overconfidence above can imply to investors causing investment Both prediction and certainty overconfidence can imply to investors causing investment Pompian .
stated the mistakes of individual investors with overconfidence bias, which can be .
overestimation of one investorAos ability to evaluate a company as a potential investment, .
the false belief that an overconfident investor that they may have acquired a special knowledge that they donAot really have, .
an unexpected loss for an overconfident investor from a poor investment performance due to them underestimating their investment risk caused by their own ignorance, and .
overconfident investors do not realize that they are accepting more risk than they should tolerate.
Herding Behavior Herding Behavior, while in the past was not defined as it is, recognized as patterns of behavior of individuals with the tendency of following the majority others, instead of making independent decisions (Keynes, 1.
Herding then later defined as a reactive phenomenon of an individual, an event that people does not take greatly into consideration, as it just 890 | P a g e https://dinastipub.
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When investors shows their tendency of Herding Behavior, their common sense and cognitive ability got overridden by their fear of missing out Ae FOMO, as they are not willing to get left behind.
In further explanation.
Pompian .
also states that investors are subject to pack mentalities, have a difficult time explaining the rationality of involving to new investment that does not fit to their long-term investment plan.
In relation to regret-aversion bias, many investors back paddle from their steps to follow the consensus of the herd.
On the other hand, investors with higher tendency of herding behavior choose to rationalize their investors, to gamble to the new investment, despite their understanding of the stakes and magnitudes of the gamble they are taking.
According to Pompian .
, there are implications of herding behavior, both to the individual investors and to the investment market in the bigger picture.
The implications are, .
there will be investors who feels that their economic status are collapsing relative to those who involves in the herding pack or crowd, .
herding behavior, although in some instances might result in positive return during bull market, it will result in investors losing in their investment during bear market, .
in the bigger picture, herding behavior can cause the market to crash, or the bubble to explode, as everyone want to get in or out of the investment market at the same time Ae this phenomenon has been shown in the 2008 subprime mortgage crises and the early 2020 investors outflux.
Self-monitoring Self-monitoring was a construct first proposed in 1974 by psychologist Mark Snyder.
refers to the difference between individuals in the way they monitor and manage their presentations of self, behaviors, and emotions.
According to Loon .
Self-monitoring can be defined in three different contexts, which are .
learning and professional In this context, self-monitoring is described as a process that has an extensive impact on generating opportunities for professional development.
It involves monitoring an individual learning prior to task, during, and after the completion of work-related task, which affects decision-making, learning behavior, use of strategy, and learning motivation, .
educational psychology, in this context self-monitoring provide the latest overview of theory and research, with the focus on cognitive and affective mechanisms, and .
social and personality psychology, in this context, self-monitoring refers to the difference between individuals in the way they view themselves and their social worlds.
It involves the individual self-observation and self-control in accordance with situational cues to social Investment Decision According to Che Hassan et al.
, .
Investment is an activity where the investors are expecting to generate returns by allocating capital to equities or debt investments.
In the current volatile and ever-changing capital market occurrences, many investors are considering whether they should revise their investment portfolios.
Under the uncertainty and unpredictability of the market environment, and in relation with behavioral finance, many investors rely on trial and error, or old Airules of thumbAn to make their investments decision (Che Hassan et al.
, 2.
During the volatile and uncertain market conditions, it is advisable for investors to manage their portfolio with active strategy to respond to the rapid market change to recompose, sell, or replace their asset in order to reduce the level of error in their asset determination (Kristanti et al.
, 2.
According to Riana & Royda .
, there are three factors to put as the basis of investment decisions, which are .
return, the investors will calculate their investment to become the expected return, and after the determined period of investment had passed, it will become the actual return, and both might be differing and thus it can be classified as the risk 891 | P a g e https://dinastipub.
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risk, the higher the expected return, the risk will also get higher The investment decision then will be down to the individual investors, whether they will go with the high risk Ae high return approach, or the low risk Ae low return approach, .
the relation between the risk level and expected return, it is a linear relationship.
Meaning that the higher risk level means the higher expected return, and vice versa.
Framework Trait Anger on Investment Decision Rahman & Gan .
