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American Journal of  Financial 
Technology and Innovation (AJFTI)

Impact of  Risk Perception, Overconfidence Bias and Loss Aversion on Investment
Decision-Making

Manoj Kumar Chaudhary1*

Volume 3 Issue 1, Year 2025
ISSN: 2996-0975 (Online)

DOI: https://doi.org/10.54536/ajfti.v3i1.4061
https://journals.e-palli.com/home/index.php/ajfti

Article Information ABSTRACT

Received: November 20, 2024

Accepted: December 21, 2024

Published: February 04, 2025

This study investigates how overconfidence, loss aversion, and perceptions of  risk affect 
investment decisions in the Nepal Stock Exchange. Making investment decisions is a 
complicated process that is influenced by several psychological elements. Using structured 
questionnaires, data was collected from individuals actively involved in stock trading. 
Employing a quantitative approach, the research utilizes a descriptive research design and 
conducts multiple regression analyses. Findings reveal that risk perception significantly 
impacts investment decisions, with individuals perceiving higher risks displaying a greater 
propensity to invest in high-risk assets. Additionally, overconfidence bias positively influences 
investment decisions, indicating that individuals with higher confidence levels tend to favour 
riskier investments. Loss aversion bias plays a significant role, as individuals averse to losses 
prefer investments that minimize potential losses. These results underscore the substantial 
impact of  behavioural biases on investment decision-making, with overconfidence bias 
exhibiting the most significant influence, followed by risk perception and loss aversion bias. 
The findings emphasize the importance of  psychological biases in understanding investment 
behaviour. Investors, financial advisors, and policymakers can all benefit from understanding 
how risk perception, overconfidence, and loss aversion affect investment decisions. Investors 
can improve portfolio performance, lessen the chance of  financial crises, and make more 
informed decisions by identifying and correcting these biases. Therefore, to encourage 
more effective and efficient investment decision-making processes, it is critical to increase 
awareness of  these biases and develop measures to mitigate their negative consequences. 
Conducting more studies to examine these biases’ additional dimensions and how they affect 
investment decisions is advisable.

Keywords
Investment Decision, Loss Aversion, 
Overconfidence Bias, Risk Perception

INTRODUCTION
Standard finance, typically referred to as traditional 
finance, is based on the (EMH) Efficient Market 
Hypothesis (Fama, 1970). Eugene Francis Fama 
presented a landmark article in 1965, introducing the 
Efficient Market Hypothesis (EMH), which states that 
stock market returns exhibit excessive fluctuations that 
depart from the average. Vaidya et al. (2022) stress the 
significance of  testing the normality of  daily returns 
within the Nepalese stock market, particularly within 
the framework of  the EMH theory. The assumption of  
normality in stock market returns serves as a foundational 
premise for the EMH theory. According to traditional 
financial theory, investors are presumed to act with 
complete rationality when making financial decisions.
However, it is acknowledged that emotions and 
psychological factors can occasionally impact these 
decisions, leading to irrational behavior (Kahneman 
& Tversky, 1979). Latif  et al. (2011) stated that many 
stock markets deviate from the rules of  EMH, leading 
to anomalies. The occurrence of  anomalies calls into 
question the idea of  market efficiency and emphasizes 
the need for more study into the behavioral elements and 
causes of  these anomalies. Behavioral finance represents 
a paradigm shift in the field of  finance, departing from 
the traditional assumption of  rationality among investors 

and instead integrating insights from psychology to 
understand financial decision-making (Kahneman & 
Tversky, 1979).
Risk perception, overconfidence, and loss aversion are 
just a few examples of  psychological biases that have 
been found to have a significant impact on investment 
decision-making. The subjective assessment of  potential 
risks connected to an investment opportunity is known as 
Risk Perception. Risk perception depends on a person’s 
knowledge, past experiences, and individual risk tolerance 
(Solvic, 1987). According to Broihanne, Merli and Roger 
(2014), investors with high-risk perceptions are more 
likely to allocate funds to low-risk assets, while investors 
with low-risk perceptions are more likely to allocate funds 
to high-risk ones. Nagriwum et al. (2023) observed that 
on the Ghana Stock Exchange, gender and nationality 
diversity significantly influence earnings quality in non-
financial listed companies, while age diversity has no any 
notable impact.
Overconfidence is defined as having a high conviction in 
one’s judgment, cognitive powers, rational thinking, and 
intellect. It frequently causes people to overestimate their 
knowledge and their ability to foresee properly (Pompian, 
2012). Overconfidence is a common bias that influences 
financial decisions. It alludes to people’s propensity to 
think highly of  themselves and the precision of  their 

