#  Rise of the Machines?
Intraday High-Frequency Trading Patterns of Cryptocurrencies

Alla A. Petukhina Humboldt-Universität zu Berlin.
Firamis GmbH, Germany.
alla.petukhina[at]wiwi.hu-berlin.de Raphael C. G. Reule Humboldt-Universität
zu Berlin.
irtg1792.wiwi[at]wiwi.hu-berlin.de Wolfgang Karl Härdle Humboldt-Universität
zu Berlin, IRTG 1792, Dorotheenstr. 1, 10117 Berlin, Germany School of
Business, Singapore Management University, 50 Stamford Road, Singapore 178899
Faculty of Mathematics and Physics, Charles University, Ke Karlovu 3, 121 16
Prague, Czech Republic Department of Information Management and Finance,
National Chiao Tung University, Taiwan, ROC haerdle[at]wiwi.hu-berlin.de

###### Abstract

This research analyses high-frequency data of the cryptocurrency market in
regards to intraday trading patterns related to algorithmic trading and its
impact on the European cryptocurrency market. We study trading quantitatives
such as returns, traded volumes, volatility periodicity, and provide summary
statistics of return correlations to CRIX (CRyptocurrency IndeX), as well as
respective overall high-frequency based market statistics with respect to
temporal aspects. Our results provide mandatory insight into a market, where
the grand scale employment of automated trading algorithms and the extremely
rapid execution of trades might seem to be a standard based on media reports.
Our findings on intraday momentum of trading patterns lead to a new
quantitative view on approaching the predictability of economic value in this
new digital market.

JEL Classification: G02, G11, G12, G14, G15, G23.
Keywords: Cryptocurrency, High-Frequency Trading, Algorithmic Trading,
Liquidity, Volatility, Price Impact, FinTech, CRIX.
The financial support of the Czech Science Foundation under grant no.
19-28231X, the Firamis GmbH, Robert-Kempner-Ring 27, 61440 Oberursel (Taunus),
the Yushan Scholar Program, and European Union’s Horizon 2020 ”FIN-TECH”
Project, under the grant no. 825215 (Topic ICT-35-2018, Type of actions: CSA),
as well as data support by dyos solutions GmbH, Oberwallstr. 8, 10117 Berlin,
are greatly acknowledged.

This is a post-peer-review, pre-copyedit version of an article published in
The European Journal of Finance. The final authenticated version is available
online at:

https://doi.org/10.1080/1351847X.2020.1789684

##  1 Motivation

High-frequency trading takes advantage of the incredible rise of computing
power provided by the steady development of ever more capable structures.
Algorithms are already major players in a variety of financial applications,
and have proven to be more efficient than their human counterparts. By
employing these so-called “algos”, positive effects can be exploited to their
maximum and market inefficiencies can potentially be eliminated (Härdle et
al., 2020). However, just like for every coin, there is a flipside, such as
the negative impact on capital markets caused by technological inefficiencies
(Emem, 2018). One of the most noted events of an early point of attack for
these algorithms was the Flash Crash of 2010.

No matter what, the machines are here to stay and their influence, possibly
powered by “learning” algorithms, will certainly increase even more with time
- especially in regards to new emerging markets such as cryptocurrencies. The
rising popularity and acceptance of this alternative asset, as it has yet to
be understood as an alternative to fiat currency, requires for specialised
strategies to maximise the potential return of investments (Akhtaruzzaman et
al., 2019; Platanakis and Urquhart, 2019; Trimborn et al., 2018; Petukhina et
al., 2020, 2019).

Figure 1: CRIX Time Series.

Yet, did the quantlets or algorithms really venture in the realm of autonomous
machines, the digital world, or are they still with the world of the humans,
the world of manual labour oil and baby nutrition companies?

This is especially of interest since the cryptocurrency market has
significantly matured in recent years and has attracted enormous investments,
not only by major players but especially by individuals. Especially $FinTech$
$Startups$ are of high interest, as absurd amounts of financial backing was
(and is still to some extent) being generated by just presenting a briefly
written $Whitepaper$-PDF marketing outlet (Zetzsche et al., 2019). The early
cryptocurrency market kick-off starting in late 2017 is evidently presenting
such happenings. The discrepancy between sentiment and tone generated by
marketing versus the delivered performance is fascinating (with further
references Härdle et al., 2020; Chen et al., 2019a; Qian et al., 2019).

In this research, we are analysing high-frequency data (5-minute intervals)
gained from the cryptocurrency market and see, if there is really 24/7
algorithmic trading, or if there are still people sitting behind their
computers creating and executing orders by hand after they have returned from
their daily jobs.

Previous research outputs on this theme, such as Zhang et al. (2019), have
used time spans ranging from 1 hour to 12 hours. Their methods yielded
results, which lead to different conclusions, yet opened up further thoughts
towards factors such as trading patterns, variations in returns, volatility
and trading volume. Zhang et al. (2018) are also looking at the same aspects
as the previous research, with the additional finding of a power-law
correlation between price and volume. Röschli et al. (2017) respectively build
a uni- and multivariate analysis of quantitative facts to show off stylized
facts of cryptocurrencies. Schnaubelt et al. (2019) analyzed limit order data
from cryptocurrency exchanges. Besides their recovery of common qualitative
facts, they find that these data exhibit many of the properties found for
classic limit order exchanges, such as a symmetric average limit order book,
the autocorrelation of returns only at the tick level and the timing of large
trades. Yet they find that cryptocurrency exchanges exhibit a relatively
shallow limit order book with quickly rising liquidity costs for larger
volumes, many small trades and an extended distribution of limit order volume
far beyond the current mid-price.

