{"title":"Artificial Intelligence in the System of Management Decision-Making","authors":"Valdemar Vitlinskyi","doi":"10.33111/nfmte.2012.097","DOIUrl":"https://doi.org/10.33111/nfmte.2012.097","url":null,"abstract":"","PeriodicalId":300314,"journal":{"name":"Neuro-Fuzzy Modeling Techniques in Economics","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115056976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"System for Assessing the Level of Use of the Strategic Potential of an Enterprise and Making Decisions to Increase It","authors":"A. Azarova, O. Antoniuk","doi":"10.33111/nfmte.2012.037","DOIUrl":"https://doi.org/10.33111/nfmte.2012.037","url":null,"abstract":"","PeriodicalId":300314,"journal":{"name":"Neuro-Fuzzy Modeling Techniques in Economics","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128580088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Kaminskyi, I. Miroshnychenko, Kostiantyn Pysanets
The active development of cryptocurrencies in recent years allows identifying the process of forming new class of alternative investment assets. There was formed a sample of cryptocurrencies based on criteria capitalization and historical returns for estimation investment risk of this asset class. The sample included 327 cryptocurrencies, each of which has a capitalization of more than $ 1 mln. Measurement of investment risk was carried out on the basis of five approaches. The first one is grounded on the variability indicators. The second approach includes risk assessment in the context of asymmetry. The third is based on the concept of capital formation as part of the risk measures VaR and CVaR. The fourth focuses on measuring sensitivity risk. The fifth approach supposes using the Hurst exponent to measure risk. Based on the measures of these approaches, a comprehensive risk assessment was carried out. To cluster cryptocurrencies by riskiness, indicators from each group were selected, to which the technique of Kohonen self-organizing map was applied. The result was a partition of cryptocurrencies into three clusters. The analysis of the results is proposed and the corresponding conclusions and recommendations are made.
{"title":"Risk and return for cryptocurrencies as alternative investment: Kohonen maps clustering","authors":"A. Kaminskyi, I. Miroshnychenko, Kostiantyn Pysanets","doi":"10.33111/nfmte.2019.175","DOIUrl":"https://doi.org/10.33111/nfmte.2019.175","url":null,"abstract":"The active development of cryptocurrencies in recent years allows identifying the process of forming new class of alternative investment assets. There was formed a sample of cryptocurrencies based on criteria capitalization and historical returns for estimation investment risk of this asset class. The sample included 327 cryptocurrencies, each of which has a capitalization of more than $ 1 mln. Measurement of investment risk was carried out on the basis of five approaches. The first one is grounded on the variability indicators. The second approach includes risk assessment in the context of asymmetry. The third is based on the concept of capital formation as part of the risk measures VaR and CVaR. The fourth focuses on measuring sensitivity risk. The fifth approach supposes using the Hurst exponent to measure risk. Based on the measures of these approaches, a comprehensive risk assessment was carried out. To cluster cryptocurrencies by riskiness, indicators from each group were selected, to which the technique of Kohonen self-organizing map was applied. The result was a partition of cryptocurrencies into three clusters. The analysis of the results is proposed and the corresponding conclusions and recommendations are made.","PeriodicalId":300314,"journal":{"name":"Neuro-Fuzzy Modeling Techniques in Economics","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132993103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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