Follow blockchain research across journals, conferences, and preprint repositories.
4,843 results · page 54 of 202
Ujkan Q. Bajra, Ermir Rogova, Sefer Avdiaj
No abstract is available for this record.
Dirk G. Baur, Jonathan R. Karlsen
This paper analyses the transition of Ethereum (ETH) from the energy-intensive Proof-of-Work (PoW) to the less energy-intensive Proof-of-Stake (PoS). We analyze returns, volatility, return correlations and volume of ETH, ETC and Bitcoin for all events in the lead-up to the actual change from PoW to PoS also labelled "the merge." The analysis suggests that some investors value the less energy-intensive mining mechanism and invest in ETH. However, since the overall effect is weak, we conclude that despite all the media attention and the stated concerns about the high energy-intensity of Bitcoin and PoW, most investors do not react to the change with an increased investment in Ethereum.
Hyung-Eun Choi
No abstract is available for this record.
Vahidin Jeleskovic, Claudio Latini, Zahid Irshad Younas, Mamdouh Abdulaziz Saleh Al‐Faryan
The growing interest in cryptocurrencies has drawn the attention of the financial world to this innovative medium of exchange. This study aims to explore the impact of cryptocurrencies on portfolio performance. We conduct our analysis retrospectively, assessing the performance achieved within a specific time frame by three distinct portfolios: one consisting solely of equities, bonds, and commodities; another composed exclusively of cryptocurrencies; and a third, which combines both 'traditional' assets and the best-performing cryptocurrency from the second portfolio.To achieve this, we employ the classic variance-covariance approach, utilizing the GARCH-Copula and GARCH-Vine Copula methods to calculate the risk structure. The optimal asset weights within the optimized portfolios are determined through the Markowitz optimization problem. Our analysis predominantly reveals that the portfolio comprising both cryptocurrency and traditional assets exhibits a higher Sharpe ratio from a retrospective viewpoint and demonstrates more stable performances from a prospective perspective. We also provide an explanation for our choice of portfolio optimization based on the Markowitz approach rather than CVaR and ES.
Shun Liu, Kexin Wu, Chufeng Jiang, Bin Huang · 5 authors
In the realm of cryptocurrency, the prediction of Bitcoin prices has garnered substantial attention due to its potential impact on financial markets and investment strategies. This paper propose a comparative study on hybrid machine learning algorithms and leverage on enhancing model interpretability. Specifically, linear regression(OLS, LASSO), long-short term memory(LSTM), decision tree regressors are introduced. Through the grounded experiments, we observe linear regressor achieves the best performance among candidate models. For the interpretability, we carry out a systematic overview on the preprocessing techniques of time-series statistics, including decomposition, auto-correlational function, exponential triple forecasting, which aim to excavate latent relations and complex patterns appeared in the financial time-series forecasting. We believe this work may derive more attention and inspire more researches in the realm of time-series analysis and its realistic applications.
Imran Yousaf, Manel Youssef, John W. Goodell
No abstract is available for this record.
Aditya Dahatonde, Lajwanti Kute, Yash Shinde, Chetan Chavan · 6 authors
Cryptocurrencies are changing how we view and interact with traditional currencies, and they have become a disruptive force in the financial industry. Accurate price prediction is becoming more and more important as the bitcoin industry grows in size and complexity. This paper provides a thorough examination of deep learning models used in bitcoin price prediction. We explore the dynamic and unpredictable character of the cryptocurrency market, where price swings can happen quickly and without warning. To comprehend the present state of the art in this domain and pinpoint the shortcomings of the deep learning models in use today, we examine the body of existing literature. The data collecting and preprocessing methods used to get the bitcoin market data ready for modeling are described in the methodology section. Numerous deep learning models—Recurrent Neural Networks among them, Convolutional neural networks (CNNs) and Long Short-Term Memory (LSTM) networks are investigated. We go over hyperparameter tweaking, model training, and the assessment metrics that are used to gauge the performance of the model. We offer a thorough case study that focuses on forecasting the price of a particular cryptocurrency, like Bitcoin, in order to offer empirical insights. Our results provide light on the difficulties and possibilities involved in this project, emphasizing the need for creative solutions to address the market
Orhan Özaydın
The World Health Organization (WHO) announced the Covid-19 pandemic in March 2020, which had a negative impact on economic activities and financial markets. Cryptocurrencies with blockchain technology, whose history is not old, took off in the Covid-19 period thanks to digital transformation and became popular in the financial markets. However, the fact that cryptocurrencies lose blood after the pandemic period. This study examines the volatility of cryptocurrencies before, during and after the pandemic Covid-19 using data from 4 cryptocurrencies (Bitcoin, Ethereum, Binance and Litecoin) and the CCI30 index, using autoregressive conditional variance models with two dummy variables. According to the results, the volatility of cryptocurrencies decreases throughout the pandemic period, moreover, decreases more after the pandemic compared to the pre-pandemic period. Investors should be cautious about investing in these risky instruments, which may become popular again in the future, just in case.
Shoaib Ali, Muhammad Naveed, Manel Youssef, Imran Yousaf
No abstract is available for this record.
Pawan Kumar, Mukul Bhatnagar, Sanjay Taneja
The temporal conduct of the cryptocurrency BIT GREEN Crypto is examined using an ARMA model. This study analyses BIT GREEN Crypto's volatility using the ARMA model. ARMA model examination of past pricing data determines BIT GREEN Crypto timing trends and variations. This study uses rigorous methods and historical data to reveal BIT GREEN Crypto's temporal patterns and changes to better cryptocurrency analysis. In the study, ARMA modelling correctly predicted BIT GREEN Crypto's volatility. The study helps investors and market participants understand cryptocurrency volatility. The results also show that the ARMA model's restrictions and the aspects of bitcoin volatility must be addressed. This study clarifies BIT GREEN Crypto's volatility and temporal dynamics. This ARMA-modelled study gives investors and market participants cryptocurrency insights and management advice.
Amit Kumar, Neha Sharma, Rahul Chauhan, Manish Sharma
The emergence of the cryptocurrency market has had a significant impact on the finance industry, presenting a dynamic and revolutionary influence that challenges conventional understandings of currency and investing. This study examines the analysis of the dynamic landscape, with a specific emphasis on prominent cryptocurrencies such as Bitcoin and Ethereum. This study examines the use of technical analysis in understanding market sentiment and identifying trends, focusing on key techniques such as candlestick charts and moving averages. Candlestick charts provide a visual depiction of price movements, allowing traders and investors to detect patterns and potential reversals in the market. Moving averages, such as the 20-day moving average (20MA), 50-day moving average (50MA), and 200-day moving average (200MA), provide several timescales that can be utilized to evaluate trends and trend biases. The interaction between these moving averages provides significant insights into the behavior of the market. Significantly, the behavior of Ethereum is noteworthy as it exhibits the 20 Simple Moving Average (SMA) consistently positioned above the 50 SMA subsequent to a substantial market occurrence. This observation highlights the subtle distinctions in the conduct of cryptocurrencies, even when subjected to comparable market circumstances. In the context of a cryptocurrency market characterized by its inherent volatility and frequent fluctuations, the utilization of technical analysis techniques becomes essential for making well-informed decisions. This study provides a comprehensive framework for understanding the intricacies of the cryptocurrency market, presenting significant perspectives for traders and investors aiming to leverage its opportunities while minimizing associated risks.
Amit Kumar, Neha Sharma, Rahul Chauhan, Manish Sharma
Cryptocurrencies, most notably Bitcoin, have experienced a significant increase in popularity, garnering the interest of both investors and scholars. The present study aims to investigate and forecast the prices of Bitcoin. The focus lies on the essential aspects of data preprocessing, exploratory data analysis, and forecasting methodologies. The dataset undergoes thorough cleaning procedures to ensure meticulousness, followed by an exhaustive exploration of the data through various analytical techniques. This study provides valuable insights into pricing trends, seasonality patterns, and relationships within the dataset. The research utilizes a range of models, such as ARIMA for short-term prediction, LSTM neural networks for intricate pattern detection, and hybrid models for enhanced resilience. The evaluation of model performance is conducted by using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE), while also employing out-ofsample testing to evaluate the model's ability to generalize. The results of this study provide a comprehensive analysis of the advantages and disadvantages associated with each technique, thereby offering significant insights for investors aiming to effectively navigate the cryptocurrency market. Furthermore, this highlights the possibility of applying this analysis to additional cryptocurrencies and improving forecasting models through the incorporation of sentiment analysis and macroeconomic indicators.
Divya Chaudhary, Sushil Kumar Saroj
As a consequence of rising geo-economic issues, global currency values have declined during the last two years, stock markets have performed poorly, and investors have lost money. Consequently, there is a renewed interest in digital currencies. Cryptocurrency is a fresh kind of asset that has evolved as a result of fintech innovations, and it has provided a major research opportunity. Due to price fluctuation and dynamism, anticipating the price of cryptocurrencies is difficult. There are hundreds of cryptocurrencies in circulation around the world and the demand to use a prediction system for price forecasting has increased manifold. Hence, many developers have proposed machine learning algorithms for price forecasting. Machine learning is fast evolving, with several theoretical advances and applications in a variety of domains. This study proposes the use of three supervised machine learning methods, namely linear regression, support vector machine, and decision tree, to estimate the price of four prominent cryptocurrencies: Bitcoin, Ethereum, Dogecoin, and Bitcoin Cash. The purpose of this study is to compute and compare the precision of all three techniques over all four datasets.
Maria Ghani, Usman Ghani, Shujahat Ali, Muhammad Mustafa · 5 authors
This research investigates the predictability of economic uncertainty indexes on the volatility of Bitcoin (BTC) during COVID-19. The economic uncertainty indexes include US economic policy uncertainty (EPU), Twitter economic uncertainty (TEU), Twitter market uncertainty (TMU), geopolitical risk index (GPR), and trade policy uncertainty (TPU) index. The empirical findings show that the Twitter market uncertainty (TMU) and geopolitical risk (GPR) uncertainty index are valuable predictors of BTC volatility. Moreover, the combination forecasts information for all economic uncertainty indexes is useful for BTC volatility forecasting. Also, we find evidence during high and low volatility and the Russia–Ukraine war. Our results show that Twitter market uncertainty and geopolitical risk uncertainty index are effective predictors of Bitcoin volatility during high volatility periods. During the Russia–Ukraine war, economic policy uncertainty (EPU), the Twitter market uncertainty index, and combination forecast information for all uncertainty indexes are effective for Bitcoin volatility prediction. Our findings are robust with the alternative method MCS test.
Zhouyun Zhao
The study explores the spillover effect on Ethereum – one of the leading cryptocurrencies – stemming from key variables in the domains of cryptocurrencies, investor sentiment, and traditional financial markets. This paper is the first to analyze the influence of such dominant representatives from diverse, external fields on cryptocurrency. We select bitcoin, the Fear and Greed index, the Standard and Poor’s 500 index and the United States Dollar to Euro Exchange Rate as representatives to investigate the spillover effect on Ethereum. Utilizing linear regression models and vector autoregressive (VAR) models, we find strong correlations between Ethereum’s return and that of Bitcoin’s, along with investor sentiment. However, the influence of financial market variables on Ethereum are found to be virtually static and negligible. This research offers valuable insights to those seeking to forecast or manipulate crypto market movement through analyzing the complex interplay between these variables and Ethereum.
Junyi Zhu
This paper illustrates the working process of predicting the Bitcoin price applying ARIMA, SARIMA and linear regression. Since more and more machine learning models were developed and tested in the financial field, these three models are selected to examine their reliabilities. In this study, three methodologies have been used for the Bitcoin predictions under the data set of Bitcoin historical prices. With the help of python notebook, order (1, 1, 1) and seasonal order (0, 1, 1, 12) were applied to the predictions in ARIMA and SARIMA respectively. In terms of linear regression, this paper used two independent variables including historical data and trading volume to predict the Bitcoin prices. It was discovered that the predictive graph for these three methodologies can match the actual value well, and linear regression performs the best. Considering the rapid development of machine learning methods, adopting alternative methods deserve in-depth investigations.