1 School of  Management, Tribhuvan University, Nepal
* Corresponding author’s e-mail: chaudharymanojk52@gmail.com



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judgments. Overconfident investors frequently assume 
they have superior knowledge and forecasting abilities, 
which causes them to take unwarranted risks and follow 
unfavorable investment strategies (Odean, 1998).
Overtrading, insufficient portfolio diversification, and 
irrational expectations of  investing returns can all be 
caused by the overconfidence bias. Another psychological 
bias that has a significant impact on financial decision-
making is loss aversion. It describes people’s propensity 
to prioritize risks over benefits. According to Kahneman 
and Tversky (1979), the pleasure experienced from 
a comparable gain is less keenly felt than the pain of  
suffering a loss. Someone tends to reject a significant loss, 
so he also tends to concentrate on preventing a loss while 
gaining profit (Pompian, 2012). 
Someone who overreacts to losses is likely to focus 
more on avoiding losses than on trying to make a profit 
with their investments. The combined impacts of  
risk perception, overconfidence, and loss aversion on 
investment decisions deserve investigation even if  each 
of  these factors has been researched separately. As an 
example, overconfidence can increase the impact of  loss 
aversion, making investors even warier of  prospective 
losses and thus encouraging irrational risk-taking 
behaviors (Barber & Odean, 2001).
Understanding how these biases interact and interact with 
one another helps give an in-depth understanding of  the 
underlying mechanisms influencing investor behavior. 
This study adds to the corpus of  knowledge in Behavioral 
finance by expanding our understanding of  how risk 
perception, overconfidence, and loss aversion affect 
investing decisions. It also has practical ramifications 
for investors and financial professionals. The ultimate 
objective is to encourage more educated, logical, and 
efficient investment decision-making processes, which 
will result in better portfolio performance and a more 
robust financial ecosystem.

LITERATURE REVIEW
Overconfidence Bias
Overconfidence bias is the tendency of  people to 
overestimate their abilities, knowledge, and the accuracy 
of  their judgments or conclusions (Barber & Odean, 
2001). Overconfident investors frequently have unrealistic 
expectations for their investments’ outcomes, resulting in 
overestimated rewards and underestimated investment 
hazards. Due to overconfidence bias, inadequate portfolio 
diversification, excessive trading, and poor investment 
performance may occur (Odean, 1998). Research has 
repeatedly shown that investors across a range of  markets 
and investment groups exhibit an overconfidence bias. 
For instance, Barber and Odean (2001) discovered that 
ordinary investors frequently exhibit greater levels of  
overconfidence in their trading skills than professional 
investors. The prevalence of  this bias was further 
highlighted by Grinblatt and Han (2001) finding that 
overconfidence is prevalent among both beginner and 
experienced investors. 

Overconfidence is a common cognitive distortion 
that leads investors to overestimate their abilities and 
knowledge in the financial domain (Kumar Dahal, 
2022). Due to this bias, people tend to overestimate 
their abilities and frequently overlook important facts 
and data, leading them to believe they are better than 
established norms (Kartini & Nahda, 2021). Dangol and 
Manandhar (2020) have identified overconfidence bias as 
one of  the four heuristic biases they analyze. A common 
cognitive bias that influences investment decision-
making is overconfidence bias, which is characterized by 
overestimation, over-placement, and over-precision. “The 
failure to acknowledge the bounds of  one’s knowledge” 
is how it is characterized (Dangol & Manandhar, 2020). 
The research also finds a strong correlation between 
overconfidence bias and irrationality in investing decision-
making. Overconfidence bias, as highlighted by Tamang 
(2022), leads investors to overestimate their analytical 
skills and the reliability of  their information. This can 
adversely affect investment decisions by causing investors 
to overlook risks and make overly optimistic assessments, 
ultimately undermining portfolio performance. Due to 
the overconfidence bias, Nepalese investors in initial 
public offerings (IPOs) tend to overestimate their 
investment abilities (Tamang, 2022).

