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Psychological barriers in the cryptocurrency market

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Abstract

Purpose The purpose of this paper is to study the existence of psychological barriers in cryptocurrencies. Design/methodology/approach To detect psychological barriers, the authors perform a uniformity test, a barrier hump test, a barrier proximity test and conditional effects test to a sample comprised by the daily closing quotes of six of the most liquid cryptocurrencies. Findings The results evidence the existence of psychological barriers in four of the cryptocurrencies under scrutiny, namely, Bitcoin, Dash, NEM and Ripple. Practical implications The fact that the cryptocurrency market has a high share of unexperienced investors and presents several cases of psychological barriers is consistent with the hypothesis that that class of investors is particularly prone to the behavioral biases which cause psychological barriers. Originality/value This paper studies, for the first time, the existence of psychological barriers in the market of cryptocurrencies.
Psychological barriers in the
cryptocurrency market
Vítor Fonseca
Faculty of Economics, Universidade do Porto, Porto, Portugal
Luís Pacheco
Department of Economics and Management,
Universidade Portucalense Infante D. Henrique, Porto, Portugal, and
Júlio Lobão
Faculty of Economics, Universidade do Porto, Porto, Portugal
Abstract
Purpose The purpose of this paper is to study the existence of psychological barriers in cryptocurrencies.
Design/methodology/approach To detect psychological barriers, the authors perform a uniformity test,
a barrier hump test, a barrier proximity test and conditional effects test to a sample comprised by the daily
closing quotes of six of the most liquid cryptocurrencies.
Findings The results evidence the existence of psychological barriers in four of the cryptocurrencies under
scrutiny, namely, Bitcoin, Dash, NEM and Ripple.
Practical implications The fact that the cryptocurrency market has a high share of unexperienced
investors and presents several cases of psychological barriers is consistent with the hypothesis that that class
of investors is particularly prone to the behavioral biases which cause psychological barriers.
Originality/value This paper studies, for the first time, the existence of psychological barriers in the
market of cryptocurrencies.
Keywords Market efficiency, Bitcoin, Psychological barriers, Cryptocurrencies
Paper type Research paper
1. Introduction
Psychological barriers are currently one of the most important topics of research in the field
of behavioral finance, with several empirical studies published since the 1990s on a variety
of financial assets, such as stock indices, single stocks, bonds, derivatives and gold, among
others. However, there is a lack of studies regarding the existence of psychological barriers
in the cryptocurrency market. This gap in the literature is surprising given the size of the
market. According to CoinMarketCap (http://coinmarketcap.com), the market capitalization
of the ten largest cryptocurrencies totaled more than US$140bn in March 2019.
Although Philip et al. (2018) defended that cryptocurrencies exhibit patterns identical to
those detected in stock markets, a question yet to be answered is whether there are
psychological barriers in those assets. In this paper we fill this gap in the literature by
examining for the first time the existence of psychological barriers on the emerging market
of cryptocurrencies. We include in our sample six of the most liquid cryptocurrencies and
apply the methodology suggested by Aggarwal and Lucey (2007) for the detection of
psychological barriers, which includes a uniformity test, a barrier proximity test, a barrier
hump test and a conditional effects test.
The results evidence the existence of psychological barriers in four of the cryptocurrencies
under scrutiny: Bitcoin, Dash, NEM and Ripple. The fact that the cryptocurrency market has a
high share of unexperienced investors and presents several cases of psychological barriers is
consistent with the hypothesis that unexperienced investors are more prone to the behavioral
biases which cause psychological barriers than professional traders. Review of Behavioral Finance
© Emerald Publishing Limited
1940-5979
DOI 10.1108/RBF-03-2019-0041
Received 8 March 2019
Revised 6 April 2019
Accepted 21 April 2019
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/1940-5979.htm
JEL Classification G14, G15, G41
Cryptocurrency
market
The remainder of this paper is organized as follows: Section 2 presents the literature
review, and Section 3 addresses the methodological aspects, namely, the data used in the
study and the various methodological steps. Section 4 discusses the empirical results.
Section 5 concludes the paper.
2. Literature review
2.1 Causes of psychological barriers
2.1.1 Behavioral biases. Psychological barriers can be seenaslimitstoarbitrageprovoked
by mental biases. For example, Mitchell (2001) pointed out that a psychological barrier can
be viewed as an impediment to an individuals mental outlook, that is, an obstacle created
by the mind, barring advance or preventing access. Accordingly, Hirshleifer (2001)
claimed that investors are affected by judgment and decision biases caused by heuristic
simplification, self-deception and emotional loss of control. Anchoring is one of those
biases and it can be defined as the phenomenon that different starting points yield
different estimates, since people tend to make estimates by starting from an initial value
and then adjust, most of the times insufficiently (Tversky and Kahneman, 1974).
Westerhoff (2003) developed a model in which investorsperception of the fundamental
value is anchored to the nearest round number. This model predicts excessive volatility,
alternation between periods of turbulence and periods of tranquility, and fluctuation of the
exchange rate around its perceived fundamental value. Because of anchoring, exchange
rates are persistently misaligned, which establishes support and resistance levels in the
limits of the fluctuation band. In other words, the perceived fundamental value acts as a
psychological barrier.
2.1.2 Aspiration levels. Sonnemans (2006) indicated that some investors, when
buying a stock, already have an idea for what price they will be able to sell the stock in the
future. This notion can be linked to aspiration levels, a concept of the psychological theory
which was introduced to the economic theory by Simon (1955), alongside the notion that
the economic agent might not pursue the optimal solution but actually settle for a
satisfactory solution.
Additionally, some financial analysts also use target prices for individual stocks and
these are also typically round numbers (Sonnemans, 2006), which will lead to many limit sell
offers being posted at round whole numbers.
2.1.3 Odd pricing. According to Sonnemans (2006, p. 1938), odd pricing is the tendency
of consumers to consider an odd price like 19.95 as significantly lower than the round price
of 20.00.This tendency could be originated by the limited amount of memory that people
have, which leads them to attach more significance to the first digits of a price as they
contain more significant information than the last digits (Brenner and Brenner, 1982).
Odd pricing is very common in consumer goods, with a number of studies (e.g.
Holdershaw et al., 1997; Folkertsma, 2002) showing that prices tend to have 9 as the last
significant digit. Additionally, Kahn et al. (1999) argued that, due to this tendency,
financial institutions would profit by quoting retail loan rates with odd-ending yields and
deposit rates with even-ending yields. The theory proposed by the authors predicts that
banks tend to set deposit rates at integers and that rates are sticky at those levels. Also,
when banks set non-integer rates, those are more likely to be just above, rather than just
below integers.
2.1.4 Option exercise prices. Dorfleitner and Klein (2009) stated that psychological
barriers could also be caused by the fact that option exercise prices are usually round
numbers. Delta hedgers are frequently most active when the price of the underlying is close
to the exercise price in other words, when the option is at the money so, purely technical
reasons can also cause additional trading activity in the underlying asset.
RBF
2.2 Previous empirical studies
Our study adds to the literature on psychological barriers by studying the phenomenon for
the first time in the market of cryptocurrencies. The first studies about psychological
barriers in financial markets took place in the 1990s. The initial research focused mainly on
stock indices of developed markets, concluding in most cases by the existence of
psychological barriers. These conclusions are shared by the studies conducted by
Donaldson and Kim (1993), Ley and Varian (1994), Koedijk and Stork (1994) and Cyree et al.
(1999), and also by the more recent study authored by Woodhouse et al. (2016).
