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High-frequency quantitative trading in the emerging digital currency market poses unique challenges due to the lack of established methods for extracting trading information. This paper proposes a deep evolutionary reinforcement learning (DERL) model that combines deep reinforcement learning with evolutionary strategies to address these challenges. Reinforcement learning is applied to data cleaning and factor extraction from a high-frequency, microscopic view-point to quantitatively explain the supply and demand imbalance and to create trading strategies. In order to determine whether the algorithm can successfully extract the significant hidden features in the factors when faced with large and complex high-frequency factors, this paper trains the agent in reinforcement learning using three different learning algorithms, including Q-learning, evolutionary strategies, and policy gradient. The experimental dataset, which contains data on sharp up, sharp down, and continuous oscillation situations, was chosen to test Bitcoin in January-February, September, and November of 2022. According to the experimental results, the evolutionary strategies algorithm achieved returns of 59.18%, 25.14%, and 22.72%, respectively. The results demonstrate that deep reinforcement learning based on the evolutionary strategies outperforms Q-learning and policy gradient concerning risk resistance and return capability. The proposed approach offers a robust and adaptive solution for high-frequency trading in the digital currency market, contributing to the development of effective quantitative trading strategies. [ABSTRACT FROM AUTHOR]

Bitcoin is a decentralized digital cryptocurrency. Its network is a Peer-to-peer(P2P) network consisting of distributed nodes. Some of these nodes are always online and in this article are called Bitcoin backbone nodes. They have a significant impact on the stability and security of the Bitcoin network, so it is meaningful to analyze and discuss them. In this paper, we first continuously collect information about Bitcoin nodes from July 2021 through June 2022 (which is the longest duration of data collection to date). In total, we collect information on 127,613 Bitcoin nodes. At the same time, we conclude that the fluctuation of Bitcoin nodes is directly related to the fluctuation of onion network nodes. Further, we filtered 2694 Bitcoin backbone nodes based on our algorithm. By analyzing the backbone nodes' attributes such as geographic distribution, client version, operator, node function, and abnormal port number, it is demonstrated that these nodes are centralized and play an important role in the Bitcoin network. Based on this, three unsupervised machine learning algorithms are selected to cluster multiple attributes of backbone nodes in a more scientific way. In this paper, the whole process from data collection to cluster analysis is completed and the best results are obtained by comparison. The experiments proved the existence of centralization of Bitcoin backbone nodes and obtained the number of nodes within each cluster. Finally, cluster nodes are de-anonymized based on the optimal results. Through our experiments, we obtain organizational information about the deployers of 103 nodes, linking the Bitcoin backbone nodes to the real world, thus accurately demonstrating the existence of Bitcoin centrality. [ABSTRACT FROM AUTHOR]

Since financial assets on stock exchanges were created, investors have sought to predict their future values. Currently, cryptocurrencies are also seen as assets. Machine learning is increasingly adopted to assist and automate investments. The main objective of this paper is to make daily predictions about the movement direction of financial time series through classification models, financial time series preprocessing methods, and feature selection with genetic algorithms. The target time series are Bitcoin, Ibovespa, and Vale. The methodology of this paper includes the following steps: collecting time series of financial assets; data preprocessing; feature selection with genetic algorithms; and the training and testing of machine learning models. The results were obtained by evaluating the models with the area under the ROC curve metric. For the best prediction models for Bitcoin, Ibovespa, and Vale, values of 0.61, 0.62, and 0.58 were obtained, respectively. In conclusion, the feature selection allowed the improvement of performance in most models, and the input series in the form of percentage variation obtained a good performance, although it was composed of fewer attributes in relation to the other sets tested. [ABSTRACT FROM AUTHOR]

Bitcoin is a payment system that generates a decentralized digital currency without ensuring temporal constraints in its transactions; therefore, it is vulnerable to double-spending attacks. Karame has proposed a formalization for a successful double-spending attack based on meeting three requirements. This focuses on fast payment scenarios where the product is delivered immediately after the payment is announced in the mempool, without waiting for transaction confirmation. This scenario is key in Bitcoin to increase the probability of a successful double-spending attack. Different approaches have been proposed to mitigate these attacks by addressing one or more of Karame's three requirements. These include the following: flooding every transaction without restrictions, introducing listeners/observers, avoiding isolation by blocking incoming connections, penalizing malicious users by revealing their identity, and using machine learning and bio-inspired techniques. However, to our knowledge, no proposal deterministically avoids double-spending attacks in fast payment scenarios. In this paper, we introduce DiFastBit: a distributed transaction differentiation scheme that shields Bitcoin from double-spending attacks in fast payment scenarios. To achieve this, we modeled Bitcoin from a distributed perspective of events and processes, reformulated Karame's requirements based on Lamport's happened-before relation (HBR), and introduced a new theorem that consolidates the reformulated requirements and establishes the necessary conditions for a successful attack on fast Bitcoin payments. Finally, we introduce the specifications for DiFastBit, formally prove its correctness, and analyze DiFastBit's confirmation time. [ABSTRACT FROM AUTHOR]

The emergence of the Bitcoin cryptocurrency marked a new era of illegal transactions. Cryptocurrency provides some level of anonymity allowing its users to create an unlimited number of wallets with alias addresses, which makes it challenging to identify the actual user. This is used by criminals for the purpose of making illegal transactions. At the same time, Bitcoin stores and provides information about all committed transactions, which opens up opportunities for identifying suspicious behavior patterns in this network using data mining. The problem of detecting suspicious activity in the Bitcoin network can be solved with sufficiently high accuracy using machine learning methods. The paper provides a comparative study of various machine learning methods to solve the mentioned problem: logistic regression, decision tree, random forest, gradient boosting. Selecting hyper parameters, rebalancing the dataset, and active learning are particularly important. The most important hyperparameters of the algorithms are described. Metrics show that the gradient boosting looks the most promising. In total 38 features of bitcoin addresses were identified. The top features are presented in the paper. [ABSTRACT FROM AUTHOR]

