Abstract
The fast pace of creating new cryptocurrencies makes it hard or even impossible to know which one of them best suits an investor’s needs. Increasingly, investors are starting to need a decision support system with which they can determine which cryptocurrencies are suitable for investment and which ones are not. In the formation of a decision support system, it is necessary to create suggestions according to personal preferences and tendencies. In this study, a decision support system was developed. The system allows investors to understand what they need and offers them cryptocurrencies that suit their preferences. On-chain parameters instead of off-chain ones were used for efficiency. In the developed system, a set of on-chain features is asked of investors, and individual weights are calculated for the selected features using the Analytic Hierarchy Process (AHP) algorithm. Using the calculated weights and the investor’s preferences, the system gives each cryptocurrency a mark of 100 and sorts the cryptocurrencies based on the mark where the system will provide different recommendations for each investor. We defined and determined the most important on-chain features. In addition, based on the answers of a focus group of cryptocurrency experts and investors, we concluded that the most important on-chain features to be considered for investment are High Volume, High Total Staked and High Percentage of Total Supply Circulating.
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References
Tasatanattakool, P., Techapanupreeda, C.: Blockchain: challenges and applications. In: International Conference on Information Networking (ICOIN) (2018)
Allad, T., Chamola, V., Parizi, R.M., Choo, K.-K. R.: Blockchain Applications for Industry 4.0 and Industrial IoT: A Review. Special Section on Distributed Computing Infrastructure (2019)
Agbo, C.C., Mahmoud, Q.H., Eklund, J.M.: Blockchain technology in healthcare: a systematic review. Healthcare 7, 56 (2019)
Song, J., Sung, J., Park, T.: Applications of blockchain to improve supply chain traceability. Procedia Comput. Sci. 162, 119–122 (2019)
Nakamoto, S.: Bitcoin: a peer-to-peer electronic cash system (2008)
https://www.statista.com/statistics/863917/number-crypto-coins-tokens/. Accessed 22 Apr 2022
Werner, R., Lawrenz, S., Rausch, A.: Blockchain analysis tool of a cryptocurrency. In: The 2020 2nd International Conference on Blockchain Technology ICBCT 2020, Hawaii, USA (2020)
Mikhaylov, A.: Cryptocurrency market analysis from the open innovation perspective. J. Open Innov. Technol. Mark. Complexity 6(4), 197 (2020)
McNally, S., Roche, J., Caton, S.: Predicting the price of bitcoin using machine learning. In: 2018 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP) (2018)
Lamon, C., Nielsen, E., Redondo, E.: Cryptocurrency price prediction using news and social media sentiment (2017)
Lánský, J.: Analysis of cryptocurrencies price development. Acta Informatica Pragensia 5(2), 118–137 (2016)
Hayes, A.: What factors give cryptocurrencies their value: an empirical analysis (2015)
Renterghem, J.V.: From bits to coins: price formation of bitcoin (2017)
Murugan, G.: Creation of a recommendation system to recommend cryptocurrency portfolio using Association rule mining. National College of Ireland, Dublin (2021)
Saaty, T.: What is the analytic hierarchy process? Math. Models Decis. Supp. 48, 109–121 (1988)
Garrido, A., López, L.J., Álvarez, N.B.: A simulation-based AHP approach to analyze the scalability of EHR systems using blockchain technology in healthcare institutions. Inf. Med. Unlocked 24, 100576 (2021)
Amroush, F., Georgantzis, N., Josean, G.I.: Three Essays on Informatics Decision Support Systems in Product Selection. University of Granada, Granada (2012)
https://www.blockchain.com/charts/blocks-size. Accessed 12 Jan 2022
Ma, N., Guan, J.: Research on AHP decision algorithms based on BP algorithm. In: AIP Conference Proceedings (2017)
Geng, Z., Zhao, S., Zhu, Q., Han, Y., Xu, Y., He, Y.: Early warning modeling and application based on analytic hierarchy process integrated extreme learning machine. In: 2017 Intelligent Systems (2018)
Lloyd, S.P.: Least squares quantization in PCM. Technical Report RR-5497, Bell Lab (1957)
MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297 (1967)
Arthur, D., Vassilvitskii, S.: k-means++: the advantages of careful seeding. In: Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms (2007)
Acknowledgment
We would like also to thank the group of experts, Adham Kahlawi, Ahmad Hazzori, Fadi Knefati, Feras Younes, Haisam Zabibi, Haiyan Alsaiyed, Houmam Homsi, Hussam Mansour, Majd Aldeen Masriah, Mohamad Sumakie, Muhammad Altabba, Rafat Katta, Safouh Kharrat, Yasser Tabbaa and Yousof Alsatom, for their help in understanding and selecting the most important on-chain parameters.
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Zakieh, A.R., Utku, S., Amroush, F. (2022). Evaluation of Cryptocurrencies Dynamically Based on Users’ Preferences Using AHP. In: Kahraman, C., Tolga, A.C., Cevik Onar, S., Cebi, S., Oztaysi, B., Sari, I.U. (eds) Intelligent and Fuzzy Systems. INFUS 2022. Lecture Notes in Networks and Systems, vol 505. Springer, Cham. https://doi.org/10.1007/978-3-031-09176-6_62
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DOI: https://doi.org/10.1007/978-3-031-09176-6_62
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