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Background and aims Crypto-currency trading is a rapidly growing form of behaviour characterised by investing in highly volatile digital assets based largely on blockchain technology. In this paper, we review the particular structural characteristics of this activity and its potential to give rise to excessive or harmful behaviour including over-spending and compulsive checking. We note that there are some similarities between online sports betting and day trading, but also several important differences. These include the continuous 24-hour availability of trading, the global nature of the market, and the strong role of social media, social influence and non-balance sheet related events as determinants of price movements. Methods We review the specific psychological mechanisms that we propose to be particular risk factors for excessive crypto trading, including: over-estimations of the role of knowledge or skill, the fear of missing out (FOMO), preoccupation, and anticipated regret. The paper examines potential protective and educational strategies that might be used to prevent harm to inexperienced investors when this new activity expands to attract a greater percentage of retail or community investors. Discussion and conclusions The paper suggests the need for more specific research into the psychological effects of regular trading, individual differences and the nature of decision-making that protects people from harm, while allowing them to benefit from developments in blockchain technology and crypto-currency.
The psychology of cryptocurrency trading: Risk
and protective factors
School of Psychology, University of Adelaide, South Australia, Australia
College of Education, Psychology and Social Work, Flinders University, South Australia, Australia
Department of Psychology, University of Lethbridge, Alberta, Canada
Received: March 15, 2021 Revised manuscript received: May 17, 2021 Accepted: May 22, 2021
Background and aims: Crypto-currency trading is a rapidly growing form of behaviour characterised by
investing in highly volatile digital assets based largely on blockchain technology. In this paper, we review
the particular structural characteristics of this activity and its potential to give rise to excessive or
harmful behaviour including over-spending and compulsive checking. We note that there are some
similarities between online sports betting and day trading, but also several important differences. These
include the continuous 24-hour availability of trading, the global nature of the market, and the strong
role of social media, social influence and non-balance sheet related events as determinants of price
movements. Methods: We review the specific psychological mechanisms that we propose to be particular
risk factors for excessive crypto trading, including: over-estimations of the role of knowledge or skill, the
fear of missing out (FOMO), preoccupation, and anticipated regret. The paper examines potential
protective and educational strategies that might be used to prevent harm to inexperienced investors when
this new activity expands to attract a greater percentage of retail or community investors. Discussion and
conclusions: The paper suggests the need for more specific research into the psychological effects of
regular trading, individual differences and the nature of decision-making that protects people from harm,
while allowing them to benefit from developments in blockchain technology and crypto-currency.
crypto-currency, trading, harm, risk factors, protective factors
Cryptocurrency trading appear to be one of the fastest growing markets in the world. Surveys
conducted by major exchanges (e.g.,, 2021;Independent Reserve, 2020) suggest
that hundreds of thousands of people are signing up to exchange platforms each month. The
current global population of crypto-currency (crypto) buyers and sellers is now estimated to
be over 106 million (, 2021). So rapidly is this growth occurring that gures re-
ported as recently as three months ago are already signicant under-estimates. Growth in
retail investors (from the general population) is paralleled by growth in the cryptocurrency
market itself. Total market capitalisation (total coins x market price) has now reached 1.75
trillion $US in February 2021 after having been $550 billion in December 2020 and $275b in
June 2020. The price of Bitcoin (BTC), the leading currency, has increased in price from $9500
(June 2020) to a peak of $58,000 in February 2021, with similar exponential growth observed
in Ethereum (ETH) and numerous other altcoins(coins other than Bitcoin). This growth in
cryptocurrency value, increasing investor interest and media attention has raised some media
commentary about whether the general community might be fully aware of the risks or harms
that might be associated with this activity. This is particularly when media activity may focus
primarily on a small minority of early investors who have achieved nancial gains, largely due
Journal of Behavioral
© 2021 The Author(s)
pCorresponding author.
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to historical factors (e.g., buying Bitcoin in 2013) or holding
coins well before the onset of periodic bull cycles.
The term cryptocurrency refers to digital coinsor assets
that are based on block-chain technology. Blockchains are
distributed ledger systems where each node of the system is
linked together in a peer-to-peer network. All transactions are
systematically validated by each component of the network.
Blocks, which form the basis for the coins, are created through
an initial coin release (or ICO), can be earned through activ-
ities (e.g., in gaming, transaction activity), or mined. Mining
usually occurs through proof-of-worksystems, whereby
cryptographic algorithms generate complex random numbers
of hashes which must be solved using the application of
computer power. The different parties that contribute to this
work (or mine the blocks) receive a fraction of the block (e.g.,
part of a Bitcoin). Such systems also use proofofstake
methods based on value-added contributions to the generation
of the coins. Coins are traded on exchanges, stored privately in
wallets using keysor identiers.
Increasingly, they can be
used in tokenised economies to pay for games, to gamble, to
pay for commodities, for producing work (e.g., Anytask and
Electroneum) or secure contracts (smart contracts, Chainlink)
and to coordinate supply chains (e.g., VeChain) or as currency
(Reserve Rights or BTC) (Gainsbury & Blaszczynki, 2017;
Scholten, Zendle, & Walker, 2020).
Cryptocurrency trading has much in common with
modern trading on the share-market (Granero et al., 2012;
Kim, Hong, Hwang, Kim, & Han, 2020). It attracts both
experienced and less experienced investors; both large
institutional investors as well as small retail investors; is
subject to market uctuations; and, trading is heavily auto-
mated, with buy and sell orders set by digital trading sys-
tems. However, there a number of clear differences. First,
trading can occur 24 hours of the day and every day of the
week. Second, crypto coins are harder to value. In contrast to
mining companies that can state potential earnings (e.g.,
based on gold deposits or barrels of oil), this more difcult
with cryptocurrencies which share many similarities with
stocks traded on venture exchanges. With such stocks, there
are often only ideas for projects and few tangible assets and
realisation of the business models. Crypto projects also often
lack statements of prot margins that might be used to es-
timate future dividend returns.
Instead, future value can
only be based on whether investors believe that the coin will
attract attention based on whether it has a good reputation
and prole,
or whether it has a genuine use case(i.e.,
utility beyond trading). Investors can only look at simple
ratios such as the maximum number of coins available vs
current supply or the current total market capitalisation as
the basis for hypothetical growth.
This is based on the
knowledge that some coins have a limited maximum and
greater likelihood of price growth, whereas others with an
innite supply (e.g., DOGE coin) are inationary and lose
value as more coins are produced. Like speculative stocks,
prices can shift rapidly overnight due to sentiment changes,
celebrity endorsements, or single comments and their
popularity on platforms such as Reddit. Although there is
evidence that price rises in crypto stocks correlate with
Twitter activity and have some predictability (see Kraaije-
veld & De Smedt, 2020), many of these other extraneous
factors are unpredictable and therefore (like an unexpected
run by a horse) has a strong element of randomness or
A third difference is that the crypto market is much more
volatile. Prices for single coins can increase over 100%
percent in a matter of hours and then drop back down again
soon after (Meng & Fu, 2020). Even Bitcoin, the dominant
coin, can vary substantially in price (e.g., in February 2021 it
lost 20% of its value in 2 days) (Dahham & Ibrahim, 2020).
