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Decentralized cryptocurrencies have gained a lot of attention over the last decade. Bitcoin was introduced as the first cryptocurrency to allow direct online payments without relying on centralized financial entities. The use of Bitcoin has vastly grown as a financial asset rather than just a tool for online payments. A lot of cryptocurrencies have been created since 2011 with Bitcoin dominating the cryptocurrencies’ market. With plenty of cryptocurrencies being used as financial assets and with millions of trades being executed through different exchange services, cryptocurrencies are susceptible to trading problems and challenges similar to those traditionally encountered in the financial domain. Price and trend prediction, volatility prediction, portfolio construction and fraud detection are some examples related to trading. In addition, there are other challenges that are specific to the domain of cryptocurrencies such as mining, cybersecurity, anonymity and privacy. In this paper, we survey the application of artificial intelligence techniques to address these challenges for cryptocurrencies with their vast amount of daily transactions, trades and news that are beyond human capabilities to analyze and learn from. This paper discusses the recent research work done in this emerging area and compares them in terms of used techniques and datasets. It also highlights possible research gaps and some potential areas for improvement. It is an open access article, you can download it from here:
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Received August 11, 2020, accepted September 11, 2020, date of publication September 21, 2020, date of current version October 6, 2020.
Digital Object Identifier 10.1109/ACCESS.2020.3025211
Cryptocurrencies and Artificial Intelligence:
Challenges and Opportunities
FARIDA SABRY 1, (Member, IEEE), WADHA LABDA 1, (Associate Member, IEEE),
1Department of Computer Science and Engineering, Qatar University, Doha, Qatar
2Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
Corresponding author: Farida Sabry (
The research in this article was done for project number NPRP X-063-1-014 under a grant from Qatar National Research Fund (QNRF).
However, the contents of the research are solely the responsibility of the authors and do not necessarily represent the official views of
QNRF. Open access funding was provided by Qatar National Library.
ABSTRACT Decentralized cryptocurrencies have gained a lot of attention over the last decade. Bitcoin
was introduced as the first cryptocurrency to allow direct online payments without relying on centralized
financial entities. The use of Bitcoin has vastly grown as a financial asset rather than just a tool for
online payments. A lot of cryptocurrencies have been created since 2011 with Bitcoin dominating the
cryptocurrencies’ market. With plenty of cryptocurrencies being used as financial assets and with millions
of trades being executed through different exchange services, cryptocurrencies are susceptible to trading
problems and challenges similar to those traditionally encountered in the financial domain. Price and trend
prediction, volatility prediction, portfolio construction and fraud detection are some examples related to
trading. In addition, there are other challenges that are specific to the domain of cryptocurrencies such
as mining, cybersecurity, anonymity and privacy. In this paper, we survey the application of artificial
intelligence techniques to address these challenges for cryptocurrencies with their vast amount of daily
transactions, trades and news that are beyond human capabilities to analyze and learn from. This paper
discusses the recent research work done in this emerging area and compares them in terms of used techniques
and datasets. It also highlights possible research gaps and some potential areas for improvement.
INDEX TERMS Artificial intelligence, machine learning, deanonymization, price prediction, fraud detec-
tion, volatility prediction, anonymity, privacy, mining, security.
This paper surveys research work applying artificial intel-
ligence and machine learning techniques in the field of
cryptocurrencies. Analyzing cryptocurrencies is considered
a relatively recent domain that became active in the last
decade. Bitcoin was announced at the end of 2008 as the first
decentralized cryptocurrency that relies heavily on the field
of cryptography for hashing and signing transactions. These
transactions are committed to a distributed blockchain ledger
to be synced and verified by nodes in a peer-to-peer network.
The Bitcoin blockchain size reached over 280 GB in June,
With this big data representing transactions in the
blockchain coupled with millions of trades being executed on
different exchange websites, growing number of tweets, posts
and articles related to Bitcoin and cryptocurrencies, there is
The associate editor coordinating the review of this manuscript and
approving it for publication was Xiaochun Cheng.
a clear need for automated tools to process and analyze this
big data. Artificial intelligence (AI) techniques can learn from
this massive amount of data by analyzing and discovering pat-
terns to ease and secure trading and mining. Discovering pat-
terns in money-laundering transactions and other fraudulent
transactions and trading schemes can help limit the crimes
involving cryptocurrencies due to privacy and security threats
they encounter. Artificial intelligence (AI) techniques are not
limited to machine learning (ML) techniques (supervised,
unsupervised, semi-supervised, and reinforcement), but also
include evolutionary-based techniques and knowledge-based
techniques [1].
The topic of data analytics for cryptocurrencies is gain-
ing importance as more entities are becoming more reliant
on cryptocurrencies. Our work differs from previous related
surveys by focusing on the research of using artificial intelli-
gence and machine learning techniques in cryptocurrencies
as digital currencies or crypto-assets and the surrounding
ecosystem. Hassani et al. [2] studied the interactions between
175840 This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see VOLUME 8, 2020
F. Sabry et al.: Cryptocurrencies and Artificial Intelligence: Challenges and Opportunities
Big Data and cryptocurrency focusing on two perspectives,
namely, ‘‘security and privacy enhancement’’, and ‘‘predic-
tion and analysis’’. They also used the word ‘‘cryptocur-
rencies’’ for its underlying blockchain technology and its
applications and not for cryptocurrencies as digital currencies
like in our work. Other authors in [3], [4] and [5] presented
surveys on blockchain applications in AI and robotics. None
of the mentioned surveys have explored the application of AI
techniques to tackle cryptocurrencies’ challenges.
Our survey considers papers from reputable peer-reviewed
conferences and journals. Papers that include mere descrip-
tive statistics or discuss the use of AI in other blockchain
applications like healthcare, smart contracts and IoT were
excluded. The following exploratory questions form the basis
for this paper:
What are the problems in the cryptocurrencies domain
that has been approached using AI techniques?
Which AI techniques have been studied in the literature
and employed in the field of cryptocurrencies, and what
are the datasets used in this domain?
What are the possible research gaps and areas of
improvement that can be further studied?
The rest of this paper is organized as follows. Section II
provides a background about Bitcoin and cryptocurren-
cies. Section III illustrates research problems related to the
cryptocurrencies domain and reviews artificial intelligence
and machine learning research addressing these problems.
Section IV discusses possible research gaps and potential
areas of improvement and conclusion of the paper is in
Section V.
Bitcoin, the cryptocurrency with the highest market capital-
ization, has been circulating for more than a decade since
January 3rd, 2009. Since then, Bitcoin has demonstrated
tremendous success as a financial asset and there are cur-
rently around 18 million Bitcoins (BTCs) being traded and
exchanged. Bitcoin, however, does not resemble other tradi-
tional assets from an econometric perspective [6].
On the technical level, Bitcoin depends on a decentralized
peer-to-peer network that aims to replace the centralized
financial system. In this decentralized network, a distributed
ledger saves all the transactions taking place in a blockchain
structure. This blockchain is synced by all nodes in the net-
work to verify the transactions. Transactions are added to the
blockchain in blocks by miners after they compete to solve a
cryptographic puzzle.
Bitcoin uses elliptic curve digital signature algorithm
(ECDSA) to sign a transaction between sender’s and
receiver’s Bitcoin addresses. Bitcoin addresses are identifiers
of 26-35 alphanumeric characters, formed by hashing of the
public keys of the sender or receiver. Using these addresses
made Bitcoin transactions look anonymous, however they
are actually pseudo-anonymous. SHA-256 hashing is also
used in Bitcoin’s proof-of-work consensus strategy. Proof-
of-work (PoW) is needed by the blockchain to secure the
FIGURE 1. Market capitalization of cryptocurrencies as per
CoinMarketCap in July 2020.
distributed ledger against any tampering attempts. This is
achieved through solving a computationally-intensive cryp-
tographic problem that is hard to solve but easy to verify. The
nodes compete to find a nonce that results in a block hash with
a certain number of leading zeroes. The winner node adds a
block of transactions to the blockchain and receives a block
reward set by the Bitcoin protocol in addition to transaction
fees from senders. The block reward was initially set to
50 BTC and it halves after every 210,000 blocks are added
to the chain. This halving process happens nearly every four
years. The block reward, at the time of writing this paper is
6.25 BTC. This process is called mining new Bitcoins. In the
early days of Bitcoin, the difficulty of the PoW problem was
relatively easy and Bitcoin mining was done using a personal
computer with good CPU. As the difficulty increases, higher
hash rates are needed to solve the crypto-puzzle. Machines
with higher computing power that use graphics processing
units (GPUs) and field-programmable gate array (FPGAs)
were used for mining new coins. Currently, mining pools
are of very high processing power and most of them use
application-specific integrated circuits (ASICs), which are
special hardware circuits designed specifically for Bitcoin
There are other alternative cryptocurrencies (altcoins) that
offer greater speed, anonymity or some other advantages
over Bitcoin. Namecoin was the first altcoin created as an
attempt to improve some features like decentralization, secu-
rity, privacy, and DNS speed, etc. Other examples are Lite-
coin, Ripple, Ethereum, Zcash, Monero, BCH, Dash, etc.
Currently, there are more than 2000 cryptocurrencies as per
CoinMarketCap.1However, Bitcoin dominates the market
with more than 70% of the market share as shown in Figure 1
as per the market capitalizations reported by CoinMarketCap
in July, 2020.