This paper explores the predictive power of economic and energy policy uncertainty indices and geopolitical risks for bitcoin's energy consumption. Three machine learning tools, SVR (scikit-learn 1.5.0),CatBoost 1.2.5 and XGboost 2.1.0, are used to evaluate the complex relationship between uncertainty indices and bitcoin's energy consumption. Results reveal that the XGboost model outperforms both SVR and CatBoost in terms of accuracy and convergence. Furthermore, the feature importance analysis performed by the Shapley additive explanation (SHAP) method indicates that all uncertainty indices exhibit a significant capacity to predict bitcoin's future energy consumption. Moreover, SHAP values suggest that economic policy uncertainty captures valuable predictive information from the energy uncertainty indices and geopolitical risks that affect bitcoin's energy consumption. [ABSTRACT FROM AUTHOR]

Financial markets are increasingly interlinked. Therefore, this study explores the complex relationships between the Tadawul All Share Index (TASI), West Texas Intermediate (WTI) crude oil prices, and Bitcoin (BTC) returns, which are pivotal to informed investment and risk-management decisions. Using copula-based models, this study identified Student's t copula as the most appropriate one for encapsulating the dependencies between TASI and BTC and between TASI and WTI prices, highlighting significant tail dependencies. For the BTC–WTI relationship, the Frank copula was found to have the best fit, indicating nonlinear correlation without tail dependence. The predictive power of the identified copulas were compared to that of Long Short-Term Memory (LSTM) networks. The LSTM models demonstrated markedly lower Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Scaled Error (MASE) across all assets, indicating higher predictive accuracy. The empirical findings of this research provide valuable insights for financial market participants and contribute to the literature on asset relationship modeling. By revealing the most effective copulas for different asset pairs and establishing the robust forecasting capabilities of LSTM networks, this paper sets the stage for future investigations of the predictive modeling of financial time-series data. The study highlights the potential of integrating machine-learning techniques with traditional econometric models to improve investment strategies and risk-management practices. [ABSTRACT FROM AUTHOR]

Due to spectacular gains during periods of rapid price increase and unpredictably large drops, Bitcoin has become a popular emergent asset class over the past few years. In this paper, we are interested in predicting the crashes of Bitcoin market. To tackle this task, we propose a framework for deep learning time series classification based on contrastive learning. The proposed framework is evaluated against six machine learning (ML) and deep learning (DL) baseline models, and outperforms them by 15.8% in balanced accuracy. Thus, we conclude that the contrastive learning strategy significantly enhance the model's ability of extracting informative representations, and our proposed framework performs well in predicting Bitcoin crashes. [ABSTRACT FROM AUTHOR]

As the trendsetter of the digital currency market, Bitcoin fluctuates dramatically in a short period of time and has received increasing attention from investors. However, its high volatility has brought great uncertainty to the financial market. In this paper, we focus on forecasting the realized volatility of Bitcoin by using an optimized deep learning model. Firstly, we construct a more comprehensive system of factor indicators and employ different methods for feature selection, and find that the Random Forest-based feature selection fits better on the deep learning model. Then, we use the particle swarm optimization (PSO) algorithm to optimize the parameters of gated recurrent unit (GRU) model to improve the prediction accuracy, and the results show that the prediction accuracy of PSO–GRU model is 10.47%, 15.28%, 21.73%, 34.79% better than the GRU model, long-short term memory model, machine learning models and the generalized autoregressive conditional heteroscedasticity model on the mean absolute error, respectively. Finally, we establish an early risk warning scheme for Bitcoin volatility and a butterfly option arbitrage strategy, that provide investors with a reference for reasonable arrangement of trading strategies. [ABSTRACT FROM AUTHOR]

We carry on a long term analysis for Bitcoin price, which is currently among the most renowned crypto assets available on markets other than Forex. In the last decade Bitcoin has been under spotlights among traders all world wide, both because of its nature of pseudo–currency and for the high volatility its price has frequently experienced. Considering that Bitcoin price has earned over five orders of magnitude since 2009, the interest of investors has been increasingly motivated by the necessity of accurately predicting its value, not to mention that a comparative analysis with other assets as silver and gold has been under investigation, too. This paper reports two approaches for a long term Bitcoin price prediction. The first one follows more standard paradigms from regression and least squares frameworks. Our main contribution in this regard fosters conclusions which are able to justify the cyclic performance of Bitcoin price, in terms of its Stock–to–Flow. Our second approach is definitely novel in the literature, and indicates guidelines for long term forecasts of Bitcoin price based on Machine Learning (ML) methods, with a specific reference to Support Vector Machines (SVMs). Both these approaches are inherently data–driven, and the second one does not require any of the assumptions typically needed by solvers for classic regression problems. [ABSTRACT FROM AUTHOR]

Cryptocurrencies like Bitcoin have received substantial attention from financial exchanges. Unfortunately, arbitrage-based financial market price prediction models are ineffective for cryptocurrencies. In this paper, we utilize standard machine learning models and publicly available transaction data in blocks to predict the direction of Bitcoin price movement. We illustrate our methodology using data we merged from the Bitcoin blockchain and various online sources. This gave us the Bitcoin transaction history (block IDs, block timestamps, transaction IDs, senders' addresses, receivers' addresses, transaction amounts), as well as the market exchange price, for the period from 13 September 2011 to 5 May 2017. We show that segmenting publicly available transactions based on investor typology helps achieve higher prediction accuracy compared to the existing Bitcoin price movement prediction models in the literature. This transaction segmentation highlights the role of investor types in impacting financial markets. Managerially, the segmentation of financial transactions helps us understand the role of financial and cryptocurrency market participants in asset price movements. These findings provide further implications for risk management, financial regulation, and investment strategies in this new era of digital currencies. [ABSTRACT FROM AUTHOR]

