Алгоритм порівняльного аналізу просторово-часових характеристик криптовалютних активів

Abstract

The current stage of development of the cryptocurrency market is characterized by a number of structural properties and transformations that cause increased interest in cryptocurrency as a means of payment. This payment option allows you to speed up the money transfer operations, requires significantly lower commissions, is not tied to time intervals, effectively solves the problem of settlements with foreign partners in any currencies, and overcomes inflationary risk. The presence of these advantages determines the relevance of studying the cryptocurrency market in order to determine the prospects for improving the modern practice of non-cash electronic payments in Ukraine, which, in turn, will contribute to increasing the level of competitiveness of domestic enterprises. The aim of the publication is to build and implement an algorithm for comparative analysis of the spatio-temporal characteristics of cryptocurrency assets, the use of which will allow to make a reasonable choice of cryptocurrency as a non-cash means of payment. This algorithm contains the following main steps: analysis of the current state and main trends in the development of the cryptocurrency market; determination of a set of basic characteristics with the help of which it is possible to describe cryptocurrency as an object in a multidimensional statistical space; classification of cryptocurrency assets; development of recommendations for the final choice of cryptocurrency as a means of non-cash payments. A comparative analysis of cryptocurrencies is carried out by indicators of risk, profitability, and market capitalization. Classification and ordering of objects in the multidimensional feature space are carried out using cluster analysis algorithms. The structure of the system of cryptocurrency objects in multidimensional space is preliminarily analyzed using agglomerative methods, then a reasonable decision is made on the optimal number of clusters and an iterative clustering algorithm is applied. A division of cryptocurrency objects into four consecutive periods has been obtained, the stability of the composition and structure of the resulting groups in dynamics has been studied. As a result, groups whose characteristics are acceptable from the point of view of the ultimate goal of the study are identified. Within these groups, the cryptocurrencies that can be used for non-cash payments have been identified.