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4,843 results · page 87 of 202
Zhenzhen Fan, Feng Jiao, Lei Lü, Xin Tong
No abstract is available for this record.
Sibtain Syed, Arshad Iqbal, Waqar Mehmood, Zain Syed · 6 authors
Cryptocurrency is a popular digital currency due to its security and peer-to-peer transferability. Predicting cryptocurrency prices is crucial for investors and traders to make informed decisions on buying, selling, or holding cryptocurrencies based on their expected value, potential risks, and returns. This study aims to identify the optimal model for predicting the prices of cryptocurrencies, such as Bitcoin (BTC) and Ethereum (ETH), using Deephaven for Data curation. The study involves extracting data from both cryptocurrencies by Deephaven and selecting the most correlating parameters through time lag adjustment. We use correlating cryptocurrency data to train models, such as Artificial Neural Networks (ANN), Long-Short Term Memories (LSTM), and Gated Recurrent Units (GRU). Where the trial-and-error technique was applied for selecting optimized hyper-parameters for each model. The models are then evaluated by statistical evaluators, such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE), separately for training and testing datasets. For Bitcoin, the results showed that the LSTM model outperform ANN and GRU models in both training and testing data with MAE, RMSE, and MAPE average values of 0.079, 1.16, and 0.0006, respectively. While for Ethereum, the results also revealed that LSTM model performance is superior with MAE, RMSE, and MAPE average values of 0.0025, 0.124 and 0.0002, respectively. While GRU (MAE 0.012, RMSE 0.117, MAPE 0.002) performs robustly against ANN (MAE 0.035, RMSE 0.149, MAPE 0.003) model.
Ahmed El Youssefi, Abdelaaziz Hessane, Yousef Farhaoui, Imad Zeroual
No abstract is available for this record.
Paola Di Casola, Maurizio Michael Habib, David Tercero‐Lucas
No abstract is available for this record.
P. V. Nagamani, Gowri Anand, Srinivasa Prasanna, Basava Raju · 5 authors
The past several years have seen an increase in interest in trading that is supported by machine learning and artificial intelligence.Utilize automated trading with the aid of machine learning and artificial intelligence to reap the maximum rewards from the cryptocurrency market.For a specific time, we keep the daily data.We achieve excellent results by utilising tactics supported by cutting-edge algorithms.The results produced the expansion in the crypto currency industry with the aid of straight forward architecture and algorithms.The rise in market capitalization has led to a rise in popularity for the cryptocurrency in 2017.Today's market involves more than 1500 crypto currencies.For usage in online transactions, the crypto currency can be created.A crypto money technology is bitcoin.Bitcoin's value changes constantly, second by second.As a result, we apply machine learning architecture to forecast the value of the bitcoin price in this case.We are working to demonstrate that, in comparison to previous techniques and architectures, this ML architecture produces results that are more accurate.Our study use the Support Vector Machine(SVM) and K Nearest Neighbor(KNN)algorithms to successfully forecast bitcoin prices.The findings demonstrate that the Support Vector Machine(SVM) method outperforms the K Nearest Neighbor(KNN) method as it is currently being used.
Hanna Hałaburda, David Yermack
No abstract is available for this record.
Ping Li, Jiahong Li, Lixin Huang, Zhenyu Cui
In his paper we explore the dynamic time-frequency volatility spillover effects between Bitcoin and Chinese financial markets, covering several main events. Results show that the volatility spillovers are asymmetric, with the Bitcoin market being the net risk receiver. The total spillovers, driven by medium and low frequency components, peak before the China stock crash in 2015, increase since the trade disputes between China and the US in 2018, and peak since the COVID-19 pandemic. The pairwise spillovers related to Bitcoins mainly concentrate on stock, foreign exchange, and copper futures markets. Although the pairwise spillovers are weak, the Bitcoin market is the net receiver in medium and low frequencies under external shocks. Therefore, investors and regulators need to assess the potential risk of Bitcoin based on asset type with the perspectives of time and frequency.
Ralitsa Petkova
No abstract is available for this record.