The rapid development of cryptocurrencies has led to an increasing severity of money laundering activities. In recent years, leveraging graph neural networks for cryptocurrency fraud detection has yielded promising results. However, many existing methods predominantly focus on node classification, i.e., detecting individual illicit transactions, rather than uncovering behavioral pattern differences among money laundering groups. In this paper, we tackle the challenges presented by the organized, heterogeneous, and noisy nature of Bitcoin money laundering. We propose a novel subgraph-based contrastive learning algorithm for heterogeneous graphs, named Bit-CHetG, to perform money laundering group detection. Specifically, we employ predefined metapaths to construct the homogeneous subgraphs of wallet addresses and transaction records from the address–transaction heterogeneous graph, enhancing our ability to capture heterogeneity. Subsequently, we utilize graph neural networks to separately extract the topological embedding representations of transaction subgraphs and associated address representations of transaction nodes. Lastly, supervised contrastive learning is introduced to reduce the effect of noise, which pulls together the transaction subgraphs with the same class while pushing apart the subgraphs with different classes. By conducting experiments on two real-world datasets with homogeneous and heterogeneous graphs, the Micro F1 Score of our proposed Bit-CHetG is improved by at least 5% compared to others. [ABSTRACT FROM AUTHOR]

The purpose of the paper is to predict Bitcoin prices using various machine learning techniques. Due to its high volatility attribute, accurate price prediction is the need of the hour for sound investment decision-making. At the offset, this study categorizes Bitcoin price by daily and high-frequency price (5-min interval price). For its daily and 5-min interval price prediction, a set of high-dimensional features and fundamental trading features are employed, respectively. Thereafter, we find that statistical methods like Logistic Regression predict daily price with 64.84% accuracy while complex machine learning algorithms like XGBoost predict 5-min interval price with an accuracy level of 59.4%. This work on Bitcoin price prediction recognizes the significance of sample dimensions in machine learning algorithms. [ABSTRACT FROM AUTHOR]

The research purpose of this paper is to obtain an algorithm model with high prediction accuracy for the price of Bitcoin on the next day through random forest regression and LSTM, and to explain which variables have influence on the price of Bitcoin. There is much prior literature on Bitcoin price prediction research, and the research methods mainly revolve around the ARMA model of time series and the LSTM algorithm of deep learning. Although it cannot be proved by the Diebold–Mariano test that the prediction accuracy of random forest regression is significantly better than that of LSTM, the prediction errors RMSE and MAPE of random forest regression are better than those of LSTM. The changes in the variables that determine the price of Bitcoin in each period are also obtained through random forest regression. From 2015 to 2018, three US stock market indexes, NASDAQ, DJI, and S&P500 and oil price, and ETH price have impact on Bitcoin prices. Since 2018, the important variables have become ETH price and Japanese stock market index JP225. The relationship between accuracy and the number of periods of explanatory variables brought into the model shows that for predicting the price of Bitcoin for the next day, the model with only one lag of the explanatory variables has the best prediction accuracy. [ABSTRACT FROM AUTHOR]

Cryptocurrencies have attracted significant awareness up to now, and Bitcoin is the most remarkable one. As they have enormous instability in their price, predicting their uctuations is a challenging task. Although several research used traditional statistical and economic methods to discover the driving variables of cryptocurrency prices, progress in developing prediction models for decision-making tools in investing techniques is still in its early stages. Many different cryptocurrency price prediction methods cover purposes, such as forecasting a one-step approach that can be done through time series analysis, neural networks, and machine learning algorithms. However, realizing the trend of a coin in the long run is required. In this paper, we aimed to investigate and forecast Bitcoin prices based on three other well-known coins (i.e., Ethereum, Zcash, and Litecoin), assuming little information about Bitcoin prices using machine learning algorithms. Moreover, we proposed a new method to predict Bitcoin's price considering different cryptocurrencies prices. The results demonstrated that Zcash had the best performance in forecasting Bitcoin price without information on Bitcoin price uctuations among other cryptocurrencies. [ABSTRACT FROM AUTHOR]

This study aims to forecast extreme fluctuations of Bitcoin returns. Bitcoin is the first decentralized and the largest, in terms of capitalization, cryptocurrency. A well-timed and precise forecast of extreme changes in Bitcoin returns is key to market participants since they may trigger large-scale selling or buying strategies that may crucially impact the cryptocurrency markets. We term the instances of extreme Bitcoin movement as 'spikes'. In this paper, spikes are defined as the returns instances that outreach a two-standard deviations band around the mean value. Instead of the unconditional historic standard deviation that is usually used, in this paper, we utilized a GARCH(p,q) model to derive the conditional standard deviation. We claim that the conditional standard deviation is a more suitable measure of on-the-spot risk than the overall standard deviation. The forecasting operation was performed using the support vector machines (SVM) methodology from machine learning. The most accurate forecasting model that we created reached 79.17% out-of-sample forecasting accuracy regarding the spikes cases and 87.43% regarding the non-spikes ones. [ABSTRACT FROM AUTHOR]

Bitcoin (BTC)—the first cryptocurrency—is a decentralized network used to make private, anonymous, peer-to-peer transactions worldwide, yet there are numerous issues in its pricing due to its arbitrary nature, thus limiting its use due to skepticism among businesses and households. However, there is a vast scope of machine learning approaches to predict future prices precisely. One of the major problems with previous research on BTC price predictions is that they are primarily empirical research lacking sufficient analytical support to back up the claims. Therefore, this study aims to solve the BTC price prediction problem in the context of both macroeconomic and microeconomic theories by applying new machine learning methods. Previous work, however, shows mixed evidence of the superiority of machine learning over statistical analysis and vice versa, so more research is needed. This paper applies comparative approaches, including ordinary least squares (OLS), Ensemble learning, support vector regression (SVR), and multilayer perceptron (MLP), to investigate whether the macroeconomic, microeconomic, technical, and blockchain indicators based on economic theories predict the BTC price or not. The findings point out that some technical indicators are significant short-run BTC price predictors, thus confirming the validity of technical analysis. Moreover, macroeconomic and blockchain indicators are found to be significant long-term predictors, implying that supply, demand, and cost-based pricing theories are the underlying theories of BTC price prediction. Likewise, SVR is found to be superior to other machine learning and traditional models. This research's innovation is looking at BTC price prediction through theoretical aspects. The overall findings show that SVR is superior to other machine learning models and traditional models. This paper has several contributions. It can contribute to international finance to be used as a reference for setting asset pricing and improved investment decision-making. It also contributes to the economics of BTC price prediction by introducing its theoretical background. Moreover, as the authors still doubt whether machine learning can beat the traditional methods in BTC price prediction, this research contributes to machine learning configuration and helping developers use it as a benchmark. [ABSTRACT FROM AUTHOR]

