Predicting the risk of death of cryptocurrencies

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buir.contributor.authorSakinoğlu, Bedirhan
buir.contributor.authorGüvenir, Altay
buir.contributor.orcidGüvenir, Altay|0000-0003-2589-316X
dc.citation.epage6en_US
dc.citation.spage1
dc.contributor.authorSakinoğlu, Bedirhan
dc.contributor.authorGüvenir, Altay
dc.coverage.spatialBerlin, Germany
dc.date.accessioned2024-03-25T08:36:09Z
dc.date.available2024-03-25T08:36:09Z
dc.date.issued2023-07-27
dc.departmentDepartment of Computer Engineering
dc.descriptionDate of Conference: 23-25 July 2023
dc.descriptionConference Name: 2023 IEEE International Conference on Omni-layer Intelligent Systems (COINS)
dc.description.abstractIn recent years, the attention drawn by cryptocurrencies has increased as their popularity grows rapidly. This situation attracts investors, entrepreneurs, regulators, and the general public. However, these coins may die and become dead coins. A coin is declared dead if no activity is recorded for more than one year. Numerous coins die without completing their one-year timeframe and this issue causes investors to lose a significant amount of money. In this study, we develop a deep neural network architecture based on long short-term memory (LSTM) to predict the death risk of a coin in a specified timeframe. In order to do this, time-series data consisting of the closing price and volume values of 4733 dead coins are utilized. The goal of our model is to inform investors about the death risk of the coin and improve their overall portfolio performance.
dc.identifier.doi10.1109/COINS57856.2023.10189205
dc.identifier.eisbn9798350346473
dc.identifier.isbn9798350346480
dc.identifier.urihttps://hdl.handle.net/11693/115112
dc.language.isoEnglish
dc.publisherIEEE - Institute of Electrical and Electronics Engineers
dc.relation.isversionofhttps://dx.doi.org/10.1109/COINS57856.2023.10189205
dc.source.title2023 IEEE International Conference on Omni-layer Intelligent Systems (COINS 2023)
dc.subjectCryptocurrencies
dc.subjectCrypto coins
dc.subjectForecasting
dc.subjectDead coins
dc.subjectMachine learning
dc.subjectLSTM
dc.titlePredicting the risk of death of cryptocurrencies
dc.typeConference Paper

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