How Irrationality Works in Indonesia:
A Case of Fake Investment
Tri Hendro Sigit Prakosa
STIE YKPN Yogyakarta, Jalan Seturan, 55281 Yogyakarta, Indonesia
Keywords: Optimism Bias, Overconfidence, Representativeness Bias, Confirmation Bias, Framing, Herding, Ponzi
Scheme, Investment Decision.
Abstract: In the conventional economic perspective, people or investors are assumed to behave rationally when
choosing investment alternatives to maximize their profits. Shiller (2000) showed that the Ponzi scheme is a
form of irrational exuberance in which people or investors act irrationally. This study was aimed to
investigate the impact of psychological biases (optimism bias, overconfidence, representativeness bias,
confirmation bias, framing, and herding) to investors’ decisions to get involved in a Ponzi scheme.
Regression analysis was employed to see the impact of these biases on the investment decisions. Data was
collected through questionnaire from 38 investors (victims) who lived in several rural areas in Yogyakarta.
The results of this study revealed that optimism bias, overconfidence, representativeness bias, confirmation
bias, framing, and herding behavior have significant impact on investment decisions. However this study
still has some limitations and needs further research.
1 INTRODUCTION
In the conventional economic perspective, people or
investors are assumed to behave rationally when
choosing investment alternatives to maximize their
profits. However, several studies noticed that human
decisions often depend on their nature, intuitions,
and habits, which formed their behavioral biases,
and in the financial context, these biases could lead
them to engage in financial frauds (Lewis, 2012,
296). According to Alan Greenspan, former
Chairman of the Federal Reserve, someone who get
involved in a financial fraud has ‘the foolish act.’
Greenspan (2009, p. 22) defined the foolish act as
“an act where someone goes ahead with a socially or
physically risky behavior in spite of danger signs, or
unresolved questions which should have been a
source of concern for the actor.”
Reurink (2016, 7) has divided financial fraud
into three categories: financial statement fraud,
investment scams (cons/swindles), and fraudulent
financial mis-selling. Investment scams are different
from financial statement frauds in which scams are
built on visible lies and completely fabricated facts.
A lot of scholars often use terms such as ‘investment
frauds,’ ‘Ponzi scheme,’ and ‘consumer scams’ to
depict investment scams. Shiller (2000) showed that
the Ponzi scheme is a form of irrational exuberance
in which people or investors act irrationally. A Ponzi
scheme is a fake investment scheme that offers
abnormaly high returns to investors providing that
these returns are not from actual investments or
products sales but by paying out the principal of
other investors (Gornall, 2010, 3). Royal Canadian
Mounted Police (RCMP) has identified some
characteristics of a Ponzi scheme that will
differentiate this scheme from a legal investment
plan: high-pressure sales tactics, closed-door
(secretive) information sessions and/or promotion
meetings, emphasis on recruitment rather than the
sale of a product or service, very high-yield return
within a short period of time, vague or non-specific
explanations as to the core nature of the business and
exactly how it makes money, and word-of-mouth
referrals (www.rcmp-grc.gc.ca/scams-fraudes).
This kind of financial fraud was named after
Charles Ponzi for his swindle in 1920 which
defrauded investors up to $15 million at that time.
During 2008-2013 there are more than 500 types of
Ponzi schemes in the U.S. which have collected
funds of more than $50 bilion from investors
(victims). One of the biggest frauds was done by
Bernard L. Madoff Investment Securities (BMIS),
which was regulated by the Securities and Exchange
314
Prakosa, T.
How Irrationality Works in Indonesia: A Case of Fake Investment.
DOI: 10.5220/0008492403140319
In Proceedings of the 7th International Conference on Entrepreneurship and Business Management (ICEBM Untar 2018), pages 314-319
ISBN: 978-989-758-363-6
Copyright
c
 2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

Commission (SEC) as a Registered Investment
Adviser (RIA). To keep this scheme working, the
schemer (promoter) will always looks for new
investors (victims) to ensure a steady cash inflow to
fund the schemer’s lifestyle and expenses from that
scheme.
In 2014, Otoritas Jasa Keuangan (OJK)
Indonesia (Indonesia Financial Services Authority)
has reported huge financial losses of Rp45 trillion
due to the fake investments, and these losses are
estimated to grow on the following years. Several
business formats or companies which have
organized Ponzi schemes are Dua Belas Suku,
Dream for Freedom, Panen Mas, Raihan Jewellery,
Lautan Emas Mulia, Golden Traders Indonesia
Syariah, Bina Sinar Sejahtera dan Virgin Gold
Mining Corporation. We can also find these kind of
scams on the internet which are classified as High
Yield Investment Programs (HYIPs). HYIPs usually
come with the attractive websites that promise very
high return to investors who are willing to invest
their funds into the HYIP providers. Data from
Google Trends (January 2004-January 2017) showed
that people are most likely to find HYIP providers in
Nigeria, Indonesia, Malaysia, Pakistan, Philippines,
South Africa, and Ukraine.
