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e-Jurnal Apresiasi Ekonomi Volume 12, Nomor 1, Januari 2024: 71-89 ISSN Cetak : 2337-3997
ISSN Online : 2613-9774
71
THE IMPACT OF AVAILABILITY BIAS AND REPRESENTATIVE BIAS ON
INVESTMENT DECISIONS AND PERFORMANCE: THE ROLE OF FOMO
AS AN INTERVENING VARIABLE
Muhammad Nizar1 , Daljono2
1,2 Master of Management, Faculty of Economics and Business, Diponegoro University
email: m.nizar.n@gmail.com, daljono.garong@gmail.com
ABSTRACT
This research starts at the root of the problem of investor irrationality in the capital market. A series of
studies in the last decade show that investors tend to behave irrationally, and phenomena or anomalies are
repeatedly found in the capital market or financial markets that are not in line with standard/traditional
finance theory or conventional/orthodox economics theory. This research aims to determine the impact of
availability bias, representative bias, and fear of missing out (FOMO) on investment decisions and
investment performance, and to find out that FOMO can act as a mediating variable between these
relationships. The population in this study were all investors who traded in the Indonesian capital market
through brokerage houses in several cities in Indonesia, and the sample size was 116 respondents, using a
purposive sampling technique. The data used is primary data, data collection techniques use questionnaires.
Structural Equation Modeling (SEM) data analysis technique with the SmartPLS analysis tool. The research
results show that availability bias has a positive and significant impact on investment decisions and
investment performance. Representative bias has a negative and insignificant impact on investment
decisions, but representative bias has a positive and significant impact on investment performance. FOMO
has a positive and significant impact on investment decisions and investment performance. Availability bias
and representative bias have a positive and significant impact on FOMO. FOMO partially mediates the
relationship between availability bias towards investment decisions and investment performance, then
representative bias towards investment performance, but FOMO fully mediates the relationship between
representative bias towards investment decisions. The results of this research would contribute to the
development of knowledge about behavioral finance and have theoretical and policy implications for
Indonesian retail investors.
Keywords: Availability Bias, Representative Bias, Fear of Missing Out (FOMO), Investment Decisions,
and Investment Performance
INTRODUCTION
This research assumes that investors can
behave irrationally in the capital market. A series
of studies in the last decade show that investors
tend to behave irrationally, and anomalies are
repeatedly found in the capital market or financial
markets that are not in line with standard finance
theory or conventional economics theory (Ritter,
2003). Some of the basic frameworks of standard
finance are Modern Portfolio Theory (MPT) by
Markowitz (1952), Capital Asset Pricing Model
(CAPM) by Sharpe (1964), and Efficient Market
Hypothesis (EMH) by Fama (1965).
However, the three standard/traditional
finance theories above, namely MPT, CAPM, and
EMH, are less able to explain several anomalies in
the capital market (Baker et al., 2019). These
phenomena include (1) the January effect
(Pompain, 2006), (2) the weekday and weekend
effect (Cross, 1973), (3) the January and monthly
effects (Rozeff and Kinney, 1976), (4) the month
change effect (Ariel, 1987), (5) the Ramadan effect
(Al-Ississ, 2015), (6) the festival effects
(Lakonishok and Smidt, 1988), (7) the internet
phenomenon (Suriani, 2022), (8) several downfalls
in capital markets (market crash) in 1929, 1987,
1998, 2008, 2015, and 2020, and (9) even the
recent market bubble phenomenon, which is
related to the FOMO (fear of missing out)
phenomenon (Gupta and Shrivastava, 2022).
Several market anomalies create market price
movements that are not normal and tend to be
extreme because they are influenced by investor
behavior factors (Woo et al., 2010).
The anomalous phenomenon in the capital
market above shows that (1) investors tend not to
be completely rational and security prices tend not
to reflect fair value; (2) investors tend not to have
portfolio uniformity (expected level of profit and
risk); and (3) investors tend to follow sentiments
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ISSN Online : 2613-9774
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that occur from the various phenomena above
(market effects, market bubbles, and market
crashes). From the various phenomena above, it
can be seen that investors tend to be irrational in
making investment decisions in the capital market
(Kim and Ha, 2010). From this point of view, it is
understandable to use a behavioral finance
approach to understand gaps in financial standards.
Behavioral finance theory assumes that
investors do not always act rationally when
deciding on an investment. Irrational investor
actions occur because of psychological factors in
making decisions (Pompain, 2006). These
investors' psychological factors make the market
abnormal (market effects, market bubbles, and
market crashes). Investors can panic buy or panic
sell only based on information that is not
completely and precisely available on the market,
so that the decisions taken by investors become
irrational (Ding et al., 2021). This irrational
investor behavior is called behavioral bias.
Behavioral biases are described as
tendencies toward errors in judgment or prediction
(Mittal, 2022). Nofsinger (2005) explains that
behavioral biases are caused by psychological
factors, which can reduce investors' capacity to
make measured investment decisions and also
cause investors to misjudge potential risks.
Behavioral biases consist of an investor's cognitive,
emotional, and social factors that have the potential
to influence investment decisions and performance.
Much research has been conducted on
behavioral biases related to cognitive, emotional,
and social issues. The first is that Jain et al. (2020)
researched the influence of behavioral biases on
investment decisions in eight aspects including
availability bias and representative bias, which
have a positive impact on investment decisions in
Punjab, India. Second, Parveen et al. (2020)
researched the influence of behavioral biases on
investment decisions in two aspects, one of which
is representative bias, which has a positive impact
on investment decisions in Pakistan. Third, Tin and
Hii (2020) researched the influence of behavioral
biases on investment performance in four aspects
including availability bias and representative bias,
which have a positive impact on investment
performance in Johor, Malaysia.
However, several studies below have found
different results. First, Dangol and Manandhar
(2020) explain the influence of behavioral biases
on investment decisions, consisting of five aspects,
including availability bias and representative bias,
which have a negative impact on investor decisions
in Nepal. Furthermore, Shah et al. (2018)
researched the influence of behavioral biases on
investment decisions, consisting of four aspects,
including availability bias and representative bias,
which have a negative impact on investor decisions
in Pakistan. Third, research by ul Abdin et al.