Markets also move in rapid cycles of boom and bust that are
more severe than observed in the standard stock-market. For
example, even in well-documented bull-runs(e.g., in 2017),
the Bitcoin price suffered multiple corrections of between 30
and 40%. Meanwhile, altcoins often display even more
extreme price movements. This creates trading opportu-
nities, but also make them higher risk. In 2017, during the
so-called crypto-bubble, some well-documented coins
(e.g., the privacy coin Verge) rose over 100,000% such that
an investment of $US100 invested at the right time would
have become over $1m in under a year. The same coin,
along with many others, also fell over 80% in the bust cycle
in 20172018 such that many altcoin holders who failed to
sell at the peak lost much of their capital gains.
A fourth difference is that crypto trading has the po-
tential create some additional uncertainties that traditional
shares often do not create. Many thousands of coins have
been produced and many have been scams, whereby coin
values have been quickly pumped(i.e., articially inated
in value through means of spreading misinformation) only
for the founders to leave the market (Kamps & Kleinberg,
2018). Crypto can also be used in criminal activities; be the
basis of operations with spurious business propositions; and
often be subject to inconsistent or knee-jerkregulations
(e.g., countries might decide to delist certain coins or ban
crypto completely such as currently been suggested in India
and Nigeria). Crypto owners can also lose their assets (the
keys to their coins) through loss of hard wallets, hard-drives,
The term proof of stake is similar to the concept of money on the tablein
business, whereby parties to a transaction demonstrate commitment and
trust by staking something of value to the business proposition.
Coins can be stored on exchanges (soft wallet) or on special USB-style
external hard drives (hard wallets).
Some crypto projects do offer what are called air-dropswhich are rewards
for holding tokens or for activity on the host platform (e.g., gambling or
playing games on certain platform that use a particular token as a cur-
The number of Google searches and Twitter search hits for a coin has been
found to correlate with future price behaviour.
If the current supply of coins is very much below the total possible supply
which can be mined or produced, then this means that the coin value will
decrease. Low market cap values are also favoured because it gives a real-
istic sense of growth. For example, a top 10 market cap will be in the order
of $1020b, so that a current market cap of $100m has a more realistic
growth potential.
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hacks launched on exchanges, or incorrect use of the tech-
A final characteristic of crypto trading which makes it
risky is that it is very difficult to engage in portfolio
balancing to protect against risk on the downside. In con-
ventional share trading, investors in speculative or high risk
or speculative stocks can also purchase more stable blue
chipshares which tend to hold their value over a longer
period. By contrast, the value of altcoins is pegged to Bitcoin
such that they usually only go up or down when BTC
changes in price (Balakrishnan, 2020). In effect, this makes
them operate more like derivatives
of BTC than indepen-
dent stocks.
As a result, if BTC suffers a major correction,
altcoin value loss will usually be even greater, so that coin
holders will have little means of creating a a balanced
portfoliowhereby there may be some hope that some coin
move against the overall market trajectory.
Crypto-trading: a form of gambling?
These many characteristics of crypto trading has led some
commentators to suggest that this new activity shares much
in common with online gambling (Gainsbury & Blaszczyn-
ski, 2017;Millar, 2018;Mills & Nower, 2019). Indeed,
similar arguments have been raised in relation to the day-
trading of shares, which appears to entail a signicant
element of luck or chance, inconsistent returns, and a like-
lihood of poor returns for most investors (e.g., Arthur &
Delfabbro, 2016;Arthur, Delfabbro, & Williams, 2015;
Arthur, Williams, & Delfabbro, 2016;Barber, Lee, Liu, &
Odean, 2009;Dorn, Dorn, & Sengmueller, 2014;Gao & Lin,
2015;Jordan & Diltz, 2003). As Arthur et al. (2015) point
out, day-trading differs from conventional long-term share
investing in that the event frequency (time between purchase
and sale) is often short. Purchases and sales may also be
more based on technical analysisthan appreciation of the
intrinsic or long-term value of the stock. Rather like race and
sports bettors studying form guides or sporting statistics, day
traders study candles, patterns, or ratios and support
levels. While these indicators can often be useful guides to
price movements, they often involve ad hoc judgments (the
price has already moved up or down) and they are not
capable of anticipating sudden market changes. As a result,
it is estimated that the majority of coin and day traders do
not make returns higher than the market and many lose
money (Melker, 2019). Few day-traders of shares last for
very long in the market, with only 7% estimated to last ve
years in the business.
Research by Arthur and Delfabbro (2016) showed that
people who gamble are signicantly more likely to engage in
day-trading. Similar ndings have emerged in relation to
crypto trading, with Mills and Nower (2019) observing, using
a sample of gamblers, that those who engaged in sports
betting and high-risk stock trading were more likely to report
crypto-trading. All of these activities were associated with a
higher risk of problem gambling, with problem gambling
found to be an independent predictor of crypto-trading after
controlling for other associated variables. The authors argued
that crypto-trading may be appealing to people who enjoy
gambling and may attract similar demographic groups or
(often younger males with higher levels of income and ed-
ucation) and people with similar personality or tempera-
ments (Conlin et al., 2015;Dixon, Giroux, Jacques, &
Gregoire, 2018;Kim et al., 2020;Kumar, 2009). These include
greater impulsivity and novelty seeking.
These emerging findings and the structural characteris-
tics described above therefore raise important questions
about the potential risks inherent in crypto-trading.
Accordingly, in this paper we examine how some of the
specific structural characteristics of this new activity might
assist in understanding how the topic might need to be
approached in psychological research. In particular, we
examine the ways in which existing insights into online
sports betting and day trading can be applied to crypto
trading. A particular focus is on whether this activity could
potentially lead to excessive behaviour and harm in some
individuals, and what specific structural characteristics are
likely to be involved. Here we outline some of the most
important psychological principles that we believe to be
central to understanding the potentially addictive elements
of this new behaviour. We then conclude the paper with
discussion of some potential protective factors that could
mitigate against the primary risk factors.
Risk factors in crypto currency trading
The illusion of control. Crypto trading, as with day trading
and sports betting, is not entirely based on chance. Skill and
strategy can make a difference to outcomes. For example,
betting on Sheffield United to win the 202122 English
Premier League or buying a cheap altcoin after a þ30% daily
surge in price during a at period of the crypto market are
both likely to be ill-advised decisions. By contrast, betting on
Manchester City and buying altcoins when the price has
recently dropped appear to be better decisions. However, all
of these activities offer many opportunities for people to
over-estimate the role that applying specic types of
knowledge or skill might play in outcomes and, conversely,
the signicant role that luck and chance are likely to play.