Given the search for the most efficient trading strategies, Caporale and
Plastun (2019) provide a range of historic scientific works on the time of day
effects to reap abnormal profits. In contrast to their work, we aim at
identifying the market drivers, which are responsible for how this new
emerging market, which is still full of conundrums for many, behaves - i.e. do
market movements fit into human activity patterns or are these independent
from time.

Preliminary research has therefore not touched the highly topical question of
human impact in the wake of digital systems. There are many papers with
interesting approaches and solutions, but only for problems that are already
known and have been rebrewn for some time now. Yet, with the advent and
popular discussion of the employment of Long Short Term Memory Neural Networks
(LSTM) and hence deep learning for finance, AI advisory, essentially based on
the human factor of sentiment in the realm of cryptocurrencies (Chen et al.,
2019a), will play a major role in especially this completely digital market.
This, as a circular argument, brings us once again to the fundamental idea of
enforcing the understanding of market behaviour based on the time of the day
and the agents acting in these markets that are predestined to be ruled by the
machines.

As a polemic term, we are using $Proof-Of-Human$ ($PoH$; derived from $Proof-
Of-Work$, $Proof-Of-Stake$ et cetera consensus algorithms) to underline the
hypotheses that not algorithms are the major players in this market, but
humans. Humans don’t act as programmed like algorithms - they act based on
biological and psychological input, such as hunger or fatigue. The majority of
humans will have certain times at which they are active, and at which they
rest and are therefore inactive. Alternatively spoken, algorithms need humans
to start and then exacerbate a price trend - the question is, therefore if the
cryptocurrency market is dominated by human or algorithmic behaviour.
Eventually, we can differentiate algorithmic and human trading patterns
expressed within the market (with further references Caporale et al., 2016).

The paper is structured by giving a brief general introduction and data source
disclosure and methodology section, followed by a respective intraday data
analysis, which is concluded by a section on Time-Of-Day effects and the
Proof-Of-Human.

All presented graphical and numerical examples shown are reproducible and can
be found on  www.quantlet.de (Borke and Härdle, 2018) and are indicated as
CCID.

##  2 High-Frequency Cryptocurrency Data

To understand the dynamics of this new high-frequency market, it is mandatory
to investigate the statistical properties of various high-frequency variables,
for example, trading volume or volatility, to find respective answers to
questions like option pricing and forecasting. Preliminary research to
visualize the cryptocurrency market was done by Trimborn and Härdle (2018)
with the CRyptocurrency IndeX,  CRIX (crix.berlin), in order to represent the
performance of the cryptocurrency market with the help of the most mature and
accepted cryptocurrencies, such as Bitcoin (BTC), Ethereum (ETH), or Ripple
(XRP) - see appendix section 5.1 for further used abbreviations. As the CRIX
index family covers a range of cryptocurrencies based on different liquidity
rules and various model selection criteria, we have chosen this as the main
data source. CRIX represents the cryptocurrency market, but by its very nature
is dominated by a few main players with BTC being the absolute market driver
over time.

Furthermore, we used data provided by dyos solutions GmbH (dyos.io) compiled
from various exchanges’ data, to ensure that our findings are coherent with
other data available. It is important to keep in mind, that the 5-minute data
analysed in this research is gained from sources located in the $European$
$markets$ (+1h GMT) and therefore the time-of-day effects may look different
for markets from the Americas or Asia. We will make an exegesis on this
important point in subsection 3.3.

In addition, the analysed data sample belongs to the time period after the
cryptocurrency market heated up immensely around the end of 2017, followed by
a sharp cooldown at the beginning of 2018. By that time a plethora of euphoric
media outlets was praising the endless possibilities which the blockchain
technology may provide - and what eventually also lead to quite a lot of ICO
scams (Zetzsche et al., 2019). At that time, algorithmic trading in
cryptocurrency markets was not seen as being a mere idea, but reality by more
or less promising FinTech startups. These emerging enterprises are offering a
wide variety of blockchain-related services, such as trading, asset
management, or technical support. Especially FinTech startups related to the
financial sector, in contrast to for example supply chain oriented ventures,
are heavily interested in ArtificialIntelligence (”learning” algorithms) and
are marketing their individual related products as groundbreaking and ready-
to-use. Given the chosen typical vacation period, July and August, one should
hence expect a less pronounced human, but algorithmic driven market behavior
to contradict the hypotheses of the PoH concept - more on that as well in
subsection 3.3.

Regarding data handling, we are coherent with previous research on high-
frequency data based on traditional data sources, such as the NYSE, which has
underlined data preparation issues and the specific statistical properties of
various high-frequency variables (Hautsch, 2011). As we are dealing with a
subject, where individuals can act directly with the market without involving
a middle-man, the characteristics of our data observed on transaction level,
therefore, are especially irregularly spaced in time and without interruption
- see section 3.

##  3 Intraday Data Analysis

In the following chapter, we provide an overview of the methods employed to
analyze our high-frequency data at hand with further statistical intraday
cryptocurrency market observations.

###  3.1 Methodology

This paper undertakes a fresh empirical investigation of key financial
variables of cryptocurrency market, such as volatility, returns and trading
volume. Following, for example, Hussein (2011), intraday return volatility is
calculated as absolute log-returns as defined in (2). As we are looking at
high-frequency data, there is no need to use measures like, for example, the
compounded annual growth rate (CAGR) instead of absolute returns, which is
used to get the per-annum returns and does not support the analysis in this
case.