(2017) regarding the influence of behavioral biases
on investment performance consists of four
aspects, including availability bias and
representative bias, which have a negative impact
on investor performance in Pakistan.
Fourth, the results of a different study
conducted by Rehan and Umer (2017) regarding
the influence of behavioral biases on investment
decisions consist of seven aspects, including
representative bias, which has a positive impact on
investor decisions, and availability bias, which has
no impact on investor decisions in Pakistan. This
shows that investors tend not to be influenced by
the availability bias factor before selecting and
assessing an investment opportunity.
Other research, such as that conducted by
Gupta and Shrivastava (2022), examined the
influence of behavioral biases on investment
decisions consisting of three aspects, namely: fear
of missing out (FOMO), loss aversion, and herd
behavior. The results of his research state that these
three variables have a positive impact on
investment decisions in India. This research
suggests examining the relationship of other
behavioral biases to investment decisions with
FOMO as a mediating variable for future research.
The following in Table 1 is a summary of several
differences in research results (research gaps).
This research sees a gap in the results of
previous research regarding the significance results
(positive or negative) between availability bias and
representative bias on investment decisions and
performance. This research also seeks to develop
previous research on FOMO, which is still limited
regarding the impact of the relationship between
behavioral biases on investment decisions and
performance. The scope of this research is limited
to retail investor research data in Indonesia, which
still has similarities with the objects of previous
research countries. This research is expected to
contribute knowledge to capital market
stakeholders in Indonesia and to the development
of behavioral finance theory in general.
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Table 1. Research Gap
No
Variable X
Variable Y
Research Gap
Author
1
Availability
Bias
Investment
Decisions
Significant
Positive
Jain et al. (2020), Khan (2017), Ikram (2016)
Significant
Negative
Dangol & Manandhar (2020), Shah et al. (2018)
Insignificant
Rehan & Umer (2017)
Investment
Performance
Significant
Positive
Tin & Hii (2020), Siraji, M (2019), Alrabadi et
al. (2018)
Significant
Negative
ul Abdin et al. (2017)
2
Representative
Bias
Investment
Decisions
Significant
Positive
Jain et al. (2020), Parveen et al. (2020), Rehan &
Umer (2017), Ikram (2016). Irshad et al. (2016),
Toma, F.M. (2015)
Significant
Negative
Dangol & Manandhar (2020), Shah et al. (2018)
Investment
Performance
Significant
Positive
Tin & Hii (2020), Siraji, M (2019), Alrabadi et
al. (2018)
Significant
Negative
ul Abdin et al. (2017)
3
FOMO
Investment
Decisions
Significant
Positive
Gupta & Shrivastava (2022), Kaur et al. (2023)
Source: Various journal sources.
Based on the research background above in
the form of phenomena and research gaps, this
research formulates the problem, namely whether
availability bias and representativeness bias have a
significant positive or negative influence on
investment decisions and performance through
FOMO as a mediating variable. From the problem
formulation, this research describes several pieces
of literature that will produce the following
hypothesis.
Literature Review
There are many theories and concepts that
can be used to explain the relationship between
behavioral biases and investment decisions. Among
them are (1) bounded rationality theory by Simon
(1955), (2) heuristics theory by Kahneman and
Tversky (1974), (3) prospect theory by Kahneman
and Tversky (1979), and (4) Thaler (1980).
However, this research only focuses on availability
bias, representative bias, and FOMO, which can
influence investment decisions and performance,
and FOMO acts as a mediating variable.
Investment Decisions and Performance
Investment is a series of asset-purchase
processes aimed at harvesting greater future
benefits. Investment performance is the result of
income, profit, or return from a portfolio of
investment assets that has an impact on the
valuation side. The behavioral finance approach
assumes that investment decisions are often
irrational and have strong psychological factors
(related to investors' mental development), caused
by (1) psychological biases (Baker and Nofsinger,
2002) or behavioral biases (Shefrin, 2007), (2)
fundamental heuristics (Baker and Nofsinger,
2002), (3) market anomalies (Ajmal et al., 2011),
(4) bounded rationality (Pompain, 2006), and (5)
imperfect information (Bikhchandani et al., 1992).
According to cognitive bias theory,
investment decisions based on heuristics can cause
individuals to participate in less rational decision-
making (Baron, 1998; Bazerman, 1998). However,
cognitive biases help individuals face difficult
decisions with strong personal beliefs (Bazerman et
al., 1984). Cognitive biases and heuristics, both of
which are mental shortcuts, are used by decision-
makers in complex and uncertain situations (Ritter,
2003) by reducing complexity (Barnes, 1984).
According to Kahneman and Tversky (1974),
because of these heuristics and cognitive biases,
systematic errors occur, and as a result, decision
results are affected (Barnes, 1984). A limited
review of previous research on heuristics is
discussed below.
Heuristics-Driven Bias
Heuristics are closely related to
irrationality and unavoidable cognitive illusions
(Piattelli-Palmerini, 1994). Heuristics are referred
to as rules of thumb or mental shortcuts, which are
used by financial practitioners (both individual and
group level) in complex and uncertain situations to
make simple and efficient decisions.
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The literature reveals that when financial
practitioners and business actors use heuristics,
they eliminate rationality, intellectual, and mental
efforts in a series of decision-making processes,
causing a number of behavioral biases. Among
these behavioral biases are availability bias and
representative bias. This research measures the
impact of availability bias and representative bias
driven by heuristics (heuristic-driven bias) on
decision-making and investment performance. A
limited review of previous research on availability
bias and representative bias driven by heuristics
and their influence on decision-making and
investment performance is discussed below.
Availability Bias, Investment Decisions, and
Performance
Availability bias is a cognitive heuristic
bias that arises when investors rely heavily on
information that is easily obtained (based on
experience) (Ngoc, 2014), namely when investors
predict possibilities that will occur or appear only
based on their memories or things they have
previously known in accordance with experience
(Brahmana et al., 2012; Kahneman & Tversky,
1974). There are four types of availability bias: the
first is retrievability, the second is categorization,
the third is the narrow range of experience, and the
fourth is resonance.