Criminals have become increasingly experienced in using cryptocurrencies, such as Bitcoin, for money laundering. The use of cryptocurrencies can hide criminal identities and transfer hundreds of millions of dollars of dirty funds through their criminal digital wallets. However, this is considered a paradox because cryptocurrencies are goldmines for open-source intelligence, giving law enforcement agencies more power when conducting forensic analyses. This paper proposes Inspection-L, a graph neural network (GNN) framework based on a self-supervised Deep Graph Infomax (DGI) and Graph Isomorphism Network (GIN), with supervised learning algorithms, namely Random Forest (RF), to detect illicit transactions for anti-money laundering (AML). To the best of our knowledge, our proposal is the first to apply self-supervised GNNs to the problem of AML in Bitcoin. The proposed method was evaluated on the Elliptic dataset and shows that our approach outperforms the state-of-the-art in terms of key classification metrics, which demonstrates the potential of self-supervised GNN in the detection of illicit cryptocurrency transactions. [ABSTRACT FROM AUTHOR]

Predicting Bitcoin price trends is necessary because they represent the overall trend of the cryptocurrency market. As the history of the Bitcoin market is short and price volatility is high, studies have been conducted on the factors affecting changes in Bitcoin prices. Experiments have been conducted to predict Bitcoin prices using Twitter content. However, the amount of data was limited, and prices were predicted for only a short period (less than two years). In this study, data from Reddit and LexisNexis, covering a period of more than four years, were collected. These data were utilized to estimate and compare the performance of the six machine learning techniques by adding technical and sentiment indicators to the price data along with the volume of posts. An accuracy of 90.57% and an area under the receiver operating characteristic curve value (AUC) of 97.48% were obtained using the extreme gradient boosting (XGBoost). It was shown that the use of both sentiment index using valence aware dictionary and sentiment reasoner (VADER) and 11 technical indicators utilizing moving average, relative strength index (RSI), stochastic oscillators in predicting Bitcoin price trends can produce significant results. Thus, the input features used in the paper can be applied on Bitcoin price prediction. Furthermore, this approach allows investors to make better decisions regarding Bitcoin-related investments. [ABSTRACT FROM AUTHOR]

In this paper, we predict money laundering in Bitcoin transactions by leveraging a deep learning framework and incorporating more characteristics of Bitcoin transactions. We produced a dataset containing 46,045 Bitcoin transaction entities and 319,311 Bitcoin wallet addresses associated with them. We aggregated this information to form a heterogeneous graph dataset and propose three metapath representations around transaction entities, which enrich the characteristics of Bitcoin transactions. Then, we designed a metapath encoder and integrated it into a heterogeneous graph node embedding method. The experimental results indicate that our proposed framework significantly improves the accuracy of illicit Bitcoin transaction recognition compared with traditional methods. Therefore, our proposed framework is more conducive in detecting money laundering activities in Bitcoin transactions. [ABSTRACT FROM AUTHOR]

In recent years, cryptocurrency has become gradually more significant in economic regions worldwide. In cryptocurrencies, records are stored using a cryptographic algorithm. The main aim of this research was to develop an optimal solution for predicting the price of cryptocurrencies based on user opinions from social media. Twitter is used as a marketing tool for cryptoanalysis owing to the unrestricted conversations on cryptocurrencies that take place on social media channels. Therefore, this work focuses on extracting Tweets and gathering data from different sources to classify them into positive, negative, and neutral categories, and further examining the correlations between cryptocurrency movements and Tweet sentiments. This paper proposes an optimized method using a deep learning algorithm and convolution neural network for cryptocurrency prediction; this method is used to predict the prices of four cryptocurrencies, namely, Litecoin, Monero, Bitcoin, and Ethereum. The results of analyses demonstrate that the proposed method forecasts prices with a high accuracy of about 98.75%. The method is validated by comparison with existing methods using visualization tools. [ABSTRACT FROM AUTHOR]

Background and Objectives: Investment has become a paramount concern for various individuals, particularly investors, in today's financial landscape. Cryptocurrencies, encompassing various types, hold a unique position among investors, with Bitcoin being the most prominent. Additionally, Bitcoin serves as the foundation for some other cryptocurrencies. Given the critical nature of investment decisions, diverse methods have been employed, ranging from traditional statistical approaches to machine learning and deep learning techniques. However, among these methods, the Generative Adversarial Network (GAN) model has not been utilized in the cryptocurrency market. This article aims to explore the applicability of the GAN model for predicting short-term Bitcoin prices. Methods: In this article, we employ the GAN model to predict short-term Bitcoin prices. Moreover, Data for this study has been collected from a diverse set of sources, including technical data, fundamental data, technical indicators, as well as additional data such as the number of tweets and Google Trends. In this research, we also evaluate the model's accuracy using the RMSE, MAE and MAPE metrics. Results: The results obtained from the experiments indicate that the GAN model can be effectively utilized in the cryptocurrency market for short-term price prediction. Conclusion: In conclusion, the results of this study suggest that the GAN model exhibits promise in predicting short-term prices in the cryptocurrency market, affirming its potential utility within this domain. These insights can provide investors and analysts with enhanced knowledge for making more informed investment decisions, while also paving the way for comparative analyses against alternative models operating in this dynamic field. [ABSTRACT FROM AUTHOR]

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In this paper we predict Bitcoin movements by utilizing a machine-learning framework. We compile a dataset of 24 potential explanatory variables that are often employed in the finance literature. Using daily data from 2nd of December 2014 to July 8th 2019, we build forecasting models that utilize past Bitcoin values, other cryptocurrencies, exchange rates and other macroeconomic variables. Our empirical results suggest that the traditional logistic regression model outperforms the linear support vector machine and the random forest algorithm, reaching an accuracy of 66%. Moreover, based on the results, we provide evidence that points to the rejection of weak form efficiency in the Bitcoin market. [ABSTRACT FROM AUTHOR]

