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2,964 results · page 30 of 124
Fenglin Wu, Yu-fan Wan, Ming-hui Wang
No abstract is available for this record.
Xiaochun Guo, Kun Guo, Shouyang Wang
No abstract is available for this record.
Kübra Saka Ilgın
Purpose : It can be stated that in today’s competitive conditions, where portfolio management is very important, it has become necessary to examine the relationship between global financial assets and major cryptocurrencies, such as Bitcoin and Ethereum. This paper aims to investigate the cointegration and causalityrelationships between Bitcoin, Ethereum, and global financial assets such as gold, oil, the S&P Global 100, the Dow Jones Commodity, and the US Dollar Indices, and to determine the diversification role of Bitcoin and Ethereum comparatively for the period between April 2016 and January 2024. Methodology: The ADF Unit Root, Johansen Cointegration, Granger Causality, Rolling Window Causality tests, and Variance Decomposition Analysis methods were used in the analysis process. Results: Based on the findings obtained from the paper, it was determined that Bitcoin and Ethereum have no cointegration with selected financial asset classes. Granger causality analysis results indicated that there were unidirectional causalities from Bitcoin and Ethereum prices to Dow Jones Commodity Index prices. In addition to the results of the Rolling Window causality tests, it was also determined that there are some causalities between Bitcoin, Ethereum, and other variables, especially after the 2021-2022 period. Conclusion: It can be concluded that Bitcoin and Ethereum are effective portfolio diversifiers throughout the entire period; however, the diversification effects of Bitcoin and Ethereum weakened towards the end of the review period. Therefore, it can be said that Bitcoin and Ethereum act similarly in the global investment portfolio.
David Krause
No abstract is available for this record.
Farwa Batool
No abstract is available for this record.
Mario Straßberger
No abstract is available for this record.
Yen-Ju Hsu, Kuang‐Chieh Yen, Yi-Chun Tsai
No abstract is available for this record.
Noyal Fernandis
No abstract is available for this record.
Ya-Hui Yang, Zhe Peng
No abstract is available for this record.
Chenglin Zhao
Bitcoin, a decentralized digital currency, has gained widespread acceptance and recognition in recent years. The prediction of Bitcoin prices is a challenging task due to its relatively young age and high volatility. Therefore, this study explores the accuracy of price prediction for Bitcoin using machine learning models and makes comparsion on the outcome of different models, Linear Regression, Long Short-Term Memory, and Recurrent Neural Network. This study utilizes the closing price of Bitcoin in USD from a Kaggle dataset as the independent variable. The study also adopts Mean Absolute Error (MAE) as the measurement indicators, and comparative performance analysis is conducted under various circumstances. The experimental results demonstrate that LR performs poorly in Bitcoin price prediction, while LSTM and RNN outperform LR. Further analysis reveals that LSTM performs better during price apexes, while RNN performs better during price recessions. Graphical representations illustrate the strengths and weaknesses of each model under different market scenarios. Through comparison, the article provides an insight for other researchers to choose corresponding machine learning models under different circumstances to predict bitcoin price.
Radovan Vojtko, Juliána Javorská
A comparative analysis between 2013-2017 and 2018-2023 reveals a significant transformation in Bitcoin and cryptocurrency investments.In the earlier phase, methods like the Markowitz Model suggested significant allocation to Bitcoin due to its high returns, diversification benefits, and low correlation with other assets.However, with the financialization of Bitcoin in December 2017, the cryptocurrency market underwent a fundamental shift, integrating into the mainstream financial system and increasing its correlation with traditional assets.In the subsequent period from 2018 to 2023, Bitcoin emerged as an average asset class with relatively high risk compared to others.Given these changes and increased institutional interest, our analysis suggests it's prudent to cap allocation to Bitcoin to maximally 2-3% of the portfolio.The analysis highlights the need for caution and realistic expectations when interpreting historical data and extrapolating long-term conclusions.
Levent Kutlu
No abstract is available for this record.
Vivek Pandey
No abstract is available for this record.
Wenhao XIE, Guangxi Cao
No abstract is available for this record.
Yanan Niu, Ilja Kantorovitch
No abstract is available for this record.
Daniel Cahill, Zhangxin Liu, Lee A. Smales
We investigate whether investors rely more on technical trading language to rationalise price movements in the absence of substantive information. Compared to equity markets, cryptocurrency markets are characterised by high volatility, often occurring without clear explanation from new information. We apply a machine-learning-based vocabulary of technical trading terms to comments from cryptocurrency- and equity-related subreddits on Reddit.com and analyse how investors use technical talk in different market conditions. We find a U-shaped relationship between technical talk and Bitcoin returns, with higher usage during extreme price movements, while technical talk on equity subreddits is concentrated around median market returns. Technical talk increases in cryptocurrency markets when news is scarce but rises in equity markets alongside greater news availability. Our results suggest that technical talk provides an important communication channel for social media users to describe price variation when information is scarce.
Lumengo Bonga‐Bonga, Muhammad Khalique
No abstract is available for this record.
Kyrylo Troian
No abstract is available for this record.
Ali Asare Nezhad, Ahmad Kalhor, Reshad Hosseini, Babak Nadjar Araabi · 5 authors
No abstract is available for this record.
Jolana Stejskalová, Dominik Krampla
No abstract is available for this record.
Jiakun Lian
This paper delves into the intriguing realm of cryptocurrency price prediction, with a specific focus on Zcash (ZEC), employing a cutting-edge deep learning approach.The study introduces two crucial features, "close_off_high" and "volatility", then systematically analyzes the correlations between these variables and the price of ZEC.By investigating the predictive accuracy of three prominent neural network architectures-Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and the Transformer model-the study discerns that LSTM and GRU models outperform the others in forecasting ZEC's price movements.Furthermore, the paper scrutinizes the influence of different activation functions on model performance, shedding light on the effectiveness of the linear activation function in this context.The research also addresses common challenges in predictive modeling, such as overfitting and multicollinearity.Moreover, it candidly acknowledges the limitations associated with solely focusing on a single cryptocurrency, recognizing that broader research efforts and interdisciplinary collaboration are required for a more comprehensive understanding of the ever-evolving cryptocurrency landscape.As the cryptocurrency market continues to evolve rapidly, this study provides invaluable insights for investors, offering a rational perspective on cryptocurrency investment.It underscores the importance of utilizing appropriate models and embracing interdisciplinary cooperation to navigate the complex and dynamic world of cryptocurrency.By bridging the gap between the cutting-edge world of deep learning and the financial market, this research paves the way for enhanced future investigations and more informed investment decisions.
sanshao peng, Syed Shams, Catherine Prentice, Tapan Sarker
No abstract is available for this record.
Yakun Liu, Yan Chen
No abstract is available for this record.
Suwan Long, Ying Xie, Zhengyuan Zhou, Brian M. Lucey · 5 authors
No abstract is available for this record.