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F. Sabry et al.: Cryptocurrencies and Artificial Intelligence: Challenges and Opportunities
Decentralization allows cryptocurrencies to be more
immune to government control and interference. On the other
hand, as a disadvantage for the lack of mature regulations
in the cryptocurrencies market; cryptocurrencies have been
used in the dark web for money-laundering, weapons and
drug dealings, and other criminal activities. Many countries
have recently been forcing know-your-customer (KYC) and
anti-money laundering (AML) policies on exchanges while
conducting research to have their government-regulated cryp-
tocurrencies and waiting for regulations acceptance. Sim-
ilarly, Facebook Libra Coin is waiting for the necessary
regulatory acceptance and was planned to launch in 2020.
The Libra coin, governed by an ‘independent’’ association,
is designed to overcome the problems of volatility and lack
of scalability in existing cryptocurrencies.
This noticeably growing market of cryptocurrencies brings
about the problem of analyzing the tremendous amount
of trades and transactions taking place for different cryp-
tocurrencies through different exchanges and over different
blockchains. Artificial intelligence (AI) is a good candidate
for solving problems involving this immense amount of data
that humans can not analyze efficiently.
Techniques and models used in artificial intelligence
tasks can be categorized into machine learning techniques,
evolutionary-based techniques, knowledge-based techniques
and other techniques in which machines think and act
humanly and rationally as per the typical definition of AI [1].
There are many key players with various needs that can be
inferred from the cryptocurrencies ecosystem (see Figure 2),
e.g. traders (including criminals), regulatory agents, miners,
security specialists, etc. Automated traders can analyze and
learn from the cryptocurrencies’ market prices and markers
for taking trading decisions and achieving high returns for
FIGURE 2. Cryptocurrencies ecosystem.
their owners. Regulatory institutions can use AI to learn from
the data about possible financial frauds and potential threats.
Miners can make use of AI techniques to increase their profit
and save electricity for environmental considerations. Secu-
rity specialists can use these techniques to analyze and assess
the security and privacy level of cryptocurrencies and spot
possible pitfalls and threats.
Cryptocurrencies face similar challenges to fiat currencies’
and other financial market assets’ challenges. According
to Business Insider Report2in June 2019, there are three
areas where AI techniques are used in banking, namely,
conversational banking, anti-fraud detection, risk assessment
and credit underwriting. Additionally, financial chatbots and
voice assistants that mimic live employees, deepen customer
relationships and provide personalized insights and recom-
mendations, are examples of software systems using AI in the
financial field. Moreover, AI is extensively used in intelligent
trading systems to do stock market prediction and currency
price prediction. This helps in taking decisions on when to
buy, hold or sell a stock based on different markers that
change over time. Furthermore, anti-fraud detection tasks
make use of machine learning to learn from spending behav-
iors and patterns and detect suspicious patterns.
Trading cryptocurrencies shares with the financial market
the aforementioned problems. Using artificial intelligence
in cryptocurrencies, like in financial services, reduces the
risk of human error and speeds up the process of trading
by predicting the value of the currency or its rise and fall
over time. In addition, there are some other challenges that
are specific to cryptocurrencies that can be deduced from
the cryptocurrencies ecosystem in Figure 2and to which AI
techniques can offer useful solutions.
From the reviewed papers in this domain, we can summa-
rize the categories for challenges facing cryptocurrencies in
a taxonomy shown in Figure 3. There are challenges that are
related to the trading process like price and trend prediction,
volatility prediction, portfolio construction, fraud detection
and other analysis tasks to get insights and indicators about
different cryptocurrencies. Trading bots do all of these tasks
for trading cryptocurrencies. These challenges involve using
machine learning techniques to learn from historical data
of prices, other market indicators and social media inter-
ests to take profitable trading decisions. Additionally, natural
language processing (NLP) -which involves using many AI
techniques- is needed for sentiment analysis and processing
of news, social media posts (e.g. Twitter, Facebook, Tele-
gram [7], LinkedIn, Reddit, etc) and domain forums like
BitcoinTalk3and Ethereum Community Forum.4The use of
word2vec-based topic modeling and other NLP techniques
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FIGURE 3. Challenges in cryptocurrencies.
was demonstrated in [8] to analyze Reddit submissions’ top-
ics temporally with the price shifts occurring during 2017 and
2018. NLP can be also useful if a trading bot is built to be a
conversational bot to ease the trading experience. Moreover,
NLP is a core component in the design of chatbots replying
to queries and questions about cryptocurrencies as proposed
in [9].
Other challenges include mining of crypto-coins,
anonymity and privacy of cryptocurrency transactions, and
security of the cryptocurrency peer-to-peer network, users’
wallets and exchange services. Transactions’ delay is a
problem faced by cryptocurrencies, and specifically Bitcoin,
as it takes some time for transactions to be approved on
the various chains and confirmed. Transaction confirmation
delay depends on many factors in the mining process so
we will classify this problem among the mining challenges.
Blockchain size and its high degree of replication is also
considered as a challenge to cryptocurrencies but it is mainly
considered as a challenge with the protocol definition and
the data storage technology which is not typically tackled
by AI techniques. The research on applying AI techniques in
the field of cryptocurrencies has grown in the last four years
especially after the price hype in 2017.
The next subsections briefly describe the cryptocurren-
cies’ challenges identified in Figure 3that can be tackled by
AI techniques. Classification of the research work is done
according to the challenge it addresses. The main research
papers in each category are reviewed briefly. There exist a
large amount of published papers particularly for tackling
the price forecasting/prediction problem. Therefore, we could
not be exhaustive in citing every piece of work that was done.
Nevertheless, we chose to include papers that use diverse
techniques of AI and that can contribute to answering the sec-
ond question of this survey as mentioned in section I, related
to the AI techniques employed in the field of cryptocurrencies
and the datasets used in this domain.
The attention to Bitcoin and other cryptocurrencies as
financial assets has grown dramatically in the last three years.
This was after the hype occurred by the end of 2017 when
the price of Bitcoin reached about 20,000 USD as shown
in Figure 6.
For trading cryptocurrencies, domain observers and traders
need to do Bitcoin/cryptocurrency analytics and to predict
the cryptocurrency price. The terms ‘price prediction’ and
‘‘price forecasting’’ are usually used in the same way to refer
to the task of predicting an estimate for the price based on
the history of past prices and other explanatory variables.
The term ‘‘prediction’’ is more general as it refers to either
prediction of the current or future prices while forecasting is
used to refer to making estimates about the future prices or
trends. The term ‘‘prediction’’ is widely used by researchers
so it is adopted in the rest of this survey.
The price of Bitcoin can be affected by many factors
(sometimes called indicators/markers/features or variables),
among them are the interaction between supply, demand and
attractiveness for investors. These factors are usually affected
by trends in social networks, forums, search engines, declara-
tions by leaders and political stability of countries. Past fluc-
tuations in cryptocurrency’s price or trades’ growth/decline
can be used to determine possible trends and predict what
could happen in the future. Other factors that might affect
cryptocurrency price are: other cryptocurrencies’ prices,
blockchain data, the gold price, silver price, oil price, stock
market variables like S&P 500 index (Standard and Poor
500 index that measures the performance of the stocks
of 500 big-size companies in the U.S. stock exchanges), and
other financial technical indicators for cryptocurrency and
other stock markets such as those used in [10]. Online factors
that represent to an extent the public adoption and awareness
of Bitcoin and other cryptocurrencies such as Reddit posts,
Wikipedia views, Google trends, etc., were used by some
researchers [11]–[16]. Table 1shows the different factors
used in literature indicating whether positive (+) or negative
(-) correlation with the Bitcoin prices is reported and whether
it is reported as a significant correlation (*).
The basic flow of most of the work done in this area
starts with the collection of time-series data for different
variables of concern. Analysis of the data and relationships
between different variables and the cryptocurrency price are
then deduced. A supervised machine learning technique is
used to learn a model from data which can then be used for
prediction. Using history of different variables makes price
prediction a time-series prediction task. It can be modeled as
a regression problem to predict the closing price based on a
set of indicators. It can also be modeled as a classification
problem to predict if there will be a rise/fall or no change
in the price of a coin by encoding the cryptocurrency price
time series output variable in terms of rise and fall. A linear
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TABLE 1. Different factors used in cryptocurrency price prediction. (+: indicates positive correlation reported, -: indicates negative correlation reported,
*: indicates significant correlation reported.)
time series model for cryptocurrency price can be generalized
by Equation 1, where ytis the cryptocurrency price response
variable, Xis the input time series of explanatory variables
and indicators such as those in Table 1, and kis the order of
the auto regressive model to represent a dependency on the
history of both the input variables and the history of the price.
Different regression models and statistical models have been
tested by researchers to fit this linear model in different ways
[11], [12], [15], [17]–[19]. Simple linear time series models
sometimes leave certain aspects of economic and financial
data unexplained [29]. That is why some authors tested
nonlinear time series models to model nonlinear behavior
in economic and financial time series data [13], [14], [16],
Table 2shows a summary for the research on price predic-
tion of cryptocurrencies comparing the used AI techniques
and datasets in each paper. They are ordered by the year of
publication. Certainly, there are more papers published in this
very active area of predicting and analyzing cryptocurrency
prices. We only included recent publications with a set of
diverse AI techniques being tested. As it can be seen from
Table 2, each study depended on a different dataset with
different features over different time periods for training and
testing. Additionally, they used different evaluation metrics
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for evaluating the results. Geometric mean return and the
Sharpe ratio were used in [23]. Young et al. [13] used accu-
racy, F1-Score and Matthews correlation coefficient (MCC).
Mean square error (MSE), root mean square error (RMSE)
and mean absolute error (MAE) were used in [21], [25].
