Herding Behavior.
Overconfidence, and Risk Tolerance on Investment Decisions in Gen Z Rizki Azhar Maulana Yusuf 1.
Ash Shoffi Hana Fadhilah2* Faizal Ridwan Zamzany3 123Management.
Faculty of Economics and Business.
Universitas Muhammadiyah Prof.
DR.
HAMKA.
Indonesia 123azharmaulana533@gmail.
ashshoffi@uhamka.
Abstract Main Purpose Ae The aim of this research is to examine the influence of Herding Behavior.
Overconfidence, and Risk Tolerance on Investment Decisions.
Method Ae This study employs a quantitative approach, utilizing the Structural Equation Modeling (SEM) technique.
The sample comprises 400 investor respondents in South Jakarta.
Main Findings Ae This studyAos findings indicate a positive and significant influence of Herding Behavior.
Overconfidence, and Risk Tolerance on investment decisions.
Theory and Practical Implications Ae these findings strengthen the theory of behavior finance.
Specifically, the studyAos results indicate that herding behavior, overconfidence, and risk tolerance contribute to deviations in indiviual behavior when they comprehend, process, and make decisions regarding information.
These deviations, which can also stem from the use of emotion and spontaneity in responding to information, are consistent with both main categories within behavioral finance.
Novelty Ae This research provides a novel contribution by comprehensively analyzing the interplay between herding behavior, overconfidence, and risk tolerance as factors influencing investment Keywords: Herding Behavior.
Overconfidence.
Risk Tolerance.
Investment.
Gen Z.
Introduction Globalization is a process where societies worldwide are integrate and interconnected across various aspects of life, including economics, politics, culture, and technology (Tang & Asandimitra, 2.
This phenomenon significantly impacts the current economy, bringing both positive and negative sides, which requires careful assessment from everyone (I.
Putri & Sudiyatno, 2.
One of globalizationAos positive benefits is the potential for increased national economic growth, especially through investment in the capital market.
As a crucial economic foundation, the capital market serves a dual function: providing companies with access to capital for operations and expansion, while also offering investors opportunities to invest their excess funds for profit (Al-Azizah et al.
, 2.
The capital market serves as a platform where publicly listed companies can sell their ownership shares to investors, thereby obtaining additional capital (I.
Putri & Sudiyatno, 2.
Similar to traditional markets, the capital market brings together supply and demand (Permata & Ghoni, 2.
However, the objects traded here are financial instruments such as stocks, bonds, and mutual funds, which represent asset ownership or claims to future income.
In capital market investments, every investor has different choices and risk tolerance, often leading to inconsistent decision-making behavior (Zainul & Suryani, 2.
These differences in investment decisions among investors are due to psychological factors influencing each individual (Aristiwati & Hidayatullah, 2.
Emotions are always a part of investment decisions, regardless of the investor.
However, behavioral biases caused by emotions are often detrimental, causing investors to make poor choices by prioritizing feelings over logic.
Market prediction becomes difficult because the market is influenced by various behavioral biases (Zainul & Suryani, 2.
Investment decision-making by investors is not always based on rational considerations.
This is due to psychological factors within investors and social influences, one of which manifests as herding behavior (Aristiwati & Hidayatullah, 2.
Herding is an irrational investment behavior where investors tend to imitate the investment decisions of others (Setiawan & Karnawati, 2.
Research conducted by (Afifah & Juwita, 2.
explains that herding positively influences investment decisions.
Meanwhile, research by (Yanti & Triono, 2.
indicates that herding does not affect investment decisions.
Another factor influencing investment decisions is overconfidence, which is a state where investors have excessive confidence in their abilities and knowledge when making investment decisions (Setiawan & Karnawati, 2.
Investors with overconfidence have a strong belief that their investment decisions are correct and they fully trust that their investment targets will be achieved as predicted (Yanti & Triono, 2.
(Aristiwati & Hidayatullah, 2.
state that overconfidence influences investment decisions, but (Setiawan & Karnawati, 2.
found that overconfidence does not influence investment decision-making.
In addition to herding and overconfidence, investment decisions are also influenced by risk According to (Nurdinda et al.
, 2.
, risk tolerance is an important factor in choosing the most suitable investment portfolio for individual needs regarding their views on risk and potential returns.
Risk tolerance reflects how investors respond to investment outcome uncertainty.