The illusion of control, dened as a subjective over-esti-
mation of the objective ability to exert control (Langer,
1975) is known to be a common feature of gambling (Wohl
& Enzle, 2002;Wood & Clapham, 2005). People believe that
strategies, skills, or certain rituals can increase the proba-
bility of winning. Such beliefs are known to be present in
both chance and more skilled games and appear to be
stronger in people experiencing gambling problems (Jeffer-
son & Nicki, 2003;Joukhador & Blaszczynski, 2004;Lambos
& Delfabbro, 2007).
By this, we do not imply that altcoins are derivatives in the traditional
sense; only that upward and downward movement are strongly correlated
with Bitcoin.
Altcoins move in the direction of BTC, but the rate of change can often be
much higher (e.g., 10x or 100x value during a time when BTC might
change by 4x).
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The illusion of control is likely to be a strong feature of
crypto trading and this feature is also likely to be common to
sports betting and day trading. The effect is likely to be
bolstered by other heuristics and biases, including: biased or
self-serving attributions (outcomes due to personal action
rather than external factors); hindsight bias (the outcome is
seen as being hypothesised all along); and, the hot-hand
fallacy (perceptions of predictable momentum shifts or
winning periods (Lambos & Delfabbro, 2007;Toneatto &
Ladouceur, 2003). Many of these effects are likely to be
strong during favourable market conditions. If BTC is
trending upwards, then nearly all coins will experience
growth over time. As a result, traders will rarely be wrong in
their choices and most decisions will be positively rein-
forced. Traders will therefore, often falsely, infer continency
between their actions and positive outcomes, an effect which
is known to be stronger when the probability of reinforce-
ment is high (Blanco, Matute, & Vadillo, 2011;Matute,
1996). As a result, traders may gain a sense of invincibility or
perception that they cannot lose and this may contribute to
greater risk taking, for example: speculation of large
amounts in just one speculative coin; not planning for
strategies to exit the market at the right time; or, moving
money from a more balanced portfolio towards purchasing
riskier altcoins.
Social learning and reinforcement. Crypto trading has also
emerged during the era of social media. This has led to the
emergence of a strong social media culture of crypto advi-
sors, spruikers/influencers and more experienced advisors
on platforms such as Youtube. Searching online quickly
shows that it is possible to nd at least one positive
endorsement of at least one major coin. Some of these arise
from what appear to be more experienced and well-
informed sources (e.g., the Coin Bureau, Michael van der
Poppe), but there are many others that are entirely specu-
lative, ill-informed, and potentially misleading because they
leave out key information (a coin might have a low market
cap which suggests growth potential, but only 10% of the
total supply of coins has been produced). Although similar
promotional information has historically been available
concerning conventional shares, the volume of material, the
interactivity and supercial condentiality of the informa-
tion (i.e., the sense that one is in the know rst, or is getting
some special insights) is much stronger. Promoters of
particular coins can show evidence of how much money
they have already earned from buying in very low, and they
can use graphics with great effect to show the anticipated
growth. This can serve to create a sense of urgency and a
need for immediate action. It also encourages a culture of
mutual social reinforcement in which followers of channels
seek to promote their successes, while also reading about the
gains scored by others.
Preoccupation. Preoccupation or salience is a recognised
feature of most major conceptual models of addiction (e.g.,
Browne & Rockloff, 2019;Grifths, 2005). Those who
engage excessively in a particular activity often nd it
difcult to disengage from the activity. They may continu-
ously think about the activity (preoccupation) and prioritize
the activity ahead of other important responsibilities. Crypto
trading would appear to be an activity that has the potential
to be highly absorbing. Like day-trading, it involves regular
scrutiny of price movements, news and other online media
about coin-related developments (e.g., coin burns), the
need to make regular buy and sell decisions and research
into the different coins (their potential value, market cap,
number and reputation) (Dixon et al., 2018). However,
because crypto markets operate continuously, it is possible
for people to be engaged with the activity at any hour of the
day. By contrast, sports bettors (unless they bet on many
different activities) often have to wait for matches to occur.
Day-traders can only be actively engaged with the activity in
the sense of buying and selling during daylight hours. This
creates the potential for crypto trading to absorb a consid-
erable amount of time and potentially with greater risk of
disruptions to sleep and other daily commitments.
Fear of Missing Out (FOMO)
One of the strongest psychological factors that appears to
influence crypto-trading is the fear of missing out (FOMO).
This term is often used by some experienced traders and a
style of thinking to avoid (Przybylski, Murayama, DeHaan,
& Gladwell, 2013). Although FOMO is likely to be a feature
of online sports betting (e.g., the belief that one could be
missing a good bet), the opportunities for FOMO seem
particularly intensied in crypto-trading. Traders are con-
fronted with displays of hundreds of coins. Some of them,
they already own; others they do not. If one which they have
purchased is going up rapidly, they may regret having not
made a larger investment. If another unpurchased coin is
going up which they had previously considered, they feel
annoyed for having missed out on the opportunity. Perhaps
most problematically of all is the situation, when they
observe a green screen of numbers. The market is going up
and they feel compelled to be part of the action. They
purchase a coin when it has reached a short-term peak, only
to watch the price fall soon afterwards. FOMO also applies
to sell decisions. When altcoins, in particular, have rapidly
increased in price (e.g., 10X), there is always the prospect
that the rise might continue. Instead of taking the prot, the
person starts to dream of what they might purchase if the
price increases 40X or 50X, but is then unprepared when the
price falls 3040% in single day when the bull-run ends.
FOMO is a construct that largely arose from social media
research and this is reflected in its associated measure
(Przybylski et al., 2013). In this sense, it is entirely appro-
priate to apply to crypto-trading given its strong presence in
online social networks. Not only do individual traders or
investors experience a FOMO in relation to their own ac-
tions, they are also exposed to testimonials from other
traders on social media sites that may then encourage them
to buy certain coins or to hang on to receive even larger
gains. In the notorious 100,000X ascent of Verge coin in the
201718 bull-run, many traders were discouraged from
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selling because of discussion of even greater potential prots
on social media communities. Even when the coin was
falling in value, some almost felt obliged to keep the faith
and not sell out, even though this would have been in their
individual best interest to take even a 50% or 30% prot
when the coin's value fell rapidly.
Anticipated regret
Cognitive psychology has recognised for some time that
many decisions are based on the desire to minimise antici-
pated regret (Miller & Taylor, 1995;Schwartz et al., 2002).