Mean absolute percentage error (MAPE) was used in [20],
[22], [26]. Accuracy, recall, precision and F1 Score were used
in [16], [20], [27]. Moreover, the results can not be easily
replicated due to the different models’ parameters that are not
thoroughly mentioned. For these reasons, it is a very difficult
task to recommend a certain model over the others as a state-
of-the-art for price prediction. However, at the end of this
section we will give summarized observations based on the
results reported in the referenced papers.
We have classified some of the state-of-art research done
in this area according to the main model(s) type it uses;
whether it is a statistical-based model, probabilistic-based
model, neural network based model or a tree-based model
(based on decision trees). Some papers tested different types
of models and will be added in a separate subsection.
There are many statistical techniques and models that have
been used in Bitcoin price prediction and analysis. Multiple
linear regression was used in [11], [12], [17] to model the
relationship between the Bitcoin cryptocurrency price and
some predictor variables. These variables included some eco-
nomic and financial variables. Additionally, measurements of
public interest in Bitcoin from Twitter feeds, Google Trends
and Wikipedia views related to Bitcoin [11] were recorded.
Twitter and Google Trends data were also used by [12] to
predict prices of Bitcoin and Ethereum. They found that
the tweet volume, rather than the tweet sentiment is a good
predictor of price.
Ordinary least squares (OLS) criterion was used to model
short-run impact of independent variables on the price of Bit-
coins while long-run relationships between the co-integrated
time series were captured using a vector error-correction
model (VECM) in [11]. OLS was used in [15] to estimate the
parameters of autoregressive distributed lag models (ARDL)
depending on features describing how political incidents and
statements mentioned in the news affect the price, the oil
price, gold price and volatility variables, Google search vol-
ume, positive and negative shocks.
Logistic regression (LR) was compared to autoregressive
integrated moving average (ARIMA) for Bitcoin Price pre-
diction in [18] and its other variants in [19]. OLS regression
and fractionally integrated ARMA (ARFIMA) were used
in [19] to investigate the stochastic properties of the top
six largest cryptocurrencies at that time (Bitcoin, Ethereum,
Ripple, Litecoin, Stellar and Tether) and their relationship to
six stock market indices.
Young et al. [13] used a deep learning model to predict
Bitcoin price and extent of transactions fluctuation of the
currency. Deep learning was also used in [28] to predict
Bitcoin and Ethereum prices in Australian dollars (AUD).
A comparative study of different deep learning models (deep
neural network (DNN), long-short term memory (LSTM) and
artificial neural network (ANN)) was done in [20] for Bitcoin
price prediction. LSTM-based prediction models slightly out-
performed other techniques for regression of Bitcoin price
while DNN-based models performed better for classification
of price changes whether up or down. They also showed
that classification models were more effective than regression
models for trading profitability.
In [21], Yiying et al. analyzed the price dynamics of
Bitcoin, Ethereum and Ripple using ANN and LSTM. They
surprisingly found that ANN relies more on long-term history
while LSTM tends to rely more on short-term dynamics
efficiently utilizing useful information hidden in them.
Many other researchers have tested neural networks and
its variants, some of them are reviewed and compared
in [22]. The authors of [22] proposed layer-wise randomness
into the observed features’ activations of multilayer percep-
tron (MLP) and LSTM to simulate market volatility. They
achieved no more than 5% average improvement in MAPE
in their best case experiment with 23 features with a window
size of 7 days.
Alessandretti et al. [23] tested three forecasting models using
daily cryptocurrency prices of 1,681 currencies. The first
model is a decision tree model to predict the return on invest-
ment for all cryptocurrencies. The second model is a gra-
dient boosting decision tree model for each cryptocurrency
to make a prediction on each cryptocurrency depending on
information about the behaviour of the whole market. In the
third method, they used a different LSTM model for each
currency, where the prediction is based on previous prices
of the currency. For parameter optimization, they used two
evaluation metrics; the geometric mean return and the Sharpe
ratio. The three methods performed better than the baseline
simple moving average strategy when applying the invest-
ment strategy for the whole period considered. The opti-
mization of parameters based on the Sharpe ratio achieved
higher returns. Methods based on gradient boosting decision
trees performed best for short-term 5/10 days predictions.
On the other hand, LSTM performed best for predictions
based on 50 days of data, since they are able to also capture
long-term dependencies and were very stable against price
Shah and Zhang [24] used Bayesian regression for predicting
Bitcoin prices. Kim et al. [14] used averaged one-dependence
estimators (AODE) as a prediction model which is a proba-
bilistic classification learning technique. They used the model
to predict fluctuations in prices and number of transactions at
different lags.
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F. Sabry et al.: Cryptocurrencies and Artificial Intelligence: Challenges and Opportunities
TABLE 2. Comparison between AI research work done for price prediction and analysis in cryptocurrency.
Hidden Markov model (HMM) has been used in [25] to
address the dynamics of Bitcoin prices and get hidden infor-
mation that cannot be directly extracted. A total of 52 features
were then fed to an LSTM to predict the Bitcoin prices.
Genetic algorithm was used in optimizing the parameters of
LSTM. Effectiveness of the resultant model HMM-LSTM
was compared to ARIMA and conventional LSTM and
was found to outperform them in terms of MSE, RMSE
and MAE.
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Bayesian neural network (BNN) was used in [26] for
regularizing weights of input variables to a neural network to
account for the high volatility of Bitcoin price. The input vari-
ables included blockchain variables, macroeconomics vari-
ables and international fiat currencies exchange rates. BNN
was found to outperform support vector regression (SVR) in
terms of RMSE and MAPE.
In [16], neural networks (NN), MLP, support vector
machines (SVM) and random forest (RF) were used for the
prediction task to predict the price of the Bitcoin, Ethereum,
Ripple and Litecoin cryptocurrency market movements. MLP
was found to outperform the other models in case of Bitcoin,
Ethereum and Ripple, while SVM performed the best in
case of Litecoin. Shintate and Pichl [27], proposed a random
sampling method (RSM) for Bitcoin time series trend pre-
diction and they used a walk forward optimization method.
They used this method to account for non-stationarity of
the high-frequency time-series trading data. They compared
the proposed approach to LSTM and MLP. The profit rates
based on RSM were higher than those based on LSTM
and MLP, and not easily biased by class imbalance. Differ-
ently, designing automatic trading strategies has been tackled
in [30] using reinforcement learning. Double and dueling
double deep Q-learning networks were used with testing two
reward functions: Sharpe ratio and profit reward function.
The system based on Sharpe ratio achieved the highest profits
for Bitcoin trading over a period of almost four years of
In [31], many statistical machine learning techniques like
support vector regressions (SVR) and Gaussian Poisson
regressions (GRP), algorithmic models such as regression
trees (RT) and the k-nearest neighbours (k-NN) and finally
artificial neural network topologies such as feedforward
(FFNN), Bayesian regularization (BRNN) and radial basis
function networks (RBFNN) to find BRNN achieving the best
accuracy. They used Bitcoin intra-day price data sampled at
5 minutes intervals in the period from January 1st, 2016 to
March 16th, 2018.
In [17], the authors compared different techniques and
least absolute shrinkage and selection operator (LASSO) was
found to dominate in predicting the 30-day returns of cryp-
tocurrencies. In [20], LSTM slightly outperformed the other
models under test when the prediction task was modeled as
a regression problem, whereas DNN slightly outperformed
the other models when perceived as a classification problem.
There is no single model that can be said to be the best for
the problem of cryptocurrency price prediction as the model
depends on many factors such as the size of the dataset,
the different indicators and features at different lags to be
used for prediction. Furthermore, the uncertainty associated
with the crypto-prices and the random black swan events
that can happen at anytime, are hard to be predicted by any
model with good accuracy. In Figures 4and 5, box plots
were plotted for the range of values reported for accuracy
FIGURE 4. Box plot of accuracy for the machine learning techniques used
in price prediction as a classification problem for papers cited in this
section with accuracy as the evaluation metric (the higher the accuracy,
the better prediction will be). On each box, the central mark is the
median and the edges of the box are the 25th and 75th percentiles. The
small circles represent outliers.
FIGURE 5. Box plot of MAPE for the machine learning techniques used in
price prediction as a regression problem for papers cited in this section
with MAPE used as the evaluation metric (the less MAPE, the better the
prediction will be). On each box, the central mark is the median and the
edges of the box are the 25th and 75th percentiles. The small circles
represent outliers.
and MAPE for some of the machine learning models used in
[13], [14], [16]–[18], [20], [22], [26], [27]. The most common
evaluation metric for the models treating price prediction as a
classification problem was accuracy as in Figure 4and MAPE
was used as the common evaluation metric for regression
models as in Figure 5. Figure 4shows that LR, linear dis-
criminant analysis (LDA) and RF achieve the best results for
classification models while MLP and stochastic MLP were
the best for regression models as shown in Figure 5. The
best reported accuracy is 72% and the best MAPE is 2.8%
but each was based on different features and time periods.
Price prediction models are away from perfect prediction due
to the speculation involved in trading and the complexities
involved in the market that affect the trading decisions but as
it is well known all models are wrong, but some are useful.
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FIGURE 6. The top cryptocurrencies’ prices until the start of March, 2020; prices are taken from Coindesk.8
Other studies [11], [12], [15], [19], [21], [23]–[25], [28] used
different evaluation metrics so they were not included in the
We believe that the best model highly depends on the
selected features and the period of study. It can be a good
approach to first test the time series for the prices and the
different indicators for non-linearity using statistical tests
and analytic techniques used in [32] to analyze the effect
of different factors on the daily prices of cryptocurrencies.
