<abstract xmlns="http://www.w3.org/1999/xhtml">

<bold>Research background:</bold> The multidimensional assessment of the attractiveness of cryptocurrency exchanges seems to be an important issue, because the risk of the collapse of such an exchange or its use for illegal purposes is higher than in the case of traditional exchanges.
<bold>Purpose:</bold> The aim of the work is to create ranking and identify groups of cryptocurrency exchanges with a similar level of attractiveness.
<bold>Research methodology:</bold> 13 different composite indicators were considered. Finally, one of them was chosen as a representative according to the similarity of the obtained rankings. Clustering methods were used to identify groups of exchanges with a similar level of the constructed measure.
<bold>Result:</bold> The best according to the adopted criteria of rankings similarity was the taxonomic measure constructed using the standardized sum method with equal weights. Combining hierarchical clustering with the <italic>k-means</italic> algorithm allowed to improve the quality of clustering measured with the silhouette index.
<bold>Novelty:</bold> The originality of the paper lies in the use of different methods of a multidimensional comparative analysis on the cryptocurrency market.
</abstract>

Research background: The multidimensional assessment of the attractiveness of cryptocurrency exchanges seems to be an important issue, because the risk of the collapse of such an exchange or its use for illegal purposes is higher than in the case of traditional exchanges.
Purpose: The aim of the work is to create ranking and identify groups of cryptocurrency exchanges with a similar level of attractiveness.
Research methodology: 13 different composite indicators were considered. Finally, one of them was chosen as a representative according to the similarity of the obtained rankings. Clustering methods were used to identify groups of exchanges with a similar level of the constructed measure.
Result: The best according to the adopted criteria of rankings similarity was the taxonomic measure constructed using the standardized sum method with equal weights. Combining hierarchical clustering with the k-means algorithm allowed to improve the quality of clustering measured with the silhouette index.
Novelty: The originality of the paper lies in the use of different methods of a multidimensional comparative analysis on the cryptocurrency market.

Ranking and Classification of Cryptocurrency Exchanges Using the Methods of a Multidimensional Comparative Analysis

Folia Oeconomica Stetinensia

This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License.

Research background: The multidimensional assessment of the attractiveness of cryptocurrency exchanges seems to be an important issue, because the risk of the...

Ranking and Classification of Cryptocurrency Exchanges Using the Methods of a Multidimensional Comparative Analysis

Online veröffentlicht: 26. Nov. 2021

Seitenbereich: 38 - 56

Eingereicht: 11. Apr. 2021

Akzeptiert: 08. Okt. 2021

DOI: https://doi.org/10.2478/foli-2021-0015

Schlüsselwörter
cryptocurrency exchanges, composite indicator, hierarchical clustering, k-means, linear ordering, weighting schemes, investments attractiveness

© 2021 Kinga Kądziołka, published by Sciendo

This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License.

Ranking and Classification of Cryptocurrency Exchanges Using the Methods of a Multidimensional Comparative Analysis

Online veröffentlicht: 26. Nov. 2021

Seitenbereich: 38 - 56

Eingereicht: 11. Apr. 2021

Akzeptiert: 08. Okt. 2021

DOI: https://doi.org/10.2478/foli-2021-0015

Schlüsselwörtercryptocurrency exchanges, composite indicator, hierarchical clustering, k-means, linear ordering, weighting schemes, investments attractiveness

© 2021 Kinga Kądziołka, published by Sciendo

This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License.

Schlüsselwörter
cryptocurrency exchanges, composite indicator, hierarchical clustering, k-means, linear ordering, weighting schemes, investments attractiveness