One of the central ndings in this area is that acts of com-
mission (doing something) usually led to stronger feelings of
regret than acts of omission (not doing something). This
asymmetry has been used to explain why people are reluc-
tant to sell shares which they have held for some time, why
they avoid hitting on 16 in blackjack (Miller & Taylor, 1995),
and why people will value lottery tickets held for a long time
as being more valuable than those held for a shorter period
(Bar-Hillel & Neter, 1995;van den Ven & Zeelenberg, 2011).
Acts of commission involving regret in trading would
include decisions where a stock or coin holding is sold, only
for it to rapidly appreciate in value. Acts of omission involve
situations where ultimately successful stocks or coins were
forgone (not purchased) for other investment decisions.
It is clear that both acts are likely to cause significant
regret in trading and that this may potentially be a risk
factor that is more strongly observed in this activity than in
most forms of gambling, with the exception of wagering. In
the gambling context, perhaps the most regretful event that
might be imagined would be a failure to buy (or to lose) a
lottery ticket in a particular week when the winning
numbers came up. Most other gaming activities, including
the higher risk ones (e.g., gaming machines, card games) are
less likely to involve situations where a person might
consider themselves to blame for missing out on a win. A
reason for this is that the games are usually of a short
duration, and decisions made in one game have no bearing
on outcomes of future games. By contrast, crypto trading
allows traders to observe the folly of their decisions over a
long period of time: what they missed out on (a coin that has
100x in value in 6 months), and what coins they sold too
early. For this reason, the act of commission and omission
effects are both likely to be very strong. However, impor-
tantly, given the very strong FOMO effects often described
as being the downfall of less experienced investors (who buy
into upwards trends), a question arises as to whether the
asymmetry between commission vs. omission based regret
observed in other contexts is as strongly observed in crypto
Protective factors and strategies
As the popularity of crypto trading rapidly increases over the
next 12 months, it is important to identify what psycho-
logical and other strategies might be used to mitigate against
the risks inherent in this new activity. How can it be kept
enjoyable, potentially productive without reducing a
person's quality of life? General strategies for avoiding harm
associated with excessive trading share some similarities
with gambling: sticking to a budget; not spending more than
can be afforded; and not chasing losses (losses are, in fact,
usually a useful tax offset; e.g., see cryptocurrency as an
investment; Similarly, it is possible to
dispel some of the erroneous beliefs associated with the
activity, including the degree to which outcomes can be
predicted or controlled. Specically, to temper over-esti-
mated perceptions of control, new investors need to be
aware that crypto value is highly correlated with BTC, so
that it is unlikely that altcoins will rise unless BTC is rising
as well. In effect, even coins based on high quality projects
with good fundamental use cases may not rise unless BTC is
stable and investors are condent about the overall health of
the market. This means that protection against downside
risk is almost more challenging than for conventional share
trading. The best a person can do is to take prots at
appropriate times, maintain some liquidity (e.g., convert
currencies into USD stable coins) and focus on investments
in projects that are likely to have greatest longevity.
Protection against social influences is also important.
Many YouTube channels and social media opinion (e.g., on
Reddit) often involves preaching to a converted audience.
Evidence is posted in support of certain coin purchases, but
often without any fundamental analysis, critical evaluation
of downsides, or consideration of opportunity cost (are there
better purchases that might be made). Such information may
be circulated rapidly and may even be exploited by bad
actors using bots or other means to amplify certain messages
to encourage buying coins to manipulate the market.
Community education around the need to seek out repu-
table and multiple information sources, including the
appropriate magnitude of investment, may therefore be
important in the near future. Inexperienced investors may
also need to be taught about the history or longer-time
horizons for this market. Many panicked in response to
negative news (e.g., the May 2021 Tweets from Tesla CEO,
Elon Musk about divesting from Bitcoin payments) even
though this would have had negligible impact on the market.
Preoccupation or salience is a difficult issue to address if
a person is a professional trader and needs to study price
movements throughout the day. However, for community or
general retail investors, it is important to recognise that
continual monitoring can be an easy habit to develop even
among casual investors. This may begin as a temptation to
check that then becomes an unconscious or automatic ten-
dency to study price movements during work, during social
activities, education, and even during the night. This activity
can disrupt sleep, productivity and be an ongoing distrac-
tion. In this way, crypto trading has the potential to combine
the financially speculative elements of gambling with social
media (e.g., Facebook update checking). Strategies for
dealing with this temptation might involve setting limits or
rules on when, or how often, the prices are checked. Other
activities might be scheduled ahead of price monitoring, so
that checking prices only occurs as a type of reward for
completing other work.
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The principal harm associated with FOMO and regret is
that people may impulsively place more money than they
can afford on risky coins that have already undertaken rapid
growth. As a result, they face a greater risk of buying into the
market when it is due for a major correction (historically
this occurs when the BTC dominance or % of total market
cap is lower). Important advice that can reduce this effect is
to encourage satisficing (Schwartz et al., 2002) in which
people are discouraged from expecting optimal or perfect
decision-making. Even experienced traders will not always
buy at the lowest price or sell at the highest price. Nor will
they pick up every coin that displays very high growth.
Instead, the aim is to encourage protective strategies: buy
before things are too high; buy the dips or corrections to
minimise downside risk; and, stay focused on the positive
outcomes rather than the outcomes that were missed.
Achievement of at least some very good outcomes in a rising
market is likely to be increased if the portfolio is spread over
a wider range of coins, but, as mentioned, cannot protect
against sudden downturns in the BTC price.
Crypto trading is a rapidly growing activity that is likely to
receive increasing mainstream acceptance over the next few
years. We believe that the topic is important in behavioral
addiction research for two reasons. The first is because it
brings together elements of risk inherent in gambling, but
also in excessive social media use. Particular features that
make this form of speculation unique include its 24-hour
availability, long-form nature, the extreme volatility of out-
comes, and the strong influence of sentiment and social in-
fluence. In this sense, it has the potential to be riskier for
inexperienced traders whose engagement in the market has
been strongly influenced by media attention or FOMO sen-
timents. A second issue is that crypto trading provides many
opportunities to examine the operation of many established
principles of social and cognitive psychology. Opportunities
exist to profile the distinct risk factors that differentiate
crypto trading from other similar activities (day trading and
online sports wagering), but also to identify protective factors
that can avoid the development of the various harms that
might arise when large numbers of inexperienced investors
enter the market. Research programs examining these factors
are likely to be influential to discussions of consumer pro-
tections and inform potential steps for regulation of trading
platforms and other activities that involve cryptocurrencies.
Funding sources: This paper was funded independently with
no support from government, industry or party external to
the University.
Authors' contribution: Initial drafting of paper (PD); Re-
visions and editing (DK, JW)
Conflict of interest: None to declare
Arthur, J. N., & Delfabbro, P. (2016). Day traders in South
Australia: Similarities and differences with traditional gamblers.
Journal of Gambling Studies,33(3), 855866.