The simple return $Ret_{t}$ is defined as

$\displaystyle Ret_{t}=\frac{P_{t}-P_{t-1}}{P_{t-1}},$ (1)

where $P_{t}$ und $P_{t-1}$ are prices of coins at time points $t$ and $t-1$
respectively. The log return $ret_{t}$ is defined as

$\displaystyle ret_{t}=\log\frac{P_{t}}{P_{t-1}}=\log(1+Ret_{t}).$ (2)

In order to expressively visualize some features of our high dimensional and
nonstationary time series gained from our large high-frequency dataset of the
specifically chosen period of time, a $Generalized$ $Additive$ $Model$ (GAM)
is best suited. A GAM is a generalized linear model (GLM), where the nonlinear
predictor is given by a specified sum of smooth functions of the covariates,
as well as a conventional parametric component of the linear predictor
(Härdle, 1990). The basic advantage of GAM is the possibility to model highly
complex nonlinear relationships given a large number of potential predictors.
In particular, recent computational developments in GAM fitting methods, such
as Wood et al. (2015), Wand (2017), and Wood (2017), have made it possible to
use these models to explore very large datasets. Moreover, in the last two
decades, GAM methods have intensively developed in terms of the range of
models that can be fitted. All these advantages make GAMs a feasible tool to
investigate intraday seasonality patterns with high-frequency trading data. In
general, the model has a structure something like:

$g\left\\{E\left(y_{i}\right)\right\\}=\beta_{0}+f_{1}\left(x_{i1}\right)+\cdots+f_{p}\left(x_{ip}\right)$
(3)

where $y=\left(y_{1},\ldots,y_{n}\right)^{\top}$ observation of a response
variable $Y$, $g$ is a link function (identical, logarithmic or inverse,
etc.), $x_{1}\dots x_{p}$ are independent variables, $\beta_{0}$ is an
intercept, $f_{1}\left(x_{i1}\right)\dots f_{p}\left(x_{ip}\right)$ are
unknown nonparametric smooth functions, and $\varepsilon_{i}$ is an i.i.d.
random error. In our application we use the identity link function, since the
LHS of our equations are features/variables observed or measured on a
continuous scale, to fit the following statistical model:

$y_{i}=f_{1}\left(x_{1,i}\right)+f_{2}\left(x_{2,i}\right)+\ldots+f_{p}\left(x_{p,i}\right)+\varepsilon_{i}$
(4)

Here $y_{i}$ will be a trading volume, volatility, or returns as defined in
(2), $x_{q,i}$ will be the daily and weekly effects. The nonlinear function
$f_{q}$ is a smooth function, composed by sum of basis functions $b_{j}^{q}$
(for example B-splines, P-splines or cubic splines) and their corresponding
regression coefficients $\beta_{q,j}$. Thus, each function $f_{q}$ is
expressed as:

$f_{q}(x)=\sum_{j=1}^{k_{q}}\beta_{q,j}b_{j}^{q}(x)$ (5)

where $k_{q}$ is the dimension of the spline basis.
The smooth function ${m(x_{1},...,x_{p})=\sum_{q=1}^{p}f_{q}(x_{q})}$ is
estimated by penalized regression:

$\sum_{i=1}^{n}\left(y_{i}-\sum_{q=1}^{p}f_{q}\left(x_{i}\right)\right)^{2}+\sum_{q=1}^{p}\lambda_{q}\int\left\|f_{q}^{\prime\prime}(x)\right\|^{2}dx$
(6)

where the penalty parameter
$\Lambda=\left(\lambda_{1},\ldots,\lambda_{p}\right)$ is a smoothing parameter
controlling the fit–smoothness trade‐off for $f_{q}$ and can be selected by
minimization of the Generalized Cross Validation (GCV) score, see (Wood, 2004)
and (Wood, 2011). Denoting $B$ the matrix formed by concatenation of the
$b^{q}_{j}$, one has to solve the following problem:

$\widehat{\beta}=\arg\min_{\lambda,\beta}\left\\{\|Y-B\beta\|^{2}+\sum_{q=1}^{p}\lambda_{\dot{q}}\beta^{\top}S_{q}\beta\right\\}$
(7)

where $\beta=\left(\beta_{1},\ldots,\beta_{p}\right)^{\top}$ is the vector of
the unknown regression parameters, $S_{q}$ is a matrix of known coefficients
(a smoothing matrix) and depends on the spline basis. Thus, given $\lambda$,
expression (7) may readily be minimized to yield the coefficient estimates
$\hat{\beta_{\lambda}}$. The method of obtaining the estimate of the $\beta$
is called Penalized Iteratively Re-weighted Least Squares (P-IRLS) which is
implemented in the mgcv R package, see (Wood, 2019).

###  3.2 Summary Statistics

As an introduction to the data analyzed in this brief research, we are
providing summary statistics regarding its statistical properties to form a
basic understanding of the market at hand. Firstly, the trading data density
of cryptocurrencies against the normal distribution of BTC is far from
normally distributed, see figure 2. Hence the behaviour of agents in this
market is far from what we would see in classic markets. This implies, that
new rules are being employed, and therefore we have to rethink our common way
on how to approach the quantitative analysis of markets in general. We will
start our discussion on the specific research question by first providing a
general overview of the cryptocurrency market with increasingly narrowed focus
and attention to detail regarding specific timeframes and parameters for
individual crypto-assets.