The work in [32] did not use any machine learning model
to learn from these factors. However, the results presented
can be analyzed and used to determine the best factors to use
for building a cryptocurrency price prediction model. In [33],
the authors tested for the importance of the economic and
technology factors affecting the Bitcoin price over different
periods of time using ANN and RF after dividing the Bitcoin
price time series into four periods. It might also be useful to
learn models based on segmented time periods. Some other
nonlinear tests and techniques listed in [34] have not been
explored in the field of cryptocurrencies price prediction.
Volatility is defined as the degree of variation of a trading
price series over time. It represents the amount of uncertainty
or risk about the size of changes in currency value. Bitcoin
and other cryptocurrencies are considered to be volatile.
Figure 6shows a graph for cryptocurrencies’ prices until
the start of March 2020. Volatility of cryptocurrencies is
mainly caused by their decentralized nature making their
prices uncontrollable by any organization or government.
Accordingly, cryptocurrencies can be considered as being
traded in a free market where the price is solely determined
by the supply and demand, however, there are other opinions
that oversee the presence of a true long-run dependency [11],
[13], [17] which invalidates the efficient market hypothesis.
There are other factors affecting the price value, other than
the interaction between supply and demand as mentioned in
the last section. People investing in Bitcoin consider high
volatility to be an indication of high-risk investment.
Volatility accounts for price movement away from its aver-
age value. A cryptocurrency price range could be estimated
if volatility can be predicted or estimated for a day or a
week based on historical data. Table 3provides a summarized
comparison of research papers using AI techniques to model
cryptocurrency volatility and the datasets used.
Generalized autoregressive conditional heteroskedasticity
(GARCH), which is a time-series statistical model, is used
for modeling volatility [35]–[37]. Twelve different GARCH
models were tested in [35] for modeling Bitcoin, Dash,
Dogecoin, Litecoin, Maidsafecoin, Monero and Ripple.
Integrated GARCH (IGARCH) and Glosten-Jagannathan-
Runkle GARCH (GJRGARCH) models were found to be the
best fit for most cryptocurrencies. Maximum likelihood was
used to fit the different GARCH-type models. Peng et al. [36]
combined the traditional GARCH model with Support Vec-
tor Regression (SVR), which can cover multi-variate and
dynamic characteristics of financial series robustly. It was
used to predict the volatility of three cryptocurrencies
(Bitcoin, Ethereum and Dash) and three fiat currencies (Euro,
British Pound and Japanese Yen) to evaluate the alternative
risky investments and guide the investment decisions. They
provided strong evidence that the SVR models significantly
outperforms the traditional GARCH models.
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TABLE 3. Comparison between AI research work done for prediction of cryptocurrency volatility.
Using and comparing GARCH models was also
investigated in [37] to model volatility in Bitcoin, Ethereum,
Litecoin, Ripple, Moreno, Dash, Stellar and NEM. Then, they
estimated the one-day-ahead value-at-risk (VaR) forecasts.
The asymmetric GARCH models with long memory property
achieved overall better performance for all cryptocurrencies.
Guo et al. [38] tested temporal mixture model using both
incremental learning and rolling procedure for performance
prediction to predict volatility of Bitcoin prices. They com-
pared their model to different statistical (GARCH, Beta-
GARCH, structural time series model, ARIMA) and machine
learning baseline models (Random Forest, Gradient Boost-
ing, Elastic-net regression, Gaussian process based regression
and LSTM). Their temporal mixture model proved to be more
accurate in most of the cases while being robust and adaptive
with respect to time-varying data.
Other linear and nonlinear machine learning models were
used for predicting realized volatility in Bitcoin prices based
only on past values of realized volatility at different lags
in [39]. Ridge regression was found to perform the best
in terms of MSE and RMSE among the tested techniques
(ANN in its different forms (MLP, GRU and LSTM), SVM,
ridge regression, and heterogeneous autoregressive realized
volatility (HAARV)).
Many cryptocurrencies’ trading bots are currently avail-
able that implement trading strategies and offer customized
customer’s strategy. Trading bots are software products or
websites that offer what is called ‘‘algorithmic trading’’ as
they automatically analyze market actions and indicators,
offer strategies to maximize trader’s gains and improve her
satisfaction. They can aggregate historical market data, calcu-
late indicators, simulate order execution and even can be set
up to execute strategies while the customer is asleep. Some
bots use natural language processing techniques to commu-
nicate with the customer in a more natural and friendly way
[9]. In the design of these trading bots, many algorithms
and techniques similar to those used for price and volatility
prediction mentioned in the last two subsections are used to
maximize the profit and develop a strategy with maximum
return. They differ in the number of exchanges they support
and the features they offer. Additionally, they can offer portfo-
lio construction and optimization to find an optimal weighing
of financial assets which might include Bitcoin, other cryp-
tocurrencies and other traditional financial assets like stocks
and bonds. This optimization aims at maximizing the over-
all return while minimizing the variance of the return. The
results in [41] suggest that investors should include Bitcoin in
their portfolio as it generates substantial higher risk-adjusted
returns through comparing eight different well-known portfo-
lio optimization techniques widely used for traditional assets.
They used the statistical-based GARCH model to learn the
dynamic conditional correlations between Bitcoin prices and
other bonds and indices.
Hierarchical risk parity approach was applied in [42]
to a large portfolio of 61 cryptocurrencies which involves
an unsupervised tree clustering, quasi-diagonalization and
recursive bisection. They carried out-of-sample compari-
son with traditional risk-minimization methods for finan-
cial assets (Inverse Volatility (IV), Minimum Variance
(MV), and Maximum Diversification (MD)). A thorough
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review of financial portfolio optimization techniques can
be found in [43]. We are here only concerned with apply-
ing AI and ML techniques in portfolio construction and
optimization for automated trading of cryptocurrencies.
Alessandretti et al. [23] used the gradient boosting decision
trees models and and LSTM prediction model to build invest-
ment portfolios for cryptocurrencies based on the predictions
optimizing the geometric mean return and the Sharpe ratio.
Nakano et al. [44] used a deep neural network for return pre-
diction in Bitcoin intra-day trading based on technical vari-
ables and time-series data every 15 minutes. Irene et al. [45]
relied on variants of GARCH models to determine the optimal
portfolio weights for a minimum variance equity portfolio.
Differently, Cocco et al. [46] viewed the trading optimization
problem from an evolutionary perspective and proposed using
genetic algorithms (GA). They simulated a Chartist trading
agent that uses the best trading rules evolved through GA
against random traders.
The area of applying artificial intelligence and machine
learning in portfolio construction and optimization for cryp-
tocurrencies is considered a recent research area. It still
needs further investigation to find the best strategy that suits
cryptocurrencies while getting benefit from machine learning
models and techniques used for other financial assets [47].
Investors classification can help investors and automated
trading bots get more insights about the cryptocurrencies mar-
ket and its price dynamics in order to develop profitable trad-
ing strategies. In [48], the socio-demographic characteristics
of cryptocurrency investors and the factors that affect their
investment decisions (whether they invested, never invested
or intend to invest in the future) for any cryptocurrency are
investigated. With about 402 survey responses for Australian
and Chinese blockchain and cryptocurrencies’ followers, they
built a multinomial Logit model to find the factors that affect
the choice of investment in cryptocurrency coins versus other
types of initial coin offering (ICO) tokens. They found age,
gender, education, occupation, and investment experience
significantly affect the decision. Having insights and clear
analysis for the reasons behind people’s investing decisions
in an ICO is critical to decide on trading strategies. It can
help in marketing ICOs of cryptocurrencies, but bigger and
more diverse datasets are required to have a good outcome.
In a recent study [49], the authors used unsupervised
clustering technique to group different types of investors.
They based their clustering on similarities in trading behav-
ior according to trade volume, average bid volume, average
relative price and average duration to finish a trade from
the time the offer is placed on a well-known exchange web-
site. They were able to classify investors into 10 clusters
(six types of investors offering Bitcoins and four types of
investors ordering Bitcoins). They used ARDL model to
identify the factors (macro-financial, technical trading indi-
cators, technological measures and market sentiment from
Twitter) that might influence the trading behavior of investor
types (speculators, cryptocurrency miners, informed traders,
large professional investors, USD-orientated investors, global
traders, etc.). They showed that the exchange rate of Bitcoin
is seen to be significantly driven by the number of orders
placed by only one cluster of investors. The discussion in their
work sheds more light on the cryptocurrency market investors
and the herd behavior shown by one cluster which results
in speculative price changes. We suggest that performing a
similar behavior clustering analysis directly on blockchain
data, although it will face many challenges, may give a
more accurate clustering. It might give more evidence about
how different indicators affect the investors’ behaviors and
accordingly affect the price.
The use and reputation of Bitcoin and other cryptocurrencies
in aiding illicit activities is a big concern, as it affects the sta-
bility and the trust in cryptocurrencies. Cryptocurrencies are
known to attract cybercriminals for their pseudo-anonymity
and for being operated outside the laws of governments
and banks. However, regulators are continuously trying to
enforce know-your-customer (KYC) and anti-money launder-
ing (AML) laws for exchanges and escrow services. There are
different types of scams and criminal activities that can occur
in cryptocurrencies, such as digital theft, hacking, phishing,
Ponzi-schemes, pump-and-dump schemes, purchasing illegal
drugs and money laundering in the black market. A Ponzi-
scheme is one of the fraudulent schemes that offer high rates
of return for early investors as it generates returns for early
investors by acquiring new customers.
Fraud detection is based on detecting anomalies and sus-
picious behaviour in the transactions and trades history [50],
especially that Bitcoin transactions are transparently recorded
on the blockchain public ledger.
