Blockchain Papers

Petr Hájek, Lubica Hikkerova, Jean‐Michel Sahut

No abstract is available for this record.

Japjeet Singh, Ruppa K. Thulasiram, A. Thavaneswaran

Cryptocurrencies have emerged as an alternative financial asset in the last decade, with their market growing exponentially in recent years. The price of cryptocurrencies is highly volatile and is prone to rapid swings within short periods of time. This behaviour makes them a high-risk and high-return financial asset. The efficacy of neural networks in forecasting the high frequency financial time series has become widely accepted in the research community. This work explored the use of Long Short Term Memory (LSTM), a neural network based non-linear sequence model, to propose a novel algorithmic trading strategy for cryptocurrencies. The proposed novel high frequency algorithmic trading strategy built over an LSTM based short-term price forecasting is used for Bitcoin and Ethereum. This simple, yet effective trading algorithm uses the network's price forecasts to make buy and short selling decisions for cryptocurrency based on certain set criteria. The proposed trading strategy gives positive returns when backtested on Bitcoin hourly prices taken from yahoo! finance. We also verified the effectiveness of the trading strategy for Ethereum, the second largest cryptocurrency, based on the positive backtesting returns. As an extension to the study, the proposed strategy is applied on an even higher frequency (minute by minute) Bitcoin price data, and the strategy gives positive backtesting returns in this extended study. We also provide fuzzy intervals for the algorithmic return of our strategy and compare those with corresponding intervals on a simple buy and hold strategy.

Jiaqi Qin, Shansong Huang, Boying Yang, Yilin Ma · 6 authors

Everyone is eager for high yield and low risk. In this research, we use Markowitz's investment theory and Monte Carlo simulation to find the optimal investment portfolio and then study the impact of adding Bitcoin to the traditional investment portfolio on the cumulative rate of return. Our results show that the return performance of the investment portfolio with Bitcoin is better than that of the traditional investment portfolio. Moreover, despite the impact of COVID-19 on the global economy and the Federal Reserve's quantitative easing policy, it is beneficial for investors to include Bitcoin in their portfolio allocation.

Gülin Vardar, Caner Taçoğlu, Berna Aydoğan

This study investigates mean and volatility spillover effects among eight major cryptocurrencies; Bitcoin, Ethereum, Litecoin, Ripple, Stellar, Bitcoin Cash, Cardano and EOS utilizing VAR-BEKK-GARCH model. The results point out that there are bidirectional and unidirectional spillover effects among these major cryptocurrencies. Moreover, the findings indicate that some cryptocurrencies are the transmitter, while others act as a receiver and among all, Litecoin is the highest transmitter, and Stellar is the only one that acts as a receiver. The interdependence among cryptocurrencies supports that they are becoming more integrated and thereby, provides important investment strategies for investors and policy implications for regulators.

Michał Frontczak, Tomasz Hachaj

The goal of our work was to select a neural network architecture that would give the best prediction of the Bitcoin exchange rate using historical data. Our work fits into the very important topic of predicting the value of the cryptocurrency exchange rate, and makes use of recent data which, as a result of the high Bitcoin exchange rate dynamics of the last year, differs significantly from those of previous years. We propose and test a number of neural network-based architectures and conduct a discussion of the results. Unlike previous state-of-the-art works, we conducted a comprehensive comparison of three different neural network-based models: MLP (multilayer perceptron), LSTM (long short-term memory) and CNN (convolutional neural network). We tested them for a wide range of parameters. The results we present are, to the best of our knowledge, the most up to date when it comes to the application of artificial intelligence methods for the prediction of cryptocurrency exchange rates. The best-performing architectures were used for a website that gives real-time predictions of the Bitcoin exchange rate. The website is available at http://stpbtc-ii.up.krakow.pl/. Source codes of our research are available to download in order to make our experiment reproducible.

Jiageng Ma, Yingjie Zhu, Jiani Xu, Yifan Li · 6 authors

Bitcoin is one of the most successful cryptocurrencies, and research on Bitcoin price prediction is getting more and more attention. Previous studies have used traditional statistical methods and machine learning models to predict Bitcoin prices. However, previous studies also have many problems, such as too few influencing factors, lack of model optimization, and poor prediction effect. This paper selects 27 factors related to Bitcoin price changes and screens the features through the XGBoost algorithm and the Random Forest algorithm (RF). In this study, combined forecasting models based on Support Vector Regression (SVR), Least Squares Support Vector Regression (LSSVR) and Twin Support Vector Regression (TWSVR) are used to predict Bitcoin price, separately. In addition, the Whale Optimization Algorithm (WOA) and Particle Swarm Optimization (PSO) are applied for parameter tuning of the models. Expected Variance Score (EVS), Coefficient of Determination (R2), Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) are used to measure the prediction accuracy of the combined models. The CPU time is used to measure the operation speed of the combined models. The experimental results show that the combined model XGBoost-WOA-TWSVR has the best prediction effect, and the EVS score of this model is 0.9547. In addition, our research verifies that Twin Support Vector Regression has advantages in both prediction effect and computational speed.