In this paper, we analyzed a dataset of over 2000 crypto-assets to assess their credit risk by computing their probability of death using the daily range. Unlike conventional low-frequency volatility models that only utilize close-to-close prices, the daily range incorporates all the information provided in traditional daily datasets, including the open-high-low-close (OHLC) prices for each asset. We evaluated the accuracy of the probability of death estimated with the daily range against various forecasting models, including credit scoring models, machine learning models, and time-series-based models. Our study considered different definitions of "dead coins" and various forecasting horizons. Our results indicate that credit scoring models and machine learning methods incorporating lagged trading volumes and online searches were the best models for short-term horizons up to 30 days. Conversely, time-series models using the daily range were more appropriate for longer term forecasts, up to one year. Additionally, our analysis revealed that the models using the daily range signaled, far in advance, the weakened credit position of the crypto derivatives trading platform FTX, which filed for Chapter 11 bankruptcy protection in the United States on 11 November 2022. [ABSTRACT FROM AUTHOR]

Forensic investigations increasingly leverage artificial intelligence (AI/ML) to identify illegal activities on bitcoin. bitcoin transactions have an original graph (network) structure, which is sophisticated and yet informative. However, machine learning applications on bitcoin have given limited attention to developing end-to-end deep learning frameworks that are modeled to exploit the bitcoin graph structure. To identify illegal transactions on bitcoin, the current paper extracts nineteen features from the bitcoin network and proposes a deep learning-based graph neural network model using spectral graph convolutions and transaction features. The proposed model is compared with two state-of-the-art techniques, viz., a graph attention network (GAT2) and an extreme gradient boosted decision tree (XGBOOST) trained on convoluted features for classification of illegal transactions on bitcoin. To understand the efficacy of the proposed model, a dataset is collected consisting of 13310125 transactions of 2059 entities having 3152202 bitcoin account addresses and belonging to 28 categories of users. Two sets of experiments are performed on the datasets: labeling transactions as legal or illegal (binary classification) and identifying the originator of the transaction to one of the twenty-eight types of entities (multi-class classification). For fast and accurate decisions, binary classification is appropriate, and for pinpointing the category of bitcoin users, a multi-class classifier is suitable. On both the tasks, the proposed models achieved a maximum of 92% accuracy, validating the methodology and suitability of the model for real-world deployment. [ABSTRACT FROM AUTHOR]

Bitcoin, the largest cryptocurrency, is extremely volatile and hence needs a better model for its pricing. In the literature, many researchers have studied the effect of data normalization on regression analysis for stock price prediction. How has data normalization affected Bitcoin price prediction? To answer this question, this study analyzed the prediction accuracy of a Legendre polynomial-based neural network optimized by the mutated climb monkey algorithm using nine existing data normalization techniques. A new dual normalization technique was proposed to improve the efficiency of this model. The 10 normalization techniques were evaluated using 15 error metrics using a multi-criteria decision-making (MCDM) approach called technique for order performance by similarity to ideal solution (TOPSIS). The effect of the top three normalization techniques along with the min–max normalization was further studied for Chebyshev, Laguerre, and trigonometric polynomial-based neural networks in three different datasets. The prediction accuracy of the 16 models (each of the four polynomial-based neural networks with four different normalization techniques) was calculated using 15 error metrics. A 16 × 15 TOPSIS analysis was conducted to rank the models. The convergence plot and the ranking of the models indicated that data normalization plays a significant role in the prediction capability of a Bitcoin price predictor. This paper can significantly contribute to the research with a new normalization technique for utilization in varied fields of research. It can also contribute to international finance as a decision-making tool for different investors as well as stakeholders for Bitcoin pricing. [ABSTRACT FROM AUTHOR]

Traders and investors are interested in accurately predicting cryptocurrency prices to increase returns and minimize risk. However, due to their uncertainty, volatility, and dynamism, forecasting crypto prices is a challenging time series analysis task. Researchers have proposed predictors based on statistical, machine learning (ML), and deep learning (DL) approaches, but the literature is limited. Indeed, it is narrow because it focuses on predicting only the prices of the few most famous cryptos. In addition, it is scattered because it compares different models on different cryptos inconsistently, and it lacks generality because solutions are overly complex and hard to reproduce in practice. The main goal of this paper is to provide a comparison framework that overcomes these limitations. We use this framework to run extensive experiments where we compare the performances of widely used statistical, ML, and DL approaches in the literature for predicting the price of five popular cryptocurrencies, i.e., XRP, Bitcoin (BTC), Litecoin (LTC), Ethereum (ETH), and Monero (XMR). To the best of our knowledge, we are also the first to propose using the temporal fusion transformer (TFT) on this task. Moreover, we extend our investigation to hybrid models and ensembles to assess whether combining single models boosts prediction accuracy. Our evaluation shows that DL approaches are the best predictors, particularly the LSTM, and this is consistently true across all the cryptos examined. LSTM reaches an average RMSE of 0.0222 and MAE of 0.0173 , respectively, 2.7 % and 1.7 % better than the second-best model. To ensure reproducibility and stimulate future research contribution, we share the dataset and the code of the experiments. [ABSTRACT FROM AUTHOR]

This paper aims to improve the predictability power of a machine learning method by proposing a two-stage prediction method. We use Group Modeling Data Handling (GMDH)-type neural network method to eliminate the user role in feature selection. To consider recent shocks in Bitcoin market, we consider three periods, before COVID-19, after COVID-19, and after Elon Musk's tweeter activity. Using time-scale analysis, we decomposed the data into different scales. We further investigate the forecasting accuracy across different frequencies. The findings show that in shorter period the first, second and third lag of daily prices and trade volume produce valuable information to predict Bitcoin price while the seven days lag can improve the prediction power over longer period. The results indicate a better performance of the wavelet base GMDH-neural network in comparison with the standard method. This reveals the importance of trade frequencies' impact on the forecasting power of models. [ABSTRACT FROM AUTHOR]