Wei Jiang, Pinlin Zhu, Aslıhan Gizem Korkmaz, Haigang Zhou
We employ a novel framework to measure the asymmetric nexus between the cryptocurrency market and the carbon futures market based on different market conditions. Specifically, we use a quantile-on-quantile regression (QQR) approach to explore the correlation between cryptocurrencies (Bitcoin, Ethereum, and Ripple) and European Union Allowance (EUA) futures. We find that there is an asymmetric relationship between markets that is affected by different cryptocurrencies and market conditions. Overall, Bitcoin or Ethereum are positively correlated with the carbon market, while the results of Ripple are more complex. Under certain conditions, EUA futures can be a better hedge against cryptocurrency risk.
Clive Walker
This paper quantifies mainstream media coverage of Bitcoin to understand how a once niche interest entered public culture. From 2011 to 2022, five key narratives are identified as criminality, culture, politics, price and technology. Price, politics, and culture have become more prominent in coverage while the technology narrative has waned. Coverage that is more political or cultural is associated with subsequently lower returns whereas the criminality narrative is associated with higher returns. Together this suggests that as narratives have become more mainstream, they have created additional demand, despite the negative association with criminal activity.
Faheem Aslam, Zil-e-huma, Rashida Bibi, Paulo Ferreira
We take the novel Twitter-based economic uncertainty (TEU) to examine if it has cross-correlation characteristics with four major cryptocurrencies i.e. Bitcoin, Ethereum, Litecoin, and Ripple. To conduct a more thorough analysis, we apply multifractal detrended cross-correlation analysis (MFDCCA) on seasonal-trend decomposition using Loess (STL) decomposed series as well as without decomposed series on the daily data, ranging from 1 June 2011 to 30 June 2021. The findings of this study indicate that: (i) all pairs of TEU with cryptocurrencies are multifractal and have power-law behavior; (ii) the pairs of Ethereum and Bitcoin with TEU are found to be the most multifractal while Litecoin with TEU has the lowest multifractal characteristics; (iii) all STL decomposed series of cryptocurrency have persistent cross-correlation with TEU with the exception of Ethereum which has anti-persistent cross-correlation with TEU; (iv) all without decomposed series of cryptocurrencies show significant persistent cross-correlation characteristics with TEU; (v) the highest linkage is found for the pair of Bitcoin with TEU. Moreover, to reveal the dynamic characteristics in the cross-correlation of TEU with cryptocurrencies, the rolling window is employed for MFDCCA. These findings have important managerial and academic implications for policymakers, investors, and market participants.
Nilcan MERT, Mustafa Caner TİMUR
<abstract> <p>Bitcoin has become quite known after the 2008 economic crisis and the COVID-19 health crisis. For some, these cryptocurrencies constitute rebellion against the existing system as governments encourage uncontrolled expansions in the money supply; for some others, it is a quick source of income. Undeniably, the volume of the crypto money market has grown considerably in recent years, regardless of the reasoning of the people who invest and trade in this field. At this point, one of the most important questions to be investigated is "what variables have caused the tremendous growth in the crypto money quantities in recent years?" This study tests the assumption that changes in cryptocurrencies are affected by changes in national currencies. Thus, the Bitcoin price is the dependent variable, and M1 monetary supply changes in the USA, European Union and Japanese economies are considered independent variables. The variables in this study were tested using the time-varying Granger causality method. The results obtained from this study confirm the philosophy of Bitcoin's emergence and the possibility that it can be a hedge against the inflationary effects of money, especially after the COVID-19 pandemic.</p> </abstract>
Jui‐Cheng Hung, Hung‐Chun Liu, J. Jimmy Yang
No abstract is available for this record.
Weige Huang, Xiang Gao
After Bitcoin futures were introduced by the Chicago Mercantile Exchange in December 2017, their trading volume has stayed in an uptrend due to speculation, though the scale is still small compared to other traditional futures. As increasing trading indicates more attention and the presence of institutional traders, there exists a need for reliable return and variance forecasts of Bitcoin futures contracts. Therefore, this paper first applies LASSO to pick out best-fitting predictors by shrinking the dimension of a universe of potential determinants sourced from intraday Bitcoin spot trades and daily futures variables. Then, a second round of predictor selection is conducted via Bayesian model averaging so that the modeling uncertainty can be mitigated. We find that factors standing out from this two-step procedure possess a strong predictive power for Bitcoin futures return and volatility in different time horizons. It is further demonstrated that the investment and hedging strategies established based on our forecasts perform well in out-of-sample validations.
