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Non-fundamental, non-parametric Bitcoin forecasting

Author

Listed:
  • Adcock, Robert
  • Gradojevic, Nikola

Abstract

Bitcoin is the largest cryptocurrency in the world, but its lack of quantitative qualities makes fundamental analysis of its intrinsic value difficult. As an alternative valuation and forecasting method we propose a non-parametric model based on technical analysis. Using simple technical indicators, we produce point and density forecasts of Bitcoin returns with a feedforward neural network. We run several models over the full period of April 2011–March 2018, and four subsamples, and we find that backpropagation neural networks dominate various competing models in terms of their forecast accuracy. We conclude that the dynamics of Bitcoin returns is characterized by predictive local non-linear trends that reflect the speculative nature of cryptocurrency trading.

Suggested Citation

  • Adcock, Robert & Gradojevic, Nikola, 2019. "Non-fundamental, non-parametric Bitcoin forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 531(C).
    • Handle: RePEc:eee:phsmap:v:531:y:2019:i:c:s0378437119309859
    DOI: 10.1016/j.physa.2019.121727
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    Citations

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    Cited by:

    1. Nicolás Magner & Nicolás Hardy, 2022. "Cryptocurrency Forecasting: More Evidence of the Meese-Rogoff Puzzle," Mathematics, MDPI, vol. 10(13), pages 1-27, July.
    2. Erdinc Akyildirim & Oguzhan Cepni & Shaen Corbet & Gazi Salah Uddin, 2023. "Forecasting mid-price movement of Bitcoin futures using machine learning," Annals of Operations Research, Springer, vol. 330(1), pages 553-584, November.
    3. Ngene, Geoffrey M. & Wang, Jinghua, 2024. "Arbitrage opportunities and feedback trading in regulated bitcoin futures market: An intraday analysis," International Review of Economics & Finance, Elsevier, vol. 89(PB), pages 743-761.
    4. Chen, Rui & Ren, Jinjuan, 2022. "Do AI-powered mutual funds perform better?," Finance Research Letters, Elsevier, vol. 47(PA).
    5. Gradojevic, Nikola & Tsiakas, Ilias, 2021. "Volatility cascades in cryptocurrency trading," Journal of Empirical Finance, Elsevier, vol. 62(C), pages 252-265.
    6. Hajek, Petr & Hikkerova, Lubica & Sahut, Jean-Michel, 2023. "How well do investor sentiment and ensemble learning predict Bitcoin prices?," Research in International Business and Finance, Elsevier, vol. 64(C).
    7. Jiri Kukacka & Ladislav Kristoufek, 2023. "Fundamental and speculative components of the cryptocurrency pricing dynamics," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-23, December.
    8. Sofiane Aboura, 2022. "A note on the Bitcoin and Fed Funds rate," Empirical Economics, Springer, vol. 63(5), pages 2577-2603, November.
    9. Hakan Pabuccu & Serdar Ongan & Ayse Ongan, 2023. "Forecasting the movements of Bitcoin prices: an application of machine learning algorithms," Papers 2303.04642, arXiv.org.
    10. Cheng, Jiyang & Tiwari, Sunil & Khaled, Djebbouri & Mahendru, Mandeep & Shahzad, Umer, 2024. "Forecasting Bitcoin prices using artificial intelligence: Combination of ML, SARIMA, and Facebook Prophet models," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
    11. Surinder Singh Khurana & Parvinder Singh & Naresh Kumar Garg, 2024. "OG-CAT: A Novel Algorithmic Trading Alternative to Investment in Crypto Market," Computational Economics, Springer;Society for Computational Economics, vol. 63(5), pages 1735-1756, May.
    12. Syed Abul, Basher & Perry, Sadorsky, 2022. "Forecasting Bitcoin price direction with random forests: How important are interest rates, inflation, and market volatility?," MPRA Paper 113293, University Library of Munich, Germany.
    13. do Nascimento, José Cláudio, 2021. "The personal wealth importance to the intertemporal choice," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    14. Jalan, Akanksha & Matkovskyy, Roman & Urquhart, Andrew & Yarovaya, Larisa, 2023. "The role of interpersonal trust in cryptocurrency adoption," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 83(C).
    15. Paweł Jakubowski & Robert Ślepaczuk & Franciszek Windorbski, 2023. "REnsembling ARIMAX Model in Algorithmic Investment Strategies on Commodities Market," Working Papers 2023-20, Faculty of Economic Sciences, University of Warsaw.
    16. Gradojevic, Nikola & Kukolj, Dragan & Adcock, Robert & Djakovic, Vladimir, 2023. "Forecasting Bitcoin with technical analysis: A not-so-random forest?," International Journal of Forecasting, Elsevier, vol. 39(1), pages 1-17.
    17. Qiutong Guo & Shun Lei & Qing Ye & Zhiyang Fang, 2021. "MRC-LSTM: A Hybrid Approach of Multi-scale Residual CNN and LSTM to Predict Bitcoin Price," Papers 2105.00707, arXiv.org.

    More about this item

    Keywords

    Bitcoin; Neural networks; Technical analysis; Density forecasting;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • G1 - Financial Economics - - General Financial Markets

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