In this paper, we investigate how a deep learning machine learning model can be applied to improve Bitcoin price forecasting and trading by incorporating unstructured information from financial news. The two-stage model we propose that includes financial news significantly outperforms machine learning models without financial news. In the first stage, we leverage long short-term memory (LSTM) networks to extract structured information from financial news. In the second stage, we apply machine learning models with structured input from financial news to the prediction of Bitcoin prices. In addition to the superior performance relative to machine learning models without input from financial news, we find that the out-of-time rate of return attained with the proposed forecasting system is substantially higher than for a buy-and-hold strategy. Our study highlights how combining deep learning and financial news offers investors and traders support for the monetization of unstructured data in finance. [ABSTRACT FROM AUTHOR]

Bitcoin has became one of the most popular investment asset recent years. The volatility of bitcoin price in financial market attracting both investors and researchers to study the price changing manners of bitcoin. Existing works try to understand the bitcoin price change by manually discovering features or factors that are assumed to be reasons of price change. However, the trivial feature engineering consumes human resources without the guarantee that the assumptions or intuitions are correct. In this paper, we propose to reveal the bitcoin price change through understanding the patterns of bitcoin blockchain transactions without feature engineering. We first propose k-order transaction subgraphs to capture the patterns. Then with the help of machine learning models, Multi-Window Prediction Framework is proposed to learn the relation between the patterns and the bitcoin prices. Extensive experimental results verify the effectiveness of transaction patterns to understand the bitcoin price change and the superiority of Multi-Window Prediction Framework to integrate multiple submodels trained separately on multiple history periods. [ABSTRACT FROM AUTHOR]

Cryptocurrency is a digital currency and also exists in the form of coins. It has turned out as a leading method for peer-to-peer online cash systems. Due to the importance and increasing influence of Bitcoin on business and other related sectors, it is very crucial to model or predict its behavior. Therefore, in recent, numerous researchers have attempted to understand and model the behaviors of cryptocurrency exchange rates. In the practice of actuarial and financial studies, heavy-tailed distributions play a fruitful role in modeling and describing the log returns of financial phenomena. In this paper, we propose a new family of distributions that possess heavy-tailed characteristics. Based on the proposed approach, a modified version of the logistic distribution, namely, a new modified exponential-logistic distribution is introduced. To illustrate the new modified exponential-logistic model, two financial data sets are analyzed. The first data set represents the log-returns of the Bitcoin exchange rates. Whereas, the second data set represents the log-returns of the Ethereum exchange rates. Furthermore, to forecast the high volatile behavior of the same datasets, we apply dual machine learning algorithms, namely Artificial neural network and support vector regression. The effectiveness of these models is evaluated against self exciting threshold autoregressive model. [ABSTRACT FROM AUTHOR]

In this paper, we address the problems of fraud and anomalies in the Bitcoin network. These are common problems in e-banking and online transactions. However, as the financial sector evolves, so do the methods for fraud and anomalies. Moreover, blockchain technology is being introduced as the most secure method integrated into finance. However, along with these advanced technologies, many frauds are also increasing every year. Therefore, we propose a secure fraud detection model based on machine learning and blockchain. There are two machine learning algorithms—XGboost and random forest (RF)—used for transaction classification. The machine learning techniques train the dataset based on the fraudulent and integrated transaction patterns and predict the new incoming transactions. The blockchain technology is integrated with machine learning algorithms to detect fraudulent transactions in the Bitcoin network. In the proposed model, XGboost and random forest (RF) algorithms are used to classify transactions and predict transaction patterns. We also calculate the precision and AUC of the models to measure the accuracy. A security analysis of the proposed smart contract is also performed to show the robustness of our system. In addition, an attacker model is also proposed to protect the proposed system from attacks and vulnerabilities. [ABSTRACT FROM AUTHOR]

Coinhive released its browser-based cryptocurrency mining code in September 2017, and vicious web page writers, called vicious miners hereafter, began to embed mining JavaScript code into their web pages, called mining pages hereafter. As a result, browser users surfing these web pages will benefit mine cryptocurrencies unwittingly for the vicious miners using the CPU resources of their devices. The above activity, called Cryptojacking, has become one of the most common threats to web browser users. As mining pages influence the execution efficiency of regular programs and increase the electricity bills of victims, security specialists start to provide methods to block mining pages. Nowadays, using a blocklist to filter out mining scripts is the most common solution to this problem. However, when the number of new mining pages increases quickly, and vicious miners apply obfuscation and encryption to bypass detection, the detection accuracy of blacklist-based or feature-based solutions decreases significantly. This paper proposes a solution, called MinerGuard, to detect mining pages. MinerGuard was designed based on the observation that mining JavaScript code consumes a lot of CPU resources because it needs to execute plenty of computation. MinerGuard does not need to update data used for detection frequently. On the contrary, blacklist-based or feature-based solutions must update their blocklists frequently. Experimental results show that MinerGuard is more accurate than blacklist-based or feature-based solutions in mining page detection. MinerGuard's detection rate for mining pages is 96%, but MinerBlock, a blacklist-based solution, is 42.85%. Moreover, MinerGuard can detect 0-day mining pages and scripts, but the blacklist-based and feature-based solutions cannot. [ABSTRACT FROM AUTHOR]

In recent years, ransomware attacks have become increasingly rampant by the offenders for which ransomware has maintained a major cyber security threat as time progresses. With paradigm shift from social to technical factors, ransomware has also maintained the equal adaptiveness by shifting its focus from initial days' scareware and locker attacks to most recent crypto-ransomware threats. There is no silver bullet available to wipe out completely crypto-ransomware attacks for its obvious relationships between social engineering which investigates more infections with encrypted malware. Bitcoin, a means of digital payment demanded by Ransomware family needs characterization and analysis to predict the cryptoransomware attack types. In this paper, at first, contractive autoencoder (CAE) is used on bitcoin transaction dataset for dimensionality reduction as a filter approach in order to obtain a reduced yet a powerful representation of the raw data and then the output of CAE is applied to the classifier for its improved performance and to make it a robust model. We use two classifiers for our experiments namely: Resilient KNN and Fuzzy Lattice Reasoning (FLR). The original KNN classifier was successful in dealing with homogenous data where the values of the numerical attribute exist completely but poses limitations while dealing with heterogeneous incomplete data containing mixed data (numeric and categorical) yet having missing values. Further, KNN used same K values for all the query objects that sometimes leads to misclassification. Resilient KNN is proposed in this paper to deal with these pitfalls effectively by assigning different k-values for different query objects, so as to obtain a most accurate predictive model. Next, the FLR is used for its ability to handle different types of data types and moreover, it is incremental and fast learning which tempted us to explore its possibility in detecting the crypto-ransomware attacks efficiently. The experimental results with several conventional and new evaluation metrics justifies the suitability of our proposed approach in building a robust and efficient classifier model to detect crypto ransomware families in comparison to existing research. [ABSTRACT FROM AUTHOR]