Tuğba Baş, Issam Malki, Sheeja Sivaprasad
No abstract is available for this record.
Weiyi Liu, Yunfei Zhao, Ye Wang, Peng Wang
No abstract is available for this record.
Gil Cohen
<abstract> <p>This research attempts to fit a polynomial auto regression (PAR) model to intraday price data of four major cryptocurrencies and convert the model into a real-time profitable automated trading system. A PAR model was constructed to fit cryptocurrencies' behavior and to attempt to predict their short-term trends and trade them profitably. We used machine learning (ML) procedures enabling our system to train using minutes' data for six months and perform actual trading and reporting for the next six months. Results have shown that our system has dramatically outperformed the naive buy and hold (B &amp; H) strategy for all four examined cryptocurrencies. Results show that our system's best performances were achieved trading Ethereum and Bitcoin and worse trading Cardano. The highest net profit (NP) for Bitcoin trades was 15.58%, achieved by using 67 minutes bars to form the prediction model, compared to −44.8% for the B &amp; H strategy. Trading Ethereum, the system generated 16.98% NP, compared to −33.6% for the B &amp; H strategy, 61 minutes bars. Moreover, the highest NPs achieved trading Binance Coin (BNB) and Cardano were 9.33% and 4.26%, compared to 0.28% and −41.8% for the B &amp; H strategy, respectively. Furthermore, the system better predicted Ethereum and Cardano uptrends than downtrends while it better predicted Bitcoin and BNB downtrends than uptrends.</p> </abstract>
Suraya Fadilah Ramli, Zahrul Azmir A. B. S. L. Kamarul Adzhar, Syed Anand Najmi Sayed Abu Bashar, Muhammad Fikri Abdullah
Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Suraya Fadilah Ramli, Zahrul Azmir A. B. S. L. Kamarul Adzhar, Syed Anand Najmi Sayed Abu Bashar, Muhammad Fikri Abdullah; Analysis of Ethereum versus Bitcoin: The GARCH approach. AIP Conference Proceedings 8 February 2023; 2500 (1): 020049. https://doi.org/10.1063/5.0112690 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Mehmet Birhan Yılmaz, Ömür Tosun
No abstract is available for this record.
Osama Liaqat, Kehkashan Nizam, Jahanzaib Alvi
No abstract is available for this record.
Andrii Bielinskyi, Vladimir Soloviev, Victoria Solovieva, Andriy Matviychuk · 5 authors
No abstract is available for this record.
Jying‐Nan Wang, Samuel A. Vigne, Hung‐Chun Liu, Yuan‐Teng Hsu
No abstract is available for this record.
Christian Urom, Gideon Ndubuisi, Khaled Guesmi
No abstract is available for this record.
Zixiong Wang, Qiuying Chen, Sang-Joon Lee
The rapid expansion of the non-fungible token (NFT) market has attracted many investors. However, studies on the NFT price fluctuations have been relatively limited. To date, the machine learning approach has not been used to demonstrate a specific error in NFT sale price fluctuation prediction. The aim of this study was to develop a prediction model for NFT price fluctuations using the NFT trading information obtained from OpenSea, the world’s largest NFT marketplace. We used Python programs to collect data and summarized them as: NFT information, collection information, and related account information. AdaBoost and Random Forest (RF) algorithms were employed to predict the sale price and price fluctuation of NFTs using regression and classification models, respectively. We found that the NFT related account information, especially the number of favorites and activity status of creators, confer a good predictive power to both the models. AdaBoost in the regression model had more accurate predictions, the root mean square error (RMSE) in predicting NFT sale price was 0.047. In predicting NFT sale price fluctuations, RF performed better, which the area under the curve (AUC) reached 0.956. We suggest that investors should pay more attention to the information of NFT creators. We anticipate that these prediction models will reduce the number of investment failures for the investors.