The main purpose of this study was to identify
the factors that are influencing investors to get
involved in the fake investment in Indonesia. As
there are limited studies about behavioral finance in
Indonesia, this study was expected to contribute
significantly to development within this field.
2 LITERATURE REVIEW
There are some psychological factors that predispose
a person to invest in this kind of scheme: optimism
bias, overconfidence, representatives bias,
confirmation bias, framing, and herding.
Optimism bias is a person's tendency to
overestimate the probability that good things will
happen and underestimate the potential for
unpleasant events. In the stock market, most
investors tend to be overly optimistic about the
markets, the economy, and the potential for positive
performance of the investments they make
(Pompian, 2006, p. 63). Then we propose the first
hypothesis (H
1
) as optimism bias influences
investment decisions.
People who have overconfidence tend to follow
their intuition and ignore some potential risks behind
the Ponzi schemes. Camerer and Lovallo (1999)
found that high risk investment instruments are most
likely to be learnt and conducted by overconfidence
investors. People who read a lot of books, read
numerous investment articles on the internet, and
even get a tip from a financial advisor often
overestimate their own predictive abilities and the
precision of the information they’ve been given to
make an investment decision. They were sure that
certain things will happen to them based on their
perceived knowledge and abilities (Pompian, 2006,
51). According to Shiller (1998), most active traders
believe that they know much more than others do,
and they, in turn, become overconfident and will
trade their stocks excessively. Pressman (1998)
stated that the main factor that drives investors to
fall down into financial fraud is overconfidence. He
also underlined that success of a Ponzi scheme was
contributed to by asymmetric information available
to investors when confronted with uncertainty or
risky situations. Based on these statements above,
we propose the second hyphotesis (H
2
) as
overconfidence bias influences investment decisions.
The representativeness bias happened to
investors (victims) because of analogical reasoning:
judging events and processes by their similarity to
other events and processes (Baddeley, 2015, 903).
Johnson (2002) said that the interpretation of new
information may use heuristic rules or stereotyping.
Some people who have received the return from an
investment scheme will be considered representative
of the conditions that will be experienced by all
investors. New members of a fake investment tend
to believe that this investment deals with ‘the law of
small numbers,’ an assumption that small samples
truly represent the whole populations (Pompian,
2006, 63). The we propose the third hypothesis (H
3
)
as representativeness bias influences the investment
decisions.
Representatives bias then bring up confirmation
bias, the tendency of a person to seek information to
support his opinion or rule out information that does
not support his opinion. Several studies proved that
people tend to put more emphasis on confirmatory
information, that is, positive or supportive data
(Pompian, 2006, 189). We propose the fourth
hypothesis (H
4
) as confirmation bias influences
investment decisions.
Framing is a tendency to make decisions based
on the information presented. A decision frame will
influence someone’s conception of the acts,
outcomes, and contingencies associated with a
particular choice (Pompian, 2006, 237). A Ponzi
scheme is often presented in a positive tone, and
provide good information to potential investors
(victims). Many Ponzi schemes were informed in
How Irrationality Works in Indonesia: A Case of Fake Investment
315


visible ways, e.g., through a website or mass media
like newspapers. In everyday life, framing bias can
influence a loss aversion feeling, and vice versa.
Suppose that a person has suffered losses, or felt
‘broke’, he/she would likely to seek risk with his/her
investments, but someone who has already gained
are more likely to invest in a sure thing. For our fifth
hyphotesis (H
5
), we propose framing bias influences
the investment decisions.
Another bias is herding behavior: a person who
follows others or mimics the behavior of groups in
making his decisions rather than decide for
him/herself. Hong et al. (2008) found that mutual
fund managers are more likely to buy stocks that
other managers in the same area are buying, which
means that social interaction among them would
create a powerful word-of-mouth effect while
selecting and choosing stocks for their portfolios.
The number of people in the neighborhood who
have already joined illegal investments will persuade
someone to join because he/she would be a part of
the ‘successful people.’ Then the Ponzi schemers
will build ‘false-trust’ to persuade participants who
are more likely to trust them. Fairfax (2001, 70)
noted that someone will pay more attention and get
attracted to somebody else if both of them are close
and have frequent contact with each other,
sometimes share values and tastes, and decide to
build an affinity link. According to Deason et al.