Figure 2: Density of intraday CCs returns. 01. July 2018 - 31. August 2018.
The probability density functions of the distributions of daily returns for
the analized cryptocurrencies with the following colour code: BCH, BTC, DASH,
ETC, ETH, LTC, REP, STR, XMR, XRP, ZEC. A normal distribution with the same
mean and standard deviation as the returns on BTC is displayed as a histogram
in the background

Secondly, using GAM, we gain interesting insights into the trading activities
in this 24/7 market. Cryptocurrencies are being traded without any forced
break, as we know it from classic markets, for example, if the stock exchange
closes for the night or especially for weekends. In addition to this fact, we
have to consider, that there is no centralized trading in the act, but a
plethora of service providers, so-called cryptocurrency exchanges. As we
disclose the origin of our data, we underline, that caused by this very
decentralized nature of cryptocurrency genesis and their respective trading,
partially greatly diverging price data is available for each individual
cryptocurrency. Again, this is caused by the decentralized root of individual,
unsupervised and unregulated, places for exchange. There is no fixed price for
BTC contrary to, for example, for exchange rates of USD-EUR.

Figure 3: Candlestick chart of CRIX. 01. July 2018 - 29. September 2018.

(a) BTC (b) ETH (c) XRP Figure 4: Chandlestick charts for individual price
movements. 01. July 2018 - 31. August 2018.

In contrast to the CRIX candlestick chart presented in figure 3, where five
minute high-frequency data is aggregated to 60 minutes, we present respective
individual plots for each examined cryptocurrency, as shown in figure 4 to
give an easier entry to understand this volatile market. Consistency between
Figure 1, 3, and 4 can be seen in the context of the findings in Chen et al.
(2019a), where the impact of sentiment on cryptocurrency prices is evident
(with further references Chen et al., 2019c; Qian et al., 2019). Furthermore,
when recurring to the observable price structure of the Flash Crash of 2010 as
well, we can see quite many jumps in these figures - a phenomenon also
described in Chen et al. (2019b) and Qian et al. (2019).

Figure 5, shows the intraday 5-minutes returns for the period from the 01.
July 2018 to the 31. August 2018. As indicated, overall returns across the
board are very extreme - a phenomenon generally unknown to classic financial
markets. In addition, we can observe an extreme activity cluster around the
second half of August. We can link this activity to increased media outlets
regarding cryptocurrencies: the more investors flooded into this market, the
higher the trading activity, fueled by sentiment, became - leading to
partially absurd returns; positive as well as negative.

(a) BTC (b) ETH (c) XRP Figure 5: Intraday Returns (5 minutes). 01. July 2018
- 31. August 2018.

(a) BTC (b) ETH (c) XRP Figure 6: Intraday Volatility. 01. July 2018 - 31.
August 2018.

Figure 6 adds to this finding, presenting the overall volatility from the
beforehand stated period. As we can see, the return activity cluster in August
from figure 5 is mirrored in the volatility activity cluster in figure 4.
Hence, we proof the beforehand stated claim of cryptocurrency activity being
fueled by media outlets as well as sentiment, as being attested.

Table 1 displays the estimated values of selected parameters for the
cryptocurrency intraday trading for the given period of the 01. July 2018 to
the 31. August 2018. The largest autocorrelation is for DASH (0.01), the
smallest autocorrelation is for STR (-0.09).

Table 1: Estimated first-order autocorrelation of the returns, $\widehat{\rho}\textsubscript{1}(ret\textsubscript{t})$, the squared returns, $\widehat{\rho}\textsubscript{1}(ret_{t}^{2})$, and the absolute returns, $\widehat{\rho}\textsubscript{1}(|ret\textsubscript{t}|)$, as well as the estimated skewness, $\widehat{S}$, the estimated excess kurtosis, $\widehat{e.Kurt}$, and the Jarque-Bera test statistic, JB, with the respective, obviously very small, p-value for the overall summed intraday high-frequency data from the 01. July 2018 to the 31. August 2018.  | $\widehat{\rho}\textsubscript{1}(ret\textsubscript{t})$ | $\widehat{\rho}\textsubscript{1}(ret_{t}^{2})$ | $\widehat{\rho}\textsubscript{1}(|ret\textsubscript{t}|)$ | $\widehat{S}$ | $\widehat{e.Kurt}$ | JB | JB p-value
---|---|---|---|---|---|---|---
BCH | -0.01 | 0.12 | 0.20 | 0.49 | 13.69 | 140148.24 | 0.00
BTC | -0.05 | 0.13 | 0.24 | 1.30 | 49.44 | 1823779.80 | 0.00
DASH | 0.01 | 0.17 | 0.20 | 0.73 | 28.98 | 626596.64 | 0.00
ETC | -0.06 | 0.26 | 0.26 | 0.70 | 26.07 | 507374.39 | 0.00
ETH | -0.01 | 0.18 | 0.27 | 0.17 | 16.34 | 198777.58 | 0.00
LTC | -0.01 | 0.11 | 0.19 | 0.44 | 14.91 | 166121.81 | 0.00
REP | -0.08 | 0.22 | 0.19 | 0.35 | 21.89 | 356937.91 | 0.00
STR | -0.09 | 0.12 | 0.18 | 0.28 | 8.12 | 49354.96 | 0.00
XMR | -0.07 | 0.13 | 0.14 | 0.03 | 10.51 | 82241.48 | 0.00
XRP | -0.05 | 0.17 | 0.25 | 0.11 | 11.44 | 97390.58 | 0.00
ZEC | -0.07 | 0.25 | 0.22 | 1.30 | 26.66 | 534032.89 | 0.00
|  |  |  |  |  |  |

While the first-order autocorrelation of the returns of all cryptocurrencies
is all close to zero and mostly negative, the autocorrelations of the squared
and absolute returns of all cryptocurrencies are positive and significantly
larger than zero. Obviously, there is a linear relationship in the absolute
and squared values of the chronologically sequential returns. Since the
autocorrelation is positive, it can be concluded, that small absolute returns
are followed sequentially by small absolute returns and large absolute returns
are followed by large ones again. This means, that there are quiet periods
with small price changes and dynamic periods with large oscillations.