Arthur, J. N., Delfabbro, P., & Williams, R. J. (2015). Is there a
relationship between participation in gambling activities and
participation in high-risk stock trading? Journal of Gambling
Business and Economics,9(3), 3453.
Arthur, J. N., Williams, R. J., & Delfabbro, P. (2016). The con-
ceptual and empirical relationship between gambling, investing,
and nancial market speculation. Journal of Behavioural Ad-
dictions,5(4), 580591.
Balakrishnan, A. (2020). Understanding how altcoins are tied to
tied-to-bitcoin/. Accessed 10th March 2021.
Bar-Hillel, M., & Neter, E. (1996). Why are people reluctant to ex-
change lottery tickets? Journal of Personality and Social Psy-
chology,70(1), 1727.
Barber, B. M., Lee, Y. T., Liu, Y. J., & Odean, T. (2009). Just how
much do individual investors lose by trading? Review of
Financial Studies,22 (2), 609632.
trollable seem controllable: The role of action in the illusion of
control. The Quarterly Journal of Experimental Psychology,64(7),
Browne, M., & Rockloff, M. J. (2020). Measuring behavioural
dependence in gambling: A case for removing harmful conse-
quences from the assessment of problem gambling pathology.
Journal of Gambling Studies,36(4), 10271044.
Conlin, A., Kryrolainen, P., Kaakinen, M., Jarvelin, M. R., Perrt-
tunen, J., & Svento, R. (2015). Personality traits and stock
market participation. Journal of Empirical Finance,33,3450. (2021). Measuring global crypto users. A study to
measure market size based using on-chain metrics.
Accessed 10th March 2021.
Dahham, A. Z. D., & Ibrahim, A. A. (2020). Effects of volatility and
trend indicator for improving price prediction of crypto-
currency. IOP Conference Series: Materials Science and Engi-
neering,928, article 032043.
Dixon, M. R., Giroux, I., Jacques, C., & Gregoire, P. (2018). What
characterizes excessive online stock trading? A qualitative
study. Journal of Gambling Issues,38(May), 826. https://doi.
Dorn, A. J., Dorn, D., & Sengmueller, P. (2014). Trading as
gambling. Management Science,61(10), 23762393. https://doi.
Gainsbury, S., & Blaszczynski, A. (2017). How blockchain and
cryptocurrency technology could revolutionize online gambling.
Gaming Law Review,21(7), 482492.
6Journal of Behavioral Addictions
Unauthenticated | Downloaded 06/22/21 02:13 AM UTC
Gao, X., & Lin, T. C. (2015). Do individual investors treat trading as
a fun and exciting gambling activity? Evidence from repeated
natural experiments. Review of Financial Studies,28(7), 2128
Granero, R., Tarrega, S., Fernandez-Aranda, F., Aymami, N.,
Gomez-Pena, M., Moragas, L., Orekhova, L., Savvidou, L. G., ...
enez-Murcia, L. (2012). Gambling on the stock market: An
unexplored issue. Comprehensive Psychiatry,53(6), 666673.
Grifths, M. D. (2005). A componentsmodel of addiction within a
biopsychosocial framework. Journal of Substance Use,10(4),
Independent Reserve. (2020). IRCI 2020. Cryptocurrency index.
Jefferson, S., & Nicki, R. (2003). A new instrument to measure
cognitive distortions in video lottery terminal users. The
Informational Biases Scale (IBS). Journal of Gambling Studies,
19(4), 387403.
Jordan, D. J., & Diltz, J. D. (2003). The protability of day traders.
Financial Analysts Journal,59(6), 85-95.
Joukhador, J., Blazczynski, A., & Maccallum, F. (2004). Superstit-
ious beliefs in gambling among problem and non-problem
gamblers: Preliminary data. Journal of Gambling Studies,20(2),
Kamps, J., & Kleinberg, B. (2018). To the moon: Dening and
detecting cryptocurrency pump-and-dumps. Crime Science,7,
Kim, H. J., Hong, J. S., Hwang, H. C., Kim, S. M., & Han, D. H.
(2020). Comparison of psychological status and investment
style between bitcoin investors and share investors. Frontiers in
Psychology,11, article 502295.
Kraaijeveld, O., & De Smedt, J. (2020). The predictive power of
public Twitter sentiment for forecasting cryptocurrency prices.
Journal of International Financial Markets, Institutions and
Money,65, March, Article 101188.
Kumar, A. (2009). Who gambles in the stock market? The Journal
of Finance,64(4), 18891933.
Lambos, C., & Delfabbro, P. H. (2007). Numerical reasoning ability
and irrational beliefs in problem gambling. International
Gambling Studies,7(2), 157172.
Langer, E. J. (1975). The illusion of control. Journal of Personality
and Social Psychology,32(2), 311328.
Matute, H. (1996). Illusion of control: Detecting response-outcome
independence in analytic but not in naturalistic conditions.
Psychological Science,7(5), 289293.
Melker, S. (2019). Day trading bitcoin: Why 95% of traders lose
money and fail.
bitcoin-why-95-of-traders-lose-money-and-fail. Accessed 10th
March 2021.
Meng, J., & Fu, F. (2020). Understanding gambling behavior and
risk attitudes using cryptocurrency based casino blockchain
data: Gambling behaviour and risk attitudes. Royal Society
Open Science,7, article 201446.
Millar, S. I. (2018). Cryptocurrency expands online gambling.
Gaming Law Review,22(3), 174174.
Miller, D., & Taylor, B. R. (1995). Counterfactual thought, regret
and superstition: How to avoid kicking yourself. In N. J. Roese,
& J. M. Olson (Eds.), What might have been: The social psy-
chology of counterfactual thinking (pp. 305332). Mahweh, NJ:
Lawrence Erlbaum Associates, Publishers.
Mills, D. J., & Nower, L. (2019). Preliminary ndings on crypto-
currency trading among regular gamblers: A new risk for
problem gambling. Addictive Behaviors,92(5), 136140. https://
Przybylski, A., Murayama, K., DeHaan, C. R., & Gladwell, V.
(2013). Motivational, emotional, and behavioral correlates of
fear of missing out. Computers in Human Behavior,29(4),
Scholten, O. J., Zendle, D., & Walker, J. A. (2020). Inside the
decentralised casino: A longitudinal study of actual crypto-
currency gambling transactions. Plos One, article e0240693.
Schwartz, B., Ward, A., Monteross, J., Lyubomirsky, S., White, K.,
& Lehman, D. (2002). Maximizing vertus satiscing: Happiness
is a matter of choice. Journal of Personality and Social Psy-
chology,83, 11781197.
Toneatto, T., & Ladouceur, R. (2003). The treatment of pathological
gambling: A critical review of the literature. Psychology of
Addictive Behaviors,17(4), 284292.
van den Ven, N., & Zeelenberg, M. (2011). Regret aversion and the
reluctance to exchange lottery tickets. Journal of Economic
Psychology,32(1), 194200.