With the scarcity of labeled incidents or examples for
different fraud activities, Monamo et al. [50] used trimmed
k-means and k-means clustering based on features from the
transactions graph in a semi-supervised way to detect fraud-
ulent activity in the Bitcoin transactions network. Both algo-
rithms achieved optimal clustering. The trimmed algorithm
was able to detect 5 from the 30 well-known anomalies such
as the Mt Gox, Linode Hack, and 50 BTC Theft as examples
for thefts and hacks. Using k-d trees, they were able to detect
2 more thefts. Based on the clustering labels for outliers,
the authors employed some supervised classification models
to understand the relation between the labels and predictor
variables. Random forest achieved the best precision.
For estimating the proportion of possible cybercriminal
entities in the Bitcoin ecosystem, Yin et al. [51] tested 13 dif-
ferent machine learning classifiers for this task. They used
LR, LDA, k-NN, classification and regression tree (CART),
Naive Bayes (NB), SVM, random forest, extremely random-
ized forests, bagging and gradient boosting. The last four
achieved the best results as reported in Table 4. The dataset
used by the authors and provided by Chainalysis12 is limited
and facing the same problem of substantial undersampling of
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TABLE 4. Comparison between AI research work done for fraud detection in cryptocurrency.
some of the 12 categories like stolen-bitcoins, ransom-ware
or mixing.
To detect Ponzi schemes automatically,
Bartoletti et al. [52] constructed a dataset of real-world Ponzi
schemes by analyzing the Bitcoin blockchain transactions
used for scams through a manual search on Reddit and bit-, they were able to find a list of 32 Ponzi schemes
for which advertisers in the forums present them as high-yield
investment programs (HYIP), or as gambling games. Then,
they collected the deposit addresses for these schemes
through manual search in the forum and on their websites.
They extended the number of addresses through address clus-
tering and multi-input heuristic. To address the well-known
class-imbalance problem in this task, they used different
under-sampled sets and different cost-matrix weights. For-
malizing the problem as a binary classification problem for an
address to be either Ponzi-related or non-Ponzi related, they
evaluated three different types of classifiers: repeated incre-
mental pruning to produce error reduction (RIPPER) which
is a rule-based technique, Bayes network and random forest.
The best classifier was random forest which correctly classi-
fied 31 Ponzi schemes out of 32. For Ponzi-schemes detection
in Ethereum, Jung et al. [53] used J48 (decision tree algo-
rithm), random forest and stochastic gradient descent (SGD)
for classification. HYIP classification has been addressed
in [54] using a dataset with a total of 2,134 HYIP addresses.
Toyoda et al. [54] used random forest (RF), gradient boosting
implementation (XGBoost), ANN, SVM and k-NN for binary
classification of HYIP and non-HYIP addresses. Random for-
est achieved the best results based on 7 features characterizing
the transactions.
A summarized comparison for the aforementioned
reviewed research work is presented in Table 4. The common
thing that can be obviously seen very challenging in this
kind of problem is the lack of a reliable labeled dataset of
reasonable size available for researchers. Even studies that
used the Chainalysis dataset still face the problem of dealing
with the high class-imbalance problem. Except for [50],
which combined semi-supervised and supervised techniques,
other efforts [51]–[54] solely relied on supervised techniques.
The high costs of manual labeling as done in [52] and the
small size of the labeled datasets for fraudulent addresses
suggest we need to explore using semi-supervised learning
techniques for fraud detection in a way similar to [55].
Privacy and anonymity are two necessary aspects for online
financial trading. Anonymity is mostly favored by criminals
to hide their identities when dealing illegally for drugs or
weapons or being involved in money laundering transactions.
However, it is also preferred by privacy-savvy people who
want to keep their identities and transactions anonymous and
private. Privacy means protecting the data of transacting users
including the traded amount, the transacting parties, their
balances and the timing of the transaction.
Trying to reveal the identities of Bitcoin users and link-
ing their Bitcoin addresses and trades usually rely on using
public data information from social media or other publicly
available data in a process called ‘‘deanonymization’’. It is
either based on heuristics to link this data to the blockchain
transactions as in [56]–[59], or it can be based on AI tech-
niques. Deanonymization has been approached using AI in
two ways; clustering [60] or classification [61], [62] and [63].
Ermilov et al. [60] used off-chain information (e.g. twit-
ter and together with the blockchain
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information based on common spending and one time change
heuristics for address clustering. They used a probabilis-
tic framework to assign an address to a cluster based on
maximizing the log-likelihood function with six clusters of
entities: mining pools, exchanges, darknet markets, mixers,
gambling and other services. The authors in [61] and [62]
considered a particular pattern of 2-motif and 3-motif features
as a potential laundering pattern used in the classification
model based on the pattern of acquisition and spending of Bit-
coin in the dataset. They derived an entity-transaction graph
from the address-transaction graph. Additionally, features
like betweenness, closeness, in-degree, out-degree, PageR-
ank and load centrality over a week, a month and a year were
used. With other temporal features; they proposed a decision
tree method with gradient boosting and compared the results
against a logistic regression algorithm. This classification
problem faces the entity-category class imbalance between
Service category and the Mining Pool category for example.
Gradient boosting classifier (GBC) achieved the best
results in [63] to classify a Bitcoin address into one of
ten categories. They used synthetic minority over-sampling
technique (SMOTE) to oversample the two minority classes
hosted-wallet and mixing to overcome the class imbalance
problem. Modeling the problem as a classification problem
as well, Lin et al. [64] proposed a different set of features
including moments of transaction time in higher order to train
different supervised machine learning models (LR, SVM, RF,
AdaBoost, LightGBM, ANN, etc.) on a labeled category data
set. Researchers in [65], used cascading of machine learning
models to enrich entities’ information with data from previ-
ous classifications. This enhanced the overall classification
performance using 34 features for initial classification. They
utilized three different models; Adaboost, random forest and
gradient boosting.
Different from all the above research work reviewed,
Juhász et al. [66] did not rely on labeled data for Bitcoin
addresses classification. They monitored Bitcoin network
messages through 140 nodes distributed over the network to
bind Bitcoin transactions to geographical locations. The mon-
itoring agents watched out for clients relaying transactions
during the first time segment; estimated to be 2 sec. The client
originating the transaction is possibly one of those agents.
A comparison between different studies, the datasets used and
the techniques tested are listed in Table 5. It can be seen that
most of the studies relied on supervised techniques, except
for [60], which used a semi-supervised method combining
clustering with labels from different public sources.
The mining process has the disadvantage of high electricity
consumption used by mining pools for participating in the
PoW computations. Only one miner succeeds to add a block
of transactions, while other mining pools are left with the
expenses of huge energy costs. This disadvantage threat-
ens the decentralization of the cryptocurrency and makes
it susceptible to monopolization, especially when the block
reward will vanish over time due to Bitcoin block reward
halving. Some researchers [68] theoretically proposed using
deep-learning tasks as a PoW to let the electricity be con-
sumed in useful tasks. A coin is rewarded when a miner
exceeds a minimum threshold for performance. They also
proposed a proof-of-storage mechanism to store the deep
learning models on distributed nodes called keepers which
are also to be rewarded as per the proposed model for keeping
secure storage for the models. While the idea of saving the
electricity for useful operations seems beneficial, yet the pro-
posed model ignored some important aspects. Among them
is the required security and protection for the training and
validation data. The challenge is how to protect the data that
must be shared with different untrusted miners in order to
solve the problem, as proposed in the model. A second issue
is the time for adding a block since some deep-learning tasks
may take days for training a model. The Bitcoin block adding
time is already criticized by people in the domain as it is
greatly limiting the transactions’ rate, but it is needed for
properly synchronizing the blockchain. Also deep-learning
problems and their training time depend on the training set
size. The authors did not mention how their proposed model
could handle these issues.
Confirmation time for transactions depends on various fac-
tors in the mining process and the network. Singh et al. [69]
addressed the confirmation time prediction for Ethereum
blockchain. For this task, they used the dataset for Ethereum
transactions until November 2018 to get a set of fea-
tures including the transactions count done by the sender,
the sender’s gas, gas price, gas used, the timestamp for send-
ing the transaction (calculated from the pending transaction
pool) and the transaction’s block timestamp. They formulated
the problem as a classification problem with eight categories
depending on the duration of confirmation of the transaction
within (15 s, 30 s, 1 min, 2 min, 5 min, 10 min, 15 min,
30 min and longer). They used SMOTE to address the class
imbalance problem as most of the transactions are confirmed
within 15s. They compared three models; Naïve Bayes, ran-
dom forest and MLP according to accuracy, null-accuracy and
Cohen’s Kappa score to account for the imbalanced nature
of the data. They found that MLP achieved the best accurate
Additionally, Feng et al. [70] proposed a Markov model
to model and analyze a selfish mining strategy in Ethereum.
They viewed the PoW mining process as a series of Bernoulli
trails that independently search for a nonce in order to gen-
erate a new block. They found that the computational power
threshold which makes selfish mining profitable in Ethereum
is lower than that in Bitcoin mining. This makes Ethereum
more vulnerable to 51% attack. They suggested designing
new reward functions to allow miners maximizing their prof-
its through a more secure and honest strategy.
Game-theoretic analysis has also been used in [71] to
prove the importance of block reward in mining to keep
the blockchain secure since the transaction fee model could
encourage selfish mining. Another study [72] used game
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TABLE 5. Comparison between AI research studies done in cryptocurrency deanonymization.
theory for analysis of some malicious mining strategies like
block withholding (BWH).