Risk Perception
As people assess and understand the potential risks 
connected to various investment possibilities, risk 
perception plays a vital role in investment decision-
making. According to Wynne (1987), risk perception is 
an appraisal that is subjective and impacted by one’s own 
experiences, knowledge, and risk tolerance. Depending 
on how they perceive risk, different investors may assess 
the same investment opportunity uniquely. According to 
Weber and Milliman’s (1997) research, those who perceive 
risk more highly tend to manage their portfolios more 
cautiously, selecting low-risk investments. On the other 
hand, investors who see risk less favorably can be more 
willing to accept greater amounts of  risk. These results 
underline the importance of  risk perception in influencing 
investing decisions. Hui and Sang (2024) demonstrated 
that combining textual and financial indicators increases 
the accuracy of  risk assessment, and deep learning is 
essential for improving financial risk prediction and 
supporting strategic decision-making.
Sapkota (2022) suggests that risk perception plays a 
crucial role in influencing investors’ decisions to invest 
in stocks. Investors may hesitate to invest if  they perceive 
higher risk, whereas lower perceived risk may encourage 
investment. Hamid et al. (2013) found that risk propensity 
positively affects risk-taking behavior, thereby impacting 
stock investment decisions. Individuals who are more 
willing to take risks are more likely to engage in riskier 
investment strategies when it comes to stocks. The study 
by Vaidya et al. (2022) explores the correlation between 
risk tolerance and demographic factors such as gender, 
education, age, income, and occupation. The findings 



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reveal that men exhibit a higher inclination towards 
risk-taking compared to women. Moreover, educated 
individuals tend to display a greater appetite for risk. 
Additionally, the research highlights that age plays a 
significant role in determining risk tolerance levels. 
Furthermore, it suggests that investors with lower wealth 
levels tend to have lower risk tolerance levels in contrast 
to wealthier investors (Vaidya et al., 2022).
In the context of  the stock market in Nepal, Rana 
(2019) highlights the profound impact of  perceived 
risk on investors’ behaviour. Specifically, investors in 
Nepal demonstrate a heightened sensitivity to financial 
risk and the potential for opportunity loss compared to 
other forms of  risk. This emphasis on financial risk and 
opportunity loss significantly influences their investment 
decisions. Moreover, varying levels of  risk perception 
among investors lead to divergent investment behaviours 
within the Nepalese stock market. Understanding and 
managing risk perception is crucial for investors to make 
informed investment decisions in the stock market of  
Nepal (Rana, 2019). 

Loss Aversion
Another psychological bias that has a significant 
impact on financial decision-making is loss aversion 
which Kahneman and Tversky identified. According 
to Kahneman and Tversky (1979), loss aversion is the 
propensity for people to prioritize costs over benefits. A 
loss causes more pain than a similar gain, which causes 
greater pleasure. This asymmetry in the perception of  risk 
and reward has significant implications for investment 
behavior, leading to conservative decision-making and 
reluctance to realize losses. Loss-averse investors exhibit 
risk-averse behavior and tend to hang onto losing assets 
for extended periods, according to research by Shefrin 
and Statman (1985). They discovered that fear of  losses 
can result in poor investing decisions and decreased 

portfolio performance. Odean (1998) investigated loss 
aversion, a Behavioral bias influencing investors’ decisions 
to sell assets. He found that investors often resist selling 
assets that have declined in value, driven by a preference 
to avoid losses over acquiring gains. This reluctance 
affects individual trading behavior in financial markets, 
underscoring the significant impact of  loss aversion on 
investment decisions. 
Barberis et al. (2001) found that losses following gains 
are perceived as less distressing, while losses following 
losses are particularly painful. This bias can lead investors 
to hold onto losing investments longer than warranted, 
a phenomenon known as the disposition effect. 
Recognizing and addressing loss aversion is crucial for 
improving investment decisions and market efficiency. 
Loss aversion is a psychological principle where the 
fear of  loss is considered to be twice as impactful as the 
potential for an equivalent gain (Pompian, 2012). Like 
this, a study by Edwards and Roy (2017) revealed that 
risk-averse investors are more likely to exhibit herding 
behavior, following the decisions of  others rather than 
making their investments. This suggests that risk aversion 
influences not only the investment decisions themselves 
but also how investors make those decisions, leading to 
herding behavior in the stock market.
Despite individual research on risk perception, 
overconfidence, and loss aversion, understanding their 
interaction in influencing investment decisions is crucial. 
Overall, research suggests that factors like overconfidence, 
loss aversion, and risk perception significantly impact 
investors’ decision-making processes. Recognizing the 
interplay between these biases can provide insights 
into investor behavior. Future studies should continue 
exploring the influence of  these biases on investment 
decisions and develop strategies to mitigate their effects.

