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A Tree Augmented Naïve Bayes-based methodology for classifying cryptocurrency trends

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  • Dag, Ali
  • Dag, Asli Z.
  • Asilkalkan, Abdullah
  • Simsek, Serhat
  • Delen, Dursun

Abstract

As the popularity of blockchain technology and investor confidence in Bitcoin (BTC) increased in recent years, many individuals started making BTC and other cryptocurrency investments, in expectation of high returns. However, as recent market movements have shown, the lack of regulation and oversight makes it difficult to guard against high volatility and potentially significant losses in this sector. In this study, we propose a data-driven Tree Augmented Naïve (TAN) Bayes methodology that can be used for identifying the most important factors (as well as their conditional, interdependent relationships) influencing BTC price movements. As the model is parsimonious without sacrificing accuracy, sensitivity, and specificity—as evident from the average accuracy value—the proposed methodology can be used in practice for making short-term investment decisions.

Suggested Citation

  • Dag, Ali & Dag, Asli Z. & Asilkalkan, Abdullah & Simsek, Serhat & Delen, Dursun, 2023. "A Tree Augmented Naïve Bayes-based methodology for classifying cryptocurrency trends," Journal of Business Research, Elsevier, vol. 156(C).
    • Handle: RePEc:eee:jbrese:v:156:y:2023:i:c:s0148296322009870
    DOI: 10.1016/j.jbusres.2022.113522
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    References listed on IDEAS

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    1. Pavel Ciaian & Miroslava Rajcaniova & d’Artis Kancs, 2016. "The economics of BitCoin price formation," Applied Economics, Taylor & Francis Journals, vol. 48(19), pages 1799-1815, April.
    2. Urquhart, Andrew, 2017. "Price clustering in Bitcoin," Economics Letters, Elsevier, vol. 159(C), pages 145-148.
    3. Obryan Poyser, 2017. "Exploring the determinants of Bitcoin's price: an application of Bayesian Structural Time Series," Papers 1706.01437, arXiv.org.
    4. Kurbucz, Marcell Tamás, 2019. "Predicting the price of Bitcoin by the most frequent edges of its transaction network," Economics Letters, Elsevier, vol. 184(C).
    5. Blau, Benjamin M., 2018. "Price dynamics and speculative trading in Bitcoin," Research in International Business and Finance, Elsevier, vol. 43(C), pages 15-21.
    6. Gottschlich, Jörg & Hinz, Oliver, 2014. "A Decision Support System for Stock Investment Recommendations Using Collective Wisdom," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 69939, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    7. Aalborg, Halvor Aarhus & Molnár, Peter & de Vries, Jon Erik, 2019. "What can explain the price, volatility and trading volume of Bitcoin?," Finance Research Letters, Elsevier, vol. 29(C), pages 255-265.
    8. Panagiotidis, Theodore & Stengos, Thanasis & Vravosinos, Orestis, 2019. "The effects of markets, uncertainty and search intensity on bitcoin returns," International Review of Financial Analysis, Elsevier, vol. 63(C), pages 220-242.
    9. Osamu Kodama & Lukáš Pichl & Taisei Kaizoji, 2017. "Regime Change And Trend Prediction For Bitcoin Time Series Data," CBU International Conference Proceedings, ISE Research Institute, vol. 5(0), pages 384-388, September.
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    Cited by:

    1. Loh, Xiu-Ming & Lee, Voon-Hsien & Leong, Lai-Ying & Aw, Eugene Cheng-Xi & Cham, Tat-Huei & Tang, Yun-Chia & Hew, Jun-Jie, 2023. "Understanding consumers’ resistance to pay with cryptocurrency in the sharing economy: A hybrid SEM-fsQCA approach," Journal of Business Research, Elsevier, vol. 159(C).

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