Wohl, M. J. A., & Enzle, M. E. (2002). The deployment of personal
luck: Sympathetic magic and illusory control in games of pure
chance. Personality & Social Psychology Bulletin,28(10), 1388
Wood, W. S., & Clapham, M. M. (2005). Development of the drake
beliefs about chance inventory. Journal of Gambling Studies,
21(4), 411.
Open Access. This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (https://, which permits unrestricted use, distribution, and reproduction in any medium for non-commercial purposes, provided the
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... Malaysia's retail investors adoption of cryptocurrencies has expanded exponentially over the past several years, while institutional adoption is still relatively low (Suhaidi, 2022). This aligns with Delfabbro et al. (2021) who state that the growth in retail investors from the general population is paralleled by growth in the cryptocurrency market itself. This implies that the vast majority of Malaysian cryptocurrency investors are retail investors. ...
Given Malaysia's stance on cryptocurrencies as a digitalisation pioneer, as well as the surge in crime and fraud, regulators and organisations are concerned about retail investors' intentions to invest in cryptocurrency. The research model integrates diffusion of innovation theory factors (compatibility, relative advantage, trialability, ease of use, and observability) with consumer behaviour theory focused on retail investors' perceptions (perceived risk and perceived value). A total of 304 responses were collected through purposive and respondent-driven sampling. The data was examined using SmartPLS Structural Equation Modelling (PLS-SEM). The findings show that compatibility, trialability, ease of use, observability, and perceived value have significant influences on the intention to invest in cryptocurrency. Meanwhile, the influence of relative advantage and perceived risk on the intention to invest in cryptocurrency is not supported. This research is vital for analysing the underlying motivations of Malaysian retail investors to invest in cryptocurrencies and further formulating the regulatory framework.
... Malaysia's retail investors adoption of cryptocurrencies has expanded exponentially over the past several years, while institutional adoption is still relatively low (Suhaidi, 2022). This aligns with Delfabbro et al. (2021) who state that the growth in retail investors from the general population is paralleled by growth in the cryptocurrency market itself. This implies that the vast majority of Malaysian cryptocurrency investors are retail investors. ...
The adoption of cryptocurrency in Malaysia in general, as well as by investors, is still in its early adopter phase. Malaysia’s 12th Plan has highlighted the importance of digitalization adoption as Malaysia aspires to be the ASEAN leader in digitalization. Due to the increasing relevance and popularity of cryptocurrencies around the world, it is vital to investigate the factors that influence Malaysian investors’ desire to invest in them. The purpose of this study is to determine the factors that drive cryptocurrency investment among Malaysian retail investors. The research model integrates diffusion of innovation factors (compatibility, relative advantage, trialability, ease of use (complexity), and observability) and consumer behavior theory, focusing on the perception of retail investors (perceived risk and perceived value). Demographic factors are also included as control variables to understand retail investors’ decisions to invest in cryptocurrencies. This research was conducted using a quantitative approach, and a total of 304 Malaysian retail investors were collected using respondent-driven sampling. The collected data was analysed using Smart PLS Structural Equation Modelling (PLS-SEM). The findings indicate compatibility, trialability, ease of use (complexity), observability, and perceived value have an influence on the intention to invest in cryptocurrency. Meanwhile, relative advantage and perceived risk are not supported. Control variables are also found to be insignificant. This research is vital for analysing and gaining an insight into Malaysian retail investors’ underlying motives for cryptocurrency investment, which will aid in the formulation of a regulatory framework.
... Social influence is defined as the influence of an individual by others on the practice of cryptocurrency [31]. The term "social influence" refers to efforts to alter another person's thoughts, feelings, or actions, whether intended or not [39]. In contrast to persuasion's deliberate and self-aware nature, social influence can be unintentional or unintended [40]. ...
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This empirical study examines the factors influencing the adoption (AD) of cryptocurrencies in Malaysia's digital market. It is assumed that the adoption of cryptocurrencies would continue to increase. The role of the dependent variables of social influence (SI), transparency (TR), price value (PV), traceability (TRA), and attitude (AT) was examined to identify customer satisfaction as a mediator variable for cryptocurrency adoption. Random sampling was used to ensure that the research objectives were adequately examined. A total of 295 respondents answered the survey questions intended for cryptocurrency users in Malaysia. Data were analyzed using partial least squares structural equation modelling (PLS-LSM). The findings revealed that SI, PV, TRA, and AT were all im-pactful in terms of AD (dependent variable) through the mediation of customer satisfaction in Malaysia's digital market. However, TR negatively impacts Malaysia's digital market. Future researchers in other regions and industries may be able to reproduce these findings and use similar constructs to add to the present body of knowledge. This study adds to the small body of literature on Bitcoin and digital money. These findings can assist researchers in understanding the role of cryptocurrency and identifying its primary influences on the Malaysian cryptocurrency market.
... 17 Research has found that cryptocurrency traders are likely to have problem gambling symptoms, 18 and has identified psychological similarities between cryptocurrency trading and online sports betting. 19 The academic literature thus shows converging trends between gambling and both financial trading and cryptocurrencies. ...
Online gambling has increased the accessibility and range of gambling products available to people all over the world. This trend has been particularly noticeable in the UK. Cryptocurrency-based gambling is a new, largely unregulated, way to gamble online, which uses mostly anonymous blockchain-based technologies, such as Bitcoin. The present research investigates consumer protection features of 40 frequently visited cryptocurrency-based online gambling operators. Overall, 22 operators allowed access from the UK, whilst 18 operators could only be accessed via a Virtual Private Network (VPN). Results revealed significant failings in the account registration process, as none of the operators verified the identity of new users, and 35 percent of operators required only an email or no information at all for signup. Overall, 37.5 percent of operators offered no safer gambling tools and a further 20 percent offered only one. Twenty-two out of 34 operators continued to email promotional material after being informed of a user’s impaired control when gambling. Less than half of the analysed operators held a valid licence, and none of the operators with an available deposit page required identity verification before enabling deposits. These results highlight the need for greater policy and research attention towards cryptocurrency-based online gambling.
... Studies have, however, found links between cryptocurrency use and problem gambling [87,88], suggesting that this may be a fruitful area for further investigation. Furthermore, cryptocurrencies may also involve different psychological factors to those at play in more traditional areas of investment [89]. ...
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Investing and gambling share key features, in that both involve risk the coming together of two or more people, and both are voluntary activities. However, investing is generally a much better way than gambling for the average person to make long-run profits. This paper reviews evidence on two types of “gamblified” investment products where this advantage does not hold for investing: high-frequency stock trading and high-risk derivatives. This review defines a gamblified investment product as one that leads most investors to lose, that attracts people at risk of experiencing gambling-related harm, and that utilizes product design principles from gambling (either by encouraging a high frequency of use or by providing the allure of big lottery-like wins). The gamblification of investing produces novel challenges for the regulation of both financial markets and gambling.