Conceptual Framework

Figure 1: Conceptual Framework
Source: Nur Aini & Lutfi (2019)

The conceptual framework shows the connections and 
interactions among overconfidence, loss aversion, and 
perception of  risk in investing decision-making. It offers a 
structured analysis of  the crucial factors and elements necessary 

to comprehend how these biases affect the procedures and 
results of  investment decision-making. A conceptual model 
has been prepared and presented in Figure 1. 
The following hypotheses have been proposed:



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Research Hypothesis
Risk Perception and Investment Decision
Investments with higher degrees of  risk often have 
the potential for higher returns, which is a well-known 
phenomenon (Chung & Chuwonganant, 2014). Investors 
who choose riskier assets often have the potential to earn 
higher profits if  those investments perform well. This 
principle underscores the importance of  balancing risk 
and potential reward in investment decisions. Sapkota 
(2022) states that risk perception has a significant positive 
influence on stock investment decisions among investors. 
According to research by Hamid et al. (2013), risk 
propensity has a positive impact on risk-taking behavior, 
which influences stock investment decisions. The study 
conducted by Aren and Zengin (2016) emphasizes the 
substantial influence of  an individual’s risk perception 
on their investment decisions, indicating that people’s 
perceptions of  risks directly shape their investment 
behavior.

H1
There is a significant positive impact of  risk perception 
on investment decisions.

Overconfidence Bias and Investment Decision
Barber and Odean (2001) found that average investors 
often display higher degrees of  overconfidence in their 
trading abilities compared to experienced investors. 
Sapkota (2022) states that overconfidence has a significant 
positive influence on stock investment decisions among 
investors. In behavioral economics, overconfidence bias 
the tendency for people to overestimate their abilities has 
received a great deal of  research (Kumar Dahal, 2022). 
Overconfidence bias has a considerable impact, causing 
people to make decisions based on an exaggerated 
sense of  confidence that, while improving investment 
decisions, carries a high risk (Gurung et al., 2024). 
Aryal (2021) concludes that the only factor significantly 
affecting Nepali investors’ investment performance is 
overconfidence bias.
According to Dangol and Manandhar (2020), investors 
and financial professionals need to be aware of  the 
impact of  overconfidence bias to make more rational 
investment decisions. Overconfidence bias, in particular, 
has been identified as a common cognitive bias that affects 
investment decision-making and can lead to suboptimal 
outcomes (Dangol & Manandhar, 2020). These findings 
highlight the importance of  understanding and managing 
overconfidence bias in investment decision-making to 
improve rationality and avoid suboptimal outcomes. 
Pandit (2021) highlights how overconfidence bias 
influences investment decisions, leading investors to 
overestimate their forecasting accuracy due to illusions 
of  knowledge and control. Despite the lack of  significant 
association with experience levels, overconfidence bias 
can affect various aspects of  investment behaviour, such 
as trading frequency and decision-making, potentially 
causing security prices to deviate from fundamentals. 

