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Predicting Hourly Bitcoin Prices Based on Long Short-Term Memory Neural Networks

In: Innovation Through Information Systems

Author

Listed:
  • Maximilian Schulte

    (Fachhochschule Aachen)

  • Mathias Eggert

    (Fachhochschule Aachen)

Abstract

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 movements 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%.

Suggested Citation

  • Maximilian Schulte & Mathias Eggert, 2021. "Predicting Hourly Bitcoin Prices Based on Long Short-Term Memory Neural Networks," Lecture Notes in Information Systems and Organization, in: Frederik Ahlemann & Reinhard Schütte & Stefan Stieglitz (ed.), Innovation Through Information Systems, pages 754-769, Springer.
    • Handle: RePEc:spr:lnichp:978-3-030-86797-3_50
    DOI: 10.1007/978-3-030-86797-3_50
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