(2015), common religion is one of the most common
reason for people to join in the affinity links. These
links will be used by the schemers to persuade others
to engage in less-than intelligent ventures with their
emphasis on: (1) reciprocation (people tend to help
others for returning a favor); (2) commitment and
consistency (people tend to honor their
commitments); (3) social proof (people tend to
follow the lead of others they trust); (4) authority
(people tend to obey authority figures), and liking
(people can be persuaded by individuals they like)
(Jacobs & Schain, 2011, 42). Previously Granovetter
(1985, 491) reminded that “the trust engendered by
personal relations presents, by its very existence,
enhanced opportunity for malfeasance. ” We
propose the sixth hypothesis (H
6
) as herding bias
influences investment decisions.
3 RESEARCH METHODOLOGY
3.1 Sampling and Questionnaire
We did a field research on the 42 investors (victims)
who lived in some rural areas in Yogyakarta,
Indonesia during September 2016-January 2017. 38
of them agreed to answer every question that we
asked to in our questionnaire, and they were also
interviewed by us. There are two objectives of
asking questions in our study: (1) to indicate their
level of agreement with some statements using
Likert scale (strongly agree, agree, neutral/neither
agree or disagree, disagree, and strongly disagree),
and (2) to indicate their opinion about an investment
scam using completely unstructured question (What
is your opinion about the investment scam?). For
operationalization of the independent variables, we
have scored answers on the ordinal data with the
following criteria: strongly agree = 5, agree = 4,
neutral = 3, disagree =2, and strongly disagree = 1.
We developed our questionnaire based on the
study of Athur (2014) with some modification. In his
study, Athur analyzed behavioural financial factors,
both cognitive and emotional factors, and their
effects on stock investment decisions by individual
investors. Our study emphasized phenomenon on the
financial fraud, not stock market, so we need to
modify some questions and/or statements in his
work so that it would fit into our study.
3.2 Data Validity and Reliability
Before we conducted the research, we did a pretest
for our questionnaire to selected samples. This
pretest was done to ensure the relevance of the items
to the study and to test the validity and reliability of
the instruments. To ensure the reliability of the
instruments, we used the Cronbach alpha measure,
and the results are as follows: optimism bias: .678;
overconfidence bias: .753; representativeness bias:
.621; confirmation bias: .791; framing bias: .724,
and herding bias: .778. We can conclude that all
instruments meet the recommended values (>.5)
(Widodo, 2006).
3.3 Regression Equation
Y = α + β
1
X
1
+ β
2
X
2
+ β
3
X
3
+ β
4
X
4
+ β
5
X
5
+ β
6
X
6
+ ε
(1)
Notes: Y: the dependent variable, represents the
investor decision to get involve in a fake investment;
α: the constant (intercept); β
1
X
1
….…X
n
: the
predictors; ε: the error term; X
1
: optimism bias; X
2
:
overconfidence bias; X
3
: representativeness bias;
X
4
: confirmation bias; X
5
: framing bias, and X
6
:
herding behavior.
Regression analysis was done using Statistical
Packages for Social Scientists (SPSS). The β
coefficients represent the strength and direction of
the relationship between the independent (X
n
) and
ICEBM Untar 2018 - International Conference on Entrepreneurship and Business Management (ICEBM) Untar
316

dependent (Y) variables. Assuming that the error
term in the linear regression model is independent of
x, and is normally distributed, with zero mean and
constant variance, by testing the null hypothesis that
β = 0, it will be realized that there is a significant
relationship between x and y, at a 0.1 significance
level.
4 DATA ANALYSIS AND
FINDINGS
4.1 Descriptive Statistics
Our respondents has been grouped into the following
categories, age 20-25 years old: 5 respondents
(13.2%); 26-30 years old: 8 respondents (21.1%);
31-35 years old: 12 respondents (31.6%); 36-40
years old: 9 respondents (23.7%), and above 40
years old: 4 respondents (10.5%). Respondents of
this study accounted of male (M): 29 respondents
(76.3%), and female (F): 9 respondents (23.7%).
From this study we found their highest degree of
education: graduate from High School: 1 respondent
(2.6%); Bachelor: 24 respondents (63.2%); Master:
12 respondents (31.6%), and Doctor/PhD: 1
respondent (2.6%).
The respondents were asked to indicate what
encouraged them to purchase their investments.