Investors with high risk tolerance tend to be brave in allocating funds to riskier assets, while those with low tolerance are more conservative (Sani & Paramita, 2.
The research results of (Afriani et al.
, 2.
indicate that risk tolerance has a positive influence on investment decisions.
However, this finding differs from the research by (Widiastuti et al.
, 2.
which states that risk tolerance does not influence investment decisions.
Based on this complex background, there is a need for further research.
Therefore, the researchers are interested in conducting research on the above topic with the title: "Herding Behavior.
Overconfidence, and Risk Tolerance on Investment Decisions in Gen Z.
LITERATURE REVIEW
Behavioral Finance In the context of investment, behavioral finance theory explains that investor decisions are often influenced by psychological aspects, not solely by rationality (L.
Putri et al.
, 2.
Deviations in investment behavior and decisions can occur due to various behavioral biases, which stem from emotions and individual preferences, pushing investors to act irrationally in their financial decisions (Aristiwati & Hidayatullah, 2.
The behavioral finance approach argues that a better understanding of financial phenomena can be achieved with models that acknowledge the existence of agents who are not entirely rational (Sani & Paramita, 2.
This field encompasses an individual's ability to manage daily finances, as well as studying the influence of psychological phenomena on economic behavior in financial decision-making, such as consumption, savings, and investment (Setiawan & Karnawati, 2.
Behavioral finance is divided into two main categories.
The first category is cognitive bias, which refers to deviant behavior when individuals understand, process, and make decisions related to information or facts.
Relevant examples of cognitive biases in investment include herding behavior and overconfidence.
The second category is emotional bias, which is deviant behavior resulting from the use of feelings and spontaneity in responding to information or facts (Afifah & Juwita, 2.
Risk tolerance is one example of an emotional bias that influences investment Investment Decisions Investment decisions refer to a strategic policy concerning the allocation of financial resources into assets with projected future profits (Aristiwati & Hidayatullah, 2.
This definition also includes an individual's actions in determining where to place their funds in various forms of investments expected to yield future returns.
According to (I.
Putri & Sudiyatno, 2.
, investment decisions are a crucial initial step in determining the required asset size for investors.
Therefore, before proceeding further, investors need to make careful and well-planned investment decisions to reduce potential future losses.
In the world of investment, there are two distinct categories of assets.
First are real assets, which have a physical form, are observable, and whose quantities can be measured.
Second are financial assets, which are non-physical and represent claims on real assets or future income, such as stocks and bonds (Afriani et al.
, 2.
Herding Behavior Herding behavior is a common behavioral bias where investors tend to imitate the investment actions taken by the majority of other market participants (Aristiwati & Hidayatullah, 2.
This behavior reflects a deviation where investors choose to follow the lead of other investors or groups perceived as more competent, rather than conducting independent analysis (Afifah & Juwita.
In essence, herding behavior is a phenomenon where individuals or investors follow the actions of the majority without considering their own subjective information or judgment (L.
Putri et al.
, 2.
This herding phenomenon shows that investors often look for shortcuts to quickly make investment decisions, especially given the abundance of information available in the market.
Research conducted by (Widyastuti & Murtanto, 2.
found that herding has a positive influence on investment decisions.
Overconfidence Overconfidence is defined as an individual's tendency to possess a level of self-assurance that exceeds their actual capabilities and knowledge (Yanti & Triono, 2.
Investors prone to overconfidence tend to have unrealistically high expectations for returns and feel superior in their investment knowledge, which motivates them to take on greater investment risks (Afifah & Juwita.
This overconfident tendency can lead investors to be overly sure of their analytical abilities and less aware of risks, ultimately resulting in overtrading and losses (Aristiwati & Hidayatullah.
Research findings, such as those by (Widiastuti et al.
, 2.
, affirm that overconfidence indeed influences investor decision-making.
Risk Tolerance Risk tolerance is the degree of risk an investor is willing to take to achieve their profit targets, which will influence their investment choices and the amount of funds allocated (Afriani et al.
Therefore, risk tolerance can be defined as the extent to which an investor is able to accept the level of risk inherent in their investments (Sani & Paramita, 2.
Research conducted by (Nurdinda et al.
, 2.
indicates that risk tolerance influences investment decisions.
RESEARCH METHOD
This research employs a quantitative method to answer its research questions.