Furthermore, whereas the estimate for skewness is mostly close to zero, except
for BTC and ZEC, the estimate for excess kurtosis is in every case
significantly larger than 3. The smallest estimated excess kurtosis is by STR
(yet with an expressive $\widehat{e.Kurt}$ of 8.12), and the largest by BTC
($\widehat{e.Kurt}$ = 49.44). These values show, that the tested constituents
are far from normally distributed. Negative skewness signals about increasing
the downside risk and is a consequence of asymmetric volatility models.
Positively skewed distributions have a longer right tail, meaning for
investors a greater chance of extremely positive outcomes. A well-known
stylized fact about returns distributions highlights their leptokurtic nature:
they have more mass around the centre and in the tails than a normal
distribution. For example, Hussein (2011) reports relatively high levels of
kurtosis in stock data from the United States of America. This phenomenon is
known as kurtosis risk.

The combined test of the normal distribution from Jarque and Bera (JB) can be
derived as asymptotically $\chi^{2}$ distribution with two degrees of freedom.
The last column in table 1 shows, that in all cases the normal distribution
hypothesis is clearly rejected. This is above all caused by the value of
kurtosis, which is significantly larger than 3, caused by a very frequent
appearance of outliers in this new market. The higher kurtosis, compared to a
normal distribution, proves that these extreme points result in $leptokurtic$
distributions and are evidence of fat tails relative to the normal
distribution’s tail. However, as this asymmetry is common to financial
markets, it is especially strong in the cryptocurrency markets with
potentially extreme returns and a very pronounced volatility.

The following tables respectively show the individual correlation to CRIX, if
the market is acting positively, table 2, or negatively, table 3. Extensive
care should be put on our main actors - BTC, ETH and XRP - when studying
these. As these enjoy a large market acceptance and hence are long-term
drivers of the cryptocurrency market, we can once again, underline our
findings given beforehand.

On a side note, tables 2 and 3 show that among the top 11 cryptocurrencies,
most pairs exhibit low return correlations, what suggest strong
diversification benefits in a portfolio, especially outside the major
cryptocurrencies presented, see also (Petukhina et al., 2020).

We can observe, that the correlation to CRIX in both tables presents itself as
clustered around well-known cryptocurrencies, namely BTC, ETH, XRP, as well as
BCH, and ETC. Therefore, this activity can be interpreted in a way, which
indicates these constituents as the market drivers. This finding also
correlates with the long term trading activity registered on many online
sources for these coins. We should note, without going into detail, that LTC
and BCH are closely related to BTC, and that ETC is closely tied to the
history of ETH. XRP itself was able to carve out its very specific niche early
enough for certain applications, especially in the banking sector - in
contrast, BTC can be seen as the genesis of digital currency without any
intrinsic value, whereas the ETH system enables many different applications,
majorly through so-called “smart contracts”.

Table 2: Pairwise crypto-currency correlations of returns for positive market-movement days, as defined by returns on CRIX. 01. July 2018 - 31. August 2018. UP | BCH | BTC | DASH | ETC | ETH | LTC | REP | STR | XMR | XRP | ZEC
---|---|---|---|---|---|---|---|---|---|---|---
BCH |  | 0.50 | 0.23 | 0.33 | 0.47 | 0.46 | 0.13 | 0.29 | 0.25 | 0.37 | 0.23
BTC | 0.50 |  | 0.27 | 0.36 | 0.55 | 0.49 | 0.18 | 0.34 | 0.30 | 0.40 | 0.27
DASH | 0.23 | 0.27 |  | 0.17 | 0.22 | 0.22 | 0.10 | 0.17 | 0.17 | 0.22 | 0.14
ETC | 0.33 | 0.36 | 0.17 |  | 0.37 | 0.31 | 0.11 | 0.21 | 0.17 | 0.28 | 0.14
ETH | 0.47 | 0.55 | 0.22 | 0.37 |  | 0.47 | 0.16 | 0.30 | 0.27 | 0.42 | 0.22
LTC | 0.46 | 0.49 | 0.22 | 0.31 | 0.47 |  | 0.17 | 0.26 | 0.25 | 0.39 | 0.23
REP | 0.13 | 0.18 | 0.10 | 0.11 | 0.16 | 0.17 |  | 0.12 | 0.11 | 0.11 | 0.11
STR | 0.29 | 0.34 | 0.17 | 0.21 | 0.30 | 0.26 | 0.12 |  | 0.18 | 0.27 | 0.19
XMR | 0.25 | 0.30 | 0.17 | 0.17 | 0.27 | 0.25 | 0.11 | 0.18 |  | 0.20 | 0.15
XRP | 0.37 | 0.40 | 0.22 | 0.28 | 0.42 | 0.39 | 0.11 | 0.27 | 0.20 |  | 0.19
ZEC | 0.23 | 0.27 | 0.14 | 0.14 | 0.22 | 0.23 | 0.11 | 0.19 | 0.15 | 0.19 |
|  |  |  |  |  |  |  |  |  |  |
Table 3: Pairwise crypto-currency correlations of returns for negative market-movement days, as defined by returns on CRIX. 01. July 2018 - 31. August 2018. DOWN | BCH | BTC | DASH | ETC | ETH | LTC | REP | STR | XMR | XRP | ZEC
---|---|---|---|---|---|---|---|---|---|---|---
BCH |  | 0.48 | 0.21 | 0.32 | 0.47 | 0.43 | 0.15 | 0.27 | 0.23 | 0.37 | 0.22
BTC | 0.48 |  | 0.26 | 0.36 | 0.52 | 0.45 | 0.19 | 0.33 | 0.30 | 0.41 | 0.24
DASH | 0.21 | 0.26 |  | 0.15 | 0.22 | 0.21 | 0.11 | 0.16 | 0.18 | 0.18 | 0.14
ETC | 0.32 | 0.36 | 0.15 |  | 0.36 | 0.30 | 0.14 | 0.21 | 0.18 | 0.30 | 0.16
ETH | 0.47 | 0.52 | 0.22 | 0.36 |  | 0.42 | 0.16 | 0.29 | 0.23 | 0.40 | 0.21
LTC | 0.43 | 0.45 | 0.21 | 0.30 | 0.42 |  | 0.16 | 0.26 | 0.24 | 0.35 | 0.19
REP | 0.15 | 0.19 | 0.11 | 0.14 | 0.16 | 0.16 |  | 0.11 | 0.12 | 0.13 | 0.08
STR | 0.27 | 0.33 | 0.16 | 0.21 | 0.29 | 0.26 | 0.11 |  | 0.16 | 0.26 | 0.16
XMR | 0.23 | 0.30 | 0.18 | 0.18 | 0.23 | 0.24 | 0.12 | 0.16 |  | 0.20 | 0.15
XRP | 0.37 | 0.41 | 0.18 | 0.30 | 0.40 | 0.35 | 0.13 | 0.26 | 0.20 |  | 0.17
ZEC | 0.22 | 0.24 | 0.14 | 0.16 | 0.21 | 0.19 | 0.08 | 0.16 | 0.15 | 0.17 |
|  |  |  |  |  |  |  |  |  |  |