Several researchers concluded that
cognitive heuristic-driven bias has a significant
positive relationship with investment management
activities. Jain et al. (2020), which confirm that
heuristic-driven biases such as representativeness,
availability, overconfidence, and anchoring lead to
investment decision-making in Punjab City, India.
Ikram's (2016) research found that bias heuristics
(overconfidence, representativeness, availability,
and anchoring) have a positive relationship with
the decisions of investors who actively trade in the
Johor Malaysia capital market and on perceived
market efficiency. This is reinforced by research by
Khan (2017), which shows that availability bias
from within investors has a positive impact on
improving investor decisions.
Jain et al. (2020) also studied heuristic-
driven bias and its influence on investor decisions
in Punjab, India. The results of their research
reveal that heuristic-driven biases such as
availability and representative bias significantly
positively cause investors to make irrational
decisions. Tin & Hii (2020) attempted to highlight
the consequences of heuristic-driven bias, namely
availability, representativeness, overconfidence,
and anchoring on the performance of each investor.
Overall, their research results show that heuristics
are the cause of stock market anomalies, resulting
in irrational decision-making that positively
influences investor performance in Johor Malaysia.
After reviewing some of the relevant literature
above, this research hypothesizes that availability
bias has a positive effect on investment decision-
making. Therefore, availability bias has a
significant positive impact on investment decisions
and performance.
H1a. Availability bias has a significant positive
impact on investment decisions.
H1b. Availability bias has a significant positive
impact on investment performance.
Representative Bias, Investment Decisions, and
Performance
Representative bias is a cognitive heuristic
bias that occurs when investors use mental
shortcuts and mental stereotypes in investment
decisions (Shefrin, 2005). Representative bias
places too much trust in stereotypes and leads
investors to make estimates that are inappropriate
for the relevant situation (Shefrin, 2008). There are
two types of representative bias: one is known as
base rate neglect, and the second is known as
sample size neglect. The consequence of heuristics-
driven representative bias is that decision makers
adopt forecasts based on small samples and
improve decisions with simple classifications
rather than very complex ones (Shah et al., 2018).
Several researchers concluded that
cognitive heuristic-driven bias has a significant
positive relationship with investment management
activities. Starting from (1), Jain et al. (2020)
concluded that investors in the city of Punjab,
India, were significantly positively influenced by
representative bias in capital market trading
activities. (2) The results of research conducted by
Parveen et al. (2020) show that investors in
Pakistan are also influenced by representative bias
in a significantly positive way in the investment
decision-making process. (3) Tin & Hii (2020)
revealed that heuristics-driven bias (availability-
representative) has a significant positive influence
on investors' investment performance in Johor
Malaysia. (4) Ikram (2016) stated that
representative bias has a significant positive effect
on investment decisions made by Pakistani
investors. This is reinforced by the research results
of Rehan & Umer (2017), which show that
heuristics (overconfidence bias, representative bias,
and anchoring bias) have a significant positive
effect on the decisions of investors who actively
trade in the Pakistani capital market and on market
efficiency. The results of these studies confirm that
heuristic-driven biases, such as representativeness,
availability, overconfidence, and anchoring, lead to
irrational decision-making and have a positive
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effect on investment decision-making. After
reviewing some of the relevant literature above,
this research hypothesizes that representative bias
has a significant positive impact on investment
decisions and performance.
H2a. Representative bias has a significant
positive impact on investment decisions.
H2b. Representative bias has a significant
positive impact on investment performance.
FOMO, Investment Decisions, and Performance
Psychologically, individuals affected by
FOMO will see, read, or learn about other people's
actions and feel anxious and may also feel lost if
they do not receive the latest news (Abel et al.,
2016). FOMO investors are investors who are
under the influence of the desire to obtain higher
profits in the future and may feel they are missing
out on return opportunities if they do not take
immediate action (Dennison, 2018; Kang et al.,
2020). Gupta and Shrivastava (2022) found in their
research that investors who are affected by FOMO,
herd bias, and loss aversion bias can influence
investor decisions in India.
FOMO can also be said to be a cognitive-
heuristic-driven bias. FOMO is part of heuristics
because it equally influences the decision-making
process by taking shortcuts (decision-making
shortcuts by Hussain and Oestreicher, 2018). This
research hypothesizes that FOMO bias has a
positive effect on irrational decision-making. So
FOMO has a significant positive impact on
investment decisions and performance.
H3a. FOMO has a significant positive impact on
investment decisions.
H3b. FOMO has a significant positive impact on
investment performance.
The Mediating Role of FOMO
Past research conducted in the context of
behavioral finance and investment decisions
provides evidence that there are several variables
that are proven to mediate the relationship between
the two. Several researchers have studied the role
of (1) risk mediation and various risk attributes that
mediate this relationship (Sadiq and Khan, 2019;
Raheja and Dhiman, 2019; Saurabh and Nandan,
2018; Hunjra and Rehman, 2016; Khan, 2014; Riaz
et al ., 2012; Sitkin and Weingart, 1995), then (2)
mediation of behavioral finance and financial strain
(Falahati et al., 2012), (3) judgment and decision-
making biases (Lakey et al., 2008), (4) ) mediation
of attitude towards the relationship between
behavioral biases and investment decisions (Ali,
2011; Jamal et al., 2015), and (5) financial literacy
and financial self-efficacy were also investigated as
mediating factors (Akhtar and Das, 2019;
Ameliawati and Setiyani , 2018).
With the same pattern of thinking, FOMO
was chosen as a mediating variable in this research.
Researchers in the past have not studied the
mediating role of FOMO on the relationship
between availability bias and representative bias
with investment decisions and performance. Thus,
the findings of this research will be very useful in
the development of behavioral finance theory,
especially regarding FOMO. After reviewing some
of the relevant literature above, this research
hypothesizes that availability bias and
representative bias have a significant positive
impact on FOMO in the context of Indonesian
capital market investors.