Donaldson and Kim (1993) tested whether DJIAs movements around key reference
points exhibited psychological barriers during the period 1974-1990. The main finding is
that those movements were indeed restrained by support and resistance levels at multiples
of 100. After breaking through a 100-level, the DJIA moved by more than otherwise
warranted. Ley and Varian (1994) confirmed these findings considering a wider timer
interval (19521993). They found fewer observations around 100-levels than what is
predicted by a uniform distribution. Additionally, they argued that there was no predictive
power on the daily closing prices resulting from psychological barriers.
Koedijk and Stork (1994) expanded the research to a number of indices, considering the
stock markets from Belgium, Germany, Japan, USA and UK. The results corroborate the
conclusions from the previous studies: psychological barriers are real, but they do not
necessarily imply predictability of stock returns.
Cyree et al. (1999) concluded that the last two digits of the DJIA, the S&P 500, the
Financial Times UK Actuaries (London) and the DAX are not equally distributed. Moreover,
their findings support the existence of psychological barriers and show that these effects are
particularly pronounced when the barrier is approached in an upward move.
Finally, Woodhouse et al. (2016) found evidence of psychological barriers at the 100-level
in the NASDAQ Composite index in the period 19902012.
Some other studies focusing on European stock markets like those conducted by
Dorfleitner and Klein (2009) and Shawn and Kalaichelvan (2012) found mixed results.
Dorfleitner and Klein (2009) analyzed the German DAZ 30, the French CAC 40, the
British FTSE 50 and the Euro-zone related DJ EURO STOXX 50 for different periods until
2003. They found fragile traces of psychological barriersin all indices at the 1,000s barrier
level and also indications of barriers at the 100 barrier level, except in the CAC 40. However,
the authors did not find any systematic barrier effect for any barrier level, thus concluding
that there are no consistent barriers in European stock indices.
Shawn and Kalaichelvan (2012) examinedfive indices (ATX, CAC, DAX, FTSE, SMI) in the
period 20012011, having found evidence for barriersin only one index (SMI) at the 1,000-level
but no significant evidence of barriers at the 100 and 1,000 levels in the other indices.
Bahng (2003) and Lobão and Fernandes (2018) carried out two of the few studies
regarding the existence of psychological barriers in emerging stock markets.
Bahng (2003) conducted the first study about the topic on Asian stock markets, using the
daily prices of seven indices (South Korea, Taiwan, Hong Kong, Thailand, Malaysia,
Singapore and Indonesia) from the beginning of 1990 to the end of 1999. The Taiwanese
index and the Indonesian index exhibited some signs of psychological barriers.
Lobão and Fernandes (2018) found no consistent psychological barriers in individual
stock prices near round numbers in the markets of Taiwan, Brazil and South Africa in the
period 20002014. The authors also documented that the relationship between risk and
return tended to be weaker in the proximity of round numbers for about half of the stocks
under study.
Different studies concluded that price barriers or at least significant deviations from
uniformity also exist in other asset classes such as bonds (Burke, 2001), commodities
(Aggarwal and Lucey, 2007) and derivatives (Schwartz et al., 2004).
Cryptocurrency
market
The present study also contributes to the emerging literature about the price dynamics of
cryptocurrencies. For example, Dwyer (2015) documented a significant difference between
the pattern of volatility in Bitcoin and that observed in gold and other currencies while
Urquhart (2016) and Barriviera (2017) found signs of inefficiency in the market of that
cryptocurrency. Fry and Cheah (2016) showed that cryptocurrency markets contain a
considerable speculative component, and Cheah and Fry (2015) and Corbet et al. (2018)
concluded that Bitcoin and Ethereum exhibited speculative bubbles.
Finally, our paper relates to the literature on price clustering in cryptocurrencies.
Mitchell (2001) draws a distinction between psychological barriers and price clustering.
While price barriers regard some numbers as support or resistance levels, price clustering is
the concentration of prices on some numbers rather than others.
Urquhart (2017) found significant evidence of clustering of daily Bitcoin closing prices on
round numbers and Hu et al. (2019) concluded that the price clustering also existed in the
intraday prices of cryptocurrencies Bitcoin, Litecoin and Ripple. Xin et al. (2019) found
significant clustering for open, high and low Bitcoin prices at various time frames, suggesting
that that can be explained by the existence of psychological barriers. Some authors have tried
to shed light on the causes of price clustering. For example, Mbanga (2019) showed that the
clustering of Bitcoin was not driven by any day-of-the-week and Baig et al. (2019) documented
the existence of a strong positive association between sentiment and price clustering.
3. Data and methodology
3.1 Data
The sample considered in the study includes six of the most liquid cryptocurrencies, namely,
Bitcoin, Dash, Ethereum, Litecoin, NEM and Ripple, which, at the end of March 2019, and
according to CoinMarketCap, account for nearly 85 percent of the cryptocurrenciesmarket
capitalization. We collected the daily closing quotes for each asset for the period between the
first trading day of the asset and December 31, 2017 from CoinMarketCap.
Table I presents the descriptive statistics of the assets included in the sample. The table
shows that all the studied cryptocurrencies yielded positive mean returns in the period
under analysis.
3.2 Methodology
3.2.1 Definition of barriers. Following Dorfleitner and Klein (2009), we will use the so-called
band technique and define barriers as an interval between two numbers at the same
distance from the number which constitutes the actual barrier. The main reason for this
technique is the idea that market players will become more active at a certain level before
the price touches a round number. Dorfleitner and Klein (2009) defined the barrier level las
the number of zeros that a barrier has; we will follow the same definition, also introducing
Return series Level series
Asset Start date End date nMean SD Skewness Kurtosis Min. Max.
Bitcoin April 28, 2013 December 31,
2017
1709 0.2727 4.4018 0.1384 8.9278 68.43 19 497.40
Dash February 14,
2014
1417 0.5608 8.5917 3.1572 41.6881 0.31 1550.85
Ethereum August 7, 2015 878 0.6397 8.5247 3.7296 64.9273 0.43 826.82
Litecoin April 28, 2013 1709 0.2328 6.9101 1.8842 26.9030 1.16 358.34
NEM April 1, 2015 1006 0.8315 9.3127 2.0248 16.8800 0.00 1.06
Ripple August 4, 2013 1611 0.3707 7.9535 2.2587 29.2155 0.00 2.30
Table I.
Descriptive statistics
RBF
the barrier levels l¼1 and 2 for the 0.1-level and 0.01-level barriers, respectively. We
define as potential barriers the multiples of 0.01, 0.1, 1, 10, 100 and 1,000 and define intervals
with an absolute length of 2, 5 and 10 percent to the corresponding barriers, thus
considering the following restriction bands:
Barrier level l¼31;000sðÞ:9801;020;9501;050;9001;100
Barrier level l¼2 100sðÞ:98102;95105;90110
Barrier level l¼1 10sðÞ:9:810:2;9:510:5;9:011:0
Barrier level l¼01sðÞ:0:981:02;0:951:05;0:901:10
Barrier level l¼10:1sðÞ:0:0980:102;0:0950:105;0:0900:110
Barrier level l¼20:01sðÞ:0:00980:0102;0:00950:0105;0:00900:0110:
For each cryptocurrency, we then examine the barrier levels which are susceptible of
constituting psychological barriers.