This underscores the importance of  investors critically 
evaluating their confidence levels to mitigate the impact 
of  overconfidence on their strategies (Pandit, 2021).
Overconfidence bias has a notable influence on investment 
decision-making, as individuals tend to overrate their 
competencies and misinterpret data (Tamang, 2022). This 
tendency can result in investors assuming undue risks and 
making less-than-ideal investment selections. Additionally, 
those affected by overconfidence bias may place less 
emphasis on fundamental or technical analyses, instead 
turning to sources like social media or personal networks 
for investment insights. Recognizing overconfidence 
bias is pivotal for fostering sound investment decisions. 
A comprehensive understanding of  personal biases, 
including overconfidence bias, is imperative for achieving 
favourable investment outcomes (Tamang, 2022). Various 
studies, including Adielyani and Mawardi (2020), Desrita 
(2022), Madaan and Singh (2019), and Sapkota (2022), 
have consistently found that overconfidence, which 
leads individuals to overestimate their own abilities, has a 
significant positive impact on stock investment decisions.

H2
There is a significant positive impact of  overconfidence 
bias on investment decisions. 

Loss Aversion on Investment Decision
Prospect theory explains the loss aversion bias, which 
is characterized by the tendency to quickly sell winning 
stocks and retain losing stocks (Odean, 1998). According 
to Kahneman and Tversky (1979), loss aversion is the 
propensity for people to prioritize costs over benefits. Their 
psychological theory provides a thorough understanding 
of  how emotions and cognitive biases impact financial 
decisions by clarifying how people evaluate possible 
losses and gains. Jain et al. (2020) pinpointed loss aversion 
as one of  the primary biases influencing investment 
decisions, which is further supported by Sapkota (2022). 
Loss aversion, positively associated with stock investment 
decisions (Sapkota, 2022), has consistently been found to 
have a significant positive impact on stock investment 
decisions across multiple studies (Hossain & Siddiqua, 
2022; Khan, 2017; Kumar & Babu, 2018; Mahina et al., 
2017). Prospect theory explains investors’ risk-averse 
tendency to hold losing stocks and sell winning stocks. 
Compared to a gain of  the same dimension, loss causes 
more fear. People prioritize possible losses over equivalent 
profits because they perceive gain and loss as unbalanced 
factors, a tendency known as loss aversion (Gurung et al., 
2024).

H3
There is a significant positive impact of  loss aversion on 
investment decisions.

MATERIALS AND METHODS
The study’s target population consists of  investors 
who are involved in stock trading in the Nepal Stock 



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Exchange. The method of  collecting primary data was 
through self-administered questionnaires. Since the 
objective was to investigate the influence of  various 
biases on investment decision-making, the researcher 
specifically targeted respondents who were investors for a 
certain period, ensuring that non-investors were excluded 
from the sample frame. The data has been gathered using 
a cross-sectional survey approach because it was done at 
a particular moment in time. The 5-point Likert scale is 
used as the basis for the structured questionnaire’s design. 
Using this scale, participants may express how much they 
agree or disagree with a set of  statements or questions 
in a standardized way. Sections on demographic data, 
risk perception, overconfidence bias, loss aversion, and 
investment decisions were included in the questionnaire. 
To determine the sample size of  the population, the rule of  
thumb proposed by Roscoe (1975) is used. Following this 
rule, 120 investors are taken as a sample. A closed-ended 
questionnaire was created and distributed to respondents, 
resulting in a sample of  120 respondents collected from 
NEPSE investors.  This study has adopted a quantitative 
research method where a descriptive research design has 
been used to describe behavioral biases that affect the 
investment decisions of  investors and a causal research 
design has been implemented to test the degree of  
impact of  those independent variables on the dependent 
variable. Cronbach’s Alpha was used to evaluate the 
instrument’s reliability. The research design has enabled 

descriptive analysis and hypothesis testing using multiple 
regression analysis between stated independent variables 
and dependent variables. To conduct further research, 
social science research software, SPSS, was utilized. 
Additionally, correlation and multiple regression tests 
were employed to explore the relationships between 
various variables in greater detail.

RESULTS AND DISCUSSIONS
The data analysis findings are presented and discussed in 
this section which includes reliability, correlation analysis, 
and regression analysis, summary of  hypotheses, findings 
and conclusion.

Reliability
The reliability of  the model was tested with the help 
of  SPSS. This analysis is measured by Cronbach’s 
alpha. Swkaran (2000) defines Cronbach’s alpha as a 
reliability measure assessing the relationship between 
items on a scale. A value above 0.6 is typically deemed 
acceptable for reliability analysis, indicating sufficient 
correlation among scale items to reliably measure the 
same underlying construct. Cronbach’s alpha values are 
generally interpreted on a scale where scores from 0.8 
to 0.9 indicate excellent reliability, scores from 0.7 and 
0.8 are considered good, and scores between 0.6 - 0.7 
are acceptable. Higher values indicate greater internal 
consistency, signifying that the items are highly correlated.