H4. Availability bias has a significant positive
impact on FOMO.
H5. Representative bias has a significant
positive impact on FOMO.
Researchers in the past have identified a
relationship between FOMO and investors using
herd behavior and aversion in the form of greed.
Dennison (2018), in his research, determined
FOMO as a significant influence that leads
investors to make hasty investment decisions in
order to follow their peers and neighbors. He also
pointed out that these investors are very driven by
the desire to get more returns quickly and thereby
hopefully avoid future losses. Kang et al. (2020)
and Tarjanne (2020) support the relationship
between FOMO and herd behavior.
When investors decide to invest in a
certain industry because they see their friends and
colleagues succeed in getting returns in that
industry, then the investor is indicated to have
FOMO in their investment decision. The spread of
FOMO leads to herd behavior, and this continues
to push up security prices (Hershfield, 2020).
Likewise, the findings of Gupta and Shrivastava
(2022) have proven that there is a partial or
complementary mediating role for FOMO in the
herd and loss aversion bias relationship in the
decisions of Indian capital market investors.
After reviewing some of the relevant
literature above, this research hypothesizes that
FOMO can mediate the relationship between
availability bias and representative bias on
investment decisions and performance.
H6a. FOMO mediates the relationship between
availability bias and investment decisions.
H6b. FOMO mediates the relationship between
availability bias and investment performance.
H7a. FOMO mediates the relationship between
representative bias and investment decisions.
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H7b. FOMO mediates the relationship between
representative bias and investment
performance.
Figure 1. Research Model
RESEARCH METHODS
The object of this research is Indonesian
retail stock investors, whose aim is to obtain
primary data. The target population in this study is
all Indonesian retail capital investors in various
cities in Indonesia, the number of which is quite
large and cannot be measured with certainty. So,
samples are needed to be used as subjects in this
research. The sampling technique is non-
probability sampling, or non-random sampling,
which is a way of taking samples without
providing identical opportunities or moments for
elements or all members of the population selected
as samples. The sample selection technique is
purposive sampling based on certain measurements
or studies (Sugiyono, 2019). The measures used
include stock investors with more than two years of
experience and a good understanding of the capital
market. This research distributed more than 200
questionnaires using Google Form as a tool for
collecting samples. Google Form is a tool that can
support collecting questionnaires online and using
statements.
The operational definition of variables is
based on a set of variables used in research. Some
of the variables in this research are: (1) availability
bias (AB), namely as an independent variable (X1);
(2) representative bias (RB), namely as an
independent variable (X2); and (3) investment
decisions (ID), namely as a dependent variable.
(Y1), (4) investment performance (IP), which is the
dependent variable (Y2), and (5) fear of missing
out (FOMO), which is the intervening or mediating
variable (Z). The following are each of these
indicators in Table 2.
Table 2. Operational Variables
Variables
References
Availability bias (AB)
Dangol dan Manandhar (2020); Shah et al. (2018);
Rasheed et al. (2018); Nada dan Moa’mer (2013)
Representative bias (RB)
Dangol dan Manandhar (2020); Shah et al. (2018);
Rasheed et al. (2018); Nada dan Moa’mer (2013)
Fear of missing out (FOMO)
Gupta dan Shrivastava (2022)
Investment Decisions (ID)
Dangol dan Manandhar (2020); Rasheed et al. (2018)
Investment Performance (IP)
Ahmad dan Shah (2022); ul Abdin et al. (2017); Waweru
(2008); Luong dan Thu Ha (2011)
Source: Various Journal Sources.
Descriptive analysis uses data and samples
that have been obtained in current conditions
without the need for in-depth analysis or making
general conclusions. This is used to provide an
overview of the topic being considered (Sugiyono,
2019). A descriptive analysis of respondents will
provide an overview of these respondents in terms
of gender, age, education, occupation, and income.
Descriptive analysis of the variable AB consists of
6 statements, RB consists of 6 statements, FOMO
consists of 6 statements, ID consists of 6
statements, and IP consists of 4 statements (for
references to the statements of each variable, see
Table 2.).
The data analysis method that will be
applied in this research is Structural Equation
Modeling-Partial Least Square (SEM-PLS), which
is included in the Structural Equation Modeling
(SEM) method group. Structural Equation
Modeling-Partial Least Square (SEM-PLS)
analysis will be carried out using SmartPLS
software version 3.2.9.
In SmartPLS, there are testing stages that
will be carried out (Hair et al., 2014; Ghozali,
2016), namely the first stage, the outer model test,
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which includes convergent and discriminant
validity tests, as well as construct reliability tests.
The conditions are loading factor indicator> 0.7,
AVE reflective construct > 0.5, the square root of
AVE must be greater than the correlation between
constructs, Cronbach’s Alpha, rho A, and
composite reliability > 0.7. Discriminant validity
test using the Fornell-Larcker criterion.
The second stage is to test the goodness of
fit model, which includes model fit SRMR < 0.10,
inner VIF value < 5, and q square predictive
relevance (to see the power of the model
predictions). The third stage is inner model testing,
which includes significance tests of p value < 0.05
and t value > 1.96 on 5,000 bootstrap samples, f
square and r square.
The structural model developed is as
follows:
Figure 2. Structural Model
RESULTS AND DISCUSSION
This research used respondents, namely
investors who trade in the Indonesian capital
market. The questionnaire was distributed via
Google Form with the following link:
https://forms.gle/nv9xmXHXCHXbR5Ev7 and
from the distribution of the questionnaire, a total of
116 answers were collected from respondents,
which were used as the sample size. The analysis
of the respondent's identity is reflected in the
following Table 3:
Table 3. Respondents’ Profile
Demographic Variables
Categories
Frequency
Percentage (%)
Sex
Male
87
75
Female
29
25
Age
21-30
6
5,2
31-40
74
63.8
41-50
29
25
>50
7
6
Education
High School
2
1,7
Associate’s Degrees
5
4.3
Bachelor’s Degrees
101
87.1
Master’s Degrees
8
6.9
Occupation
Private Sector Employees
41
35.3
Entrepreneurs
14
12.1
State-Owned Company Employees
59
50.9
Government Employees
1
0.9
Other Professions
1
0.9
Monthly Income
<IDR 50 million
96
82.8
IDR 50-100 million
12
10.3
IDR 101-300 million
6
5.2
>IDR 301 million
2
1.7
Source: Author’s Calculation, 2023
Out of the 116 respondents, 75% were
male and 25% were female investors. Sixty-three-point-eight percent of the respondents were from
31–40 age groups, while 5.2%, 25%, and 6% were
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from 21–30, 41–50, and above 50 age groups.