Bitcoin has recently attracted a lot of media and public attention as a result of its recent price boom and crash. Similarly, numerous researchers have explored many aspects influencing the Bitcoin price and the patterns underlying its fluctuations, specifically utilizing various machine learning methods. In this work, authors investigate and analyze various cutting-edge machine learning algorithms for Bitcoin price prediction, such as a logistic regression and a long short-term memory (LSTM) model. Although LSTM-based prediction models outperformed other prediction models for Bitcoin price prediction (regression), a simple profitability analysis revealed that classification models were more effective than regression models for algorithmic trading. Overall, the suggested ML learning-based prediction models performed similarly. This work performs an in-depth investigation on the evolution of Bitcoin, as well as a thorough review of several machine learning methods used for price prediction. It’s included is a Bitcoin price prediction. The Existing work fails to predict day to day bitcoin price changes. And has given less accuracy compared to the proposed work. Authors have overcome this limitation in the proposed work by predicting the day to day BTC price for the upcoming month. [ABSTRACT FROM AUTHOR]

Bitcoin and Ethereum blockchains are exploding with data. While blockchain is becoming increasingly popular, the sheer volume of data obscures important concerns about safety, and privacy. Data analysis can aid in identifying problems and recommending possible solutions, and is also essential to make blockchain useful in a wide range of industries. The paper reviews the current state of research in four key areas: security, privacy, performance and price prediction, and compiles relevant literature. Finally, it predicts future trends and challenges in this field, which will serve as a roadmap for researchers. Blockchain data analysis will increasingly rely on Machine Learning. [ABSTRACT FROM AUTHOR]

In this study, we explore the research published from 2009 to 2021 and summarize what extant literature has contributed in the last decade to the analysis of volatility and risk management in cryptocurrency investment. Our samples include papers published in journals ranked across different fields in ABS ranked journals. We conduct a bibliometric analysis using VOSviewer software and perform a literature review. Our findings are presented in terms of methodologies used to model cryptocurrencies' volatility and also according to their main findings pertaining to volatility and risk management in those assets and using them in portfolio management. Our research indicates that the models that consider the Markov-switching regime seem to be more consensual among the authors, and that the best machine learning technique performances are hybrid models that consider the support vector machines (SVM). We also argue that the predictability of volatility, risk reduction, and level of speculation in the cryptocurrency market are improved by the leverage effects and the volatility persistence. [ABSTRACT FROM AUTHOR]

In this article forecasting of daily closing price series of Bitcoin, Ripple, Dash, Litecoin and Ethereum crypto currencies, using data on prices (open, low, high), market capital and volumes using prior days is focused. The value conduct of cryptographic forms of money remains to a great extent neglected, giving new chances to scientists and business analysts to feature the likenesses and contrasts with standard monetary costs. Hence the paper is focused on this area. he results are compared with various benchmarks. Predictions are done using statistical techniques and machine learning algorithms. A simple linear regression (SLR) model that uses only a single-variable sequence of closing prices for forecasting, and a multiple linear regression (MLR) model that uses a multivariate sequence of prices and quantities at the same time. The simple linear regression (SLR) model for univariate serial forecasting uses only closing prices. Mean Absolute Percentage Error (MAPE) and relative Root Mean Square Error (relative RMSE) performance measures are considered. The accuracy achieved by the ARIMA model on our dataset is the highest, followed by Multivariable Linear Regression and LSTM. [ABSTRACT FROM AUTHOR]

Cryptocurrency is a new sort of asset that has emerged as a result of the advancement of financial technology and it has created a big opportunity for researches. Cryptocurrency price forecasting is difficult due to price volatility and dynamism. Around the world, there are hundreds of cryptocurrencies that are used. This paper proposes three types of recurrent neural network (RNN) algorithms used to predict the prices of three types of cryptocurrencies, namely Bitcoin (BTC), Litecoin (LTC), and Ethereum (ETH). The models show excellent predictions depending on the mean absolute percentage error (MAPE). Results obtained from these models show that the gated recurrent unit (GRU) performed better in prediction for all types of cryptocurrency than the long short-term memory (LSTM) and bidirectional LSTM (bi-LSTM) models. Therefore, it can be considered the best algorithm. GRU presents the most accurate prediction for LTC with MAPE percentages of 0.2454%, 0.8267%, and 0.2116% for BTC, ETH, and LTC, respectively. The bi-LSTM algorithm presents the lowest prediction result compared with the other two algorithms as the MAPE percentages are: 5.990%, 6.85%, and 2.332% for BTC, ETH, and LTC, respectively. Overall, the predictionmodels in this paper represent accurate results close to the actual prices of cryptocurrencies. The importance of having these models is that they can have significant economic ramifications by helping investors and traders to pinpoint cryptocurrency sales and purchasing. As a plan for future work, a recommendation is made to investigate other factors that might affect the prices of cryptocurrency market such as social media, tweets, and trading volume. [ABSTRACT FROM AUTHOR]

Since financial assets on stock exchanges were created, investors have sought to predict their future values. Currently, cryptocurrencies are also seen as assets. Machine learning is increasingly adopted to assist and automate investments. The main objective of this paper is to make daily predictions about the movement direction of financial time series through classification models, financial time series preprocessing methods, and feature selection with genetic algorithms. The target time series are Bitcoin, Ibovespa, and Vale. The methodology of this paper includes the following steps: collecting time series of financial assets; data preprocessing; feature selection with genetic algorithms; and the training and testing of machine learning models. The results were obtained by evaluating the models with the area under the ROC curve metric. For the best prediction models for Bitcoin, Ibovespa, and Vale, values of 0.61, 0.62, and 0.58 were obtained, respectively. In conclusion, the feature selection allowed the improvement of performance in most models, and the input series in the form of percentage variation obtained a good performance, although it was composed of fewer attributes in relation to the other sets tested.

