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Crypto coins by category | The sub-periods that are formed by leaving the , or observations out as the out-of-sample part of the data are utilized as well. Mathematics 7 10 Lo, A. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. Flach, P. Using artificial neural network models in stock market index prediction. Consent for publication Not applicable. |
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Bitcoin philippines 2018 | Google Scholar Huang, W. Springer, Singapore. Other studies have already partly addressed these issues; however, the originality of our paper comes from the combination of all these features, that is, from an overall analysis framework. However, Urquhart and Bariviera also point out that after an initial transitory phase, as the market started to mature, bitcoin has been moving toward efficiency. Focus to learn more DOI s linking to related resources. Econ Rev 23 1 � Google Scholar. |
Cryptocurrency machine learning | Kennis, M. Google Scholar Bariviera, A. The classification and regression methods use attributes from trading and network activity for the period from August 15, to March 03, , with the test sample beginning on April 13, In the validation sub-sample, the success rates of the classification models range from Exchange trading information�the closing prices the last reported prices before UTC of the next day and the high and low prices during the last 24 h, the daily trading volume, and market capitalization�come from the CoinMarketCap site. |
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