Around 87.1% of respondents had bachelor’s
degrees, while 1.7%, 4.3%, and 6.9% had high
school, associate’s degrees, and master’s degrees.
About 50.9% of respondents work as state-owned
company employees, while 35.3%, 12.1%, 0.9%,
and 0.9% work as private sector employees,
entrepreneurs, government employees, and other
professions. About 82.8% of the respondents had a
monthly income of less than IDR50 million,
compared to 10.3% in the income group of IDR50
million to IDR100 million. The remaining 5.2%
and 1.7% of respondents earned between IDR101
million and IDR300 million and more than IDR301
million per month, respectively.
The description of the variables is used to
determine respondents' perceptions about the
variables AB, RB, FOMO, ID, and IP. The results
of the variable description analysis were reviewed
based on the frequency of respondents' answers to
each statement item. Descriptive analysis of these
variables is expressed at various scale levels as
follows:
RS = m – n
b
RS = 6 – 1 = 0.83
6
Information:
RS = Range (level) of scale
m = Maximum score value on the scale
n = Minimum score value on the scale
b = Total categories or used
Thus, the scale categories can be
determined as follows:
1.00 – 1.83 = Strongly Disagree
1.84 – 2.67 = Disagree
2.68 – 3.51 = Disagree
3.52 – 4.35 = Quite Agree
4.36 – 5.19 = Agree
5.20 – 6.00 = Strongly Agree
The results of data processing in this
research related to research variable statistics can
be presented in the following tables.
Table 4. Descriptive Analysis
Variables
Indicators
Mi
n
Max
Modus
Mea
n
Availability Bias
(AB)
I prefer to sell stocks when the composite index is
downward trend (AB1).
1
6
4
4.02
I prefer to buy stocks when the composite index is
upward trend (AB2).
1
6
4
3.88
I prefer to buy local stocks rather than international
stocks because local stock information is more
widely available (AB3).
3
6
5
4.86
I prefer to buy stocks that are recommended by close
friends or relatives (AB4).
1
6
4
4.34
I prefer to buy local stocks rather than trading
international stocks (AB5).
1
6
5
4.87
I prefer to buy stocks that are recommended by
financial experts or stock experts (AB6).
3
6
5
5.10
Mean
4.51
Representative Bias
(RB)
I avoid buying stocks that have performed poorly in
the past (RB1).
2
6
4
4.69
I prefer to buy stocks that have performed well in the
past because I believe that good performance will
continue in the future (RB2).
2
6
5
4.76
I prefer to buy stocks that have good fundamentals
(consistent earnings growth in the past) (RB3).
3
6
6
5.15
I prefer to buy stocks that are doing well in the local
composite index rather than stocks that will perform
poorly in the near future (RB4).
2
6
4
4.64
I definitely check the past performance of a stock
before deciding to buy it (RB5).
3
6
5
5,03
I use trend analysis before deciding to buy stocks
(RB6).
2
6
5
4,92
Mean
4.87
Fear of Missing Out
I feel uncomfortable if I don't immediately hear the
2
6
5
4.59
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(FOMO)
latest news or news about the stocks I own
(FOMO1).
I'm worried if I don't know the future corporate and
business plans of the stocks that I currently own
(FOMO2).
1
6
4
4.57
I want to immediately find out the trend of the stocks
I currently own (FOMO3).
3
6
5
4.90
I feel anxious when I cannot check my current stock
portfolio (FOMO4).
2
6
5
4.97
I would feel disappointed if I lost the opportunity to
buy or own stocks offered by other investors (FOMO
5).
2
6
4
4.42
I feel afraid of being the last to know about news that
is relevant to the stock portfolio that I own (FOMO
6).
2
6
5
4.60
Mean
4.68
Investment Decisions
(ID)
I trust my inner or heart before deciding to buy a
stock (ID1).
2
6
4
4.43
The stocks I bought were good stocks, according to
my feelings (ID2).
2
6
5
4.51
I buy and sell stocks based on instinct (ID3).
1
6
4
3.98
I sold a stock that I felt was bad (ID4).
1
6
4
3.98
I buy and sell stocks using intuition (ID5).
2
6
4
4.09
I buy and sell stocks based on my feelings rather than
logical or rational reasons (ID6).
1
6
4
3.60
Mean
4.10
Investment
Performance (IP)
I feel satisfied with the returns from my stock
investment portfolio recently (IP1).
3
6
5
4.76
I feel confident that my recent stock portfolio returns
are at least the same, better, or higher than the
average return given by the market or local
composite index (IP2).
3
6
4
4.61
I feel satisfied with the results of my recent stock
investment decisions (including buying, selling, stock
selection, and determining stock trading volume)
(IP3).
3
6
5
4.76
I feel satisfied with the results of my stock
investment in the local composite index because the
results are in accordance with my financial planning
needs recently (IP4).
3
6
5
4.86
Mean
4.75
Source: Author’s Calculation, 2023.
Table 3 shows that the average AB
response index value is 4.51 (agree or high
category). The AB6 indicator has the highest
average value, namely 5.10 (agree), and AB2 has
the lowest average value, namely 3.88 (quite
agree). The average RB response index value is
4.87 (agree or high category). The RB3 indicator
has the highest average value, namely 5.15 (agree),
and RB4 has the lowest average value, namely
4.64 (agree). The average FOMO response index
value is 4.68 (agree or high category). The
FOMO4 indicator has the highest average value,
namely 4.97 (agree), and FOMO5 has the lowest
average value, namely 4.42 (agree).
