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Predicting Bitcoin Volatility with LSTM by iskrenatanasov2007 is a document available to read on EtoBox.

This research develops machine learning models using LSTM neural networks to predict Bitcoin price fluctuations with 82% accuracy over 3-day forecasts. Key findings highlight that Federal Reserve announcements and social media sentiment significantly contribute to volatility, while the model successfully predicted 3 out of 4 major crashes. The paper also provides regulatory recommendations and notes limitations in effectiveness during extreme market events, emphasizing the need for human oversight.

Author

iskrenatanasov2007

Language

EN