Akhter Mohiuddin Rather

No abstract is available for this record.

Akif Akgül, Eyyüp Ensari Şahin, Fatma Yıldız ŞENOL

Crypto assets succeeded in making their name known to large masses with Bitcoin, which emerged as a result of the creation of the first genesis block in 2008. Until 2010, the aforementioned recognition showed itself mostly in areas such as games, but over time it managed to enter the portfolios of individual investors. Especially as of end of 2017, the rapid increases in monetary value quickly attracted the attention of corporate companies and then the (Central Banks). These assets have created different alternatives (also know as altcoins) by working and have managed to become one of the important financial instruments today. This study has examined in detail the techniques (Chaos theory, Onchain analysis and Sentiment analysis) developed on the price predictions of crypto assets, which are very important in terms of the number and quality of investors. In the study, findings were obtained that new techniques such as onchain and sentiment are more prominent in estimating crypto asset prices compared to traditional asset price estimation methods of crypto assets and that these techniques can make consistent estimations.

Uwais Suliman, Terence L. van Zyl, Andrew Paskaramoorthy

Cryptocurrencies are peer-to-peer digital assets monitored and organised by a blockchain network. Price prediction has been a significant focus point with various machine learning algorithms, especially concerning cryptocurrency. This work addresses the challenge faced by traders of short-term profit maximisation. The study presents a deep reinforcement learning algorithm to trade in cryptocurrency markets, Duelling DQN. The environment has been designed to simulate actual trading behaviour, observing historical price movements and taking action on real-time prices. The proposed algorithm was tested with Bitcoin, Ethereum, and Litecoin. The respective portfolio returns are used as a metric to measure the algorithm's performance against the buy-and-hold benchmark, with the buy-and-hold outperforming the results produced by the Duelling DQN agent.

Jerome Branny, Rolf Dornberger, Thomas Hanne

In this paper, we investigate how to forecast Non-Fungible Token (NFT) sale prices by using multiple multivariate time series datasets containing features related to the NFT market space. We examined eight recent studies regarding the forecasting and valuation of NFTs and compared their most important findings. This laid the fundamental work for two separate machine learning prototypes based on Long Short-Term Memory (LSTM) which are able to forecast the sale price history of an individual NFT asset. Root Mean Squared Errors (RMSE) of 0.2975 and 0.24 were obtained which appears to be promising.

Jantima Polpinij, Napat Saktong

This study aimed to evaluate the effectiveness of several algorithms for predicting the close-price of various cryptocurrencies. Three algorithms employed in this comparative study were Support Vector Regression (SVR), Random Forest (RF), and Long Short-Term Memory (LSTM), while the three cryptocurrency datasets examined were Bitcoin, Ethereum, and Litecoin. Furthermore, in the stage of the data preparation, we compared two popular data normalization methods: min-max and z-score. After examining the close-price prediction results of each approach using Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE), it was revealed that the predictive model generated by the LSTM algorithm together with z-score normalization yielded the most effective results for each cryptocurrency dataset.

Qasem Abu Al‐Haija

Bitcoin (BTC) is a distributed virtual paradigm that uses a peer-to-peer network to provide a means of digital money. Bitcoin pricing was changing monthly, and the BTC price observations have been collected since October 2013. In this paper, we propose a neural network-based autoregressive predictive model to forecast the monthly pricing of cryptocurrency bitcoin technology based on 100 historical observations for the bitcoin prices from Oct-2013 to Sep-2021 (in us dollars). Specifically, the proposed scheme uses a nonlinear autoregressive neural network with external input (NARX) by detaining the maximum regression coefficient corresponding to the most prediction accuracy and the least normalized prediction error. The simulation results showed that the highest prediction accuracy for the identified cryptocurrency, bitcoin pricing is 99.1%. The subsequent perdition model was effectively used to anticipate the evolution of forthcoming 12-month data records for the n cryptocurrency bitcoin prices from Oct-2021 to Sep-2022. The forecast values reveal a very slow, linearly developing tendency in the prices of cryptocurrency bitcoin released monthly over the past ten years' records for the global cryptocurrency bitcoin pricing time series.