, studied that Trait Anger does not significantly affect investment decision with the Pearson correlation value of -0.
Although in the beta coefficient, the researcher interpreted that that trait anger is marginally significant and positively affected the investment decision making due to the small beta coefficient of 0.
Rahman & Gan .
argued that the insignificancy is due to trait anger being classified as an occurrent emotion.
Gambetti & Giusberti .
, conducted the research, in which the results indicate that trait anger foresees risky financial decisions, as it also associated positively with the intention to invest money in various stocks, with the results shown within the hypothesis testing of low-risk vs medium-risk.
=9,65, p < 0,01.
RA change = 0,01.
Usriyono & Wahyudi .
, also conducted the research of which one of the variables is trait anger, and found that trait anger shows a significant and positive influence towards investment decision.
The positive influence and significance were shown with the MRA result values for Trait Anger to Investment Decisions models of Adjusted RA = 0,056.
Sig.
0,010, and t-value = 2,620.
Trait Anxiety on Investment Decision Rahman & Gan .
indicated that trait anxiety has a negative and significant impact on the investment decision, due to the coefficient values of -0.
Rahman & Gan .
argued that trait anxiety will trigger an unconfident feeling amongst investors on their ability to evaluate investment options.
Gambetti & Giusberti .
, stated with the result of their research, that trait anxiety relates to an individualsAo ability to predict more conservative financial decisions, in contrary to the trait anger, and it also correlated with an individualAos decision to not invest their savings.
Trait anxiety also negatively and significantly influenced investment decisions, as it is shown in the hypothesis testing of low-risk vs high-risk, with the values of F.
=8,80.
P< 0,01, and adjusted RA = 0,10.
Usriyono & Wahyudi .
, stated with their version of the similar research, that trait anxiety shows the tendency of positive and significant influence towards investment decisions.
The tendency were shown by the MRA results value for the Trait Anger to Investment Decisions model of Adj.
RA=0,056,
Sig.
= 0,010, and t-value = 2,620.
Overconfidence on Investment Decision According to the findings of correlation analysis on Rahman & Gan .
, overconfidence are negatively correlated to investment decisions with the P value of -0.
It implies that individual investors with higher overconfidence tendency, has the lower accuracy of investment decisions.
According to Antony & Joseph .
of their research, it was shown that overconfidence is the most impactful bias towards investment decisions, with the values of the priority vector of 29,21%.
It put the irrational behavior side of the investors into picture, as they are overly confident that that they can rely on their own abilities and optimistic assessment.
Raut et al.
, with their study of behavioral finance with structural equation modelling (SEM) being employed, shows the result which indicates the positive and significant influence of overconfidence towards individual investorsAo decisionmaking, with the value of = 0,30, t = 8,291, and RA=0,54.
The research also indicated overconfidence as one of the most crucial factor in the investment decision making, and 892 | P a g e https://dinastipub.
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It implies higher herding factors, significantly lower investment decision and Herding behavior does not significantly affect investment decision with the coefficient value of 0.
074 (Rahman & Gan, 2.
Rizal & Damayanti .
, with the behavioral research in the Indonesian Islamic Stock Market.
The result shows the influence of herding behavior in the Indonesian Islamic Stock Market, as the result of GARCH-type methods shows that the markets return square coefficient is negative statistically, therefore showing the tendency of individual investors to make their investment decision based on the other investors.
Adiputra et.
, conducted the research which indicated that herding behavior has a positive and significant influence on investment decisions, supported with the results of Path-coefficient analysis, with herding behavior classified as a positive predictor, with patch coefficient value of 0,621.
Self-monitoring on Investment Decision According to the research of Rahman & Gan .
Self-monitoring positively correlated to an investment decision with the P-value of 0.
This indicates that the higher the self-monitoring level, the investment decision will improve.
Self-monitoring also has a positive impact on investment decision, with the coefficient value of 0.
Rahman & Gan .
argued that the better trading performance is more feasible for those who self-monitor themselves since they behave strategically.
According to Adiputra et.