###  3.3 Time-Of-Day Effects and Proof-Of-Human

To support our hypothesis of mostly dealing with human agent initiated trades,
which we coin as PoH, we present our findings regarding the time-of-day
trading in this section. Additional material on information arrival, news
sentiment, volatilities and jumps of intraday returns can also be taken from
Qian et al. (2019).

Cryptocurrency exchanges, as introduced in section 2, are often designed to
serve a certain target group, for example by emphasizing compliance with
national regulatory frameworks. By plotting the trade volume against the
timestamps, we can also observe certain properties of market activity and draw
coherent conclusions to the origin of the market participants: are these
mostly human, who are doing trades by hand, or are we looking at a well oiled
automatic machinery full of algorithms - just as commonly portrayed. Keep in
mind, as mentioned in section 2, that our data is gained from Europe-based
sources, and taken from periods that are overwhelmingly identifiable by
corporate staff vacations. One should hence expect a less pronounced human,
but algorithmic driven market behaviour to contradict our hypotheses.

To underline this argument, it is useful to imagine a transitional system,
whereas human interference is completely removed or not relevant to a market
system (e.g. Caporale et al., 2016), and where the trading pattern will,
therefore, be independent of the time-of-day effects:

$human+human+human\>\widehat{=}\>human\>driven\>network$
$human+algorithm+human\>\widehat{=}\>predominantly\>human\>driven\>network$
$human+algorithm+algorithm\>\widehat{=}\>predominantly\>machine\>driven\>network$
$algorithm+algorithm+algorithm\>\widehat{=}\>algorithmic\>driven\>network$

With increasing market participation of algorithms, we expect, for example,
nighttime to have a negligible impact on the market activity. In contrast, we
expect nighttime to have an impact on market activity if the market is
dominated by human interaction.

The following figures employ GAM to observe daily and weekly patterns for
intraday volatility and trading volume. For daily seasonality cubic regression
splines, for weekly seasonality $P$-splines are used, and a number of knots
are logically set to the number of unique values, i.e 62 for daily patterns
and 7 for weekly. The summary statistics of GAM for all cryptocurrencies
demonstrate a high significance of smooth terms combined with a quite low
explanatory power (coefficients of determination are around 1%). Nevertheless,
we can observe distinct intraday seasonality patterns.

(a) BTC (b) ETH (c) XRP Figure 7: Daily seasonality: fit of Generalized
Additive Model (5 min nodes) with cubic regression splines for absolute
returns of cryptocurrencies (shaded regions represent confidence bands for
smooths), 01. July 2018 - 31. August 2018.

(a) BTC (b) ETH (c) XRP Figure 8: Weekly seasonality: fit of Generalized
Additive Model with p-splines for absolute returns of cryptocurrencies (shaded
regions represent confidence bands for smooths), 01. July 2018 - 31. August
2018.

Assuming that the majority of employed persons do work from 09:00 to 17:00
o’clock in Europe, figures 7 and 8 (data time is +1 GMT) present us with a
very clear picture of returns and volume. Characteristic $human$ $activity$
curves are presented by figure 7 showing the daily seasonality - a curve
driven by algorithms as the main actor, or $Artificial$ $Intelligence$ in a
FinTech startup buzzword context, should not present such a comparatively
extreme low around a typical time for the majority of humans to be asleep.
Following that point, the curves expresse a significant growth, only to flat
out again around lunch break time. Most figures present a peak between 17:00
and 20:00 o’clock, just when most people finish their daily routine jobs,
followed by an expressive decline of the curves. This is surprising, as media
outlets and startup marketing generally praise the non-stop availability and
easy access to cryptocurrency exchanges, and hence we would presume to see a
curve different to that of a “routine”-job. Further adding to this argument of
trading being mostly done by humans organized in cooperations (regarding
figure 8 with the seven numbers indicating the days of the week), is research
on anomalies such as the “Monday Effect” applied to our findings (e.g. Cross,
1973; Basher and Sadorsky, 2006). By applying both parametric and
nonparametric methods, Caporale and Plastun (2019) find abnormal returns for
no other cryptocurrency than BTC, and that only on Mondays - yet, in figure 8
we can observe that weekly absolute returns across cryptocurrencies reach
their peak only in the period from Tuesdays to around Thursdays, with a steep
decline in activity during the weekends.