Izzati Izyani Japar, Dharini Pathmanathan, Shafiqah Azman

Abstract Cryptocurrency investment especially Bitcoin, has become favourable over recent years due to promising returns in the future. However, the movement of price is mainly speculation-based as these currencies are still new in the market. The COVID-19 outbreak boosted research interest in predicting the price fluctuation of Bitcoin since cryptocurrency trading produced many millionaires. Five lexicon-based Twitter sentiment analysis approaches are examined to see the effect of Tweets on the price of Bitcoin during the pre- and post- COVID-19 period. Results show that negative Twitter sentiments affected the price of Bitcoin pre- COVID-19 and the second year of post- COVID-19 when Elon Musk actively criticised Bitcoin on Twitter.

Junho Kim, Hanul Sung

Since bitcoin has gained recognition as a valuable asset, researchers have begun to use machine learning to predict bitcoin price. However, because of the impractical cost of hyperparameter optimization, it is greatly challenging to make accurate predictions. In this paper, we analyze the prediction performance trends under various hyperparameter configurations to help them identify the optimal hyperparameter combination with little effort. We employ two datasets which have different time periods with the same bitcoin price to analyze the prediction performance based on the similarity between the data used for learning and future data. With them, we measure the loss rates between predicted values and real price by adjusting the values of three representative hyperparameters. Through the analysis, we show that distinct hyperparameter configurations are needed for a high prediction accuracy according to the similarity between the data used for learning and the future data. Based on the result, we propose a direction for the hyperparameter optimization of the bitcoin price prediction showing a high accuracy.

Samuka Mohanty, Rajashree Dash

Bitcoin is yet to be assumed as a worthy cryptocurrency and rewarding asset in the global market. As polynomial-based neural networks (PBNNs) are very robust and more accurate in modeling stock price prediction, their advantage in Bitcoin pricing needs to be analyzed. In this study, the robustness of PBNNs, based on Chebyshev (CPBNN) and Legendre (LPBNN), is blended with the proposed algorithm, coined as the mutated climb monkey algorithm (MCMA), to control the estimation of network parameters to accurately predict the one-day-ahead Bitcoin price. The performance was evaluated by a comparative analysis of the testing of both CPBNN and LPBNN with each of the six algorithms under consideration on three different datasets collected within the same time interval. As the use of a few evaluation criteria will not be able to identify an efficient predictor model, this study also proposes the use of a Multi-Criteria Decision-Making (MCDM) framework to rank all models using 15 different evaluation criteria. The ranking of the models clearly indicates that the proposed MCMA algorithm outperforms all other algorithms under study. The convergence plots of the top two models for the datasets also indicate that the PBNN using MCMA for learning predicts better results.

Mohsin Ghaffar Ghouri, Mohsin Ashraf

Cryptocurrency is the most secure, traceable, and reliable intangible currency because it uses blockchain technology. It eliminates the middle layer of financial institutes in the traditional economic system. Because of high returns in cryptocurrencies, investors and other firms invest a lot of money. But the prices of the cryptocurrencies are uncertain. Prices of cryptocurrencies are influenced by many factors like sentiments, trading volume, and similar. Researchers are doing plenty of work to predict the accurate prices of various cryptocurrencies. However, many of these methods cannot be used in real-time. Several deep learning models such as Neural networks (NN), Long short-term memory (LSTM), and Gated recurrent unit (GRU) have been utilized by researchers for predicting the price of cryptocurrencies and yet, are unable to achieve significant results. This work combines LSTM and GRU with sentiment analysis to precisely estimate bitcoin values. We have used Root means square error (RMSE) to evaluate the model performance with and without sentiments. Empirically, we have compared the results with the other state-of-the-art models and found better results using the proposed hybrid model incorporated with sentiments.

Darwin Nesakumar, Srinath Bhavyasri, Tanguturi Keerthana, Yelisetty Meghana · 6 authors

This work explains the role of tree regressions, long-term short-term memory models and ARIMA models in predicting Bitcoin values. In this study, we used Bitcoin Sets data to test and train the Ml and the Ai models. The data filtration process was completed with the help of the Python Library. When we understand the data, we adjust it for using those characteristics or attributes that are best suited for model. Here this model was implemented by recording the results. The accuracy of the decision tree regression model was found to be high when comparing with other machine learning models; R flat is found that is equal to 0.67.On the other hand, the R of the LSTM model is flat 0.49, the R of the ARIMA model is flat. Ninety-four. By this, we can see that deep learning model is highly improved model when compared with machine learning model.