, based on the conducted research, shown that Self-monitoring can predict investment decision positively, as the statement supported with the result of Path-coefficient analysis, which shows the value of Self-monitoring path coefficient of 0,227.
According to Masriani et.
Selfmonitoring cannot moderate the variables of their research subject, which are financial literacy, herding variable, and risk tolerance, as the significance value from the regression analysis, respectively of each variable are 0,000, 0,000, and 0,010.
Research Hypothesis Research hypothesis is a tentative statement or assumption regarding the solution to the problem of study, it is the assumption which is used to draw the logical consequences (Patel & Patel, 2.
According to the formulation of problems in Chapter 1, the hypothesis is acquired as following:
Ha1 : Trait Anger partially significantly influence on the investment decision making among Generation Z in the Greater Bandung Area.
Ha2 : Trait Anxiety partially significantly influence on the investment decision making among Generation Z in the Greater Bandung Area.
Ha3 : Overconfidence partially significantly influence on the investment decision making among Generation Z in the Greater Bandung Area.
Ha4 : Herding behavior partially influence on the investment decision making among Generation Z in the Greater Bandung Area.
Ha5 : Self-monitoring partial significantly influence on the investment decision making among Generation Z in the Greater Bandung Area.
Ha6 : Trait Anger.
Trait Anxiety.
Overconfidence.
Herding Behavior, and Self-monitoring simultaneously significantly influence the investment decision making among Generation Z in the Greater Bandung Area.
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The result indicates that the Gen Z investor population is dominated by female respondents, as 53.
4% of the respondents, equals to 261 respondents are females.
Based on year of birth, 51.
5% or 252 of the respondents were born between the year 2000-2003, and they can be classified as students or fresh graduates with limited investing experience.
The majority of the respondent, being 58.
9% or 288 respondents, are classified as students.
This result strengthens the statement in CNBC .
, which indicated that within the trend of 11 million newly registered investors, 26.
68% of them are students.
Looking from the domicile of the Gen Z investors, most of them domiciled in the Bandung City area, being the 44% or 215 respondents.
the phenomenon can be correlated with the fact that Bandung City have the highest gross domestic regional product (GDRP) per capita in the West Java Province (AyoBandung, 2.
As the data of KSEI .
indicated the growth of investors phenomenon, this research also indicated the similarities in between, as the majority of the respondents of this research appears to be novice investors, with 50.
or 245 of the respondents have only less than a year investing experience.
For the preferred or selected investment instrument, stock is the most preferred investment instrument with 22.
or 111 respondents preferring it, indicating that the investors prefers the less risky investment instrument due to their limited understanding of investment, both fundamental and technical.
Multiple Linear Regression Analysis Where:
= Investment Decision = Trait Anger = Trait Anxiety = Overconfidence = Herding Behavior = Self-monitoring T-Test Result Table 1.
T-Test Result Coefficientsa Unstandardized Coefficients Std.
Error (Constan.
Dependent Variable: Y Model Standardized Coefficients Beta Sig.
Based on Table 1 above, t-test can be conducted to find out the influence of each independent variable on the dependent variable.
The result suggested that:
The t-stat value of Trait Anger is -3.
As the value of the t-stat < t-table of 1.
is accepted.
Therefore, it can be concluded that Trait Anger does not partially influence the investment decision of Generation Z in the Greater Bandung Area.
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As the value of the t-stat > t-table of 1.
is rejected.
Therefore, it can be concluded that Trait Anxiety partially influence the investment decision of Generation Z in the Greater Bandung Area.
The t-stat value of Overconfidence is 1.
As the value of the t-stat > t-table of 1.
H0 is rejected.
Therefore, it can be concluded that Overconfidence partially influence the investment decision of Generation Z in the Greater Bandung Area.
The t-stat value of Herding Behavior is 2.
As the value of the t-stat < t-table of 1.
H0 is accepted.
Therefore, it can be concluded that Herding Behavior does not partially influence the investment decision of Generation Z in the Greater Bandung Area.
The t-stat value of Self-monitoring is 6.
As the value of the t-stat > t-table of 1.
H0 is rejected.
Therefore, it can be concluded that Self-monitoring partially influence the investment decision of Generation Z in the Greater Bandung Area.