According to (Nurfadilah et al.
, 2.
, the quantitative method has a strong philosophical foundation that guides how researchers study a population or sample.
This method is often referred to as a traditional method because of its long-standing use, positivistic due to its reliance on positivism philosophy, and scientific because it adheres to scientific principles such as being concrete, objective, measurable, rational, and systematic (Sugiyono, 2.
This study aims to uncover the influence of three independent variablesAiHerding Behavior.
Overconfidence, and Risk ToleranceAion one dependent variable, namely Investment Decisions.
This study established its sample criteria as follows: investors residing in South Jakarta, born between 1995-2010 (Generation Z), and either male or female.
To determine the sample size, the research utilized the Slovin formula, which is n=N/.
NOe 2 ), where n represents the sample size.
N is the total population, and e signifies the margin of error.
By choosing a 5% margin of error, this research aimed to achieve efficiency in both cost and time while maintaining an adequate level of representation.
Based on calculations using the Slovin formula with this error margin, the result was 399 respondents.
This number was then rounded up by the researchers to 400 respondents for the final sample.
This research employs the Partial Least Squares Structural Equation Modeling (PLS-SEM) method using SmartPLS4 software.
Structural Equation Modeling (SEM) is a multivariate analysis that combines factor analysis and complex simultaneous equation models between variables, both recursive and non-recursive, to obtain comprehensive results in answering phenomena (Herianti.
With SEM, researchers can test the relationships between latent variables .
ariables that cannot be directly measure.
and their indicators (Setiabudhi et al.
, 2.
RESULTS
Outer Model Convergent Validity Table 1.
Outer Loadings X1.
X1.
X1.
X1.
X1.
X1.
X1.
X2.
X2.
X2.
X2.
X2.
X3.
X3.
X3.
X3.
X3.
X3.
Source.
Data processed by eviews, 2025 Outer loadings indicate the strength of the relationship between an indicator .
easured variabl.
and the latent construct it measures.
An outer loading value greater than 0.
7 is considered to show a good contribution of the indicator in measuring its latent construct.
Overall, all indicators for constructs X1 (Herding behavio.
X2 (Overconfidenc.
X3 (Risk Toleranc.
, and Y (Investment Decision.
have outer loadings above 0.
This demonstrates that these indicators significantly contribute to measuring their respective latent constructs.
No indicators need to be removed, as all meet the criteria for convergent validity.
Table 2.
Average Variance Extracted Average Variance Extracted (AVE) Source.
Data processed by eviews, 2025 All AVE (Average Variance Extracte.
values are above 0.
5, which means that the constructs demonstrate good convergent validity.
This indicates that more than 50% of the variance in the indicators can be explained by their respective latent constructs.
Discriminant Validity Table 3.
Fornell-Larcker Criterion Discriminant Validity Test Source.
Data processed by eviews, 2025 The research findings indicate that each construct demonstrates good discriminant validity.
This is because the square root of the AVE (Average Variance Extracte.
n the diagona.
are higher than the inter-construct correlations.
This implies that the constructs are significantly distinct from one another, and their indicators more strongly measure their own respective constructs compared to other constructs.
Reliability Test Table 4.
Reliability Test Cronbach's Composite Composite reliability reliability .
Source.
Data processed by eviews, 2025 The reliability test results indicate that all constructs possess excellent reliability, as all generated Cronbach's Alpha and Composite Reliability values are greater than 0.
This means that the questionnaire instrument used in this research is trustworthy for accurately measuring each construct, and there are no issues with the internal consistency among indicators within each Consequently, the findings of this study can be relied upon.
Multicollinearity test Multicollinearity occurs when there is a very high relationship among indicators in a reflective If high multicollinearity exists, the estimation of model parameters can be distorted.
VIF
(Variance Inflation Facto.
is used to check for multicollinearity among indicators in a reflective A VIF value greater than 5 indicates the presence of multicollinearity that needs to be Table 5.
Variance Inflation Factor (VIF) VIF
VIF
VIF
VIF
X1.
162 X1.
685 X3.
618 Y.
X1.
613 X2.
806 X3.
886 Y.
X1.
012 X2.
612 X3.
832 Y.
X1.
913 X2.
555 X3.
877 Y.
X1.
887 X2.
869 X3.
804 Y.
X1.
869 X2.
591 X3.
Source.