It can be concluded that ideas using AI techniques in the
mining process to replace solving the crypto-puzzle in the
PoW, or entirely replace the PoW by an AI-based consen-
sus mechanism, have not been thoroughly investigated and
evaluated. AI techniques can be employed by mining pools
to choose which cryptocurrency to mine and which mining
pool to join in order to reduce the electricity consumption
and increase their profit based on historical data. A study done
in [73] used prospect theory in comparison with utility theory
to predict the profitability of a miner. They learned from
data obtained from mining with 5 pools (AntPool, F2Pool,, SlushPool and BatPool) for 40 consecutive days
using AntMiner S5. The data included parameters related to
the mining pools (e.g. hash distribution and reward sharing
method) and other parameters specific to the miner (e.g. its
hash rate and electricity cost) in addition to the current value
of the currency and the value of the block reward. They con-
cluded that their prospect theoretic approach predicted their
profits more accurately than the expected utility approach.
Despite the security and privacy properties that exist in
blockchain-based cryptocurrencies which were surveyed
in [74], there are several security threats that are facing the
cryptocurrency ecosystem [75]. They can be classified as
attacks on the distributed network, mining process attacks,
double spending and transaction malleability attacks. There
are also client-side security attacks and privacy threats to wal-
let, exchange or escrow services [58]. In this paper, we only
focus on research papers related to security that rely on
AI techniques.
Johnson et al. [76] used game-theoretical models of com-
petition between two mining pools of varying sizes to find the
trade-off between mining strategies. Triggering a distributed
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denial-of-service (DDoS) attack to lower the chance that a
competing mining pool wins in solving the crypto-puzzle and
adds a block to the chain. They considered differences in
costs of both investment and attack, as well as uncertainty
of the success of a DDoS attack. By analyzing the game’s
equilibria, they found that pools have a greater incentive to
attack large pools than small ones. It was also concluded that
larger mining pools have a greater incentive to attack than
smaller ones.
In a different context, DDoS attack detection in
Bitcoin-related services (e.g. mining pools, currency
exchanges, e-Wallet, gambling services) has been studied
in [77]. Johnson et al. used MLP trained with features based
on data collected in [78] and blockchain blocks and transac-
tions to detect DDoS attacks. Blocks were labeled as either
‘‘DDoS’’ or ‘‘nonDDoS’’ to reflect whether the block was
created on the day the DDoS attack occurred. The inadequacy
of the tested model with its low accuracy and some limitations
are reported in the paper.
A cryptocurrency network is more vulnerable to a 51%
attack [75] if selfish miners controlled more than a cer-
tain threshold of computational hash power. Selfish mining
behavior has been modeled by a Markov-model and analyzed
in [70] for Ethereum as discussed in the last section. Self-
ish mining poses a serious threat to cryptocurrencies adopt-
ing PoW. Another type of attack called block with-holding
(BWH) has been analyzed in [79] based on the work done
in [72]. In a BWH attack, a pool of miners sends a share of
its mining power to another pool and does not announce any
PoW solutions it finds thus ‘‘withholding’’ them while still
collecting the shared reward generated by other non-attacking
miners. Consequently, the BWH attack lowers the effective-
ness of the victim pool while increasing the effectiveness
of the attacker’s pool. Eyal et al. [72] considered BWH as
a rational behavior that is profitable for the attacker. Profit
analysis for a realistic and complex BWH attack scenario
was done in [79] using game theory to derive a mathematical
representation for BWH attacks. The analysis revealed that
pools attacking each other can reach a nash equillibrium and
that largest pools benefit from BWH while smaller pools
lose if there is an attack so they have to attack to maximize
their profits. The smallest pools, however, are better not to
attack in any scenario. In a similar fashion, Li et al. [80]
analyzed the nash equillibrium of the mining process through
numerical simulations exploring the influence of the mining
pools’ power, the ratio of the power to be infiltrated, and the
betrayed rate of dispatched miners.
Caporale et al. [81] used a Markov-switching non-linear
specification to analyze the effects of cyber attacks on returns
in the case of four cryptocurrencies (Bitcoin, Ethereum, Lite-
coin and Stellar) over the period August 2015 to Febru-
ary 2019. In addition to the daily prices data and the VIX data
obtained from the Federal Reserve of St.Louis., they used the
data source for cyber attacks from Hackmageddon.14 They
included not only the crypto attacks targeting cryptocurren-
cies, but also other cyber attacks as they see that the media
coverage for these attacks could also affect the investors’ per-
ception of cryptocurrencies which rely on cyber security. The
results suggest significant negative effects of cyber attacks
on the probability of cryptocurrencies staying in the low
volatility regime. This work can be seen as statistical learning
from data which can further be used for price prediction after
the occurrence of cyber attacks.
The aforementioned research for security analysis either
used supervised machine learning models or game-theory
techniques. Some other recent ML solutions and proposals
to address the problem of detecting suspicious activities in
Bitcoin and blockchain were surveyed in [82]. Some of the
referenced work are better categorized as fraud detection or
anonymity and deanonymization and we have already cov-
ered them in the previous subsections.
Technology advances have impacted the cryptocurrencies
evolution by creating new ways to mine new coins, store
the blockchains over distributed nodes, secure the network
and analyze the huge amount of trades and blockchain trans-
actions that are beyond human capabilities. In this study,
we presented a survey for the state-of-art research that makes
use of artificial/machine intelligence techniques to address
the challenges facing cryptocurrencies.
The AI research studies addressing Bitcoin are remarkably
more than those researching other altcoins as seen in Table 6.
The possible dependencies between cryptocurrencies’ prices
should be further identified. The possibility of using AI
techniques to address security, anonymity and privacy level
of other cryptocurrencies is recommended for further explo-
ration as security and privacy are major and critical concerns
for traders to gain more trust while trading.
Although this topic is the most studied among other topics
using AI to tackle cryptocurrency issues, there is still room
for more research on applying AI techniques for cryptocur-
rency price predictions in new contexts. Most of the papers
predicted Bitcoin prices in USD but few papers cover price
prediction of other altcoins with other fiat currencies. Con-
ducted research relied mostly on the cryptocurrency price
history indicators such as open, high, low and closing prices,
while few studies take advantage of different sources of
social media, online metrics and other stock markets indi-
cators. Among the used social media sources are tweets
and their sentiments, Reddit posts, Wikipedia views, and
Google Trends data. Few research efforts considered using
BitcoinTalk forum posts. Other news sources, such as news-
papers and news agencies have not been taken into account,
although such sources can shape the opinion of some novice
investors and influence users’ buying or selling behaviour.
Technical news about security breaches or instability of the
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TABLE 6. AI research work for Bitcoin and other altcoins.
cryptomarket might affect the price as well. Moreover, almost
all the studies depend merely on posts and content in the
English language for their analysis while investors from all
over the world contribute to the cryptocurrency market.
The results of research efforts in price prediction depended
on different datasets with features at different time peri-
ods. Therefore, the results can not be fairly compared
to reach a conclusion for recommending or favoring one
price prediction model over another. Tree-based models and
probabilistic-based models were the least models to be tested.
Probabilistic-based models are better to be investigated in
more depth to model the uncertainty in the domain of cryp-
tocurrencies. Introduction of some sort of trust or confidence
score measure for the prediction accuracy or performance of
the model to account for the uncertainty and missing factors
or explanatory variables is recommended. Nonlinear tests
and techniques listed in [34] have not yet been thoroughly
explored for application on cryptocurrencies time series and
need further investigation to capture the non-linear dependen-
cies on the explanatory variables.
Volatility prediction has been mostly approached using
GARCH model variants. GARCH models are preferred by
financial experts because they provide a more real-world
context to predict the prices and financial returns. Most of
the volatility prediction research was conducted by finance
researchers using GARCH variants and was based on his-
torical prices. Only [39] explored other techniques but with
only the past values of the realized volatility. No volatility
prediction research took advantage of social media and news
metrics that can give an estimate of the user adoption and
interest in the cryptocurrency market. Investors classification
[49] and estimation of the size of short-term traders cluster
that shows herd behavior can be seen to directly affect the
volatility of Bitcoin price and need to be further investigated.
Additionally, [36] was able to combine a regression technique
with GARCH and the results were promising. There is a need
for additional research efforts to combine other regression
techniques and to use different indicators.
There are many online commercial trading bots currently
available that apply price and volatility prediction techniques
and portfolio trading strategies for portfolio management that
were reviewed in section III-B. Most of the portfolio trad-
ing strategies research is based on the portfolio construction
strategies used for traditional financial assets. We believe
there is still a room in this area to learn the best strategy
for trading of cryptocurrencies that can adapt dynamically
to new ICOs and take into account the behavior of different
clusters of investors. The portfolio construction may be also
approached using game theory approaches with the design of
a proper payoff.
Fraud detection research suffered from the unavailability of
sufficient reliable datasets. The class imbalance problem is a
challenge facing fraud detection since only a few addresses
are marked as fraudulent. Different oversampling and under-
sampling approaches to address the class imbalance problem
need to be investigated in the domain of cryptocurrencies.
Semi-supervised techniques need to be investigated as they
can be a way for learning from a small labeled dataset while
having many unlabeled examples. AI solutions for other types
of a scam like fake ICOs and pump-and-dump schemes have
not been examined as well.
Clustering techniques need to be further investigated for their
ability to cluster addresses based on the patterns of exchanges
between addresses. This can help to regulate authorities
to spot addresses suspected for money-laundering or other
illegal behavior. Additionally, this can help in investor clas-
sification and in gaining more insights about the cryptocur-
rency market dynamics to develop better trading strategies.
Furthermore, most of the deanonymization research was done
for Bitcoin [59]. Deanonymization of other cryptocurrencies
remains to be explored as well.