(a) BTC (b) ETH (c) XRP Figure 9: Daily and weekly seasonality: fit of
Generalized Additive Model with cubic and p-splines for trading volume of
cryptocurrencies (5 min nodes), 01. July 2018 - 31. August 2018. 01. July 2018
- 31. August 2018.

Figure 9 presents us a respective lower trading volume during the weekends,
compared to for example Thursdays or especially Fridays. Similar results can
be seen in figure 10, presenting us with low volatility on the cryptocurrency
market at said times - one assumption from this could be taken from the
immense influx of financially potent startups organized as cooperations in
this emerging market (c.f. Benedetti and Kostovetsky, 2018). Yet, we can see
that human interaction is shaping how the market behaves during the given time
frames. Trade limited to regular working hours and days in Europe leads to the
conclusion, that the majority of trades are not done by algorithms, which are
active 24/7, but by human agents themselves making transactions and orders
individually and by hand. This is especially obvious through figure 8, which
is presenting a much lower activity pattern observable during the weekends.
Should algorithms really be the drivers in this, technically predestined,
fully digitized market, then this curve should not drop off as observable on
Saturdays and Sundays. These findings are similar across the board (see
appendix sections 5.2 - 5.4). While there is a plethora of well working, open-
source trading bots available for these markets, for example via Github
(Nevskii, 2019), as well as an abundance of commercially available trading
bots (Norry, 2020), the trust in these - or the knowledge of how to employ
them in this emerging market - is certainly low. This is especially
surprising, as the possibility for arbitrage or mean reversion is obvious with
multiple exchanges trading the same assets each with individually different
prices, see section 3. The inherent possibility to take advantage of this
inefficiency of the distributed trading, with near-simultaneous transactions,
leads to great opportunities for traders unseen in most traditional markets
for most assets. Hence we can assume, as algorithms need humans to get
deployed and take action, like reacting to price changes, that the overall
impact of these is not significant, if not negligible at all.

(a) BTC (b) ETH (c) XRP Figure 10: Daily and weekly seasonality: fit of
Generalized Additive Model with cubic and p-splines for volatility of
cryptocurrencies (5 min nodes), 01. July 2018 - 31. August 2018. 01. July 2018
- 31. August 2018.

In total we can observe, that the activity patterns displayed in this market
not only tend to express human interaction but also corporate structures as
well, as most trading is done Mondays to Fridays, with the weekends expressing
a low intensity of trades taking place. The previously mentioned immense
increase of financially potent FinTech entities have attracted absurd amounts
of financial backing compared to the output delivered via initial coin
offerings, ICOs for short (c.f. Benedetti and Kostovetsky, 2018; Zetzsche et
al., 2019). To enable new industries using the blockchain technology, startups
and commercial companies have been launching ICOs, similar to the initial
public offerings (IPOs) of companies, to sell tokens in a transparent and
decentralized manner and therefore creating a new method of raising funds
without intermediaries, like traditional financial institutes. Some of these
tokens are pegged to other (monetary) systems or even cryptocurrency
constructions directly, as these have already gained a high market acceptance
- especially the Ethereum ecosystem is facilitating this by providing
excessive tools and documentaries, paired with a focused and growing community
of developers, to create what they coined as “coloured coins” in order to
expand the utility of the existing blockchain (Walters, 2018). Besides the
fact, that the legality of ICOs is disputed and potential responses from
regulatory agencies are growing to be imminent, ICOs enable anyone within the
community to participate in the investment, providing opportunities for small-
scale investors. Hence the assumption would be, that especially these
specialized corporate startups are working on their backend and maintain their
ecosystem, whilst being active drivers of trading in this market - yet
predominantly human ones.

Coming back to the 2010 Flash Crash mentioned in the introduction of this
paper, one could argue, that such a flash crash is not possible due to the
delay that is inherent to blockchains - the so-called blocktime (Härdle et
al., 2020). However, as research has shown, it is easily possible to derive
sentiment and therefore market reactions from Twitter, Facebook, Stocktwits,
or similar public forums. As most activity can be seen on the respective
cryptocurrency exchanges, where the order books are not handled on-chain, but
necessarily off-chain to quickly process the exchange users trading requests
(Chen et al., 2019c, a; Qian et al., 2019). Therefore a crash related to
certain cryptocurrency prices may be seen only after the respective
information has been seeded into the network and accepted as new information
to the individual blockchain. This is creating an inherent risk, as market
behaviour can not be seen by only relying on on-chain data to predict certain
price movements. The previously mentioned “‘learning” algorithms could
therefore be, if they are employed in this manner, be dangerous if a “false-
postive” is identified and results in a respective process leading to dumping
a certain asset, which in turn could then generate a waterfall when other
algorithms, that respond to blockchain price data, reply to this movement
(Zinovyeva et al., 2019). Hence, a grand scale application of algorithms needs
to be finely tuned in order to avoid any humanely unforeseeable, but
technically feasible, consequences.

With the cryptocurrency market being easy to join and to actively participate
in, financial traders are becoming redundant - unless they provide specialized
services. Making many transactions doesn’t cost time to interact with a trader
and money to pay this person, as one can do that by hand at home with very low
transaction costs. This said, there is a big competition going on between the
exchanges, who themselves may act as traders or brokers. The future has to
tell if through this competition the rise of the machines and the respective
mass employment of algorithmic trading in this digital realm will become
reality.