Data processed by eviews, 2025 All VIF values in the table are below 5, ranging from 1.
555 to 2.
The highest VIF values are for indicators X1.
and X1.
, which are still within the acceptable limit below Lower VIF values are found for indicator X2.
Based on these VIF results, all values are below the common threshold of 5, meaning the model does not suffer from multicollinearity as there are no excessively high relationships between the indicators.
Inner Model R-Square ( yacya ) Table 6.
R-Square R Square R Square Adjusted 0,651 0,648 Source.
Data processed by eviews, 2025 The R-Square results for the model indicate that the R-square value for the dependent variable.
Investment Decisions (Y), is 0.
This means that the three independent variablesAiherding behavior, overconfidence, and risk toleranceAican explain 65.
1% of the variation in investment The remaining 34.
9% is influenced by other factors not included in this model.
With an R-square value of 64.
8%, this model is categorized as strong, signifying that the model used has good predictive capability in explaining investment decisions among Generation Z in South Jakarta.
Effect Size .
a ya ) Table 7.
Effect Size
0,189
0,057
0,094
Source.
Data processed by eviews, 2025 From the results above.
Herding Behavior (X.
has an f-squared value of 0.
189, indicating that herding has a moderate effect on investment decisions among Generation Z in South Jakarta.
This means that the presence of herding behavior significantly contributes to shaping investment Meanwhile.
Overconfidence (X.
has an f-squared value of 0.
057, which falls into the category of a small effect.
This implies that overconfidence has a sufficiently strong influence on investment decisions among Generation Z in South Jakarta.
For Risk Tolerance (X.
, with an fsquared value of 0.
094, it also falls into the category of a small effect.
This shows that the risk tolerance factor influences the investment decisions of Generation Z in South Jakarta.
Hypothesis Testing Table 8.
Hypothesis Sample Standar
Path
TMean
P-values Coefficient Statistics (M) (STDEV) X1 -> Y
X2 -> Y
X3 -> Y
Source.
Data processed by eviews, 2025 Herding Behavior (X.
exhibits the largest influence on Investment Decisions (Y), evidenced by both its highest path coefficient .
and t-statistic .
This indicates that imitating behavior, recommendations from friends, or perceived more competent groups play a crucial role in the investment decisions of Gen Z.
Risk Tolerance (X.
also has a significant influence with a path coefficient of 0.
281, showing that the level of risk an investor is willing to undertake to achieve their profit targets affects investment decisions.
Overconfidence (X.
has a slightly smaller influence compared to the other two variables .
but remains significant.
This suggests that excessive self-confidence impacts investment decisions.
DISCUSSION
The Influence of Herding Behavior on Investment Decisions The analysis reveals that herding behavior has a significant positive influence on investment decisions, evidenced by a path coefficient of 0.
392 and a significance level .
00 (< 0.
Herding behavior accounts for 37.
9% of the variance in investment decisions, suggesting that investors with a strong tendency towards herding are more likely to follow the investment choices of other investors or larger groups.
This aligns with behavioral finance theory, which posits that psychological aspects, including emotional biases and individual preferences, frequently influence investor actions, leading to irrational decisions rather than purely rational ones (Aristiwati & Hidayatullah, 2.
(L.
Putri et al.
, 2.
These findings are consistent with studies by (Salsabila & Arifin, 2.
, (Setiawan & Karnawati, 2.
, (Humairo & Panuntun, 2.
, and (Afifah & Juwita, 2.
, all of which also found a positive and significant impact of herding on investment decisions.
The tendency to follow others' decisions is often attributed to disparities in information and varying levels of experience among investors, prompting some to imitate.
However, a contrasting result was observed by (I.
Putri & Sudiyatno, 2.
, whose research indicated a negative influence of herding.
This suggests that students in KSPM Semarang analyze their investments thoroughly before making decisions, rather than simply following other investors.
The Influence of Overconfidence on Investment Decisions The analysis reveals that overconfidence significantly and positively influences investment decisions, with a path coefficient of 0.
223 and a p-value of 0.
This indicates that overconfidence accounts for 22.
4% of the influence on investment decisions, suggesting that an excessive belief in potential returns drives investors to commit larger investment amounts.
This aligns with behavioral finance theory, which posits that a better understanding of financial phenomena can be achieved through models acknowledging that financial actors are not always fully rational.