3.2.2 M-values. The concept of M-values was introduced by Donaldson and Kim (1993),
who considered potential barriers at the levels , 300, 400, , 3,400, 3,500, , i.e. at:
kX100;k¼1;2; ::: (1)
De Ceuster et al. (1998) found two problems with this approach: not only it was too narrow,
as the series was not multiplicatively regenerative, leading to 3,400 being considered a
barrier while 340 was not, for instance, but also the gap between barriers, as defined by
Equation (1), would tend to zero as the price series increased, intuitively reducing the
probability of those levels to represent psychological barriers. Thus, the authors claim we
should consider the possibility of barriers at the levels , 10, 20, , 100, 200, , 1,000,
2,000, , i.e. generally at:
kX10l;k¼1;2;...;9;l¼:::; 1;0;1;...;(2)
and also at the levels , 10, 11, , 100, 110, , 1,000, 1,100, , i.e. generally at:
kX10l;k¼10;11;...;99;l¼:::; 1;0;1;... (3)
The M-values we will use in our study can now be defined according to these barriers:
Mk¼Pt100
k

mod 100;k¼0:01;0:1;1;10;100;1;000;(4)
where Pt100
ðÞ
=k
ðÞ

is the integer part of Pt100
ðÞ
=k
ðÞ
, and mod 100 is the reduction
modulo of 100.
Illustrating this with a purely theoretical quote of 1,234.56789, the M0.01 is 78, the M0.1 is
67, the M1 is 56, the M10 is 45, the M100 is 34 and the M1000 is 23.
3.2.3 Uniformity test. After defining the M-values, the next step is to examine if they
follow a uniform distribution, through the uniformity test introduced by Ley and Varian
(1994), which consists of a KolmogorovSmirnov Z-statistic test where we will be testing: H
0
uniform distribution against H1 non-uniform distribution. In the presence of
psychological barriers, it is expected to reject the null hypothesis. However, it is important to
underline that this rejection does not by itself confirm the existence of such barriers.
Cryptocurrency
market
Additionally, De Ceuster et al. (1998) stated that, as the series grows, the interval between
barriers widens and, as a result, the distribution of digits and their frequency of occurrence
tends to stop being uniform.
3.2.4 Barrier tests. We will perform two barrier tests, a barrier proximity test and a
barrier hump test. The purpose of these tests is to assess if the series observations on or
near a barrier occur less frequently than what would be predicted by a uniform distribution,
examining the shape of the M-values distribution.
A barrier proximity test examines the frequency of M-values in the proximity of
potential barriers, applying the following equation:
fMðÞ¼aþbDþe;M¼00;01;...;99;(5)
where f(M) is defined as the frequency with which a quote closes with its last two digits in
cell M, minus 1 percentage point, and Dis a dummy variable which takes the value 1 if the
price of the asset is at the potential barrier and 0 elsewhere. Besides the strict dummy, which
takes the value 1 if M¼00 and takes the value 0 otherwise, we will study 3 dummies for
each potential barrier level:
D9802 ¼1ifMX98 or Mp02;¼0 otherwise;
D9505 ¼1ifMX95 or Mp05;¼0 otherwise;
D9010 ¼1ifMX90 or Mp10;¼0 otherwise:
The βcoefficients are expected to be negative and statistically significant in the presence of
psychological barriers.
The barrier hump test examines the entire shape of the distribution of M-values and is
broader than the barrier proximity test as it does not focus solely on the proximity of the
potential barriers. We will implement this test using the following equation, which was
introduced by Bertola and Caballero (1992):
fMðÞ¼aþgMþdM2þe;M¼00;01;...;99;(6)
where f(M) is once again defined as the frequency with which a quote closed with its last
two digits in cell M, minus 1 percentage point, and the independent variables are the M-
value and its square.
In the presence of psychological barriers, the M-values are expected to follow a hump-
shape distribution, which will be reflected in Equation (6) through negative and statistically
significant δ, whereas under the null hypothesis of no barriers, δshould be zero and the M-
values should follow a uniform distribution.
3.2.5 Conditional effects test. The final test of our methodology was introduced by Cyree
et al. (1999) and is designed to detect changes in the conditional mean and variance of the
distribution of returns during the sub-periods before and after crossing a barrier, either from
above or below. We use a five-day window before and after crossing a barrier.
In order to identify if a barrier is crossed in an upward or downward movement and
examine the difference in returns between the five-day periods before and after the barrier is
crossed, we will use four dummy variables: UB for the five-day period before prices cross a
barrier on an upward movement, UA for the five-day period after prices cross a barrier on
an upward movement, DB for the five-day period before prices cross a barrier on a
downward movement and DA for the five-day period after prices cross a barrier on a
downward movement. Each of these dummies will take the value 1 on the identified days
and the value 0 elsewhere. Taking into account, as stated by Cyree et al. (1999), that the
distributional shifts implied by psychological barriers invalidate the basic assumptions of
RBF
OLS, we will then regress the following equations using a GARCH (1,1) model:
Rt¼b1þb2UBtþb3UAtþb4DBtþb5DAtþet;(7)
eteN0;Vt
ðÞ;(8)
Vt¼a1þa2UBtþa3UAtþa4DBtþa5DAtþa6Vt1þa7e2
t1þZt:(9)
In the absence of barriers, it is expected that the coefficients of the indicator variables will take the
value zero both in the mean and variance equations, whereas any coefficient significantly
different from zero (either positive or negative) will indicate the presence of psychological barriers.
The four null hypotheses to be tested using a Wald test are the following:
H1. There is no significant difference in the conditional mean return before and after an
upwards crossing of a barrier.
H2. There is no significant difference in the conditional mean return before and after a
downwards crossing of a barrier.
H3. There is no significant difference in the conditional variance before and after an
upwards crossing of a barrier.
H4. There is no significant difference in the conditional variance before and after a
downwards crossing of a barrier.
4. Empirical results
4.1 Uniformity test
Table II shows the results of the uniformity tests for each cryptocurrency, using a
KolmogorovSmirnov Z-test. Overall, the studied financial assets show signs of psychological
M0.01 M0.1 M1 M10 M100 M1000
Bitcoin
Z-stat. ––1.900118 1.095154 3.199988 6.327789
p-value ––0.0015*** 0.1815 0.0000*** 0.0000***
Dash
Z-stat. 0.111838 0.026889 0.172266 0.186074 0.264230
p-value 0.0000*** 0.2538 0.0000*** 0.0000*** 0.0000***
Ethereum
Z-stat. 5.269663 2.057020 3.405512 7.523218
p-value 0.0000*** 0.0004*** 0.0000*** 0.0000***
Litecoin
Z-stat. ––3.569276 14.03169 15.61957
p-value ––0.0000*** 0.0000*** 0.0000***
NEM
Z-stat. 8.549875 13.32748 17.56190 ––
p-value 0.0000*** 0.0000*** 0.0000*** ––
Ripple
Z-stat. 0.215958 0.631059 0.810789 ––
p-value 0.0000*** 0.0000*** 0.0000*** ––
Notes: The results of a KolmogorovSmirnov test for uniformity. Z-stat. stands for the value of the test
statistic; p-value shows the marginal significance of this statistic. H
0
: uniformity; H1: non-uniformity.
*,**,***Significant at the 10, 5 and 1 percent levels, respectively
Table II.
Uniformity test results
Cryptocurrency
market
barriers, as there are statistically significant evidence at a 1 percent significance level that M-
values do not follow a uniform distribution at, at least, three barrier levels for all the six assets.
Additionally, the Ethereum, Litecoin, NEM and Ripple cryptocurrencies reject uniformity at a
5 percent significance level for every potential barrier level.
4.2 Barrier tests
4.2.1 Barrier proximity test. Table IIIVI show the results of the barrier proximity
tests performed on the selected cryptocurrencies. As previously mentioned, in the
presence of a psychological barrier, it is expected that βis negative and significant,
which means that there is a lower frequency on the M-values which constitute the
potential barrier.