The average ID response index value is
4.10 (quite agree or quite high category). The ID2
indicator has the highest average value, namely
4.51 (agree), and ID6 has the lowest average value,
namely 3.60 (quite agree). The average IP response
index value is 4.75 (agree or high category). The
IP4 indicator has the highest average value, namely
4.86 (agree), and IP2 has the lowest average value,
namely 4.61 (agree).
SEM-PLS Analysis
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The following are the results of the
convergent validity testing, which are presented in
Table 5:
Table 5. Convergent Validity Testing
Variables
Indicato
rs
Loadin
g
CR
CA
rho A
AVE
Availability Bias (AB)
AB1
0.717
0.88
1
0.838
0.842
0.552
AB2
0.748
AB3
0.765
AB4
0.718
AB5
0.762
AB6
0.746
Representative Bias
(RB)
RB1
0.782
0.92
4
0.902
0.906
0.671
RB2
0.811
RB3
0.812
RB4
0.775
RB5
0.869
RB6
0.861
Fear of Missing Out
(FOMO)
FOMO1
0.876
0.92
0
0.895
0.901
0.657
FOMO2
0.778
FOMO3
0.833
FOMO4
0.846
FOMO5
0.733
FOMO6
0.790
Investment Decisions
(ID)
ID1
0.790
0.92
9
0.907
0.916
0.686
ID2
0.753
ID3
0.894
ID4
0.853
ID5
0.911
ID6
0.753
Investment Performance
IP1
0.853
0.93
8
0.911
0.912
0.790
IP2
0.900
IP3
0.904
IP4
0.898
Notes: CR (Composite Reliability), CA (Cronbach’s Alpha)
Source: Author’s Calculation, 2023.
The loading factor value for each variable
indicator is > 0.70. This result means that all
indicators used for AB, RB, FOMO, ID, and IP can
be considered valid. Reliability analysis of
Cronbach's Alpha, Composite Reliability, and rho
A values for each AB, RB, FOMO, ID, and IP
variable is > 0.70. These results indicate that each
variable is considered reliable and thus meets the
requirements to be used as a research object. The
analysis results show that the Average Variant
Extracted (AVE) value for each AB, RB, FOMO,
ID, and IP is > 0.5. These results mean that each
measure of each variable is considered valid.
The following is Table 6 regarding
discriminant validity testing using the Fornell-
Larcker Criterion, which is as follows:
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Table 6. Discriminant Validity Testing Using Fornell-Larcker Criterion
Variables
AB
FOMO
ID
IP
RB
AB
0.743
FOMO
0.726
0.811
ID
0.546
0.544
0.828
IP
0.694
0.748
0.538
0.889
RB
0.707
0.763
0.447
0.722
0.819
Source: Author’s Calculation, 2023.
Table 6 shows that all the root values of
the AVE (Fornell-Larcker criterion) for each
variable are greater when compared to the
correlation values with other variables. This can be
taken as an example from the Fornell-Larcker
criterion value for the AB variable of 0.743, which
is greater than the correlation value with other
variables. This is also shown in each of the RB,
FOMO, ID, and IP variables. This means that the
conditions for the discriminant validity of the
model have been met. The following Table 7 is the
result of the goodness-of-fit model:
Table 7. Model_Fit Results
Model_Fit
Saturated
Model
Estimated Model
SRMR
0,071
0,073
Source: Author’s Calculations, 2023.
The Model_Fit results show that the
SRMR (standardized root mean square residual)
value for both the saturated model and the
estimated model is 0.071 and 0.073. This value is
less than 0.10, so it can be concluded that the
resulting model is fit. The following Table 8 shows
the results of the inner VIF value testing:
Table 8. Inner VIF Values
Variables
FOMO
ID
IP
AB
1.998
2.401
2.401
RB
1.998
2.710
2.710
FOMO
2.870
2.870
Source: Author’s Calculations, 2023.
The results of the inner VIF values show
that each independent variable used in each model
has a VIF value smaller than 5. This means that
there is no strong correlation between the
independent variables used in the first, second, and
third models, so it is concluded that all models
result in no multicollinearity. The following is
Table 9 regarding q square to measure the relevant
predictive value.
Table 9. Q Square
Variables
SSO
SSE
Q² (=1-
SSE/SSO)
AB
696.000
696.000
FOMO
696.000
405.922
0.417
ID
696.000
534.414
0.232
IP
464.000
235.699
0.492
RB
696.000
696.000
Source: Author’s Calculations, 2023.
The Q-Square values for each of the first,
second, and third models are 0.417, 0.232, and
0.492, where these values are greater than 0, so it
can be said that the three models produced have a
relevant predictive value or could predict well.
Next, below is Table 10 regarding the f
square and Table 11 regarding the r square.
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Table 10. F Square
Variables
FOMO
ID
IP
AB
0.201
0.071
0.060
RB
0.356
0.011
0.078
FOMO
0.059
0.131
Source: Author’s Calculations, 2023.
The influence of AB on FOMO and the
influence of RB on FOMO are included in the
moderate influence, considering the value is
between 0.15 and 0.35. The influence of AB on ID,
the influence of FOMO on ID, the influence of AB
on IP, the influence of RB on IP, and the influence
of FOMO on IP can be included in the weak
influence category, considering that the values are
between 0.02 and 0.15, while the influence of RB
on IP has no effect because the value is lower than
0.02.
Table 11. R Square
Variables
R Square
R Square
Adjusted
FOMO
0.652
0.645
ID
0.345
0.327
IP
0.637
0.627
Source: Author’s Calculations, 2023.
The r square value of the first model is
0.652. This means that AB and RB can explain
65.2% of the variation in the FOMO variable,
while the remaining 34.8% of the variation in the
FOMO variable is expressed by other variables,
which are not the focus of this research. The r
square value shows that the first model is
moderate.
The r square value of the second model is
0.345. This means that AB, RB, and FOMO can
explain 34.5% of the variation in the ID variable,
while the remaining 65.5% of the variation in the
ID variable is explained by other variables that are
not the focus of this research. The r square value
shows that the second model is moderate.