Table 1: Reliability statistics
Factors on scale                                   Cronbach's Alpha     No. of Items
Risk perception 0.690 5
Overconfidence bias           0.763 5
Loss Aversion 0.613 4
Investment Decision 0.713 4

In Table 1, Cronbach’s alpha of  all 18 of  these variables 
was above 6 which means that there was strong internal 
reliability was strong among the items. The reliability 
statistics table uses Cronbach’s alpha to assess how 
consistently items within different scales measure their 
intended constructs. Risk perception achieves an alpha 
of  0.690 with 5 items, indicating moderately acceptable 
internal consistency. Overconfidence bias shows 
strong reliability with an alpha of  0.763 across 5 items, 
suggesting these items reliably measure overconfidence. 
Loss aversion exhibits lower but potentially acceptable 
reliability with an alpha of  0.613 from 4 items, indicating 
room for improvement in item consistency. Similarly, 
the investment decision scale demonstrates acceptable 
reliability with an alpha of  0.713 measured by 4 
items, showing effectiveness in measuring investment 
tendencies. Overall, while all scales are acceptable for 
research purposes, enhancing the reliability of  scales with 
lower alpha values, such as loss aversion, could improve 

their accuracy in measuring psychological constructs.

Correlation
Correlation analysis is a statistical technique used to 
evaluate the strength and direction of  the relationship 
between two variables. The outcome, known as the 
correlation coefficient, ranges from -1 to 1. A coefficient 
near 1 signifies a strong positive relationship, where an 
increase in one variable corresponds to an increase in the 
other. A coefficient near -1 indicates a strong negative 
relationship, where an increase in one variable corresponds 
to a decrease in the other. A coefficient around 0 implies 
no linear relationship between the variables. This analysis 
aids in understanding how variables are interconnected, 
supporting decision-making and predicting future trends. 
Significant correlations, often highlighted in studies, 
indicate statistically meaningful relationships that are 
unlikely to be due to random chance. The study measured 
the relationship between the variables using Pearson’s 
coefficient of  correlation.



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Table 2 reveals relationships between variables: a moderate 
positive correlation exists between Risk perception and 
overconfidence bias, and a strong positive correlation 
is observed between risk perception and investment 
decision. The correlation table outlines the relationships 
between risk perception (RP), overconfidence bias 
(OB), loss aversion (LA), and investment decision 
(ID), including their means and standard deviations. 
RP, with a mean of  3.21 and a standard deviation of  
0.804, shows significant correlations with OB (0.446**) 
and ID (0.809**). OB, having a mean of  3.45 and a 
standard deviation of  0.486, significantly correlates with 
RP (0.446**) and ID (0.750**). LA, which has a mean 
of  2.93 and a standard deviation of  0.477, significantly 
correlates with ID (0.363**). ID, with a mean of  3.32 and 
a standard deviation of  0.484, has significant correlations 
with RP (0.809**), OB (0.750**), and LA (0.363**). The 
significant correlations, denoted by **, indicate strong 
relationships among these variables, especially between 
RP and ID, and OB and ID.
These findings suggest that individuals perceiving higher 
risk levels may also exhibit greater overconfidence and 
make more investment decisions. The strong correlations 
(**, p < 0.01) affirm the reliability of  these observed 

relationships, with less than a 1% chance that they are 
random. These results demonstrate how psychological 
factors such as risk perception and overconfidence bias 
significantly influence specific investment decisions, 
underscoring the crucial role of  these traits in financial 
decision-making.

Regression
The regression analysis has been carried out to assess the 
impact of  different independent variables on investment 
decisions. Regression analysis is a statistical technique 
used to investigate the relationships between a dependent 
variable and one or more independent variables, aiming 
to understand how variations in the independent variables 
influence the dependent variable. Popular types include 
simple linear, multiple linear, logistic, and polynomial 
regression. Essential elements are the dependent 
variable, independent variables, regression coefficients, 
and R-squared value. The steps involve specifying the 
model, estimating the coefficients, validating the model, 
and interpreting the results. In the context of  investment 
decisions, regression analysis can evaluate how factors 
like interest rates and market volatility impact investment 
decisions, aiding in strategic planning and predictions.