Testing for psychological barriers in cryptocurrencies, when we consider a barrier to
be in the exact zero modulo point (Table III), we find no negative and significant
βestimates for any asset or any barrier level, even though we find negative (but not
significant) estimates for all assets except NEM. When we assume a barrier in the 98-02
interval (Table IV), Bitcoin and Dash present negative estimates for βfor the 1,000-level
potential barrier, both significant at a 10 percent level. Widening the interval to 95-05
(Table V), we find 9 negative and significant βestimates: Bitcoin at the 100 and 1,000-level
barriers, Dash at the 1-, 10-, 100- and 1,000-level barriers, Litecoin at the 10- and 100-level
barriers and Ripple at the 0.01-level barrier. Finally, considering a barrier to be in the 90-10
interval (Table VI), we find the same negative and significant estimates for βof the
previous table except for Dash at the 1-level barrier, leading to a total of eight negative
and significant βestimates. Once again, as we widen the barrier intervals some estimates
change signal, becoming positive with the enlargement of the intervals, namely, for
Ethereum at the 10- and 100-level barriers, Litecoin at the 1-level barrier and Ripple at the
0.1-level barrier.
Summing up, we are not able to reject the no-barrier hypothesis for Ethereum and NEM
at any potential barrier level; Bitcoin presents some signs of the existence of a psychological
barrier around the 100- and 1,000-level round numbers; Dash presents some signs of the
existence of a psychological barrier around the 1-, 10-, 100- and 1,000-level round numbers;
Litecoin presents some signs of the existence of a psychological barrier around the 10 and
100-level round numbers and the same happens with Ripple at the 0.01-level round numbers.
4.2.2 Barrier hump test. The barrier hump test examines the entire shape of the
distribution of M-values. As mentioned before, in the presence of barriers, these values are
assumed to follow a hump-shape distribution, and thus δis expected to be negative and
significant in the presence of such barriers. Table VII shows the results of this test on the
selected cryptocurrencies, which seem to corroborate most of the results obtained from the
barrier proximity test.
We find negative and significant δestimates for Bitcoin at the 100- and 1,000-level,
Dash at the 10-level, Litecoin at the 10- and 100-level and Ripple at the 0.01-level, leading to
a total of six potential barriers. This means that the barrier hump test does not detect
signs of psychological barriers in three of the nine barrier levels which, according to the
proximity test, exhibited some signs of psychological barriers, namely, Dash at the 1-, 100-
and 1,000-level. The results also corroborate the absence of psychological barriers for
Ethereum and NEM.
Summing up, at this point of our battery of tests, we have found consistent signs of the
existence of psychological barriers around round numbers for four of the six selected
cryptocurrencies Bitcoin, Dash, Litecoin and Ripple. We also observe that, as we widen the
barrier intervals, the existence of evidence supporting psychological barriers tends to
become more frequent and also more significant.
RBF
M0.01 M0.1 M1 M10 M100 M1000
Cryptocurrency β
p-
value R²β
p-
value R²β
p-
value R²β
p-
value R²β
p-
value R²β
p-
value R²
Bitcoin –––– – –0.8221 0.0017*** 0.0963 0.1235 0.6000 0.0028 0.1826 0.5887 0.0039 0.6555 0.4233 0.0066
Dash ––8.5420 0.0030*** 0.0861 0.1546 0.5953 0.0029 0.3685 0.6301 0.0024 0.6536 0.4320 0.0063 0.9388 0.3446 0.0091
Ethereum ––1.9811 0.0000*** 0.2638 0.0253 0.9440 0.0001 0.0897 0.8709 0.0003 0.6649 0.6899 0.0016 ––
Litecoin –––– – –0.4782 0.1363 0.0225 0.8919 0.4467 0.0059 0.8919 0.4781 0.0051 ––
NEM 2.3033 0.3506 0.0089 33.2289 0.0000*** 0.8285 69.3757 0.0000*** 0.9918 ––––––––
Ripple 0.0069 0.9926 0.0001 0.6966 0.8064 0.0006 64.9506 0.0000*** 0.9596 ––––––––
Notes: The results of a barrier proximity test using the regression f(M)¼α+βD+ε, where the dependent variable is the frequency of appearance of M-values, minus 1 percentage
point, and ð·is a dummy variable that takes the value 1 if M¼00 and 0 otherwise (see Section 3.2.4. for further details). H
0
:β¼0; H1:βo0. *,**,***Significant at the 10, 5 and 1
percent levels, respectively
Table III.
Barrier proximity test
results for the strict
dummy
Cryptocurrency
market
M0.01 M0.1 M1 M10 M100 M1000
Cryptocurrency β
p-
value R²β
p-
value R²β
p-
value R²β
p-
value R²β
p-
value R²β
p-
value R²
Bitcoin ––––0.3271 0.0065*** 0.0731 0.0455 0.4290 0.0064 0.1657 0.2815 0.0118 0.6584 0.0762* 0.0317
Dash ––0.9382 0.4850 0.0050 0.1166 0.3798 0.0079 0.5029 0.1481 0.0212 0.5178 0.1714 0.0190 0.8001 0.0760* 0.0318
Ethereum ––0.5539 0.0014*** 0.0990 0.0024 0.9884 0.0001 0.2182 0.3858 0.0077 0.1175 0.8773 0.0002 ––
Litecoin ––––0.1287 0.3815 0.0078 0.6708 0.2089 0.0161 0.8555 0.1344 0.0227 ––
NEM 5.4138 0.0000*** 0.2358 8.6157 0.0000*** 0.2672 14.705 0.0000*** 0.2138 ––––––– –
Ripple 0.4515 0.1820 0.0181 0.8174 0.5283 0.0041 16.223 0.0000*** 0.2873 ––––––– –
Notes: The results of a barrier proximity test using the regression f(M)¼α+βD+ε, where the dependent variable is the frequency of appearance of M-values, minus 1 percentage point,
and ð·is a dummy variable that takes the value 1 if M98 or M02 and 0 otherwise (see Section 3.2.4. for further details). H
0
:β¼0; H1:βo0. *,**,***Significant at the 10, 5 and 1
percent levels, respectively
Table IV.
Barrier proximity test
results for the 98-02
dummy
RBF
M0.01 M0.1 M1 M10 M100 M1000
Cryptocurrency βp-value R²βp-value R²βp-value R²βp-value R²βp-value R²βp-value R²
Bitcoin ––––0.1196 0.1589 0.0201 0.0717 0.3379 0.0094 0.2271 0.0326** 0.0458 0.6813 0.0078*** 0.0700
Dash ––0.1216 0.8967 0.0002 0.2009 0.0282** 0.0482 0.4892 0.0423** 0.0414 0.5613 0.0321** 0.0460 0.8208 0.0083*** 0.0689
Ethereum –– –0.2143 0.0821* 0.0305 0.0184 0.8726 0.0003 0.1910 0.2752 0.0121 0.0416 0.9374 0.0001 ––
Litecoin ––––0.0418 0.6839 0.0017 0.6454 0.0814* 0.0307 0.9742 0.0134** 0.0607 ––
NEM 2.5418 0.0009*** 0.1071 5.5067 0.0000*** 0.2250 6.6845 0.0023*** 0.0911 ––––– ––
Ripple 0.4705 0.0446* 0.0405 1.5457 0.0849* 0.0300 7.5502 0.0003*** 0.1282 ––––– ––
Notes: The results of a barrier proximity test using the regression f(M)¼α+βD+εwhere the dependent variable is the frequency of appearance of M-values, minus 1 percentage point, and ð·is
a dummy variable that takes the value 1 if M95 or M05 and 0 otherwise (see section 3.2.4. for further details). H
0
:β¼0; H1:βo0. *,**,***Significant at the 10, 5 and 1 percent levels,
respectively
Table V.
