پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 161 |
| تعداد شمارهها | 6,573 |
| تعداد مقالات | 71,037 |
| تعداد مشاهده مقاله | 125,522,612 |
| تعداد دریافت فایل اصل مقاله | 98,782,287 |
Cryptocurrency Market Efficiency: Does Distributed Ledger Technology Matter? | ||
| Iranian Economic Review | ||
| مقاله 13، دوره 28، شماره 4، اسفند 2024، صفحه 1396-1424 اصل مقاله (1.84 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22059/ier.2024.347819.1007521 | ||
| نویسندگان | ||
| Arman Kave؛ Sakine Owjimehr* | ||
| Department of Economics, Faculty of Economics, Management, and Social Sciences, Shiraz University, Shiraz, Iran. | ||
| چکیده | ||
| In recent years, due to its use in transactions with encrypted assets, distributed ledger technology has attracted the attention of the financial sector on the one hand. On the other hand, the expansion of the projects has the potential to increase the efficiency, transparency, speed, and flexibility of financial transactions underlying infrastructure processes. The present article aims to calculate the efficiency of various modes of this technology. Calculating and examining the efficiency of financial markets are remarkably effective in choosing the investors’ strategy. There are several methods to calculate efficiency. In the present study, the method of analyzing Multi Fractal Detrended Fluctuation Analysis (MFDFA) has been implemented to calculate such an efficiency. Hence, different forms of distributed ledger technology have been investigated including: “Blockchain” and “Directed Acyclic Graph (DAG)”. The DAG technology itself is classified into two modes: 1. Tangle and 2. Hashgraph. To calculate Blockchain efficiency, the hourly data from September 2019 to November 2022 were utilized for Bitcoin (BTC) and Ethereum (ETH) cryptocurrencies. For Tangle technology, Iota cryptocurrencies were used, and for Hashgraph technology, Fantom (FTM) and Hederahashgraph (HBAR) cryptocurrencies were implemented. The results reveal that the distributed ledger technology of cryptocurrencies influences their efficiency. Hashgraph technology (the cutting-edge type of distributed ledger) proves the highest efficiency compared to other technologies. | ||
| کلیدواژهها | ||
| Cryptocurrencies؛ Distributed Ledger Technology؛ Efficiency؛ Multi Fractal Detrended Fluctuation Analysis | ||
| مراجع | ||
|
Apopo, N., & Phiri, A. (2021). On the (in)efficiency of Cryptocurrencies: Have they Taken Daily or Weekly Random Walks? Heliyon, 7(4), e06685, 1-10.
Aslam, F., Latif, S., & Ferreira, P. (2020). Investigating Long-Range Dependence of Emerging Asian Stock Markets Using Multifractal Detrended Fluctuation Analysis. Symmetry, 12(7), 1-19.
Bencic, M., & Zarko, I. (2018). Distributed Ledger Technology: Blockchain Compared to Directed Acyclic Graph (1569-1570). 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS), Retrieved from https://arxiv.org/pdf/1804.10013
Ghazani, M., & Jafari, M. (2021). Cryptocurrencies, Gold, and WTI Crude Oil Market Efficiency: A Dynamic Analysis Based on the Adaptive Market Hypothesis. Financial Innovation, 7(29), 1-26.
Hou, W., Feng, G., Yan, P., & Li, S. (2018). Multifractal Analysis of the Drought Area in Seven Large Regions of China from 1961 to 2012. Meteorology and Atmospheric Physics, 130(4), 459-471.
Ivan, O . (2021). Altcoin Season – Exploring Alt Seasons and Crypto Market Cycles. Working Paper, Retrieved from https://academy.moralis.io/blog/altcoin-season-exploring-alt-seasons-and-crypto-market-cycles
Jia, Y. (2017). Demystifying Hashgraph: Benefits and Challenges. Working paper, Retrieved from https://hackernoon.com/demystifying-hashgraph-benefits-and-challengesd605e5c0cee5
Kakinaka, S., & Umeno, K. (2022). Cryptocurrency Market Efficiency in Short- And Long-Term Horizons during COVID-19: An Asymmetric Multifractal Analysis Approach. Finance Research Letters, 46, 102319.
Kantelhardt, J. W., Zschiegner, S. A., Koscielny-Bunde, E., Havlin, S., Bunde, A., & Stanley, H. E. (2002). Multifractal Detrended Fluctuation Analysis of Nonstationary Time Series. Physica A: Statistical Mechanics and its Applications, 316(1-4), 87-114.
Mnif, E., Jarboui, A., & Mouakhar, K. (2020). How the Cryptocurrency Market Has Performed during COVID 19? A Multifractal Analysis. Finance Research Letters, 36, 101647.
Norouzzadeh, P., & Jafari, G. R. (2005). Application of Multifractal Measures to Tehran Price Index. Physica A: Statistical Mechanics and its Applications, 356(2-4), 609-627.
Peng, C. K., Buldyrev, S. V., Havlin, S., Simons, M., Stanley, H. E., & Goldberger, A. L. (1994). Mosaic Organization of DNA Nucleotides. Physical Review, 49(2), 1685-1690.
Peters, E. (1994). Fractal Market Analysis: Applying Chaos Theory to Investment and Economics, 24. New Jersey: John Wiley & Sons.
Popov, S. (2018). The Tangle. White Paper, 1(3), 1-28.
Schueffel, P. (2017). Alternative Distributed Ledger Technologies: Blockchain vs. Tangle vs. Hashgraph - A High-Level Overview and Comparison. SSRN, 3144241, 1-8.
Tran, V., & Leirvik, T. (2019). Efficiency in the Markets of Cryptocurrencies. Financial Research Letters, 35, 1013820.
Ugarte, J. (2018). Distributed Ledger Technology (DLT): Introduction. Economic Bulletin, Analytical Articles, 4/2018, 1-11.
Wawrzaszek, A., Modzelewska, R., Krasińska, A., Gil, A., & Glavan, V. (2022). Fractal Dimension Analysis of Earth Magnetic Field during 26 August 2018 Geomagnetic Storm. Entropy, 24(5), 1-19.
Yuan, Y., Zhuang, X. T., & Jin, X. (2009). Measuring Multifractality of Stock Price Fluctuation Using Multifractal Detrended Fluctuation Analysis. Physica A: Statistical Mechanics and its Applications, 388(11), 2189-2197.
Zhang, Y., Chan, S., Chu, J., & Sulieman, H. (2020). On the Market Efficiency and Liquidity of High-Frequency Cryptocurrencies in a Bull and Bear Market. Risk and Financial Management, 13(8), 1-14.
Zhang, W., Wang, P., Li, X., & Shen, D. (2018). The Inefficiency of Cryptocurrency and its Cross-Correlation with Dow Jones Industrial Average. Physica A: Statistical Mechanics and its Applications, 510, 658-670.
Zhuang, X., Wei, Y., & Ma, F. (2015). Multifractality, Efficiency Analysis of Chinese Stock Market and its Cross-Correlation with WTI Crude Oil Price. Physica A: Statistical Mechanics and its Applications, 430, 101-113. | ||
|
آمار تعداد مشاهده مقاله: 18 تعداد دریافت فایل اصل مقاله: 41 |
||
سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب