Follow blockchain research across journals, conferences, and preprint repositories.
4,843 results · page 96 of 202
Nassar S. Al-Nassar, Sabri Boubaker, Anis Chaibi, Beljid Makram
No abstract is available for this record.
Yongjian Ma
Nowadays, the Russian-Ukrainian war has been a hotly topic, and the war has shaken the global economy, especially in the international crude oil market. Also, as a popular financial instrument, the investors like to see Bitcoin as a hedging tool, but the problem of whether cryptocurrencies can hedge the volatility of commodity markets lacks a unified explanation. Therefore, the paper wants to find the relationship between Bitcoin and crude oil during the Russian-Ukrainian war. This paper uses data from Bitcoin, crude oil WTI futures, and crude oil Brent futures, and constructs the VAR model and ARMA-GARCH model based on these data. Ultimately, the article finds that the volatility of the international crude oil market only has little impact on Bitcoin. Thus, the investors do not need to worry about the high crude oil price caused by the war will affect Bitcoin’s yield and volatility, so Bitcoin seems like a great hedging instrument against the shock of the international crude oil market.
Abtin Ijadi Maghsoodi
No abstract is available for this record.
P. Umamaheswari, S. Abiramasundari, M. Kamaladevi, P. Dinesh
Bitcoin is a type of digital currency or computerized money that is utilised for speculation around the world. Bitcoins are files that are saved in a digital wallet programme on a mobile phone or a PC. Every transaction and its timestamp data are recorded in a common list known as blockchain. In this research, the cost of bitcoin is estimated utilising data mining techniques and machine learning algorithms. The dataset is preprocessed with the use of data mining algorithms, which reduces data noise. Bitcoin's price fluctuates, and it is estimated using long short-term memory (LSTM), a type of neural networking, to extract acceptable patterns for modelling and prediction. Discovering recurring patterns in the bitcoin market is a necessary endeavour in order to achieve optimal bitcoin price functionality. The dataset consists of numerous regularly reported bitcoin price features every year. Linear regression (LR) technique is used to estimate the future cost of bitcoin. Daily price shift with the best possible precision by using the available data is also estimated.
Weike Yang, Zheng Tao
In this paper, we analyze the time-series graphs of Bitcoin price and Twitter-based economic uncertainty index over the past two years and use a wavelet coherence graph to determine their relationship. We found a causal relationship between Bitcoin (BTC) and Twitter-based economic uncertainty (TEU) index in different frequency bands, which would help predict Bitcoin price movements in the future. Our study provides reference to academics and investors.
Juliet U. Elu, Miesha Williams
No abstract is available for this record.
Hyeonoh Kim, Chang Yong Ha, Kwangwon Ahn
No abstract is available for this record.
Yeonwoo Son, Soham Vohra, Rohit Vakkalagadda, Michael Zhu · 7 authors
The volatility of cryptocurrencies and exclusivity of crypto communities has made cryptocurrency investment inaccessible for common people. With machine learning, harnessing social media trends that affect price in a random field like cryptocurrency will provide everybody the ability to earn money. Although existing research utilizes sentiment analysis to label posts based solely on English, this project will use NLP to perform stance detection with respect to a certain entity to make predictions. The second part of this project will apply this stance detection to real-world prices, using an RNN to turn stance data into price data. The stance detection model, RoBERTa, reached an accuracy of 80%. An independent price prediction model using an RNN achieved a mean absolute error of $1144, a relatively minimal error considering that the price of crypto reaches $60000. This endeavor proves the difficulty in proving cryptocurrency prices, but the model's steady improvement indicates that future work on social media trends may be promising after all.
Alexandru-Costin Baroiu, Gabriela Dobrița Ene
Still in the inception of Web 3.0, the present study stands as an important incursion into the future of the Internet and displays how different technologies, Social Media and Cryptocurrencies, can influence each other and the world around them in these new, exciting, times. This paper seeks to find if there is a relationship between Twitter Sentiment and Bitcoin price. To this end, data is collected and preprocessed to construct a dataset consisting of two time series, one for Twitter Sentiment regarding Bitcoin and one for Bitcoin Price. The relationship between the two is studied through a Vector Autoregressive model. The results show that each exerts influence on the other. Twitter Sentiment is quicker to absorb the information, while Bitcoin Price takes longer to intake the most recent events. Future avenues of study are identified, with topic analysis of Bitcoin discourse being highlighted due to its potential to unlock new knowledge.
Carmen López-Martín
This paper analyses the effects known as the day of the week and the month of the year in the cryptocurrency markets. The closing values of eleven cryptocurrencies have been considered. The study employs dummy variable regression techniques, ANOVA and Friedman tests for assessing two calendar anomalies, the day-of-week and month-of-year effects. To test these calendar effects, we have applied both full sample and rolling-regression techniques for two lengths of the rolling sample intervals. Furthermore, we have examined the existence of long memory in day-of-the- week and month-of-the-year cryptocurrency returns. The results provide evidence about the existence of day-of-the-week and month-of-the-year effects in cryptocurrency returns, in particular, on Thursdays and in November. In addition, it should be added that the general results of the current study show that the calendar effect in the cryptocurrency market is dynamic rather than static, which indicates that the calendar effect is a phenomenon that varies over time.
Sudhi Sharma, Aviral Kumar Tiwari, Samia Nasreen
No abstract is available for this record.
Ruchi Gupta, Jagannath E. Nalavade
Bitcoin has recently been greatly regarded as an investment asset. It is incredibly unpredictable despite being the biggest digital currency. Therefore, accurate forecasting is essential for making investment strategies. This is a difficulty that the latest research effort takes on to construct a revolutionary Bitcoin price prediction model by incorporating new feature engineering and price prediction methods. The original features are first retrieved from the actual Bitcoin data obtained. This work is well-fit and accurate by developing a novel feature computing framework. The proposed decomposed inter-day difference based features and the second order technical indicator are generated within the feature extraction stage. Following that, the developed two-level ensemble classifier is used to accurately forecast the Bitcoin price value using extracted and original features. The two-level ensemble classifier blends the outstanding classifiers support vector machine and artificial neural networks. It is intended to adjust the weight parameter throughout training the ensemble method to accommodate the unpredictability features of Bitcoin prices better. The article presented the novel self-adaptive bat algorithm as a solution. With regard to specific performance metrics, the output of the two-level ensemble classifier is contrasted with that of the current models.
Abdussalam Aljadani
Abstract Cryptocurrencies are distributed digital currencies that have emerged as a consequence of financial technology advancement. In 2017, cryptocurrencies have shown a huge rise in their market capitalization and popularity. They are now employed in today’s financial systems as individual investors, corporate firms, and big institutions are heavily investing in them. However, this industry is less stable than traditional currency markets. It can be affected by several legal, sentimental, and technical factors, so it is highly volatile, dynamic, uncertain, and unpredictable, hence, accurate forecasting is essential. Recently, cryptocurrency price prediction becomes a trending research topic globally. Various machine and deep learning algorithms, e.g., Neural Networks (NN), Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM) were utilized to analyze the factors influencing the prices of the cryptocurrencies and accordingly predict them. This paper suggests a five-phase framework for cryptocurrency price prediction based on two state-of-the-art deep learning architectures (i.e., BiLSTM and GRU). The current study uses three public real-time cryptocurrency datasets from “Yahoo Finance”. Bidirectional Long Short-Term Memory and Gated Recurrent Unit deep learning-based algorithms are used to forecast the prices of three popular cryptocurrencies (i.e., Bitcoin, Ethereum, and Cardano). The Grid Search approach is used for the hyperparameters optimization processes. Results indicate that GRU outperformed the BiLSTM algorithm for Bitcoin, Ethereum, and Cardano, respectively. The lowest RMSE for the GRU model was found to be 0.01711, 0.02662, and 0.00852 for Bitcoin, Ethereum, and Cardano, respectively. Experimental results proved the significant performance of the proposed framework that achieves the minimum MSE and RMSE values.
Emmanuel Joel Aikins Abakah, Aviral Kumar Tiwari, Chi‐Chuan Lee, Matthew Ntow‐Gyamfi
Abstract This research explores the distributional and directional predictabilities among Fintech, Bitcoin, and artificial intelligence stocks from March 2018 to January 2021 using nonparametric causality‐in‐quantile and crossquantilogram approaches. We also examine connectedness across the assets using a quantile VAR approach. The results indicate the existence of bidirectional causality‐in‐variance between the variables in a normal market. We also find that directional predictability among the assets is oscillatory over time lags. Finally, we observe a strong price connectedness for highly positive and negative changes. These results further document the diversification potential and safe‐haven properties of technology‐related assets for portfolio investors.
Sisa Shiba, Juncal Cuñado, Rangan Gupta, Samrat Goswami
This paper examines the forecasting power of daily infectious disease-related uncertainty in predicting the realized volatility of nine foreign exchange futures and the Bitcoin futures series using the heterogeneous autoregressive realized variance model. Our results indicate that the infectious diseases-related uncertainty index plays a crucial role in predicting the future path of foreign exchange and Bitcoin futures realized volatility in all the selected time intervals. These findings have important implications for portfolio managers and investors during periods of high levels of uncertainty associated with infectious diseases.
Xi Yuan, Chi‐Wei Su, Adelina Dumitrescu Peculea
With the control of the cryptocurrency market in environmental protection, investors pay attention to the risk conduction mechanism between energy consumption and the Bitcoin market. This paper applies quantile connectedness to analyse the overall situation and dynamic evolution of information spillover in the system of the Bitcoin market. The results show that the hashrate and electricity demand are the primary sources of risk in the information network, and their fluctuations have intensified the risk spillover effects in the system. In addition, the spillover level is more prominent in extreme cases, which means the information linkage in the system is integrated. The spillover effect of each variable fluctuates and is uncertain with time. This helps in the sustainable development of Bitcoin and guides the government's policy development and supervision of cryptocurrencies. The risk infection path helps prevent the risk of infection in the Bitcoin market and improves the sustainability of the encrypted market.
Remzi Gök, Elie Bouri, Eray Gemi̇ci̇
No abstract is available for this record.
Forbes Kaseke, Shaun Ramroop, Henry Mwambi
Despite the rapid growth of developing markets, aided by globalization, comparative studies of cryptocurrency and stock market volatility have focused on the developed markets and neglected developing ones. In this regard, this study compares cryptocurrency volatility with that of the Johannesburg Stock Exchange (JSE), a developing market. GARCH-type models are applied to daily log returns of Bitcoin, Ethereum, and the FTSE/JSE 4O in two ways. Firstly, the models are applied directly; secondly, structural breaks are tested and accounted for in the models. The sample period was from September 18, 2017, to May 27, 2021. The results show higher volatility and higher volatility persistence in cryptocurrency than in the JSE market. They also show that persistence is overestimated for cryptocurrencies when structural breaks are not accounted for. The opposite was true for the JSE.Moreover, the two cryptocurrencies were found to have close to identical volatility plots that differ from that of the JSE. High volatility periods of cryptocurrency also did not coincide with that of JSE and those of JSE did not coincide with the cryptocurrency ones. There is also evidence of an inverse leverage effect in cryptocurrency, which opposes the normal leverage effect of the JSE market.
Naman Shah, Sonal R Dave
Among the new way of exchanging money, using crypto currency has been very popular. Its also an investment to get good returns over the period of time. Cryptocurrency has grown to more than 120 million investors around the world as per a survey of 2021.Its growing at the 15 to 20% ratio around the world every year. This fact leads to a serious consideration of security and its vulnerabilities in block chain. Apart from market risks, high volatility, lack of rules and regulations, cyber risks are one of the most required types which needs proper attention and technical understanding. Because the crypto currencies are fully decentralized the risk of attacks is exposed and in most of the cases defenseless. Proof of stake and proof of work are two major algorithms followed by almost all crypto currencies to allot stocks to the holders. In this paper, different types of risks and attacks with POS and POW are explained with its mitigation. The problems and outcomes are examined, reviewed and conferred in case of Ethereum and Bitcoin crypto currencies. These currencies decentralized frameworks and anonymity attracts unlawful activities. Recognizing and preventing them needs understanding of the mechanism of attacks which are discussed in easiest possible ways for even a new-bee or an outsider person.
Bhavay Malhotra, Chittaranjan Chandwani, P.N. Agarwala, Suman Mann
A cryptocurrency or ‘coins' is a virtual asset which can be used as an alternative to physical currency via a computer network that is not reliant on the government or any bank, to uphold or maintain it. This has led to the creation of numerous brands of cryptocurrencies. It was only after the boom in their price in 2011 that they began to be regarded as an investment asset. Since these coins are highly volatile in their pricing, there is a need for a good prediction of their closing price on which investment decisions can be made. To address this requirement, this paper studies the relative performances of different machine learning algorithms for a well-known cryptocurrency - the ‘Bitcoin’. The performance measures of different machine learning models were undertaken to get the accuracy of the models for Bitcoin and results were obtained. The results show that the Auto Regressive Integrated Moving Average (ARIMA) is better than the other models and has the least mean absolute error. It is observed that the quality of training data and amount of the dataset used plays an important role for a successful prediction. When comparing the predicted value of Bitcoin through ARIMA with its actual value, the results obtained are found to be comparable for the entire four months of analysis.
Nguyễn Đình Thuân, Nguyen Thi Viet Huong
No abstract is available for this record.
Michael R. Williams, Mucahit Kochan, David Green
We examine the relationships among Bitcoin (BTC), the Chinese Yuan (CNY), and Chinese capital outflows between 2014-2021. We find that BTC returns strongly comove with CNY returns after 2018Q1, while no significant BTC/CNY relationship exists before 2018Q1. Further, the strength of the BTC/CNY relationship increases throughout 2018 to the present date. Yet, this relationship strength cannot be explained by periods of ascending BTC prices, changes in crypto mining location, nor changes in the use of BTC "mining pools". Instead, we find that the strength of the BTC/CNY relationship is strongly and directly related to Chinese capital outflows. We find no similar relationship with a "bogey" currency, the Euro, implying that the capital outflows -to- BTC/CNY relationship is unique to China and its capital outflow environment. In total, our novel results suggest that BTC is used as part of a process to move economically significant amounts of capital from mainland China.
Talie Kassamany, Étienne Harb, Roland Baz
Given the broad scope of Ethereum and the wide range of its decentralized applications, this paper investigates its hedging and safe haven capabilities against main fiat currencies, stock and bond indices in the US and Europe, and crude oil and gold markets. We use daily data from January 2016 until February 2021 and apply percentile regressions and crisis event interaction analysis by selecting four worldwide events including US presidential elections, the Brexit referendum, and COVID-19. We reveal that Ethereum does not act as a hedge or a safe haven against fiat currencies, stock and bond indices, and gold. However, it does act as a strong safe haven against crude oil in calm and turbulent periods and against European bonds during market turbulence. The study provides insights to regulators and investors into the potential role of Ethereum in investment decision-making and protecting financial market participants in the US and EU.
Ishanka Dias, J. M. R. Fernando, P. N. D. Fernando
No abstract is available for this record.