Barrier proximity test
results for the 95-05
dummy
Cryptocurrency
market
M0.01 M0.1 M1 M10 M100 M1000
Cryptocurrency βp-value R²βp-value R²βp-value R²βp-value R²βp-value R²βp-value R²
Bitcoin ––0.0243 0.7108 0.0014 0.0455 0.4290 0.0064 0.1830 0.0247** 0.0504 0.6698 0.0006*** 0.1147
Dash ––0.6314 0.3795 0.0079 0.0620 0.3831 0.0078 0.5342 0.0035*** 0.0836 0.4576 0.0227** 0.0518 0.6575 0.0058*** 0.0750
Ethereum ––0.0454 0.6336 0.0023 0.0730 0.4069 0.0070 0.4093 0.0019*** 0.0944 0.4642 0.2528 0.0133 ––
Litecoin ––0.1097 0.1620 0.0199 0.7932 0.0048*** 0.0785 0.9872 0.0010*** 0.1057 ––
NEM 1.2327 0.0392** 0.0424 3.3898 0.0001*** 0.1445 3.4257 0.0446** 0.0405 ––––––
Ripple 0.6073 0.0006*** 0.1144 3.0857 0.0000*** 0.2025 3.8714 0.0166** 0.0571 ––––––
Notes: shows the results of a barrier proximity test using the regression f(M)¼α+βD+εwhere the dependent variable is the frequency of appearance of M-values, minus 1 percentage point,
and ð·is a dummy variable that takes the value 1 if M90 or M10 and 0 otherwise (see Section 3.2.4. for further details). H
0
:β¼0; H1:βo0. *,**,***Significant at the 10, 5 and 1 percent levels,
respectively
Table VI.
Barrier proximity test
results for the 90-10
dummy
RBF
M0.01 M0.1 M1 M10 M100 M1000
Cryptocurrency δp-value R²δp-value R²δp-value R²δp-value R²δp-value R²δp-value R²
Bitcoin ––––1.11E-05 0.7567 0.0052 3.78E-05 0.2298 0.0154 8.72E-05 0.0398** 0.1445 0.0004 0.0001*** 0.1985
Dash ––6.67E-05 0.8660 0.0031 2.53E-05 0.5153 0.0105 0.0002 0.0624* 0.1104 2.55E-05 0.8136 0.0636 0.0001 0.3873 0.1311
Ethereum –––– –2.62E-05 0.5644 0.1177 0.0004 0.0000*** 0.3347 0.0006 0.0038*** 0.0939 ––
Litecoin ––––0.0001 0.0006*** 0.2150 0.0005 0.0003*** 0.2485 0.0006 0.0001*** 0.2898 ––
NEM 0.0007 0.0225** 0.1302 0.0019 0.0000*** 0.2641 0.0022 0.0145** 0.1046 ––––
Ripple 0.0005 0.0000*** 0.3389 0.0013 0.0001*** 0.3081 0.0024 0.0050*** 0.1312 ––––
Notes: The results of a barrier hump test using the regression f(M)¼α+γM+δM
2
εwhere the dependent variable is the frequency of appearance of M-values, minus 1 percentage point, regressedto the said
M-values and the respective squares (see Section 3.2.4. for further details). H
0
:δ¼0; H1:δo0. *,**,***Significant at the 10, 5 and 1 percent levels, respectively
Table VII.
Barrier hump
test results
Cryptocurrency
market
4.3 Conditional effects test
Tables VIIIX present the results of the conditional effects test, where we examine the
behavior of the selected cryptocurrenciesprices in the five-day periods before and after
crossing a barrier from below (thus, constituting a potential resistance level), and also in the
five-day periods before and after crossing a barrier from above (thus, constituting a
potential support level). We assess if the return series exhibit a pattern on these days which
is significantly different from that when prices are not in the proximity of any barrier.
We perform this test for one potential barrier level only for each asset, chosen as the most
likely to constitute an actual barrier level, according to the results from the previous tests.
Therefore, this test is applied to the 0.1-level barrier for NEM and Ripple, to the 10-level barrier
for Ethereum and Litecoin and to the 100-level barrier for Bitcoin and Dash. The results of the
mean return equation are shown in Table VIII. We may observe that the mean return after
crossing a barrier from below is positive for all the six cryptocurrencies and significant at a 5
percent level for all of them except Litecoin while before crossing a barrier in such movement
it is positive for all cryptocurrencies but only significant at a 5 percent level for two of them:
Bitcoin and Ripple. Still regarding the upward movements, the results show that the
magnitude of returns is higher after crossing a barrier for all assets except Litecoin. The
crossing of a barrier from below does not originate a change in the signal of the mean returns
and regarding crossings from above we observe that the mean return is negative for all
assets after crossing a barrier in such movement significant at a 5 percent level for all
cryptocurrencies but Ripple and also negative before crossing the barrier for four
assets Dash and Ripple being the exceptions. Curiously, there are no negative and significant
mean returns for any of the six studied cryptocurrencies in thefive-day periods before crossing
CUBUADBDA
Bitcoin
Coefficient 0.0473 0.6532 1.4683 0.1983 1.7376
p-value 0.5462 0.0131** 0.0000*** 0.5782 0.0000***
Dash
Coefficient 0.1795 0.8628 2.5630 0.6653 3.0760
p-value 0.2156 0.4316 0.0268** 0.0559* 0.0000***
Ethereum
Coefficient 0.3179 0.2525 1.3925 0.1249 1.6333
p-value 0.1601 0.6747 0.0049*** 0.8291 0.0023***
Litecoin
Coefficient 0.1418 2.2870 1.3822 0.6813 1.8873
p-value 0.3025 0.0908* 0.1519 0.5085 0.0086***
NEM
Coefficient 0.3632 1.0636 5.9505 1.2022 5.1457
p-value 0.1155 0.1095 0.0106** 0.0937* 0.0062***
Ripple
Coefficient 0.4388 3.4611 3.9798 1.9683 1.8759
p-value 0.0000*** 0.0475** 0.0154** 0.4140 0.1406
Notes: The results of the mean equation of a GARCH estimation of Rt¼b1þb2UBtþb3UAtþb4DBtþ
b5DAtþet;eteN0;Vt
ðÞVt¼a1þa2UBtþa3UAtþa4DBtþa5DAtþa6Vt1þa7e2
t1þZt, where UB,UA,
DB and DA are dummy variables. UB takes the value 1 in the 5 days before crossing a barrier from below; UA
takes the value 1 in the 5 days after crossing a barrier from below; DB takes the value 1 in the 5 days
before crossing a barrier from above; DA takes the value 1 in the 5 days after crossing a barrier from above.
*,**,***Significant at the 10, 5 and 1 percent levels, respectively
Table VIII.
Conditional effects
test results return
equation
RBF
a barrier in a downward movement. The magnitude of returns is higher in the five-day periods
after crossing a barrier for all assets and the crossing of a barrier in a downward movement
promotes a sign change in the mean return of only two cryptocurrencies: Dash and Ripple.
Table IX shows the results of the variance equation. In the presence of psychological
barriers, we should find positive variance indicators before crossing a barrier meaning that
the market is turbulent and negative indicators after crossing a barrier meaning that the
market is calmer. Regarding upward movements, we find positive variance indicators before
crossing a barrier for five cryptocurrencies and negative indicators after crossing a barrier for
also five cryptocurrencies. As for downward movements, we find positive indicators before
crossing barriers for two cryptocurrencies and negative indicators after crossing barriers for
six cryptocurrencies. We observe that volatility tends to increase after crossing a barrier from
below and decrease after crossing a barrier from above which, considering the results obtained
for the mean return equation, is in line with the efficient market hypothesis and the reasoning
that higher returns tend to compensate higher volatility levels. The GARCH term is positive
and significant at a 1 percent level for every asset, indicating significant GARCH effects.
Finally, Table X exhibits the results of the Wald test to the hypotheses listed in Section
3.2.5. We find significant (at a 5 percent significance level) changes in the conditional mean
returns after crossing a barrier in an upwards movement for two cryptocurrencies (Bitcoin
and NEM), while for downwards movements, we observe that changes in the conditional
mean returns are significant at 5 percent for three cryptocurrencies (Bitcoin, Dash and
NEM). As for differences in the conditional variance, we observe significant results for four
cryptocurrencies Bitcoin, Ethereum, Litecoin and NEM concerning upwards movements
CRESID(1)
2
GARCH(1) UB UA DB DA
Bitcoin
Coefficient 0.3054 0.1192 0.8413 2.0988 1.3859 2.8976 0.9813
p-value 0.0000*** 0.0000*** 0.0000*** 0.0000*** 0.0000*** 0.0000*** 0.0051***
Dash
Coefficient 2.1703 0.2091 0.7567 11.277 8.5335 5.2712 6.5721
p-value 0.0000*** 0.0000*** 0.0000*** 0.0021*** 0.0214** 0.0405** 0.0073***
Ethereum
Coefficient 3.1468 0.2749 0.6608 7.4373 0.3394 4.4360 0.4347
p-value 0.0000*** 0.0000*** 0.0000*** 0.0000*** 0.8561 0.0000*** 0.6087
Litecoin
Coefficient 1.8237 0.0916 0.8484 42.705 22.821 1.3866 1.9194
p-value 0.0000*** 0.0000*** 0.0000*** 0.0000*** 0.0000*** 0.3691 0.1763
NEM
Coefficient 20.019 0.4368 0.3565 2.7009 83.051 19.261 8.6145
p-value 0.0000*** 0.0000*** 0.0000*** 0.3486 0.0000*** 0.0000*** 0.4938
Ripple
Coefficient 6.5235 0.7574 0.3392 26.087 1.3252 58.358 0.7711
p-value 0.0000*** 0.0000*** 0.0000*** 0.1615 0.9389 0.0669* 0.9360
Notes: The results of the variance equation of a GARCH estimation of Rt¼b1þb2UBtþb3UAtþ
b4DBtþb5DAtþet;eteN0;Vt
ðÞVt¼a1þa2UBtþa3UAtþa4DBtþa5DAtþa6Vt1þa7e2
t1þZt,where
UB,UA,DB and DA are dummy variables. UB takes the value 1 in the 5 days before crossing a barrier
from below; UA takes the value 1 in the 5 days after crossing a barrier from below; DB takes the value 1 in the
5 days before crossing a barrier from above; DA takes the value 1 in the 5 days after crossing a barrier
from above. V
t1
refers to the moving average parameter and e2
t1stands for the GARCH parameter.
*,**,***Significant at the 10, 5 and 1 percent levels, respectively
Table IX.
Conditional effects
test results variance
equation
Cryptocurrency
market
and regarding downwards movements we find significant differences for three
cryptocurrencies, namely, Bitcoin, Ethereum and Ripple.
Summing up, the conditional effects test indicates that the magnitude of the mean returns
is higher in the five-day period after crossing a barrier, both for upward and downward
movements. Also, we observe that markets tend to be turbulent before crossing a barrier and
calmer after that barrier is crossed, but results also show that volatility tends to stay aligned
with returns, as predicted by the efficient markets hypothesis. Finally, analyzing the results of
the Wald test, we observe significant signs of the existence of psychological barriers in all
cryptocurrencies. Clearly, Bitcoin presents the stronger case for the existence of psychological
barriers, as all four null hypotheses are rejected at a 5 percent significance level.
We are now in position to summarize the results of each test, reaching the global results
shown in the last column of Table XI. The disagreement across tests in some of the
H1 H2 H3 H4
Bitcoin
χ
2
4.2549 10.061 69.024 33.263
p-value 0.0391** 0.0015*** 0.0000*** 0.0000***
Dash
χ
2
1.0170 27.605 0.1577 0.0897
p-value 0.3132 0.0000*** 0.6913 0.7645
Ethereum
χ
2
1.3740 1.9733 5.3936 6.8673
p-value 0.2411 0.1601 0.0202** 0.0088***
Litecoin
χ
2
0.2872 0.9152 150.6705 0.0404
p-value 0.5920 0.3387 0.0000*** 0.8407
NEM
χ
2
4.2216 4.0788 20.1286 0.7282
p-value 0.0399** 0.0434** 0.0000*** 0.3935
Ripple
χ
2
0.0400 2.3157 0.9561 3.1178
p-value 0.8415 0.1281 0.3282 0.0774**
Notes: The results of a Wald test to four hypotheses. H1: there is no significant difference in the conditional
mean return before and after an upwards crossing of a barrier; H2: there is no significant difference in the
conditional mean return before and after a downwards crossing of a barrier; H3: there is no significant
difference in the conditional variance before and after an upwards crossing of a barrier; H4: there is
no significant difference in the conditional variance before and after a downwards crossing of a barrier.
*,**,***Significant at the 10, 5 and 1 percent levels, respectively
Table X.
Conditional effects
test results
Uniformity
test
Barrier proximity
test
Barrier hump
test
Conditional effects
test
Psychological
barriers?
Bitcoin Yes Yes Yes Yes Yes
Dash Yes Yes Yes Yes Yes
Ethereum Yes No No Yes No
Litecoin Yes Yes Yes Yes Yes
NEM Yes No No Yes No
Ripple Yes Yes Yes Yes Yes
Table XI.
Summary of results
from the various tests
RBF
cryptocurrencies stem from the different features of psychological barriers that were captured
by the tests applied to our sample. For example, while uniformity tests only examine whether
M-values follow a uniformdistribution,barrier proximity tests assess whether observations in
the vicinity of a potential barrier are relatively rarer than would be expected. Overall and
considering the four tests we had conducted in this study, we can conclude that there are
strong signs of psychological barriers in four of the cryptocurrencies under scrutiny: Bitcoin,
Dash, Litecoin and Ripple. It is also noteworthy that the cryptocurrencies that present the least
signs of psychological barriers are among the ones that exhibited the highest return and
volatility (measured by the standard deviation of the returns).
5. Conclusion
In this paper, we conducted the first study on psychological barriers in the
cryptocurrencies market.
We considered a sample of six of the most liquid cryptocurrencies. After analyzing the
range of each assets quotes and defining all potential psychological barriers, we started by
performing a uniformity test, observing that all assets rejected the null hypothesis, which
claimed that the respective M-values followed a uniform distribution. Then, we conducted a
barrier proximity test using several intervals to each of the previously defined potential
barrier levels, finding signs of the existence of psychological barriers in four cryptocurrencies.
The following test was a barrier hump test, which focused on the whole shape of the M-values
distribution, assessing if they follow a uniform distribution or a hump-shape distribution as
should be the case in the presence of psychological barriers and it confirmed the majority of
theprevioustests results. Finally, we conducted a conditional effects test, in its three
modalities: mean return equation, variance equation and hypotheses test. The mean return
equation indicated that in all financial markets the magnitude of the mean returns tended to
be higher in the five-day period after crossing a barrier, both for upward and downward
movements; the variance equation led to the conclusion that markets were significantly more
volatile before crossing a barrier; and, through the hypotheses test, we observed signs of the
existence of psychological barriers in four cryptocurrencies.