From our study we found that the respondents were
encouraged by their friends: 36 respondents (95%),
and by themselves: 2 respondents (5%). When the
respondents were asked what the purpose of their
investment was, we found their purpose on
investment was to achieve financial freedom: 19
respondents (50%); to receive additional income: 8
respondents (21%); to have growth in income: 8
respondents (21%), and to satisfy their curiosity: 3
respondents (8%). Finally, the respondents were
asked to indicate what duration they would like their
investment to be, and we found that they would like
their investment to be maximum 6 months: 11
respondents (29%); between 6 months to one year:
15 respondents (39%), and more than one year: 12
respondents (32%).
4.2 Some Findings
4.2.1 Analysis of Investment Decision
In this study, we used the investor annual expected
return as a dependent variable. The respondents
were asked what their annual expected return from
their investment would be. We classified the
expected return based on the work of Athur (2014).
From our study, we found that their annual return
would be expected to be: between 5% and 10%: 3
respondents (8%); between 11% and 15%: 3
respondents (8%); between 16% and 20%: 15
respondents (39%), and above 20%: 17 respondents
(45%).
4.2.2 Analysis of Irrational Aspects
Table 1: The Result of Regression Analysis.
Unstandardized
Coefficients
Standardized Coefficients
Model B
Std.
Error
Beta t Sig.
(Constant) 4.339 .319 13.592 000
Optimism bias .288 168 .297 .714 .095
Overconfidence
b
ias
.189 .088 .241 2.157 .034
Representativene
ss bias
.044 .074 .078 .589 .552
Confirmation
b
ias
.144 .086 .218 1.677 .099
Framing bias .149 .077 .198 1.939 .055
Herding bias .021 .257 .006 .022 .938
*Note: dependent variable: investment decisions
The following is the regression equation:
Y = 4.339 + .288 X
1
+ .189 X
2
+ .044 X
3
+ .144 X
4
+ .149 X
5
+ .021 X
6
(2)
4.2.3 Hypotheses Testing
Table 2: Hypotheses Testing and Their Results.
No
Null
Hypothesis
Statement/Q
uestion
Sig. Result
H
1
Optimism
bias
influences the
investment
decisions
I believe
that bad
investment
will not
happen to
me
.095 Supported
H
2
Overconfiden
ce bias
influences the
investment
decisions
When it
comes to
trust my
judgments, I
can usually
rely on my
intuitive
feelings
.034 Supported
How Irrationality Works in Indonesia: A Case of Fake Investment
317


Table 2: Hypotheses Testing and Their Results. (cont.)
No
Null
Hypothesis
Statement/Q
uestion
Sig. Result
H
3
Representativ
eness bias
influences the
investment
decisions
I believe
that past
history
influences
present
investment
decisions
.552 Supported
H
4
Confirmation
bias
influences the
investment
decisions
I believe in
making my
investments
because I
have
informed
about all the
fundamental
s of the
company
.099 Supported
H
5
Framing bias
influences the
investment
decisions
The
previous
returns
generated
by the
company
made it very
attractive to
me to invest
in it
.055 Supported
H
6
Herding bias
influences the
investment
decisions
I follow an
investment
because of a
person that I
know or I
like has
joined this
investment
.983 Supported
*Note: significance level: .1 (10%)
4.2.4 Model Summary
Table 3: Model Summary.
Model R R Square
Adjusted
R Square
Std Error of
the Estimate
1 .589 .345 .48 987.5444
*Notes: predictors: Constant, Optimism Bias, Overconfidence
Bias, Representativeness Bias, Confirmation Bias, Framing Bias,
and Herding Bias
.
A multiple regression analysis of the influence of
psychological biases in the investor decisions was
made to determine the extent to which such biases
explained the investment decisions. Output of SPSS
shows that the R
2
= .345 which means that 34.5% of
the variance in investment decisions was explained
by the regression model.
5 CONCLUSIONS AND
RECOMMENDATIONS
5.1 Conclusions
This study was aimed to investigate the impact of
psychological biases (optimism bias,
overconfidence, representativeness bias,
confirmation bias, framing, and herding) towards
investor’s decisions in getting involved in a Ponzi
scheme. Regression analysis was employed to see
the impact of these biases on the investment
decisions. Data was collected through a
questionnaire given to 38 investors (victims) who
lived in several rural areas in Yogyakarta. The
results of this study revealed that optimism bias,
overconfidence, representativeness bias,
confirmation bias, framing, and herding behavior
have significant impact on investment decisions. In
other words, people who are getting involved in a
kind of investment scam i.e. Ponzi scheme has no
doubt acted irrationally, both logically and
emotionally.