F-Test Result Table2.
F-Test Result ANOVAa Model Sum of Squares Regression 1119.
Residual Total Dependent Variable: Y Predictors: (Constan.
X5.
X2.
X4.
X3.
X1 Mean Square Sig.
As it shown previously on Table 2, with F-table of 489 respondents and 5 independent variables value being 2.
2326 and F-stat value being 102.
569, then F-stat > F-table, therefore H0 is rejected.
It can then be concluded that Trait Anger.
Trait Anxiety.
Overconfidence.
Herding Behavior, and Self-monitoring simultaneously influence investment decision of Generation Z in the Greater Bandung Area.
It means that the more investment decision value, the higher the simultaneous influence of Trait Anger.
Trait Anxiety.
Overconfidence.
Herding Behavior, and Self-monitoring.
Determination Coefficient Test Result Table 3.
The Determination Coefficient Test Result Model Summaryb Model R Square Adjusted R Square Std.
Error of the Estimate Predictors: (Constan.
X5.
X2.
X4.
X3.
X1 Dependent Variable: Y Based on the Adjusted R Square value on Figure 3 above, it is found that the Determination Coefficient value of the model is 51%.
It means that the independent variables of trait anger, trait anxiety, overconfidence, herding behavior, and self-monitoring can influence the investment decision by 51%, whilst the rest of the percentage being 49%, are factors that are not observed within the research.
CONCLUSION
Based on the research of Trait Anger.
Trait Anxiety.
Overconfidence.
Herding Behavior, and Self-monitoring on Investment Decision of Generation Z in the Greater Bandung Area from a sample of 489 respondents, it can be concluded that from the research, the answers from the formulated research questions are the following trait Anger does not 895 | P a g e https://dinastipub.
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Trait Anxiety influences the investment decision of Generation Z in the Greater Bandung Area.
Additionally.
Trait Anger Ae partially influences the investment decision of Generation Z in the Greater Bandung Area, overconfidence influences the investment decision of Generation Z in the Greater Bandung Area.
Additionally.
Trait Anger Ae partially influences the investment decision of Generation Z in the Greater Bandung Area, herding Behavior does not partially influence the investment decision of Generation Z in the Greater Bandung Area, self-monitoring influences the investment decision of Generation Z in the Greater Bandung Area.
Additionally.
Self-monitoring partially influences the investment decision of Generation Z in the Greater Bandung Area.
And Trait Anger.
Trait Anxiety.
Overconfidence.
Herding Behavior, and Self-monitoring simultaneously influence the investment decision of Generation Z in the Greater Bandung Area.
The research findings is hoped to be beneficial for enhancing educational materials within the context of both schools and universities.
These results can serve as valuable references for students seeking to develop their investment literacy.
This research is hopefully to become an encouragement for next researchers to conduct research of which the results can fill the gap in the field that has not been researched yet, by conducting the research on different research object or different independent variables.
It is also encouraged for future researcher to be able to cover as many segments of population as possible during the sampling process, therefore the result of the research have more validity to represent the entire population.
Researchers in the future can also edit the variable, adding more variables to fill in more gap within the field.
By utilizing this research, the Indonesian Government through the relevant authorities can therefore expected to provide the proper knowledge and practical training of investment for investors and prospective investors.
In the case of investment.
The Indonesian Stock Exchange (IDX) and The Indonesian Financial Service Authority (OJK) as the legal authorities that oversee the investing field are expected to educate and train investors more intensively, including when it comes to the behavioral and psychological biases.
Particularly for Generation Z investors, behavioral bias is one of the factors that has to be considered within the investment decision making process, as the variables of Trait Anger.
Trait Anxiety.
Overconfidence.
Herding Behavior, and Self-monitoring may have effects on Investment Decision.
Therefore, both financial and psychological analysis are crucial in order to make the best investment decisions possible that will result in the best return of As discussed in this research, each and every of investment decisions come with a certain level of risk and error, as the investment decisions being made can be diverted from the most rational decision, and irrational investment decisions were being made due to the behavioral bias of each individual investors.
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