Panagiotis Vionis, Theodore Kotsilieris
The energy sector is undergoing a period of technological transformation, driven by the emergence of blockchain and smart contracts. These technologies have the potential to revolutionize energy markets and significantly reduce transaction costs, improve efficiency, and increase transparency. The rising energy prices in recent years have been a cause for global concern. As the EU recorded historically high energy prices in 2022, according to the EU Council, this price rise is linked to increased energy demand following the COVID-19 pandemic, the war in Ukraine, and the acceleration of climate change. This paper aims to critically examine the current state of blockchain and smart contracts technology in the energy sector, focusing on use cases, key challenges, and potential solutions. It further explores the impact of these technologies on energy markets and their potential to contribute to a sustainable, low-carbon energy future. Finally, it examines the prospects of blockchain and smart contract technologies to transform the energy industry and the policy implications for governments and regulators.
Ahmed Bouteska, Mohammad Zoynul Abedin, Petr Hájek, Kunpeng Yuan
Cryptocurrency price forecasting is attracting considerable interest due to its crucial decision support role in investment strategies. Large fluctuations in non-stationary cryptocurrency prices motivate the urgent need for accurate forecasting models. The lack of seasonal effects and the need to meet a number of unrealistic requirements make it difficult to make accurate forecasts using traditional statistical methods, leaving machine learning, particularly ensemble and deep learning, as the best technology in the area of cryptocurrency price forecasting. This is the first work to provide a comprehensive comparative analysis of ensemble learning and deep learning forecasting models, examining their relative performance on various cryptocurrencies (Bitcoin, Ethereum, Ripple, and Litecoin) and exploring their potential trading applications. The results of this study reveal that gated recurrent unit, simple recurrent neural network, and LightGBM methods outperform other machine learning methods, as well as the naive buy-and-hold and random walk strategies. This can effectively guide investors in the cryptocurrency markets.
Muhammad Abubakr Naeem, Afzol Husain, Ahmed Bossman, Sitara Karim
No abstract is available for this record.
Volodymyr Vrydnyk
The article thoroughly investigates the topic of development and regulation of digital currencies. Considering the global spread of digital assets, particularly cryptocurrencies, the article analyzes current challenges arising in the context of monetary policy and financial stability. The article examines the main concepts of digital currencies, including blockchain technologies and decentralized finance, as well as the characteristics of the approach to regulating these new assets in view of potential challenges for lawmakers, regulators, and central banks. The impact of digital currencies on monetary policy is analyzed in terms of potential effects on macroeconomic development: inflation, currency control, and financial stability. Also, the challenges and opportunities that digital currencies present for the traditional banking system are discussed. Monetary regulation of cryptocurrencies is a critically important aspect within the broader regulatory spectrum, involving supervision and control of digital currencies by central authorities, both from the perspective of fiat currencies and in terms of the processes of digitization overall. Cryptocurrencies, such as Bitcoin, typically have a capped supply, distinguishing them from traditional fiat currencies. This impacts monetary policy instruments like interest rates and money supply control, as there is no central authority regulating these parameters. Digital assets seamlessly operate across borders, challenging traditional structures of monitoring and controlling international transactions, making them vulnerable to cyber attacks and fraud. Especially in the early stages of development, cryptocurrencies may lack clear foundations or intrinsic value, unlike traditional assets such as stocks, often evaluated based on quarterly reports, earnings, and transparent financial indicators. Cryptocurrencies may be subject to more subjective influences, such as the impact of social media or the media, potentially leading to the formation of a phenomenon known as the ‘cryptocurrency bubble,’ driven by unjustified fluctuations in the prices of Bitcoin and other altcoins. This phenomenon resembles economic bubbles in traditional financial markets, characterized by sharp increases in asset prices driven by speculation and excessive buying rather than fundamental factors like underlying value or utility. The article provides general recommendations and practices for the regulation of digital currencies, supporting the innovative nature of digital assets in contemporary realities. With a primary focus on digital assets and their regulation, particularly cryptocurrencies, the article examines current challenges in the realm of monetary policy and financial stability, as well as fundamental concepts related to digital assets.