... As a result, a significant social media culture of crypto advisors, strikers/influencers, and more seasoned advisers has emerged on channels like as YouTube. A brief web search reveals that at least one major currency has at least one good recommendation (Delfabbro, P., (2021) [36]). ...
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Purpose: The current study investigates the behavioral intention to use cryptocurrencies. The study's major goal is to prioritize the key motivations behind it mainly Investment in cryptocurrency and to learn the investors behavioral intentions. Design/Methodology: This study examines whether different factors determine the investors towards cryptocurrency usage like Ease of use, Social Impact, Convenience, Trust, Price volatility, Individual believes, Privacy, Risk and Decision making. Findings: This research's findings are intended to provide useful information on behavioral intentions of cryptocurrency users and merchants will be able to construct a viable business strategy to stay competitive. Originality: A literature review is conducted to examine the cryptocurrency usage behavior of Investors. The goal is to review the existing cryptocurrency behavior & try classifying and provide an exhaustive analysis of the determinants influencing the cryptocurrency behavioral intention of its users. Academic references, as well as essential facts and data taken from websites, scholarly articles were used in the study. Paper Type: Review Paper
... A diferencia del mercado accionario, el territorio de las criptodivisas permite realizar y consultar transacciones 7x24, lo cual constituye un factor de riesgo para la ludopatía, que se suma a la volatilidad y la alta incertidumbre del mercado. El estudio The psycology of cryptocurrency trading: Risk and protective factors (Delfabbro, P., King, D. L., & Williams, J., 2021) es uno de diversos trabajos que alertan sobre este tema. ...
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Colección de señales y tendencias reseñadas por estudiantes y egresados de la especialidad de Diseño del mañana de CENTRO.
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Background and aims Play-to-earn (P2E) gaming is a newly emerging form of gaming increasingly based on blockchain technology. In this paper, we examine the mechanics and business model of these games and their potential benefits and risks for players. Methods The paper draws upon and critically synthesises the developing published literature on predatory monetization in gaming as well as objective market data drawn from credible online sources. Results P2E gaming blurs the boundaries between gaming and trading and may not yield many of the benefits promoted to consumers or otherwise conveyed through marketing and social media messaging. Particular risks include the deflationary nature of reward currencies and the asymmetric reward structures that heavily favour early investors and exploit late adopters. Discussion and conclusions This paper highlights the need for greater consumer awareness of the mechanics and risks of these new gaming models. It will be important for business models to be more transparent and designed so as to encourage more equitable game outcomes, sustainable returns, a balance between intrinsic and extrinsic rewards, and protection for potentially vulnerable players.
This research paper revolves around several factors that affect cryptocurrency and its efciency shortly. Many great scholars have talked differently about this concept. To nd where cryptocurrency is bound to be, a survey was conducted with necessary questions and a total of 200 responses were received which in turn, helped us to analyze and interpret various economic and social factors impacting its standing in the business sector. Certain limitations and suggestions were generated at the end of the research analysis, followed by the conclusion. It also explores the users' condence in dealing with cryptocurrency in a time when the usage of such virtual cash was not fully managed and regulated. Besides, the paper is aimed to measure the spread of cryptocurrency use to have a clear photo from the practical view. The paper additionally analyses how certain remarkable international locations have responded in terms of recommendations.
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In recent years, the tendency of the number of financial institutions to include cryptocurrencies in their portfolios has accelerated. Cryptocurrencies are the first pure digital assets to be included by asset managers. Although they have some commonalities with more traditional assets, they have their own separate nature and their behaviour as an asset is still in the process of being understood. It is therefore important to summarise existing research papers and results on cryptocurrency trading, including available trading platforms, trading signals, trading strategy research and risk management. This paper provides a comprehensive survey of cryptocurrency trading research, by covering 146 research papers on various aspects of cryptocurrency trading ( e . g ., cryptocurrency trading systems, bubble and extreme condition, prediction of volatility and return, crypto-assets portfolio construction and crypto-assets, technical trading and others). This paper also analyses datasets, research trends and distribution among research objects (contents/properties) and technologies, concluding with some promising opportunities that remain open in cryptocurrency trading.
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The purpose of this research is to identify how effective the determinants of the improved price changes in cryptocurrencies are and if they are predictable. The study addresses several independent variables that are in our consideration which may impact the prices the most. To obtain the results, panel data has been used to run fixed effects models. Then we treated them as time series data to run dynamic trend indicator and first-differencing volatility regression model. Important political shocks and instabilities have been analyzed and interpreted in this paper. In the light of our findings we were able to comment on the complex relation between cryptocurrency prices and socio-political situations throughout the time range. The results address that cryptocurrency price changes are predictable. It is easy to say that major stakeholders (Apple, Amazon, Facebook, Google, Tesla) affect the most prices. Internet search trends seem to have an impact but at the end it has been found that the correlation is strong. We have evaluated all the major cryptocurrency prices with exact accuracy of 95.38% using the volatility regression model effectively. All the cryptocurrencies are evaluated against US dollars in regard of different cryptocurrency like Bitcoin, Ethereum, Litecoin and Ripple digital currency. Cryptocurrencies shouldn’t be seen as a gambling medium and should be taken more seriously like an investment medium. In some specific occasions investing in cryptocurrencies may lead lucrative income.
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Bitcoin has unique characteristics that have inspired people to invest in it as well as distinct drawbacks. With a rapid increase in Bitcoin prices in the short term, more investors enthusiastically began investing in it, raising concerns about a speculative bubble. This study investigated the multiple factors involved in the Bitcoin craze despite concerns about its shortcomings. In what concerns to personality traits and psychological states, online use patterns, and investment patterns, we first hypothesized that Bitcoin investors would show differences in multiple factors when compared to share investors. Based on our assumptions about these differences, we secondly hypothesized that investors' personality, psychological states, and investment patterns could predict whether they would invest in Bitcoin or shares. In total, 307 respondents completed the research protocol and were sorted into Bitcoin investors (n = 101), share investors (n = 102), and non-investors (n = 104). A self-report questionnaire on demographic data, online use patterns, investment patterns as well as the Fear of Missing Out (FoMO) scale, Temperament and Character Inventory-Revised-Short (TCI-RS), Mood Disorder Questionnaire (MDQ), trait anxiety part of the State-Trait Anxiety Inventory (STAI-T), and the Korean version of the Canadian Problem Gambling Index (K-CPGI) were administered. The results of this study indicated that Bitcoin investments can be attributed to the interaction of multiple factors, among which personality, psychological states, and investment patterns are particularly important. Specifically, the investment pattern is the strongest predictive factor for Bitcoin investment. Bitcoin investors were distinct with regard to higher novelty seeking, higher gambling tendencies, and unique investment patterns. Thus, personality, psychological states, and investment patterns could explain the substantial investments in Bitcoin.