Overall, we found evidence of the existence of psychological barriers in four of the
cryptocurrencies under study: Bitcoin, Dash, Litecoin and Ripple. Among all the
cryptocurrencies, Bitcoin the quasi-monopolist leader of the cryptocurrencies market is
by far the one which presents stronger signs of psychological barriers.
The results of our study may potentially be used by investors to build more profitable
strategies when in presence of psychological barriers. Moreover, our results seem difficult to
reconcile with the efficient market paradigm, since one of the chief features of an efficient
capital market is that prices should not exhibit any particular patterns (Fama, 1970).
Our results are also relevant to the debate about the prevalence of decision-making
biases in different categories of investors. Given that the cryptocurrency market is
essentially inhabited by unexperienced investors (Yermack, 2015; Kow, 2017), our results
suggest that this category of investors is particularly susceptible to the decision-making
biases usually associated with the formation of psychological price barriers.
Our study presents several limitations which may lead to future research on this topic:
for instance, studies with broader samples, namely, a larger set of cryptocurrencies, could
lead to stronger results; it could be fruitful to analyze the prevalence of psychological
barriers in different time periods according to the price trend prevailing in the market at the
time; it would be interesting to investigate the characteristics of cryptocurrencies (in terms
of liquidity, volatility, transaction volume, etc.) that tend to lead to a higher prevalence of
psychological barriers; and, finally, the finding of significant psychological barriers in some
cryptocurrencies implies the need to address its practical implications, namely, the
possibility to earn extraordinary profits exploiting that anomaly.
Cryptocurrency
market
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Corresponding author
Luís Pacheco can be contacted at: luisp@upt.pt
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Cryptocurrency
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... Additionally, fractional trading in cryptocurrencies has resulted in small ticket size investments by investors who lack adequate information and jump on the bandwagon during bull periods to make quick profits. This exacerbates short-term noise trading in the market, exposing it to the behavioural biases of herding and convergent trading (Fonseca et al., 2019;Gurdgiev, and O'Loughlin, 2020). The findings also show that during high market volatility, the interplay of fear and anxiety impacts herd behaviour significantly (Shaikh and Padhi, 2015;Youssef, 2020). ...
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This paper examines the evidence of herding in the revolutionary cryptocurrency market for the period from January 2017 to December 2020. The study employs quantile regression technique for investigating herd behaviour during market asymmetries of rising and falling returns, extreme market returns, high volatility, and the exogenous event of the COVID-19 pandemic. The results provide evidence of pronounced herding during the bull phase, extreme down-markets, and high volatility. These results indicate that herd hunch is prevalent in the cryptocurrency market as investors exhibit imitation while ignoring their own knowledge and beliefs. Also, the phenomenon is more vividly observed during the panic period of COVID-19.
... In the past six years, more attention has been paid to motivational factors [43][44][45][46][47][48][49]. In contrast, barriers were not analyzed as much [50][51][52]. Despite this, it can be stated that motivational factors as well as barriers to using cryptocurrency constitute a topic gaining in popularity, and some of the aforementioned articles have been cited by dozens of other authors. ...
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The cryptocurrency market is very young, volatile, and highly risky. By the end of 2020, a new bull run started, and the prices of several cryptocurrencies reached record-breaking highs. The factors affecting this rise of cryptocurrencies include the impacts of the COVID-19 pandemic, the economic crisis and the global increase in the inflation rate, as well as the gradual acceptance and adoption of cryptocurrencies by people worldwide. This exploratory research is focused on this last factor, i.e., using cryptocurrency and with it, the associated support of its ecosystem (e.g., mining, staking). A survey was carried out investigating the motivational factors and barriers to investment in cryptocurrency for Czech representatives of Generations Y and Z (18–42 years; n = 468). The geographic scope was nationwide, and quota sampling was used. Notably, this survey was carried out prior to the global COVID-19 pandemic outbreak, and it is thus not affected by the pandemic and its related economic impacts. The article investigates the dependency between the individual motivational factors and barriers from the perspective of the tendency to take risks (using the risk propensity scale), according to gender and representation of Generations Y and Z. The lack of information on this form of investment is considered as the main barrier to investment in cryptocurrency, with respect to sex and generations. Compared to that, a negative experience with investment in cryptocurrency constitutes the most minor barrier. Respondents that have a tendency to take risks are mostly put off by their lack of experience with investment in general. The main motivational factor for investment in cryptocurrency, with respect to sex and generations, is considered to be the speed of increase in cryptocurrency value. On the other hand, the least encouraging factor is the opportunity to use the high volatility of cryptocurrency for speculative trading. Interestingly, this factor mostly encourages respondents that do not have a tendency to take risks. The findings are discussed, along with the presentation of their implications for practice and the directions of further explanatory research.
... Similarly, Grobys and Junttila (2020) contemplate that the crypto market is inherently speculative. As a result, psychological impediments, especially convergent trading or herding, are bound to occur in such a market (Fonseca et al., 2019;Gurdgiev and O'Loughlin, 2020). Consequently, researchers began examining such mass behaviour in the crypto market using ordinary least squares (OLS hereafter) specifications of return-based methods (Kumar, 2020;Philippas et al., 2020;Raimundo Junior et al., 2020;Senarathne and Jianguo, 2020;Susana et al., 2020). ...
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Purpose With the unprecedented growth of digitalization across the globe, a new asset class, that is cryptocurrency, has emerged to attract investors of all stripe. The novelty of this newly emerged asset class has led researchers to gauge anomalous trade patterns and behavioural fallacies in the crypto market. Therefore, the present study aims to examine the herd behaviour in a newly evolved cryptocurrency market during normal, skewed, Bitcoin bubble and COVID-19 phases. It, then, investigates the significance of Bitcoin in driving herding bias in the market. Finally, the study gauges herding contagion between the crypto market and stock markets. Design/methodology/approach The study employs daily closing prices of cryptocurrencies and relevant stocks of S&P 500 (USA), S&P BSE Sensex (Index) and MERVAL (Argentina) indices for a period spanning from June 2015 to May 2020. Quantile regression specifications of Chang et al.’ s (2000) absolute deviation method have been used to locate herding bias. Dummy regression models have also been deployed to examine herd activity during skewed, crises and COVID-19 phases. Findings The descriptive statistics reveal that the relevant distributions are leptokurtic, justifying the selection of quantile regression to diagnose tails for herding bias. The empirical results provide robust evidence of crypto herd activity during normal, bullish and high volatility periods. Next, the authors find that the assumptions of traditional financial doctrines hold during the Bitcoin bubble. Further, the study reveals that the recent outbreak of COVID-19 subjects the crypto market to herding activity at quantile ( t ) = 0.60. Finally, no contagion is observed between cryptocurrency and stock market herding. Practical implications Drawing on the empirical findings, it is believed that in this age of digitalization and technological escalation, this new asset class can offer diversification benefits to the investors. Also, the crypto market seems quite immune to behavioural idiosyncrasies during turbulence. This may relieve regulators of the possible instability this market may pose to the entire financial system. Originality/value The present study appears to be the first attempt to diagnose leptokurtic tails of relevant distribution for crypto herding in the wake of two remarkable events: the crypto asset bubble (2016–2017) and the outbreak of coronavirus (early 2020).
