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Trend Prediction Classification for High Frequency Bitcoin Time Series with Deep Learning

Author

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  • Takuya Shintate

    (Graduate School of Arts and Sciences, International Christian University, Osawa 3-10-2, Mitaka, Tokyo 181-8585, Japan)

  • Lukáš Pichl

    (Graduate School of Arts and Sciences, International Christian University, Osawa 3-10-2, Mitaka, Tokyo 181-8585, Japan)

Abstract

We provide a trend prediction classification framework named the random sampling method (RSM) for cryptocurrency time series that are non-stationary. This framework is based on deep learning (DL). We compare the performance of our approach to two classical baseline methods in the case of the prediction of unstable Bitcoin prices in the OkCoin market and show that the baseline approaches are easily biased by class imbalance, whereas our model mitigates this problem. We also show that the classification performance of our method expressed as the F-measure substantially exceeds the odds of a uniform random process with three outcomes, proving that extraction of deterministic patterns for trend classification, and hence market prediction, is possible to some degree. The profit rates based on RSM outperformed those based on LSTM, although they did not exceed those of the buy-and-hold strategy within the testing data period, and thus do not provide a basis for algorithmic trading.

Suggested Citation

  • Takuya Shintate & Lukáš Pichl, 2019. "Trend Prediction Classification for High Frequency Bitcoin Time Series with Deep Learning," JRFM, MDPI, vol. 12(1), pages 1-15, January.
  • Handle: RePEc:gam:jjrfmx:v:12:y:2019:i:1:p:17-:d:199465
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    References listed on IDEAS

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    Cited by:

    1. Yang, Haijun & Xue, Feng, 2021. "Analysis of stock market volatility: Adjusted VPIN with high-frequency data," International Review of Economics & Finance, Elsevier, vol. 75(C), pages 210-222.
    2. Suhwan Ji & Jongmin Kim & Hyeonseung Im, 2019. "A Comparative Study of Bitcoin Price Prediction Using Deep Learning," Mathematics, MDPI, vol. 7(10), pages 1-20, September.
    3. Chenlu Dang & Fan Wang & Zimo Yang & Hongxia Zhang & Yufeng Qian, 2022. "RETRACTED ARTICLE: Evaluating and forecasting the risks of small to medium-sized enterprises in the supply chain finance market using blockchain technology and deep learning model," Operations Management Research, Springer, vol. 15(3), pages 662-675, December.
    4. Vladimir Petrov & Anton Golub & Richard Olsen, 2019. "Instantaneous Volatility Seasonality of High-Frequency Markets in Directional-Change Intrinsic Time," JRFM, MDPI, vol. 12(2), pages 1-31, April.
    5. Shigeyuki Hamori, 2020. "Recent Advancements in Section “Financial Technology and Innovation”," JRFM, MDPI, vol. 13(12), pages 1-2, December.
    6. Krzysztof Piasecki & Michał Dominik Stasiak, 2020. "Optimization Parameters of Trading System with Constant Modulus of Unit Return," Mathematics, MDPI, vol. 8(8), pages 1-17, August.
    7. Gyana Ranjan Patra & Mihir Narayan Mohanty, 2023. "Price Prediction of Cryptocurrency Using a Multi-Layer Gated Recurrent Unit Network with Multi Features," Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1525-1544, December.
    8. Carvajal-Patiño, Daniel & Ramos-Pollán, Raul, 2022. "Synthetic data generation with deep generative models to enhance predictive tasks in trading strategies," Research in International Business and Finance, Elsevier, vol. 62(C).
    9. Christian M. Hafner, 2020. "Alternative Assets and Cryptocurrencies," JRFM, MDPI, vol. 13(1), pages 1-3, January.
    10. Helder Sebastião & Pedro Godinho, 2021. "Forecasting and trading cryptocurrencies with machine learning under changing market conditions," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-30, December.

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