Table 2: Correlation
Variables Mean Std. Dev. RP OB LA ID
1. Risk perception (RP) 3.21 .804 1
2. Overconfidence bias (OB) 3.45 .486 .446** 1
3. Loss Aversion (LA) 2.93 .477 .063 .175 1
4. Investment Decision (ID) 3.32 .484 .809** .750** .363** 1

**Correlation is significant at the 0.01 level (2-tailed)

Table 3: Regression
Models Intercept Regression Coefficients of R2 F

AB PAB OB                         
1 1.756 0.487 .655 91.077

(0.000) * (0.000) * (0.000)
2 0.745 0.746 .562 61.527

(0.029) * (0.000) * (0.000) *
3 2.242 0.368 .132 7.286

(0.000) * (0.010) * (0.010) *
4 0.508 0.357 0.483 .843 126.654

(0.015) * (0.000) * (0.000) * (0.000) *
5 0.865 0.475 0.318 0.75 71.541

(0.001) * (0.000) * (0.000) * 3 (0.000) *
6 0.180 0.704 0.242 .117 4.853

(0.638) (0.000) * (0.012) * (0.010) *
7 -0.81 0.437 0.252 0360 .903 143.182

(0.680) (0.000) * (0.000) * (0.000) * (0.000) *
*Denote that the results are significant at a 1 per cent level of  significance



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shows an intercept of  0.180 with significant coefficients 
for PAB and OB, explaining 11.7% of  the variance.
Model 7 features an intercept of  -0.810 with significant 
coefficients for AB, PAB, and OB, explaining 90.3% of  
the variance with a highly significant F-statistic. Overall, 
these regression models assess the relationships between 
predictors (AB, PAB, and OB) and a dependent variable. 
The significance of  intercepts, coefficients, R2 values, 
and F-statistics offers insights into the models’ fit to 
the data and the predictive strength of  the variables 
in explaining variability in the dependent variable. All 
models demonstrate statistical significance at a 5% level.

Summary of  Hypothesis
A total of  3 hypotheses were examined and the outcomes 
are presented in Table 4. It was found that all these 
hypotheses were supported at a significance level of  
5 per cent. In other words, most of  the hypotheses 
yielded statistically significant results, indicating a strong 
relationship between the variables being tested. The 
significant findings highlight the interconnected nature of  
these variables and their impact on each other, supporting 
their theoretical underpinnings and suggesting potential 
implications for further research or practical applications 
in relevant domains.

Table 3 presents the outcomes of  regression analysis 
with 7 distinct models, showcasing intercepts, regression 
coefficients, R-squared values, and F-statistics for 
each. These models examine various combinations of  
independent variables with the dependent variable, 
providing insights into their strength and significance. 
The table presents multiple regression models analyzing 
the associations between predictors and a dependent 
variable. The R2 values indicate how well each model 
explains the variance in the dependent variable, with 
higher values suggesting a better fit. The F-statistics test 
the overall significance of  the models, highlighting the 
strength of  predictors in explaining variability.
Model 1 has an intercept of  1.756 and significant 
coefficients for AB and OB, explaining 65.5% of  the 
variance with a highly significant F-statistic. Model 2 
features an intercept of  0.745 with significant coefficients 
for PAB and OB, explaining 56.2% of  the variance. Model 
3 shows an intercept of  2.242 with a significant coefficient 
for PAB, explaining 13.2% of  the variance. Model 4 
displays an intercept of  0.508 with significant coefficients 
for AB, PAB, and OB, explaining 84.3% of  the variance 
with a highly significant F-statistic. Model 5 indicates an 
intercept of  0.865) with significant coefficients for AB, 
PAB, and OB, explaining 75.3% of  the variance. Model 6 

Table 4: Summary of Hypothesis
Hypothesis Results
H1: There is a significant positive impact of  risk perception on investment decisions. Supported
H2: There is a significant positive impact of  overconfidence bias on investment decisions. Supported
H3: There is a significant positive impact of  loss aversion on investment decisions. Supported

