Predicting hourly bitcoin prices based on long short-term memory neural networks

  • Bitcoin is a cryptocurrency and is considered a high-risk asset class whose price changes are difficult to predict. Current research focusses on daily price movements with a limited number of predictors. The paper at hand aims at identifying measurable indicators for Bitcoin price movement s and the development of a suitable forecasting model for hourly changes. The paper provides three research contributions. First, a set of significant indicators for predicting the Bitcoin price is identified. Second, the results of a trained Long Short-term Memory (LSTM) neural network that predicts price changes on an hourly basis is presented and compared with other algorithms. Third, the results foster discussions of the applicability of neural nets for stock price predictions. In total, 47 input features for a period of over 10 months could be retrieved to train a neural net that predicts the Bitcoin price movements with an error rate of 3.52 %.

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Metadaten
Author:Maximilian Schulte, Mathias Eggert
Parent Title (English):Proceedings of the International Conference on Wirtschaftsinformatik (WI) 2021
Document Type:Conference Proceeding
Language:English
Year of Completion:2021
Date of the Publication (Server):2021/03/03
Length:16 Seiten
Note:
16th International Conference on Wirtschaftsinformatik,
March 2021, Essen, Germany
Link:https://aisel.aisnet.org/wi2021/XStudent/Track03/3/
Zugriffsart:campus
Institutes:FH Aachen / Fachbereich Wirtschaftswissenschaften