The r square value of the third model is
0.637. This means that AB, RB, and FOMO can
explain 63.7% of the variation in the IP variable,
while the remaining 36.3% of the variation in the
IP variable is explained by other variables that
were not studied. The r square value shows that the
third model is moderate.
The full structural model obtained based
on processing results using SmartPLS was shown
in Figure 3 below:
Figure 3. Full Structural Model
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Table 12. Hypothesis Testing
Hypothesis
Relationship
Std. β
t value
p value
Confidence
Interval
Supported
H1a
AB -> ID
0.335
2.973
0.003
0.107, 0.542
Yes
H1b
AB -> IP
0.230
2.161
0.031
0.024, 0.448
Yes
H2a
RB -> ID
-0.044
0.366
0.714
-0.273, 0.191
No
H2b
RB -> IP
0.278
2.038
0.042
0.018, 0.549
Yes
H3a
FOMO -> ID
0.334
2.695
0.007
0.075, 0.570
Yes
H3b
FOMO -> IP
0.370
2.631
0.009
0.068, 0.613
Yes
H4
AB -> FOMO
0.374
4.404
0.000
0.199, 0.535
Yes
H5
RB -> FOMO
0.498
5.718
0.000
0.323, 0.673
Yes
H6a
AB -> FOMO -> ID
0.125
2.601
0.009
0.027, 0.218
Yes (Partially)
H6b
AB -> FOMO -> IP
0.138
2.387
0.017
0.025, 0.254
Yes (Partially)
H7a
RB -> FOMO -> ID
0.166
2.182
0.029
0.034, 0.340
Yes (Fully)
H7b
RB -> FOMO -> IP
0.184
2.426
0.015
0.037, 0.336
Yes (Partially)
Source: Author’s Calculation via Bootstrapping, conducted through SmartPLS, 2023.
AB has a significant positive impact on ID,
IP, and FOMO. AB -> ID has a p value < 0.05, t
value > 1.96, and β = 0.335, which means it
supports H1a. AB -> IP has a p value < 0.05, t
value > 1.96, and β = 0.230, which means it
supports H1b. AB -> FOMO has a p value < 0.05,
t value > 1.96, and β = 0.374, which means it
supports H4.
RB has no positive and insignificant
impact on ID, but RB has a positive and significant
impact on IP and FOMO. RB -> ID has a p value >
0.05 and β = -0.044, even though the t value >
1.96, which means it does not support H2a. RB ->
IP has a p value < 0.05, t value > 1.96, and β =
0.278, which means it supports H2b. RB ->
FOMO has a p value < 0.05, t value > 1.96, and β
= 0.498, which means it supports H5.
FOMO has a significant positive impact on
ID and IP. FOMO -> ID has a p value < 0.05, t
value > 1.96, and β = 0.334, which means it
supports H3a. FOMO->IP has a p value < 0.05, t
value > 1.96, and β = 0.370, which means it
supports H3b.
FOMO partially mediates the relationship
between AB -> ID, AB -> IP, and RB -> IP. AB ->
FOMO -> ID has p value <0.05, t value > 1.96, and
β = 0.125, which means it supports H6a. AB ->
FOMO -> IP has p value <0.05, t value > 1.96, and
β = 0.138, which means it supports H6b. RB ->
FOMO -> IP has p value <0.05, t value > 1.96, and
β = 0.184, which means it supports H7b. However,
FOMO fully mediates the relationship between RB
-> ID. RB -> FOMO -> ID has p value <0.05, t
value > 1.96, and β = 0.184, which means it
supports H7a.
Discussion of the impact of AB and RB on
FOMO, then the impact of AB, RB, and FOMO on
ID and IP, as well as the role of FOMO as a
mediator between AB-RB and ID-IP, is as follows.
Availability bias (AB) has a significant
positive impact on investment decisions (ID). The
results of this research support the results of
research conducted by Jain et al. (2020), Khan
(2017), and Ikram (2016), but do not support the
results of research by Dangol & Manandhar (2020)
and Shah et al. (2018), which stated that AB has a
significant negative impact on ID, and do not
support the results of Rehan's research and Umer's
(2017), which state that AB does not have a
significant impact but is positive on ID.
Availability bias (AB) has a significant
positive impact on investment performance (IP).
The results of this research support the results of
research conducted by Tin & Hii (2020), Siraji, M.
(2019), and Alrabadi et al. (2018), but do not
support the results of research by ul Abdin et al.
(2017), which states that AB has a significant
negative impact on IP.
Representative bias (RB) has no significant
and negative impact on investment decisions (ID).
The results of this study do not support the results
of research by Jain et al. (2020), Parveen et al.
(2020), Rehan & Umer (2017), Ikram (2016),
Irshad et al. (2016), and Toma, F.M. (2015), which
state that RB has a significant positive impact on
ID, and do not support the results of research by
Dangol & Manandhar (2020) and Shah et al.
(2018), which state that RB has a significant
negative impact on ID.
Representative bias (RB) has a significant
positive impact on investment performance (ID).
The results of this research support the results of
research by Tin & Hii (2020), Siraji, M. (2019),
and Alrabadi et al. (2018), but do not support the
results of research by ul Abdin et al. (2017), which
states that RB has a significant negative impact on
ID.
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Fear of missing out (FOMO) has a
significant positive impact on investment decisions
(ID). The results of this research support the results
of research by Gupta & Shrivastava (2022) and
Kaur et al. (2023).
Fear of missing out (FOMO) has a
significant positive impact on investment
performance (IP); availability bias (AB) has a
significant positive impact on fear of missing out
(FOMO); representative bias (RB) has a significant
positive impact on fear of missing out (FOMO);
fear of missing out (FOMO) plays a role in
partially mediating the relationship between
availability bias (AB) and investment decisions
(ID); fear of missing out (FOMO) plays a role in
partially mediating the relationship between
availability bias (AB) and investment performance
(IP); Fear of missing out (FOMO) plays a role in
mediating the relationship between representative
bias (RB) and investment decisions (ID) fully, and
fear of missing out (FOMO) plays a role in
mediating the relationship between representative
bias (RB) and partial investment performance (IP).