Amrita Sen, Gunamani Jena

Cryptocurrency has gained popularity because it has the potential to control the total quantity of money ever generated and because it can be used to monitor cash transactions without the interference of a central bank. Cryptocurrencies are used by many networks and apps to represent valuable and intangible goods such as virtual worlds, peer-to-peer networks, online social systems, and social gaming platforms. The usage of virtual money has increased recently across many different platforms. This study suggests a forecasting system that uses one hot code, random work simulation, and LGBM regression. Preprocessing involves data normalization utilizing exogenous variables. From the preprocessed, the SHA-256 bitcoin algorithm and bitcoin halving characteristics are chosen. The symmetric distribution is used to visualize the data. The utilization of data visualization is to effectively explain a portion of data's significance. The results were compared with conventional methods. The algorithm has been used in a cryptocurrency system, and its effectiveness is evaluated. The findings show that the proposed strategy performance is superior.

Xiao Li, Linda Du

No abstract is available for this record.

Dimitar Kitanovski, Miroslav Mirchev, Ivan Chorbev, Igor Mishkovski

As of the end of 2013 till now we are witnessing huge volatility and risk in the cryptocurrency market compared to flat currency or stock market. Thus, in this market the portfolio diversification is of big importance in order to reduce volatility and keep the optimal return for the investors. A usual approach for portfolio construction is to keep a balance between returns and volatility, based on their interdependence and individual returns. One way of diversification is employing clustering or community detection algorithms to select a more diverse set of assets. We study the utilization of the Louvain algorithm and affinity propagation for community detection, based on correlation and mutual information between cryptocurrencies, for potential application in portfolio diversification.

Preeti Sharma, R. M. Pramila

No abstract is available for this record.

Marko Stankovic, Nebojša Bačanin, Miodrag Živković, Luka Jovanović · 6 authors

Cryptocurrencies have established a firm position in the economic world in the past decade, with thousands of distinctive currencies available for electronic payments. The majority of cryptocurrencies, however, experience extremely volatile price perturbations, drastically affecting investors and traders. To address this problem, this paper proposes long short-term memory approach tuned by salp swarm metaheuristics. This hybrid model has been validated on a benchmark financial dataset, and the outcomes have been compared to other cutting-edge methods. The results suggest that the proposed method outperformed the competitors, showing significant potential in time-series prediction tasks.

Rasoul Amirzadeh, Asef Nazari, Dhananjay Thiruvady

The total capital in cryptocurrency markets is around two trillion dollars in 2022, which is almost the same as Apple’s market capitalisation at the same time. Increasingly, cryptocurrencies have become established in financial markets with an enormous number of transactions and trades happening every day. Similar to other financial systems, price prediction is one of the main challenges in cryptocurrency trading. Therefore, the application of artificial intelligence, as one of the tools of prediction, has emerged as a recently popular subject of investigation in the cryptocurrency domain. Since machine learning models, as opposed to traditional financial models, demonstrate satisfactory performance in quantitative finance, they seem ideal for coping with the price prediction problem in the complex and volatile cryptocurrency market. There have been several studies that have focused on applying machine learning for price and movement prediction and portfolio management in cryptocurrency markets, though these methods and models are in their early stages. This survey paper aims to review the current research trends in applications of supervised and reinforcement learning models in cryptocurrency price prediction. This study also highlights potential research gaps and possible areas for improvement. In addition, it emphasises potential challenges and research directions that will be of interest in the artificial intelligence and machine learning communities focusing on cryptocurrencies.

Gül Cihan Habek, Mansur Alp Toçoğlu, Aytuğ Onan

As the cryptocurrency trading market has grown significantly in recent years, the number of comments related to cryptocurrency has increased tremendously in social media platforms. Due to this, sentiment analysis of the cryptocurrency-related comments has become highly desirable to give a comprehensive picture of peoples’ opinions about the trend of the market. In this regard, we perform cryptocurrency-related text sentiment classification using tweets based on positive and negative sentiments. For increasing the efficacy of the sentiment analysis, we introduce a novel deep neural network hybrid architecture which is composed of an embedding layer, a convolution layer, a group-wise enhancement mechanism, a bidirectional layer, an attention mechanism, and a fully connected layer. Local features are derived using a convolution layer, and weight values associated with intuitive features are developed using the group-wise enhancement mechanism. After feeding the improved context vector to the bidirectional layer to grab global features, the attention mechanism and the fully connected layer have been employed. The experimental findings indicate that the proposed architecture outperforms the state-of-the-art architectures with an accuracy value of 93.77%.