Trend Prediction Classification for High Frequency Bitcoin Time Series with Deep Learning
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References listed on IDEAS
- Gkillas, Konstantinos & Katsiampa, Paraskevi, 2018. "An application of extreme value theory to cryptocurrencies," Economics Letters, Elsevier, vol. 164(C), pages 109-111.
- Zihao Zhang & Stefan Zohren & Stephen Roberts, 2018. "BDLOB: Bayesian Deep Convolutional Neural Networks for Limit Order Books," Papers 1811.10041, arXiv.org.
- Zihao Zhang & Stefan Zohren & Stephen Roberts, 2018. "DeepLOB: Deep Convolutional Neural Networks for Limit Order Books," Papers 1808.03668, arXiv.org, revised Jan 2020.
- Justin Sirignano & Rama Cont, 2018. "Universal features of price formation in financial markets: perspectives from Deep Learning," Working Papers hal-01754054, HAL.
- Koutmos, Dimitrios, 2018. "Bitcoin returns and transaction activity," Economics Letters, Elsevier, vol. 167(C), pages 81-85.
CitationsCitations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
- Vladimir Petrov & Anton Golub & Richard Olsen, 2019. "Instantaneous Volatility Seasonality of High-Frequency Markets in Directional-Change Intrinsic Time," Journal of Risk and Financial Management, MDPI, Open Access Journal, vol. 12(2), pages 1-31, April.
- Christian M. Hafner, 2020. "Alternative Assets and Cryptocurrencies," Journal of Risk and Financial Management, MDPI, Open Access Journal, vol. 13(1), pages 1-3, January.
More about this item
Keywordscryptocurrency; metric learning; classification framework; time series; trend prediction;
- C - Mathematical and Quantitative Methods
- E - Macroeconomics and Monetary Economics
- F2 - International Economics - - International Factor Movements and International Business
- F3 - International Economics - - International Finance
- G - Financial Economics
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