This study aims to forecast extreme fluctuations of Bitcoin returns. Bitcoin is the first decentralized and the largest, in terms of capitalization, cryptocurrency. A well-timed and precise forecast of extreme changes in Bitcoin returns is key to market participants since they may trigger large-scale selling or buying strategies that may crucially impact the cryptocurrency markets. We term the instances of extreme Bitcoin movement as ‘spikes’. In this paper, spikes are defined as the returns instances that outreach a two-standard deviations band around the mean value. Instead of the unconditional historic standard deviation that is usually used, in this paper, we utilized a GARCH(p,q) model to derive the conditional standard deviation. We claim that the conditional standard deviation is a more suitable measure of on-the-spot risk than the overall standard deviation. The forecasting operation was performed using the support vector machines (SVM) methodology from machine learning. The most accurate forecasting model that we created reached 79.17% out-of-sample forecasting accuracy regarding the spikes cases and 87.43% regarding the non-spikes ones.

The volatile and unpredictable nature of the cryptocurrency market makes it particularly challenging to make profitable investment decisions. different machine learning-based techniques have been employed for forecasting cryptocurrency value. However, although some works have addressed incorporating the Blockchain transactions' data into the analysis, none of them has provided a hybrid solution, including features obtained through complex network modeling. In this paper, we investigated the use of machine learning and complex network techniques to improve the profitability of a cryptocurrency portfolio during a downtrend period. We extracted features through a complex network-building methodology based on the Bitcoin blockchain transactions, merged them with the historical cryptocurrency values, and generated the predictions using different machine-learning models. The results indicated that incorporating complex network features improved the performance in retaining the initial capital at the end of the experiment, leading to an increment of 7.09% and 4.33% for the CNN and LSTM models, respectively. Our findings suggest that the proposed method enhanced the performance of cryptocurrency investment strategies during downtrend periods. [ABSTRACT FROM AUTHOR]

Machine learning and deep learning algorithms produce very different results with different examples of their hyperparameters. Algorithm parameters require optimization because they are not specific to all problems. This paper used Long Short-Term Memory (LSTM) and eight different hyperparameters (go-backward, epoch, batch size, dropout, activation function, optimizer, learning rate, and the number of layers) to examine daily and hourly Bitcoin datasets. The effects of each parameter on the daily dataset on the results were evaluated and explained. These parameters were examined with the hparam properties of Tensorboard. As a result, it was seen that examining all combinations of parameters with hparam produced the best test Mean Square Error (MSE) values with hourly dataset 0.000043633 and daily dataset 0.00061806. Both datasets produced better results with the tanh activation function. Finally, when the results are interpreted, the daily dataset produces better results with a small learning rate and dropout values. In contrast, the hourly dataset produces better results with a large learning rate and dropout values. [ABSTRACT FROM AUTHOR]

Bitcoin is a kind of cryptocurrency that has become a popular stock market investment and it has been steadily rising in recent years, and occasionally falling without warning, on the stock market. Because of its fluctuations, an automated tool for predicting bitcoin on the stock market is required. However, because of its volatility, investors will need a prediction tool to help them make investment decisions in bitcoin or other cryptocurrencies. In this paper, Deep learning mechanisms like Recurrent Neural Network (RNN) and Long short-term memory (LSTM) are proposed to develop a model to forecast the bitcoin price trend in the market. Finally, the predictions result for the Bitcoin price trend are presented over the next 15, 30, and 60 days. Each model is evaluated in terms of Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE) forecasting error values. The LSTM model is found to be the better mechanism for time-series cryptocurrency price prediction, but it takes longer to compile. [ABSTRACT FROM AUTHOR]

Following the rise and fall of cryptocurrency fees in recent years, Bitcoin has increasingly become a source of finance. There might be a need for great projections on which to base financing decisions due to its incredibly unstable nature. While the current study used system mastery to anticipate Bitcoin charges with more accuracy, few studies have examined the practicality of applying alternative modelling approaches to samples with different fact systems and dimensional capacities. We first divide the Bitcoin charge into daily and highfrequency components in order to predict it at various frequencies by employing system mastering techniques. Logistic Regression and Linear Discriminant Analysis are two statistical techniques for Bitcoin. Daily charge prediction with high-dimensional capabilities performs better than more difficult system mastering techniques with a 66 percent accuracy rate. With the best statistical techniques and system mastery algorithms with accuracy rates of 66 percent and 65.3 percent, respectively, we outperform benchmark effects for daily charge prediction. Statistics are updated from ML, models including "Random Forest, XG Boost, Quadratic Discriminant Analysis, Support Vector Machine, and Long Short-term Memory for Bitcoin 5-minute C language charge prediction, and they achieve an accuracy of 67.2 percent". When examining the significance of pattern measurement in system mastering tactics, our research on the prediction of Bitcoin charges may be taken into account. [ABSTRACT FROM AUTHOR]

The purpose of this study is to create an overview of the current industry practices concerning time series analysis specifically focused on cryptocurrencies. This study attempts to initially introduce the classical time series models slowly diving deeper to survey the field of altcoin price prediction to shed some light on currently available literature. Some of the models discussed below include ARIMA and its variations, LTSM, and other sentiment analysis procedures majorly targeting various alt-coins but as the first cryptocurrency, Bitcoin is observed to be the sole focus of the many studies discussed here. The study largely focuses on Time series data with social media sentiment analysis, the various factors affecting cryptocurrency prices, and finally discusses the prominent findings and results in the industry. This study is conducted with a focus to increase awareness and knowledge of the mentioned topics among fellow researchers and working professionals. [ABSTRACT FROM AUTHOR]