Xiaoke Song
The study aims to predict the close prices of four different cryptocurrencies (Bitcoin, Ethere-um, Dogecoin, and Cardano) using machine learning techniques and determine which of these cryptocurrencies is suitable for investment. To achieve this goal, we used two popular gradi-ent boosting algorithms: Extreme Gradient Boosting (XGBoost) and Light Gradient-Boosting Machine (LightGBM). Prediction accuracy of the trained model is evaluated by Mean Abso-lute Error (MAE) generated by the methodology of Cross-Validation. Our results show that both XGBoost and LightGBM can effectively predict the close prices of the four cryptocur-rencies, with LightGBM achieving slightly better performance in terms of prediction accura-cy. Based on our analysis, we were able to identify which cryptocurrencies were suitable for investing and provide recommendations for potential investors. Overall, our study highlights the potential of machine learning techniques in predicting cryptocurrency close prices and identifying suitable investment opportunities.
Andromahi Kufo, Ardit Gjeçi, Artemisa Pilkati
The blossoming of cryptocurrencies during the last decade has largely influenced both the financial and the technological world. Bitcoin emerged on the edge of the financial crisis in 2008, signaling the very beginning of a financial and technological innovation, which in continuance would eventually create a lot of questions and debate previously unforeseeable. This paper aims to explore the impact of factors such as trading volume, information demand, stock returns, and exchange rates on the volatility of returns for decentralized and unbacked cryptocurrencies from 2016 to 2022 by employing the GARCH model. Based on each coin’s innate functional characteristics and market performance quantified by their respective market capitalization, the selection included Bitcoin, Ether, and XRP as representative crypto coins for the category of decentralized and unbacked cryptocurrencies. The implementation of correlation analysis and the use of the GARCH model on influencing factors for each coin revealed that decentralized and unbacked cryptocurrencies are positively related to trading volume, information demand, and exchange rates while being indifferent to a certain extent to the stock market returns of the world stock index MSCI ACWI. The results of this study provide further insight into the behavior of cryptocurrency return volatility in the new, ever-changing, and highly unpredictable crypto market as well as aid investors in their decision-making process concerning portfolio optimization.
Zekai ŞENOL
Kripto varlıklar pay senetleri ve emtialar gibi geleneksel yatırım araçlarıyla karşılaştırıldığında daha az düzenleme, düşük işlem maliyetleri, merkeziyetsizlik gibi bazı avantajlara sahiptirler. Kripto varlıklar ortaya çıkışlarından günümüze kadar fiyat, hacim ve değer bakımından artarak portföylerde kendilerine yer edinmeye başlamışlardır. Kripto varlıkların geleneksel yatırım araçlarıyla olan ilişkileri portföy yönetimi açısından sonuçlar ortaya çıkarabilir. Bu çalışmada bitcoin ile altın, petrol, doğal gaz ve emtia endeksinden oluşan emtialar arasındaki volatilite yayılımları incelenmiştir. Çalışmada 24 Ağustos 2016 – 13 Ocak 2023 dönemine ait günlük veriler varyansta nedensellik ve Lu, Hong, Wang, Lai ve Liu (2014) tarafından geliştirilen zamanla değişen varyansta nedensellik testiyle incelenmiştir. Çalışmada bitcoinden altın ve emtia endeksine doğru ve doğal gazdan bitcoine doğru tek yönlü volatilite yayılımı görülmüştür. Bitcoin ile emtilar arasında düşük düzeyde zamanla değişen volatilite yayılımı belirlenmiştir. Sonuçlar portföy yönetimi, portföy riskinin yönetilmesi, yatırım kararları açısından önem taşımaktadır.
Zhou En, Xinyu Wang
No abstract is available for this record.