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Decentralised gambling applications are a new way for people to gamble online. Decentralised gambling applications are distinguished from traditional online casinos in that players use cryptocurrency as a stake. Also, rather than being stored on a single centralised server, decentralised gambling applications are stored on a cryptocurrency’s blockchain. Previous work in the player behaviour tracking literature has examined the spending profiles of gamblers on traditional online casinos. However, similar work has not taken place in the decentralised gambling domain. The profile of gamblers on decentralised gambling applications are therefore unknown. This paper explores 2,232,741 transactions from 24,234 unique addresses to three such applications operating atop the Ethereum cryptocurrency network over 583 days. We present spending profiles across these applications, providing the first detailed summary of spending behaviours in this technologically advanced domain. We find that the typical player spends approximately $110 equivalent across a median of 6 bets in a single day, although heavily involved bettors spend approximately $100,000 equivalent over a median of 644 bets across 35 days. Our findings suggest that the average decentralised gambling application player spends less than in other online casinos overall, but that the most heavily involved players in this new domain spend substantially more. This study also demonstrates the use of these applications as a research platform, specifically for large scale longitudinal in-vivo data analysis.
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The statistical concept of gambler’s ruin suggests that gambling has a large amount of risk. Nevertheless, gambling at casinos and gambling on the Internet are both hugely popular activities. In recent years, both prospect theory and laboratory-controlled experiments have been used to improve our understanding of risk attitudes associated with gambling. Despite theoretical progress, collecting real-life gambling data, which is essential to validate predictions and experimental findings, remains a challenge. To address this issue, we collect publicly available betting data from a DApp (decentralized application) on the Ethereum blockchain, which instantly publishes the outcome of every single bet (consisting of each bet’s timestamp, wager, probability of winning, userID and profit). This online casino is a simple dice game that allows gamblers to tune their own winning probabilities. Thus the dataset is well suited for studying gambling strategies and the complex dynamic of risk attitudes involved in betting decisions. We analyse the dataset through the lens of current probability-theoretic models and discover empirical examples of gambling systems. Our results shed light on understanding the role of risk preferences in human financial behaviour and decision-makings beyond gambling.
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Behavioural dependence (BD) for gambling has traditionally been subsumed under the concept of ‘problems’: a hybrid construct that includes both indicators of BD, and adverse consequences (harm) arising from excessive time and money expenditure. Although progress has been made towards specific measurement of harm, dedicated measures of BD do not exist. Theory led us to expect that (1) dependence and harm are measurably distinct constructs, (2) harm mediates the relationship between dependence and wellbeing, and finally, that (3) separate measures should be more effective than a unidimensional problems measure in predicting wellbeing. Candidate BD items from six existing measures of gambling problems were extracted and evaluated with respect to DSM-5 criteria and content overlap, leading to 17 candidate items. This was further reduced to 8 items based on both item content and psychometric criteria, using data from an online panel of 1524 regular gamblers, with demographic characteristics similar to Australian population norms. Participants also completed measures of harm, problems, and subjective wellbeing. All three hypotheses were confirmed. BD was shown to be highly reliable and unidimensional, and measurably distinct from gambling harms. Harm mediated the negative relationship between BD and wellbeing. The harm + BD model yielded better predictions of personal wellbeing that a unidimensional, continuous problems measure—and explained about twice the variance of a simple contrast between problem and non-problem gamblers. We conclude that is psychometrically justified to specifically measure gambling BD, and this may be of particular use in theoretically-driven applications.
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Abstract Pump-and-dump schemes are fraudulent price manipulations through the spread of misinformation and have been around in economic settings since at least the 1700s. With new technologies around cryptocurrency trading, the problem has intensified to a shorter time scale and broader scope. The scientific literature on cryptocurrency pump-and-dump schemes is scarce, and government regulation has not yet caught up, leaving cryptocurrencies particularly vulnerable to this type of market manipulation. This paper examines existing information on pump-and-dump schemes from classical economic literature, synthesises this with cryptocurrencies, and proposes criteria that can be used to define a cryptocurrency pump-and-dump. These pump-and-dump patterns exhibit anomalous behaviour; thus, techniques from anomaly detection research are utilised to locate points of anomalous trading activity in order to flag potential pump-and-dump activity. The findings suggest that there are some signals in the trading data that might help detect pump-and-dump schemes, and we demonstrate these in our detection system by examining several real-world cases. Moreover, we found that fraudulent activity clusters on specific cryptocurrency exchanges and coins. The approach, data, and findings of this paper might form a basis for further research into this emerging fraud problem and could ultimately inform crime prevention.
The purpose of the present study was to investigate whether or not there is an association between engaging in traditional forms of gambling and engaging in high-risk stock trading and, if so, to examine game play patterns of high-risk stock traders, as well as identify any socio-demographic similarities or differences between the two groups. Logistic regressions on data from two large Canadian data sets were undertaken to examine which variables best differentiate traditional gamblers from high-risk stock traders. The results indicate that high-risk stock traders have a higher frequency of gambling, engage in a larger range of gambling activities, and are more likely to be problem gamblers. Additionally, the type of gambling activities that high-risk stock traders participate in suggests that they are a sub-group of skill-based gamblers who also prefer gambling on casino table games, sports betting, dog and horse race betting, and games of skill for money over chance based games such as electronic gaming machines, bingo, and instant win tickets. High-risk stock traders, compared to traditional gamblers were more likely to be male, have a higher income, be better educated, and to be of Asian or “other” descent, not be divorced, widowed or separated, and be self-employed or employed full-time. However, unlike other skill-based gamblers, high-risk stock traders tended to be older rather than younger, and had a high income rather than a low income.
Cryptocurrencies have become a very popular topic recently, primarily due to their disruptive potential and reports of unprecedented returns. In addition, academics increasingly acknowledge the predictive power of Twitter for a wide variety of events and more specifically for financial markets. This paper studies to what extent public Twitter sentiment can be used to predict price returns for the nine largest cryptocurrencies: Bitcoin, Ethereum, XRP, Bitcoin Cash, EOS, Litecoin, Cardano, Stellar and TRON. By using a cryptocurrency-specific lexicon-based sentiment analysis approach, financial data and bilateral Granger-causality testing, it was found that Twitter sentiment has predictive power for the returns of Bitcoin, Bitcoin Cash and Litecoin. Using a bullishness ratio, predictive power is found for EOS and TRON. Finally, a heuristic approach is developed to discover that at least 1-14% of the obtained Tweets were posted by Twitter “bot” accounts. This paper is the first to cover the predictive power of Twitter sentiment in the setting of multiple cryptocurrencies and to explore the presence of cryptocurrency-related Twitter bots.