Empirical Analysis ot the Top 800 Cryptocurrencies using Machine Learning Techniques
| dc.contributor.advisor | Georg, Co-Pierre | |
| dc.contributor.author | Riedl, Anna Teresa | |
| dc.date.accessioned | 2020-02-20T12:38:31Z | |
| dc.date.available | 2020-02-20T12:38:31Z | |
| dc.date.issued | 2019 | |
| dc.date.updated | 2020-02-14T09:45:17Z | |
| dc.description.abstract | 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. | |
| dc.identifier.apacitation | Riedl, A. T. (2019). <i>Empirical Analysis ot the Top 800 Cryptocurrencies using Machine Learning Techniques</i>. (). ,Faculty of Commerce ,African Institute of Financial Markets and Risk Management. Retrieved from http://hdl.handle.net/11427/31210 | en_ZA |
| dc.identifier.chicagocitation | Riedl, Anna Teresa. <i>"Empirical Analysis ot the Top 800 Cryptocurrencies using Machine Learning Techniques."</i> ., ,Faculty of Commerce ,African Institute of Financial Markets and Risk Management, 2019. http://hdl.handle.net/11427/31210 | en_ZA |
| dc.identifier.citation | Riedl, A. 2019. Empirical Analysis ot the Top 800 Cryptocurrencies using Machine Learning Techniques. | en_ZA |
| dc.identifier.ris | TY - Thesis / Dissertation AU - Riedl, Anna Teresa AB - 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. DA - 2019 DB - OpenUCT DP - University of Cape Town KW - Mathematical Finance LK - https://open.uct.ac.za PY - 2019 T1 - Empirical Analysis ot the Top 800 Cryptocurrencies using Machine Learning Techniques TI - Empirical Analysis ot the Top 800 Cryptocurrencies using Machine Learning Techniques UR - http://hdl.handle.net/11427/31210 ER - | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11427/31210 | |
| dc.identifier.vancouvercitation | Riedl AT. Empirical Analysis ot the Top 800 Cryptocurrencies using Machine Learning Techniques. []. ,Faculty of Commerce ,African Institute of Financial Markets and Risk Management, 2019 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/31210 | en_ZA |
| dc.language.rfc3066 | eng | |
| dc.publisher.department | African Institute of Financial Markets and Risk Management | |
| dc.publisher.faculty | Faculty of Commerce | |
| dc.subject | Mathematical Finance | |
| dc.title | Empirical Analysis ot the Top 800 Cryptocurrencies using Machine Learning Techniques | |
| dc.type | Master Thesis | |
| dc.type.qualificationlevel | Masters | |
| dc.type.qualificationname | MPhil |