##  4 Closing remarks

We have shown, that meanwhile there are certainly grand-scale employers of
algorithmic trading around in this new emerging market of cryptocurrencies,
yet, based on the time-of-day effects and the evidence gained, we can
conclude, that the impact of 24/7 algorithmic trading is rather negligible
given the empirical facts we have at hand. This leads us to the conclusion,
that even though this new digital market appears predestined to be ruled by
algorithms and specialised AI advisors, the digital realm of cryptocurrencies
has yet to be conquered by the machines and is still firmly in the hands of
humans or generally driven by respective startup’s.

Further research should certainly step into this breach, that we have proven
to be existent, and create means on how to best exploit this open ground on a
market-oriented basis, as well as on an individual level, say in regards to
the exchanges. Necessarily, such research not only needs to be of quantitative
or technical origin, but also needs to include a regulatory point of view, as
especially this field on blockchain research is more and more characterized by
its evident interdisciplinary nature.

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##  5 Appendix

###  5.1 List of cryptocurrencies in this research

Abbrev. | CC | Website
---|---|---
BCH | Bitcoin Cash | bitcoincash.org
BTC (XBT) | Bitcoin |  bitcoin.com, bitcoin.org
DASH | Dash | dash.org
ETC | Ethereum Classic | ethereumclassic.github.io
ETH | Ethereum | ethereum.org
LTC | Litecoin |  litecoin.com, litecoin.org
REP | Augur | augur.net
STR | Stalker | staker.network
XMR | Monero | getmonero.org
XRP | Ripple | ripple.com
ZEC | Zcash | z.cash

###  5.2 Appendix-Statistics for BCH, ETC and LTC

(a) BCH (b) ETC (c) LTC Figure 11: Candlestick charts for individual price
movements. 01. July 2018 - 31. August 2018.

(a) BCH (b) ETC (c) LTC Figure 12: Intraday 5-minutes log-returns. 01. July
2018 - 31. August 2018.

(a) BCH (b) ETC (c) LTC Figure 13: Intraday volatility (absolute values of
5-minutes log-returns) . 01. July 2018 - 31. August 2018.

(a) BCH (b) ETC (c) LTC Figure 14: Generalized additive model of volatility.
01. July 2018 - 31. August 2018.

(a) BCH (b) ETC (c) LTC Figure 15: Generalized Additive Model of trading
volume of cryptocurrencies. 01. July 2018 - 31. August 2018.

(a) BCH (b) ETC (c) LTC Figure 16: Daily and weekly seasonality: fit of
Generalized Additive Model with cubic and p-splines for trading volume of
cryptocurrencies (5 min nodes), 01. July 2018 - 31. August 2018. 01. July 2018
- 31. August 2018.

(a) BCH (b) ETC (c) LTC Figure 17: Daily and weekly seasonality: fit of
Generalized Additive Model with cubic and p-splines for volatility of
cryptocurrencies (5 min nodes), 01. July 2018 - 31. August 2018. 01. July 2018
- 31. August 2018.

###  5.3 Appendix-Statistics for DASH, REP and STR

(a) DASH (b) REP (c) STR Figure 18: Candlestick charts for individual price
movements (60-minutes intervals). 01. July 2018 - 31. August 2018.

(a) DASH (b) REP (c) STR Figure 19: Intraday log-returns (5-minutes). 01. July
2018 - 31. August 2018.

(a) DASH (b) REP (c) STR Figure 20: Intraday volatility (absolute 5-minutes
log-returns). 01. July 2018 - 31. August 2018.

(a) DASH (b) REP (c) STR Figure 21: Generalized Additive Model of volatility
of cryptocurrencies. 01. July 2018 - 31. August 2018.

(a) DASH (b) REP (c) STR Figure 22: Generalized Additive Model of intraday
trading volume of cryptocurrencies. 01. July 2018 - 31. August 2018.

(a) DASH (b) REP (c) STR Figure 23: Daily and weekly seasonality: fit of
Generalized Additive Model with cubic and p-splines for trading volume of
cryptocurrencies (5 min nodes), 01. July 2018 - 31. August 2018. 01. July 2018
- 31. August 2018.

(a) DASH (b) REP (c) STR Figure 24: Daily and weekly seasonality: fit of
Generalized Additive Model with cubic and p-splines for volatility of
cryptocurrencies (5 min nodes), 01. July 2018 - 31. August 2018. 01. July 2018
- 31. August 2018.

###  5.4 Appendix-Statistics for XMR and ZEC

(a) XMR (b) ZEC Figure 25: Chandlestick charts for individual price movements.
01. July 2018 - 31. August 2018.

(a) XMR (b) ZEC Figure 26: Intraday 5-minutes log-returns. 01. July 2018 - 31.
August 2018.

(a) XMR (b) ZEC Figure 27: Intraday Volatility. 01. July 2018 - 31. August
2018.

(a) XMR (b) ZEC Figure 28: Generalized Additive Model of volatility of
cryptocurrencies. 01. July 2018 - 31. August 2018.

(a) XMR (b) ZEC Figure 29: Generalized Additive Model of the 62 intraday
trading volume of cryptocurrencies. 01. July 2018 - 31. August 2018.

(a) XMR (b) ZEC Figure 30: Daily and weekly seasonality: fit of Generalized
Additive Model with cubic and p-splines for trading volume of cryptocurrencies
(5 min nodes), 01. July 2018 - 31. August 2018. 01. July 2018 - 31. August
2018.

(a) XMR (b) ZEC Figure 31: Daily and weekly seasonality: fit of Generalized
Additive Model with cubic and p-splines for volatility of cryptocurrencies (5
min nodes), 01. July 2018 - 31. August 2018. 01. July 2018 - 31. August 2018.