5.2 Suggestions
We are still continuing this study to gain deeper
insights on the Ponzi scheme. We realize that our
study still has some limitations. First, the sample
used in our study is too small for generalization of
results. For the upcoming research we need to
include more respondents and also investigate other
psychological biases and their impacts towards
investment decisions. Second, we do not treat male
respondents and female respondents differently.
Third, we do not specifically describe cultural
impacts on the investor decisions. Fourth, since the
author has limited time and costs to do this study,
results of this study tend to look too simplistic, and
insufficient to give more insights about investment
scams in Indonesia to the readers. However, we
hope this study provides a threshold for upcoming
researchers to investigate investment scam practices,
as well as provide views on psychological and
cultural aspects on investor decision making in
Indonesia.
ICEBM Untar 2018 - International Conference on Entrepreneurship and Business Management (ICEBM) Untar
318

REFERENCES
Athur, A. Dakane. 2014. Effect of Behavioural Biases on
Investment Decisions of Individual Investors in Kenya.
Master Of Science In Finance Thesis, School Of
Business, University Of Nairobi, pp. 1-75.
Baddeley, Michelle. 2015. “Herding, Social Influences
and Behavioural Bias in Scientific Research.” EMBO
Reports, Vol 16, no. 8, pp. 902-905.
Camerer, C., and D. Lovallo. 1999. “Overconfidence and
Excess Entry: An Experimental Approach.” The
American Economic Review, Vol. 89, no.1, pp. 306-
318.
Deason, S., S. Rajgopal, and G.B. Waymire. 2015. “Who
Gets Swindled in Ponzi Schemes?” Available at SSRN
2586490.
Elan, Seth L. 2010. Behavioral Patterns and Pitfalls of
U.S. Investors. Federal Research Division, Library of
Congress, Washington DC., pp. 1-10.
Fairfax, L. 2001. 'With Friends Like These...': Toward a
More Efficacious Response to Affinity-based
Securities and Investment Fraud.” Georgia Law
Review, no. 64, pp. 63-119.
Gornall, William J. 2010. Financial Fraud: A Game of
Cat and Mouse. Master Thesis, University of Ontario,
Waterloo, Canada.
Granovetter, M. 1985. “Economic Action and Social
Structure: the Problem of Embeddedness.” American
Journal of Sociology , Vol. 91, no.3, pp 481-510.
Greenspan, Alan. 2009. “How Bernard Madoff Made off
with My Money or Why Even an Expert on Gullibility
Can Get Gulled.” Skeptic, Vol. 14, no. 2, pp. 20-25.
Hong, H. K. 2005. “Thy Neighbour's Portfolio: World-of-
Mouth Effects in the Holdings and Trades of Money
Managers.” Journal of Finance, Vol. 60, no. 28, pp. 1-
24.
Hong, H., J. D. Kubik, and J. C. Stein. 2008. “The Only
Game in Town: Stock-Price Consequences of Local
Bias.” Journal of Financial Economics, Vol. 90, no. 1,
pp. 20–37.
Jacobs, Pearl, and L. Schain. 2011. “The Never Ending
Attraction of the Ponzi Scheme.” Journal of
Comprehensive Research,Vol. 9, pp. 40-46.
Johnson, M. L. 2002. Behavioural Finance: And the
Change of Investor Behaviour During and After the
Speculation Bubble at the end of the 1990s. Master's
Thesis in Finance, Lund University.
Lewis, M.K. 2012. “New Dogs Old Tricks. Why do Ponzi
Schemes Succeed?” Accounting Forum, no. 36, pp.
294-309.
Pressman, Steven. October 1998. “On Financial Frauds
and Their Causes: Investor Overconfidence.”
American Journal of Economics and Sociology, Vol.
57, no. 4, pp. 405–421.
Reurink, Arjan. 2016. Financial Fraud: A Literature
Review. Cologne: Max Planck Institute for the Study
of Societies, pp. 1-80.
Shiller, Robert J. 1998. “Human Behavior and the
Efficiency of the Financial System.” National Bureau
of Economic Research Working Paper: W6375.
Shiller, Robert. J. 2000. Irrational Exuberance. UK:
Princeton University Press.
Widodo, P.B. 2006. “Reliabilitas dan Validitas Konstruk
Skala Konsep Diri untuk Mahasiswa Indonesia.”
Jurnal Psikologi, Universitas Diponegoro, Vol. 3, no.
1, pp. 1-17.
How Irrationality Works in Indonesia: A Case of Fake Investment
319