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We examine for the first time the major stock markets of eight Latin American countries for indication of psychological barriers at round numbers. We test for uniformity in the trailing digits of the indices and use regression and GARCH analysis to assess the differential impact of being above or below a possible barrier. The Chilean stock market seems to be significantly different from its counterparts as it is the only one that showed virtually no signs of psychological barriers. There is mild to strong evidence of barriers in the remaining markets. These findings challenge the notion that most Latin American markets are unpredictable and lend credit to the claim that technical analysis strategies can be useful in some of these markets.
Article
Purpose This article aims to verify if there are detectable barriers in price levels that are understood to be psychologically important (psychological barriers) in a set of hourly electricity prices. These barriers manifest themselves when the market struggles with a difficulty in crossing the barrier to a different level. Psychological barriers focus on directional price movements around regions of the barrier, thus the importance of understanding investor behavior. The authors intend to contribute empirically to the scarce literature on psychological influences in individuals trading in the energy market, hereby enhancing the knowledge concerning the behavior of investors in this market. Design/methodology/approach The present work aims to test psychological barriers in the Nord Pool electricity market. Through a sample of hourly data on the Elspot day-ahead market, from 2013 to 2017, three groups of tests were made, following the M-values methodology: (1) uniformity tests, which clearly rejected the uniformity in hourly prices; (2) barrier tests, which included the barrier proximity and barrier hump tests, evidencing psychological barriers and (3) conditional effects tests, which allowed us to conclude in favor of effects of positive returns after approaching a barrier on an upward movement, i.e. the barrier breaches due to the fact that increasing prices tend to lead to further price increases, on average. Findings Uniformity tests, rejected the uniformity in hourly prices; barrier tests, included the barrier proximity and barrier hump tests, evidencing psychological barriers and conditional effects tests, allowed us to conclude in favor of effects of positive returns after approaching a barrier on an upward movement, i.e. the barrier breaches due to the fact that increasing prices tend to lead to further price increases, on average. Another relevant conclusion is that the period from midnight to 9 a.m. is very sensitive, since there is evidence of return and variance effects simultaneously. The implications of these results are potentially relevant, since changes on the variance are usually perceived as a proxy for risk, with changes on the return. It was also concluded that with the increase of the time span from 5 to 10 days on the conditional effects difference tests, there were significant changes on the results, the variance effect is stronger, while the return effect weakens. Research limitations/implications However, this research presents some limitations that result in representing opportunities for future research. The fact that there are reduced data available for other markets end up limiting the study of the global electricity market. Although Nord Pool is Europe's leading energy market and is seen as one of the most successful energy markets in the world, it would be interesting to do a study with more than one electricity market to make comparative considerations. Although the spot market is the main arena for energy trade, while the intraday market works as a compliment, it would be equally interesting to do a similar study for the intraday market and then compare conclusions. Moreover, in the present study, it was used standard methods in the literature on psychological barriers, but other methods could have been used–for example, those that assume that prices follow the Benford's distribution (Lu and Giles, 2010), which also present a path for future research and opportunity for confirming the robustness of the present results. Practical implications When the presence of psychological barriers is detected it means that the risk-return relationship becomes weaker around the psychological barrier (round numbers, meaning that electricity traders anchor). Identification of psychological barriers supports the claim that technical analysis strategies based on price support and resistance can be profitable. Therefore, more profitable strategies can be built by traders, but no reconciliation with the efficient market hypothesis (EMH) (provided that in inefficient markets prices should not exhibit any particular pattern). The finding of significant psychological barriers in specific hourly time intervals implies the need to address its practical implications in electricity markets, being so specific, namely, the possibility to earn extraordinarily profits exploiting this anomaly and who wins. Originality/value The electricity sector is a determinant sector in economic growth and a factor of development. Herein lies the importance of studying this market, which until now has not occurred in this subject, as far as it was possible to gauge. Are there barriers in the electricity market and should such a presence be taken into account? Investigating the existence of psychological barriers in the electric market becomes relevant, because knowing that investors are psychologically affected by a psychological barrier, can become a useful tool in negotiation, as it can function as another variable in the “equation” which is to trade in a complex market like this. Proving the potential presence of a psychological barrier may lead investors to believe in the idea of levels of resistance or levels of support, affecting their decision-making and price dynamics.
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Following Urquhart (2017) who finds evidence of price clustering in Bitcoin, we answer the question of whether the documented price clustering in Bitcoin is driven by any given day-of-the-week. We find evidence that Bitcoin prices cluster around whole numbers more on Fridays and least on Mondays. We also show that Bitcoin price clustering around the top three most frequent two-digit decimals is primarily a Friday phenomenon.
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Purpose – The purpose of the study is to examine the prices of some of the most widely traded stocks from Taiwan, Brazil and South Africa for indications of psychological barriers at round numbers. Design/methodology/approach – The sample under study includes a group of 24 stocks (8 for each one the emerging markets) during the period 2000-2014. We test for uniformity in the trailing digits of the stock prices and use regression and GARCH analysis to assess the differential impact of being above or below a possible barrier. Findings – We found no consistent psychological barriers in individual stock prices near round numbers. Moreover, we document that the relationship between risk and return tends to be weaker in the proximity of round numbers for about half of the stocks under study. Originality/value – This is the first study to examine the prices of single stocks from emerging markets for indications of psychological barriers at round numbers. Our results advocate special reflection regarding trading strategies linked to support and resistance levels in stock prices.
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While prior research provides evidence of price clustering in bitcoin, this study seeks to explain the unusual level of bitcoin price clustering using various measures of bitcoin-level and market-wide sentiment. Our results suggest that sentiment has a strong positive association with price clustering. In economic terms, a one standard deviation increase in sentiment – measured by Google Trends – explains about 2.5% to 5% of the unusual level of price clustering in Bitcoin. We also find that our results are robust when we use other measures of investor sentiment.
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This paper extends current literature on price clustering in Bitcoin market. We analyze intraday data of various time frames and document evidence of clustering for open, high and low prices. We discover and explain different patterns of clustering and their relation with the time frame. We further examine the effect of price level on these findings.
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We are the first to investigate intraday price behavior of cryptocurrencies. Trade prices cluster on round numbers throughout the day. The clustering increases with price level and pricing uncertainty. There is also strategic pricing at just below or above round numbers. At the transaction level, we find that prices are mainly formed due to negotiations and strategic trading, instead of based on psychologically appealing numbers in the order of 0, 5, and others.
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We examine the existence and dates of pricing bubbles in Bitcoin and Ethereum, two popular cryptocurrencies using the (Phillips et al., 2011) methodology. In contrast to previous papers, we examine the fundamental drivers of the price. Having derived ratios that are economically and computationally sensible, we use these variables to detect and datestamp bubbles. Our conclusion is that there are periods of clear bubble behaviour, with Bitcoin now almost certainly in a bubble phase.
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This letter revisits the informational efficiency of the Bitcoin market. In particular we analyze the time-varying behavior of long memory of returns on Bitcoin and volatility 2011 until 2017, using the Hurst exponent. Our results are twofold. First, R/S method is prone to detect long memory, whereas DFA method can discriminate more precisely variations in informational efficiency across time. Second, daily returns exhibit persistent behavior in the first half of the period under study, whereas its behavior is more informational efficient since 2014. Finally, price volatility, measured as the logarithmic difference between intraday high and low prices exhibits long memory during all the period. This reflects a different underlying dynamic process generating the prices and volatility. To help you access and share your article, we are providing you with the following personal article link, which will provide free access to your article, and is valid for 50 days, until November 16, 2017 https://authors.elsevier.com/a/1Vo7kbZedevty