Findings
The research findings emphasize that behavioral biases, 
such as risk perception, overconfidence bias, and loss 
aversion, significantly shape investment decision-making 
processes. Risk perception plays a pivotal role as individuals 
who perceive higher risks tend to favor investments with 
potentially higher returns but also greater volatility. This 
reflects their willingness to take on risk based on their 
subjective assessment of  market conditions and asset 
performance. Similarly, overconfidence bias influences 
investment decisions by leading individuals to overestimate 
their abilities and underestimate risks, thereby opting for 
riskier investments. Overconfidence bias can result in 
excessive trading and suboptimal investment outcomes, 
as highlighted in studies by Odean (2011) and others, 
which link overconfidence to increased trading frequency 
and poorer performance over time.
Loss aversion, on the other hand, manifests as a 
preference for investments that minimize potential 
losses rather than maximizing gains. Investors exhibiting 
this bias are more likely to avoid risky assets that could 
result in significant losses, even if  those investments 
offer higher potential returns. This cautious approach is 
rooted in the psychological discomfort associated with 
financial losses, as documented by behavioral economists 
such as Kahneman and Tversky (1979). Overall, these 

biases collectively exert substantial effects on investment 
decisions, particularly among active investors who 
frequently engage in financial markets. The consistency 
of  these findings across various studies, including those 
by Odean (1998), Bondt and Thaler (1985), and Barber 
and Odean (2001), underscores the robustness of  these 
behavioral influences in shaping investor behavior 
globally.
However, recent studies, such as Aryal (2021) work on 
Nepalese investors, provide nuanced insights into how 
cultural and regional factors can moderate these biases. 
Aryal (2021) findings suggest that while loss aversion is 
generally significant, cultural contexts may influence the 
extent to which investors are willing to tolerate losses. 
Such insights highlight the need for a comprehensive 
understanding of  behavioral biases in diverse economic 
and cultural environments, informing more tailored 
investment strategies and policy interventions aimed at 
mitigating biased decision-making in financial markets.

CONCLUSION
The research findings underscore that investment 
decisions are significantly shaped by psychological 
factors such as risk perception, overconfidence, and loss 
aversion. Risk perception influences individuals to favor 
investments with higher potential returns but also higher 



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Am. J. Financ. Technol. Innov. 3(1) 14-22, 2025

volatility, reflecting their subjective assessment of  market 
risks. Overconfidence biases investors to overestimate 
their abilities and underestimate risks, leading them to 
favor riskier assets with potentially greater rewards. This 
tendency can result in increased trading and suboptimal 
investment outcomes over time, as documented in 
behavioral finance literature. Moreover, loss aversion 
plays a crucial role as investors tend to prioritize avoiding 
losses over seeking gains. This bias leads them to choose 
investments that minimize potential losses, even if  those 
investments may offer lower returns compared to higher-
risk alternatives. The psychological discomfort associated 
with financial losses, as outlined by Kahneman and 
Tversky’s prospect theory, underscores why investors 
often make conservative decisions to protect against 
potential losses.
However, the study acknowledges several limitations. 
These include potential biases in self-reported 
data, which may skew the accuracy of  responses 
regarding risk perception, overconfidence, and loss 
aversion. Additionally, concerns are raised about the 
representativeness of  the sample used in the study, which 
may limit the generalizability of  the findings to broader 
investor populations. The cross-sectional study design is 
also noted for potentially overlooking changes in biases 
and investment decision-making over time, suggesting a 
need for longitudinal studies to capture these dynamics 
more comprehensively. Despite these limitations, the 
research employs a systematic and rigorous approach 
to investigating how behavioral biases impact investor 
decisions. By shedding light on the roles of  loss aversion, 
overconfidence, and risk perception in shaping investment 
behavior, the study contributes valuable insights to 
behavioral finance literature. These insights are crucial 
for financial professionals and investors seeking to better 
understand and navigate the complexities of  market 
behavior influenced by psychological biases. Future 
research should continue to explore these biases across 
diverse markets and under varying economic conditions 
to deepen our understanding of  their implications for 
investment outcomes. 

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