Financial behavior plays a significant role in investment decision-making, as investors prone to overconfidence tend to hold unrealistically high expectations for profits and perceive themselves as superior in investment knowledge, leading them to undertake greater investment risks (Aristiwati & Hidayatullah, 2.
(Afifah & Juwita, 2.
These findings are consistent with studies by (Wayan Rona & Kadek Sinarwati, 2.
, (Pertiwi & Panuntun, 2.
, (Yanti & Triono, 2.
, and (Widyastuti & Murtanto, 2.
, all of whom found a positive and significant impact of overconfidence on investment decisions.
This consistent result is often attributed to the strong influence of cognitive and internal personal factors that lead investors to excessive self-confidence, often without adequately considering external factors, fundamental or technical analysis, or market information, trends, and facts.
However, research by (Setiawan & Karnawati, 2.
presented a contrasting view, showing that overconfidence does not affect investment decisions.
This discrepancy might arise because highly overconfident investors may also tend to conduct their own thorough information gathering and analysis before making investment choices.
The Influence of Risk Tolerance on Investment Decisions The analysis indicates that risk tolerance has a positive and significant influence on investment decisions, with a path coefficient of 0.
281 and a significance level .
01 (< 0.
Risk tolerance accounts for 29.
5% of the influence on investment decisions, suggesting that investors with higher risk tolerance are inclined to take bolder investment steps compared to those with lower tolerance.
This aligns with behavioral finance theory, which encompasses an individual's ability to manage daily finances, including planning, budgeting, monitoring, controlling, and recording fund sources.
Risk tolerance falls under the emotional bias category within behavioral finance, denoting a deviation in behavior due to the use of feelings and spontaneity in responding to information or facts (Afifah & Juwita, 2.
These findings are consistent with research by (Afriani et al.
, 2.
, (Nurdinda et al.
, 2.
(Sani & Paramita, 2.
, and (L.
Putri et al.
, 2.
, all of whom also found a positive and significant influence of risk tolerance on investment decisions.
These studies suggest that a higher risk tolerance correlates with a greater capacity for investors to accept investment risk.
However, a differing result was reported by (Widiastuti et al.
, 2.
, whose research indicated that risk tolerance does not influence investor decisions in stock investments on the Indonesia Stock Exchange.
This contrasting finding suggests that an individual's financial choices might not always be driven by their risk tolerance.
CONCLUSION
Based on the data analysis and discussion, several conclusions can be drawn: Herding Behavior positively and significantly influences investment decisions among Generation Z, leading to the acceptance of H1.
This indicates that herding behavior reflects a deviation where investors prefer to follow the lead of other investors or groups deemed more competent in making investment decisions, rather than conducting independent analysis.
Overconfidence also positively and significantly impacts investment decisions among Generation Z, leading to the acceptance of H2.
This means that investors prone to overconfidence tend to have unrealistically high expectations for returns and feel superior in their investment knowledge, which encourages them to take on greater investment risks.
Finally.
Risk Tolerance positively and significantly affects investment decisions among Generation Z, leading to the acceptance of H3.
This suggests that investors with high risk tolerance will take bolder investment steps compared to those with low
SUGGESTION
Practical Suggestions Enhancing Financial Literacy and Regulation: Relevant parties, including investment platforms and government bodies like the Financial Services Authority (OJK), must collaborate to provide educational materials specifically for Generation Z.
This education should focus on how to avoid behavioral biases .
uch as herding and overconfidenc.
and the importance of portfolio diversification.
Furthermore, the government needs to tighten oversight on investment promotions, especially those by influencers, to ensure the information is transparent and not misleading.
Theoretical Suggestions Model Development and Comparative Research: Future researchers are advised to expand on this study's model by incorporating other relevant variables, such as the role of social media and family influence.
Additionally, a comparative study between Gen Z and other generations could provide a deeper understanding of how psychological factors affect investment decisions across different age groups.
ACKNOWLEDGEMENTS
To Myself: Thank you for your dedication, unwavering spirit, and resilience in overcoming every challenge throughout my studies and the completion of this thesis.
Let's continue to grow and develop into a better individual in the future.
To Supervisors and Colleagues: Sincere thanks to Thesis Supervisors 1 and 2 for their invaluable support, guidance, and direction in completing this research.
Appreciation also goes to my friends for their continuous support and encouragement.
REFERENSI