Real-Time Prediction of BITCOIN Price using Machine Learning Techniques and Public Sentiment Analysis
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References listed on IDEAS
- Dixon, Matthew & Klabjan, Diego & Bang, Jin Hoon, 2017. "Classification-based financial markets prediction using deep neural networks," Algorithmic Finance, IOS Press, vol. 6(3-4), pages 67-77.
- Magdalena Daniela NEMES & Alexandru BUTOI, 2013. "Data Mining on Romanian Stock Market Using Neural Networks for Price Prediction," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 17(3), pages 125-136.
- David Garcia & Frank Schweitzer, 2015. "Social signals and algorithmic trading of Bitcoin," Papers 1506.01513, arXiv.org, revised Sep 2015.
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Cited by:
- Sudersan Behera & Sarat Chandra Nayak & A. V. S. Pavan Kumar, 2024. "Evaluating the Performance of Metaheuristic Based Artificial Neural Networks for Cryptocurrency Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 64(2), pages 1219-1258, August.
- Mingzhe Wei & Georgios Sermpinis & Charalampos Stasinakis, 2023. "Forecasting and trading Bitcoin with machine learning techniques and a hybrid volatility/sentiment leverage," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 852-871, July.
- Yanzhao Zou & Dorien Herremans, 2022. "PreBit -- A multimodal model with Twitter FinBERT embeddings for extreme price movement prediction of Bitcoin," Papers 2206.00648, arXiv.org, revised Oct 2023.
- Zi Ye & Yinxu Wu & Hui Chen & Yi Pan & Qingshan Jiang, 2022. "A Stacking Ensemble Deep Learning Model for Bitcoin Price Prediction Using Twitter Comments on Bitcoin," Mathematics, MDPI, vol. 10(8), pages 1-21, April.
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NEP fields
This paper has been announced in the following NEP Reports:- NEP-BIG-2020-07-13 (Big Data)
- NEP-CMP-2020-07-13 (Computational Economics)
- NEP-FOR-2020-07-13 (Forecasting)
- NEP-PAY-2020-07-13 (Payment Systems and Financial Technology)
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