The results of this research contribute to academic
discoveries about the variables studied, specifically
the impact of the direct relationship between the
variables AB-FOMO and RB-FOMO, which is
then related to the impact of the indirect
relationship between AB-FOMO-ID, AB-FOMO-
IP, RB-FOMO-ID, and RB-FOMO-IP.
CONCLUSIONS, IMPLICATIONS,
LIMITATIONS, AND FUTURE
RESEARCH AGENDA
According to the research findings,
availability bias has a positive and significant
effect on investment decisions and performance.
Representative bias has a negative and
insignificant impact on investing decisions, but a
positive and significant impact on investment
performance. FOMO has a significant positive
impact on investing decisions and performance.
FOMO is impacted positively and significantly by
availability bias and representative bias. FOMO
partially mediates the association between
availability bias and investment performance, then
representative bias and investment performance,
whereas FOMO fully mediates the relationship
between representative bias and investment
decisions.
It is known that the variables that have the
most significant positive impact on ID are AB,
FOMO, and RB, which are known to be
insignificant. Regarding the impact of AB on ID, it
is known that investors prefer to invest
domestically rather than abroad because the
information is easily accessible, so it can be seen
that in information that is easy to obtain, there is a
role for financial experts or stock experts who
often refer to stocks’ choice of domestic index for
investors. If these financial experts or stock
experts had referred to non-domestic stocks, the
results would have been different. Therefore,
investors should re-examine the references for
domestic stocks presented by financial experts or
stock experts. The stock reference must be
revalidated and matched with the company's
financial reports, considering future prospects and
risks. Then, investors can also compare the results
of stock reference presentations between financial
experts and stock experts so that more moderate
conclusions can be drawn. For this reason,
investors must absorb as much information as
possible from competent parties in order to
minimize future risks.
Regarding the impact of FOMO on ID, it
is known that the feeling of anxiety, worry, and
discomfort felt by investors regarding their
investment portfolio is exacerbated by irrational
decision-making, which will increase the feeling
of anxiety, worry, and discomfort itself. Therefore,
investors should decide on all forms of investment
on rational grounds to be free from feelings of
anxiety and so on. In order to make rational
decisions, investors must understand the ins and
outs of the company whose stocks they want to
buy. Both in terms of fundamentals, technical
risks, and future prospects. An investor must
diligently improve his investment abilities at all
times so that he has strong confidence, no longer
hesitates, and is afraid of future losses.
It is known that the variables with the
most significant positive impact on IP are FOMO,
RB, and finally AB. Regarding the impact of
FOMO on IP, it is known that even though the
foundation of the stock portfolio owned is not
strong, causing feelings of discomfort and worry
among investors who own it, investors still feel
satisfied with the results of their investment
performance, which is also in accordance with
their financial planning needs. It could be that
FOMO investors are still enjoying results that are
in line with their estimates, even though that
satisfaction is based on feelings of anxiety, worry,
or discomfort. Of course, the results will be more
satisfying if they are not accompanied by feelings
of anxiety, worry, or discomfort. Therefore,
continuing the researcher's suggestion on the
previous page, investors should improve their
investment abilities at all times so that they have
strong beliefs, are no longer doubtful, and are
afraid of future losses.
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Regarding the impact of RB on IP, it is
known that investors are satisfied with their
investment results, which are in accordance with
planning, because they contain stocks that have
had good fundamentals in the past. Stocks that had
good fundamentals in the past may not necessarily
be good in the future. There are various
possibilities that investors should be aware of.
Investors must always check their performance
developments, whether quarterly, semi-annually,
or annually. The aim is that if it is known that there
are things that will not be good in the future,
investors can anticipate this by rebalancing their
portfolio with other stocks that will perform better.
Investors can look at other stocks in similar
industries that have better performance.
It is known that the presence of FOMO in
the indirect relationship between AB-FOMO-ID,
AB-FOMO-IP, RB-FOMO-ID, and RB-FOMO-IP
can weaken the relationship between them. This is
caused by investors simply buying stock
recommendations without examining the
fundamentals, prospects, and risks in the future
more carefully. This happens because investors
may have limited abilities in terms of proper stock
analysis. So, as suggested above, investors must be
rational before investing, use common sense,
financial ratios, and predictive ability for future
potential and risks, and there is no harm in taking
references from financial experts or stock experts
and matching them directly to stock financial
reports and comparing them with the analysis
between financial experts and stock experts
themselves.
This research is clearly not without limits.
The goal for the future is that other scholars
working on the same issue will enhance and
perfect their work. The following research
limitations have been summarized based on the
findings of this study:
First, the number of respondents for this
study is still restricted to a few places in Indonesia.
Second, the factors investigated continue to be
confined to availability bias, representative bias,
fear of missing out, investment decisions, and
performance in investments. Many more variables,
particularly those connected to behavioral finance,
need to be investigated further in the future. Third,
men and employees of state-owned companies
tend to dominate the demographic composition of
respondents, with nearly identical demographics.
Based on the research's limitations, it is
hoped that future research will improve and refine
the findings. So, here are a few recommendations
that might be incorporated into future research
agendas on related topics:
First, future research should be more
robust regarding respondent demographics, which
are not just dominated by males and identical
occupations, such as education level, monthly
salary, flying hours in the capital market, and so
on. The number of respondents must be raised
with an equitable distribution throughout
Indonesia to derive significant results.
Second, future research could explore the
impact of additional behavioral finance factors on
investing decisions and performance using the fear
of missing out as a mediator or moderator. As an
outcome, it is possible to investigate the
significance of the impact and the role that the fear
of missing out plays in mediating or moderating
this link.
BIBLIOGRAPHY
Abel, J. P., Buff, C. L., & Burr, S. A. (2016).
Social Media and the Fear of Missing Out:
Scale Development and Assessment.
Journal of Business & Economics Research
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