The sharp drop in the Bitcoin price after the 2017 hype,
together with the increased difficulty of mining a new block
and the uncertainty about what might happen when reaching
the maximum limit of Bitcoins, create an environment where
mining becomes less profitable and unpredictable than in the
past. This creates a serious risk that impacts the trust in cryp-
tocurrencies. This urges miners to employ data analysis for
VOLUME 8, 2020 175855
F. Sabry et al.: Cryptocurrencies and Artificial Intelligence: Challenges and Opportunities
mining in order to implement new mining strategies that pre-
dict profit based merely on transactions fees as income, and
ignore block reward as a major source of income. The mining
strategy was best modeled using game theoretic approaches.
However, miners’ data of total computation power and elec-
tricity consumption is not available. The mining research
work reviewed for modifying the consensus protocol or the
PoW puzzle to be solved is still immature and requires further
investigation and more concrete evidence of effectiveness.
It is thought that the data of cryptocurrencies’ network events
can hold significant information about miners, their devices
and their network joining and leaving patterns. AI techniques
can help detect attacks or suspicious activities on the network
based on this data. This direction has not been explored yet.
One of the reasons for that is the unavailability of a dataset for
attacks and suspicious activities to use for learning. Building
big labeled datasets for security attacks is an expensive task.
This suggests semi-supervised learning as a good candidate
to solve this kind of problem as long as the assumptions for
semi-supervised techniques are met.
In summary, this survey navigates through and organizes
the vast amount of diverse research work that applies AI
techniques in the field of cryptocurrencies. The state-of-art
research efforts were classified into six classes. For each
class, a comparison of different research work according to
the used techniques and datasets was provided. We high-
lighted possible research gaps and open directions that
require future development in this highly dynamic field.
Although we have not cited all the research papers in the
field, yet we did our best to cite recent papers that investi-
gated a wide spectrum of different AI techniques to tackle
different challenges. This survey can greatly help researchers
interested in the application of AI and machine learning
techniques in the field of cryptocurrencies. It gives them
a quick, yet full, overview of this multidisciplinary area;
through presenting simplified reviews of some of the research
done in this area and the used techniques while listing some
of the available datasets they used to address the different
cryptocurrencies challenges.
The research in this article was done for project num-
ber NPRP X-063-1-014 under a grant from Qatar National
Research Fund (QNRF). However, the contents of the
research are solely the responsibility of the authors and do not
necessarily represent the official views of QNRF. They would
like also to acknowledge Qatar National Library (QNL) for its
Open Access Fund to support the article processing charges.
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FARIDA SABRY (Member, IEEE) received the M.Sc. and Ph.D. degrees in
computer engineering from the Faculty of Engineering, Cairo University,
Egypt. She is currently a Lecturer with the Computer Engineering Depart-
ment, Faculty of Engineering, Cairo University. She is also working as a
part-time Postdoctoral Researcher with the Department of Computer Sci-
ence and Engineering, Qatar University. Her main research interest includes
machine intelligence and its applications.
WADHA LABDA (Associate Member, IEEE) received the M.Sc. degree in
information systems and the Ph.D. degree from The University of Manch-
ester, U.K., in 2018 and 2012, respectively. She is currently working as an
Assistant Professor with the Department of Computer Science and Engineer-
ing, College of Engineering, Qatar University, where she is also the Head of
the Technology Innovation and Engineering Education (TIEE) Unit, College
of Engineering. Her research interests include privacy of data, knowledge
engineering, and blockchain and its applications.
AIMAN ERBAD (Senior Member, IEEE) received
the M.C.S. degree in embedded systems and
robotics from the University of Essex, U.K., and
the Ph.D. degree in computer science from The
University of British Columbia, Canada. He is
currently an Associate Professor with the College
of Science and Engineering, Hamad Bin Khalifa
University (HBKU). His research interests include
cloud computing, edge computing, the IoT, pri-
vate and secure networks, and multimedia systems.
He received the Platinum award from H. H. Emir Sheikh Tamim bin Hamad
Al Thani at the Education Excellence Day 2013 (Ph.D. category). He also
received the 2020 Best Research Paper Award from Computer Communica-
tions, the IWCMC 2019 Best Paper Award, and the IEEE CCWC 2017 Best
Paper Award. He is an Editor of KSII Transactions on Internet and Informa-
tion Systems and was a Guest Editor of IEEE Network.
QUTAIBAH MALLUHI (Member, IEEE) received
the B.S. degree from the King Fahd Univer-
sity of Petroleum & Minerals, and the M.S. and
Ph.D. degrees from the University of Louisiana
at Lafayette. He was the Head of the Depart-
ment, from 2006 to 2012, and the Director of
the KINDI Center for Computing Research, Qatar
University (QU), from 2012 to 2016. He was
the Co-Founder and CTO of Data Reliability Inc.
He served as a Faculty for Jackson State Univer-
sity. He is currently a Professor with the Department of Computer Science
and Engineering, Qatar University (QU).
175858 VOLUME 8, 2020
... Варто зосередитися також на роботах F. Sabry, W. Labda та ін. [5], які досліджували використання методів штучного інтелекту та машинного навчання в криптовалютах як цифрових валютах чи криптоактивах і навколишній екосистемі. В икористання тематичного моделювання на основі word2vec та інших методів нейролінгвістичного програмування (НЛП) було продемонстровано в дослідах A. Burnie, E. Yilmaz [6] для аналізу тем, поданих на Reddit, про зміни цін у 2017 та 2018 рр. ...
... Обговорення в їхній роботі проливає більше світла на інвесторів ринку криптовалют і поведінку «стада», яку демонструє один кластер, що призводить до спекулятивних змін цін. Своєю чергою, автори F. Sabry, W. Labda, A. Erbad, Q. Malluhi [5] вважають, що виконання подібного аналізу кластеризації поведінки безпосередньо на даних блокчейну може дати більш точну кластеризацію. Окрім цього, це може надати більше доказів щодо того, як різні показники впливають на поведінку інвесторів і, відповідно, -на ціну. ...
Today, cryptocurrencies and topics related to information technology are attracting more attention not only on the part of traders, but also scientists. More research is being carried out aimed at the thorough study of cryptocurrencies, as well as the search for ways to facilitate interaction with blockchain. The topic of data analysis for cryptocurrencies is becoming increasingly important as the number of companies dependent on cryptocurrencies is growing rapidly. There are problems related to the cryptocurrency trading process, such as forecasting prices and trends, forecasting volatility, building a portfolio, detecting fraud, analyzing indicators for various cryptocurrencies. To solve these problems, trading bots are used. Trading bots are software products or websites that offer so-called «algorithmic trading», as they automatically analyze the actions and indicators of the market, offer strategies to maximize the trader’s profits and increase his satisfaction. They can aggregate historical market data, calculate indicators, model the order fulfillment and can even be set up to execute strategies while the customer is asleep. When analyzing the needs of the market, it turned out that there was a lack of a chat bot that would help traders or simply persons interested in the topic of cryptocurrencies to receive fresh information about the latest changes in the market. The article considers the functions and examples of performance of the chat bot CryptoAlert, created by one of the authors, which helps users to always be aware of the latest changes in the cryptocurrency market. The main function of the bot is to receive notifications about significant changes in the price of the selected coin. The use of CryptoAlert facilitates the trader’s work and significantly increases the likelihood of successful trading in the market.
The proposed project work is totally supported and easy yet effective strategy named as Martingale. An automatic system which only requires only some pre-coded instructions to execute trades on variety of market variables starting from asset price to trading volume. The strategy along with each cryptocurrency, the benchmark against which the algorithm is tested is that the market’s performance. Returns are compared with the buying and so multiplying the trade volume at each loss and different scenarios are analysed to work out the chance related to the buying compared with an algorithmic strategy. Results are going to be in love with the market’s actual trends and also with some alternate possible trends to check all market scenarios. An internet interface will accompany the presentation allowing the users to check the strategies by entering their parameters and instantly seeing the results
Conference Paper
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This article aims to start from the properties of technologies, blockchain, artificial intelligence, internet of things, and the cloud. Explore the possibilities of combining them and solve practical problems. What are blockchains, who is artificial intelligence, where is the cloud and how the internet of things are the basis of what we call global integration. We give a position to future integrations as well as a review of what is currently published. (spanish)
Decarbonization, Digitalization and Decentralization are the three key pillars to meet the significant rise of energy demand with the rapid development of urbanization, which enable the global low-carbon economy with transactive energy market. The total energy consumption of buildings and transport accounts for more than 70% in global final energy consumption but renewables only meet less than 20% in the demand of heating, cooling and transport. Therefore, buildings and electric vehicles have great potentials in allowing the optimization and balance of supply and demand with their cross-sector transactive behaviors for full-scale flexibility. This paper provides a systematic overview of the positive roles of buildings and interactive transaction behaviors of electric vehicles in establishing sustainable transactive energy community from energy physical space, data cyber space, and human social space. Low-carbon transactive energy solutions with key technologies and latest advances for net zero energy building with high electric vehicle density is discussed in a hierarchical way. Internet of things as the fundamental architecture enables digitalization and interoperability of transactive energy. Blockchain as the core element enables decentralization and transparency of transactive energy. Edge computing as the accelerator alleviates the issues of blockchain and speeds up blockchain-based transactive energy. A comprehensive survey of currently known projects and startups on blockchain-based transactive energy for cross-sector local community with buildings and electric vehicles is provided in the end with the discussion of the open challenges and future perspectives for this promising area.
