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Empirical Analysis ot the Top 800 Cryptocurrencies using Machine Learning Techniques
The International Token Classification (ITC) Framework by the Blockchain Center in Frankfurt classifies 795 cryptocurrency tokens based on their economic, technological, legal and industry categorization. This work analyzes cryptocurrency data to evaluate the categorization with real-world market da...
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| Format: | Thesis |
| Language: | English |
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African Institute of Financial Markets and Risk Management
2020
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| _version_ | 1867613742824423424 |
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| access_status_str | Open Access |
| author | Riedl, Anna Teresa |
| author2 | Georg, Co-Pierre |
| author_browse | Georg, Co-Pierre Riedl, Anna Teresa |
| author_facet | Georg, Co-Pierre Riedl, Anna Teresa |
| author_sort | Riedl, Anna Teresa |
| collection | Thesis |
| description | The International Token Classification (ITC) Framework by the Blockchain Center in Frankfurt classifies 795 cryptocurrency tokens based on their economic, technological, legal and industry categorization. This work analyzes cryptocurrency data to evaluate the categorization with real-world market data. The feature space includes price, volume and market capitalization data. Additional metrics such as the moving average and the relative strengh index are added to get a more in-depth understanding of market movements. The data set is used to build supervised and unsupervised machine learning models. The prediction accuracies varied amongst labels and all remained below 90%. The technological label had the highest prediction accuracy at 88.9% using Random Forests. The economic label could be predicted with an accuracy of 81.7% using K-Nearest Neighbors. The classification using machine learning techniques is not yet accurate enough to automate the classification process. But it can be improved by adding additional features. The unsupervised clustering shows that there are more layers to the data that can be added to the ITC. The additional categories are built upon a combination of token mining, maximal supply, volume and market capitalization data. As a result we suggest that a data-driven extension of the categorization in to a token profile would allow investors and regulators to gain a deeper understanding of token performance, maturity and usage. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/31210 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:40:59.498Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2020 |
| publishDateRange | 2020 |
| publishDateSort | 2020 |
| publisher | African Institute of Financial Markets and Risk Management |
| publisherStr | African Institute of Financial Markets and Risk Management |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/31210 Empirical Analysis ot the Top 800 Cryptocurrencies using Machine Learning Techniques Riedl, Anna Teresa Georg, Co-Pierre Mathematical Finance The International Token Classification (ITC) Framework by the Blockchain Center in Frankfurt classifies 795 cryptocurrency tokens based on their economic, technological, legal and industry categorization. This work analyzes cryptocurrency data to evaluate the categorization with real-world market data. The feature space includes price, volume and market capitalization data. Additional metrics such as the moving average and the relative strengh index are added to get a more in-depth understanding of market movements. The data set is used to build supervised and unsupervised machine learning models. The prediction accuracies varied amongst labels and all remained below 90%. The technological label had the highest prediction accuracy at 88.9% using Random Forests. The economic label could be predicted with an accuracy of 81.7% using K-Nearest Neighbors. The classification using machine learning techniques is not yet accurate enough to automate the classification process. But it can be improved by adding additional features. The unsupervised clustering shows that there are more layers to the data that can be added to the ITC. The additional categories are built upon a combination of token mining, maximal supply, volume and market capitalization data. As a result we suggest that a data-driven extension of the categorization in to a token profile would allow investors and regulators to gain a deeper understanding of token performance, maturity and usage. 2020-02-20T12:38:31Z 2020-02-20T12:38:31Z 2019 2020-02-14T09:45:17Z Master Thesis Masters MPhil http://hdl.handle.net/11427/31210 eng application/pdf African Institute of Financial Markets and Risk Management Faculty of Commerce |
| spellingShingle | Mathematical Finance Riedl, Anna Teresa Empirical Analysis ot the Top 800 Cryptocurrencies using Machine Learning Techniques |
| thesis_degree_str | Master's |
| title | Empirical Analysis ot the Top 800 Cryptocurrencies using Machine Learning Techniques |
| title_full | Empirical Analysis ot the Top 800 Cryptocurrencies using Machine Learning Techniques |
| title_fullStr | Empirical Analysis ot the Top 800 Cryptocurrencies using Machine Learning Techniques |
| title_full_unstemmed | Empirical Analysis ot the Top 800 Cryptocurrencies using Machine Learning Techniques |
| title_short | Empirical Analysis ot the Top 800 Cryptocurrencies using Machine Learning Techniques |
| title_sort | empirical analysis ot the top 800 cryptocurrencies using machine learning techniques |
| topic | Mathematical Finance |
| url | http://hdl.handle.net/11427/31210 |
| work_keys_str_mv | AT riedlannateresa empiricalanalysisotthetop800cryptocurrenciesusingmachinelearningtechniques |