The research work is focused on examining the role of artificial intelligence (AI) in addressing challenges associated with cryptocurrencies like Bitcoin, Ethereum, etc. The popularity of Bitcoin has sparked the emergence of new alternative cryptocurrencies, commonly referred to as ‘altcoins'. Simultaneously with its growing popularity and public awareness, the Bitcoin system has been branded as a haven for security breaches, selfish mining, money laundering, extreme volatility, and unpredictability of future prices. To address these challenges, stakeholders accepting cryptocurrencies must apply AI techniques to process and analyze large amounts of cryptocurrency data. In this context, this chapter discusses the recent research work to assist the researchers and practitioners in the cryptocurrency domain to make fact-based decisions by using AI techniques. Consequently, the chapter provides a detailed review of the background on fiat currencies, cryptocurrencies, challenges associated with cryptocurrencies, and the role of AI techniques in addressing those challenges.
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The modern financial world has seen a significant rise in the use of cryptocurrencies in recent years, partly due to the convincing lure of anonymity promised by these schemes. Bitcoin, despite being considered as the most widespread among all, is claimed to have significant lapses in relation to its anonymity. Unfortunately, studies have shown that many cryptocurrency transactions can be traced back to their corresponding participants through the analysis of publicly available data, to which the cryptographic community has responded by proposing new constructions with improved anonymity claims. Nevertheless, the absence of a common metric for evaluating the level of anonymity achieved by these schemes has led to numerous disparate ad hoc anonymity definitions, making comparisons difficult. The multitude of these notions also hints at the surprising complexity of the overall anonymity landscape. In this study, we introduce such a common framework to evaluate the nature and extent of anonymity in (crypto) currencies and distributed transaction systems, thereby enabling one to make meaningful comparisons irrespective of their implementation. Accordingly, our work lays the foundation for formalizing security models and terminology across a wide range of anonymity notions referenced in the literature, while showing how “anonymity” itself is a surprisingly nuanced concept, as opposed to existing claims that are drawn upon at a higher level, thus missing out on the elemental factors underpinning anonymity.
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Blockchain technology, has the characteristics of decentralization, openness and transparency, so that everyone can participate in database recording. Therefore, blockchain technology has a good application prospect in various industries. As the most successful application of blockchain technology, the Bitcoin system applies the Proof of Work (PoW) consensus mechanism. Under the PoW consensus mechanism, each miner competes through his own power to solve a SHA256 mathematical problem together, so as to gain profits. Due to the difficulty of the cryptography puzzle, miners tend to join the mining pool to obtain stable income. And the block withholding attacks will be carried out between the mining pools, so as to maximize his own income by controlling the infiltration rate dispatched to other mining pools. In this paper, we build a game model between mining pools based on the PoW consensus algorithm, and analyze its Nash equilibrium from two perspectives. The influence of the mining pools’ power, the ratio of the power to be infiltrated, and the betrayed rate of dispatched miners on the mining pool’s infiltration rate selection and income were explored, and the results were obtained through numerical simulations.
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Robust portfolio optimization refers to finding an asset allocation strategy whose behavior under the worst possible realizations of the uncertain inputs, e.g., returns and covariances, is optimized. The robust approach is in contrast to the classical approach, where one estimates the inputs to a portfolio allocation problem and then treats them as certain and accurate. In this paper we provide a categorized bibliography on the application of robust mathematical programming to the portfolio selection problem. With no similar surveys available, one of the aims of this review is to provide quick access for those interested, but maybe not yet in the area, so they know what the area is about, what has been accomplished and where everything can be found. Toward this end, a total of 148 references have been compiled and classified in various ways. Additionally, the number of Scopus© citations by contribution and journal is recorded. Finally, a brief discussion of the review’s major findings is provided and some solid leads on future directions are given.
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Over the past few years, with the advent of blockchain technology, there has been a massive increase in the usage of Cryptocurrencies. However, Cryptocurrencies are not seen as an investment opportunity due to the market's erratic behavior and high price volatility. Most of the solutions reported in the literature for price forecasting of Cryptocurrencies may not be applicable for real-time price prediction due to their deterministic nature. Motivated by the aforementioned issues, we propose a stochastic neural network model for Cryptocurrency price prediction. The proposed approach is based on the random walk theory, which is widely used in financial markets for modeling stock prices. The proposed model induces layer-wise randomness into the observed feature activations of neural networks to simulate market volatility. Moreover, a technique to learn the pattern of the reaction of the market is also included in the prediction model. We trained the Multi-Layer Perceptron (MLP) and Long Short-Term Memory (LSTM) models for Bitcoin, Ethereum, and Litecoin. The results show that the proposed model is superior in comparison to the deterministic models. INDEX TERMS Cryptocurrency, Multilayer Perceptron, Long short-term memory, Random walk, Stochas-ticity.
Conference Paper
Driven by innovative information technologies, the financial industry is facing a recent disruptive fintech revolution. One emerging technology within this field is cryptocurrency, aiming to change the future means of payment. In this paper, we study Bitcoin exchange trading and examine what factors influence the behavior of different cryptocurrency investor types. To answer this question, market bids are considered in form of investors' offers and orders as a proxy for their trading behavior. First, an unsupervised clustering technique is applied in order to group different types of investors based on similarities in trading behavior. Second, a supervised classification mechanism is used on social media news to measure the sentiment influencing trading decisions. Among other indicators this bullishness is integrated in an autoregressive distributed lag (ARDL) model to identify the factors influencing the trading behavior of investor types. Besides large investors, foreign traders and speculators, cryptocurrency-specific market participants are characterized in the form of miners. With identifying indicators driving investors' actions (i.e., macro-financial fundamentals, technical trading indicators, technological measures and market sentiment), this study contributes to recent research by explaining the trading behavior on cryptocurrency markets and its impact on exchange rates.
It has long been known that estimating large empirical covariance matrices can lead to very unstable solutions, with estimation errors more than offsetting the benefits of diversification. In this study, we employ the Hierarchical Risk Parity approach, which applies state-of-the-art mathematics including graph theory and unsupervised machine learning to a large portfolio of cryptocurrencies. An out-of-sample comparison with traditional risk-minimization methods reveals that Hierarchical Risk Parity outperforms in terms of tail risk-adjusted return, thereby working as a potential risk management tool that can help cryptocurrency investors to better manage portfolio risk. The results are robust to different rebalancing intervals, covariance estimation windows and methodologies.
In recent years, Bitcoin exchange rate prediction has attracted the interest of researchers and investors. Some studies have used traditional statistical and econometric methods to understand the economic and technology determinants of Bitcoin, few have considered the development of predictive models using these determinants. In this study, we developed a two-stage approach for exploring whether the information hidden in economic and technology determinants can accurately predict the Bitcoin exchange rate. In the first stage, two nonlinear feature selection methods comprising an artificial neural network and random forest are used to reduce the subset of potential predictors by measuring the importance of economic and technology factors. In the second stage, the potential predictors are integrated into long short-term memory (LSTM) to predict the Bitcoin exchange rate regardless of the previous exchange rate. Our results showed that by using the economic and technology determinants, LSTM could achieve better predictive performance than the autoregressive integrated moving average, support vector regression, adaptive network fuzzy inference system, and LSTM methods, which all use the previous exchange rate. Thus, information obtained from economic and technology determinants is more important for predicting the Bitcoin exchange rate than the previous exchange rate.
Due to the remarkable boost in cryptocurrency trading on digital blockchain platforms, the utilization of advanced machine learning systems for robust prediction of highly nonlinear and noisy data, gains further popularity by individual and institutional market agents. The purpose of our study is to comparatively evaluate a plethora of Artificial Intelligence systems in forecasting high frequency Bitcoin price series. We employ three different sets of models, i.e., statistical machine learning approaches including support vector regressions (SVR) and Gaussian Poisson regressions (GRP), algorithmic models such as regression trees (RT) and the k-nearest neighbours (kNN) and finally artificial neural network topologies such as feedforward (FFNN), Bayesian regularization (BRNN) and radial basis function networks (RBFNN). To the best of our knowledge, this is the first time an extensive empirical investigation of the comparative predictability of various machine learning models is implemented in high-frequency trading of Bitcoin. The entropy analysis of training and testing samples reveals long memory traits, high levels of stochasticity, and topological complexity. The presence of inherent nonlinear dynamics of Bitcoin time series fully rationalizes the use of advanced machines learning techniques. The optimal parameter values for SVR, GRP and kNN are found via Bayesian optimization. Based on diverse performance metrics, our results show that the BRNN renders an outstanding accuracy in forecasting, while its convergence is unhindered and remarkably fast. The overall superiority of artificial neural networks is due to parallel processing features that efficiently emulate human decision-making in the presence of underlying nonlinear input-output relationships in noisy signal environments.
Highlights • The significant factors of the choice of investment in cryptocurrency include age, gender, education, occupation, and previous investment experience. • Chinese and Australian investors rank the ICO attributes differently. • The deterrence factors, and investment strategies vary between Chinese and Australians. Abstract This study investigates the socio-demographic characteristics that individual cryptocurrency investors exhibit and the factors which go into their investment decisions in different Initial Coin Offerings (ICOs). A web-based revealed preference survey was conducted among Australian and Chinese blockchain and cryptocurrency followers, and a 2 Multinomial Logit model was applied to inferentially analyze the characteristics of cryptocurrency investors and the determinants of the choice of investment in "cryptocurrency coins" versus other types of ICO tokens. The results show a difference between the determinant of these two choices among Australian and Chinese cryptocurrency folks. The significant factors of these two choices include age, gender, education, occupation, and investment experience, and they align well with the behavioural literature. Furthermore, alongside differences in how they rank the attributes of ICOs, there is further variance between how Chinese and Australian investors rank deterrence factors and investment strategies.