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What drives bitcoin? An approach from continuous local transfer entropy and deep learning classification models

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  • Andr'es Garc'ia-Medina
  • Toan Luu Duc Huynh3

Abstract

Bitcoin has attracted attention from different market participants due to unpredictable price patterns. Sometimes, the price has exhibited big jumps. Bitcoin prices have also had extreme, unexpected crashes. We test the predictive power of a wide range of determinants on bitcoins' price direction under the continuous transfer entropy approach as a feature selection criterion. Accordingly, the statistically significant assets in the sense of permutation test on the nearest neighbour estimation of local transfer entropy are used as features or explanatory variables in a deep learning classification model to predict the price direction of bitcoin. The proposed variable selection methodology excludes the NASDAQ index and Tesla as drivers. Under different scenarios and metrics, the best results are obtained using the significant drivers during the pandemic as validation. In the test, the accuracy increased in the post-pandemic scenario of July 2020 to January 2021 without drivers. In other words, our results indicate that in times of high volatility, Bitcoin seems to autoregulate and does not need additional drivers to improve the accuracy of the price direction.

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  • Andr'es Garc'ia-Medina & Toan Luu Duc Huynh3, 2021. "What drives bitcoin? An approach from continuous local transfer entropy and deep learning classification models," Papers 2109.01214, arXiv.org.
    • Handle: RePEc:arx:papers:2109.01214
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    References listed on IDEAS

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    1. Das, Sonali & Demirer, Riza & Gupta, Rangan & Mangisa, Siphumlile, 2019. "The effect of global crises on stock market correlations: Evidence from scalar regressions via functional data analysis," Structural Change and Economic Dynamics, Elsevier, vol. 50(C), pages 132-147.
    2. Philippas, Dionisis & Rjiba, Hatem & Guesmi, Khaled & Goutte, Stéphane, 2019. "Media attention and Bitcoin prices," Finance Research Letters, Elsevier, vol. 30(C), pages 37-43.
    3. Shen, Dehua & Urquhart, Andrew & Wang, Pengfei, 2019. "Does twitter predict Bitcoin?," Economics Letters, Elsevier, vol. 174(C), pages 118-122.
    4. Lahmiri, Salim & Bekiros, Stelios & Bezzina, Frank, 2020. "Multi-fluctuation nonlinear patterns of European financial markets based on adaptive filtering with application to family business, green, Islamic, common stocks, and comparison with Bitcoin, NASDAQ, ," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 538(C).
    5. Geert Bekaert & Eric C. Engstrom & Nancy R. Xu, 2022. "The Time Variation in Risk Appetite and Uncertainty," Management Science, INFORMS, vol. 68(6), pages 3975-4004, June.
    6. Toan Luu Duc Huynh, 2019. "Spillover Risks on Cryptocurrency Markets: A Look from VAR-SVAR Granger Causality and Student’s-t Copulas," JRFM, MDPI, vol. 12(2), pages 1-19, April.
    7. Gronwald, Marc, 2019. "Is Bitcoin a Commodity? On price jumps, demand shocks, and certainty of supply," Journal of International Money and Finance, Elsevier, vol. 97(C), pages 86-92.
    8. Urquhart, Andrew, 2018. "What causes the attention of Bitcoin?," Economics Letters, Elsevier, vol. 166(C), pages 40-44.
    9. Huynh, Toan Luu Duc & Nasir, Muhammad Ali & Vo, Xuan Vinh & Nguyen, Thong Trung, 2020. "“Small things matter most”: The spillover effects in the cryptocurrency market and gold as a silver bullet," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    10. Chaim, Pedro & Laurini, Márcio P., 2019. "Is Bitcoin a bubble?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 517(C), pages 222-232.
    11. Thampanya, Natthinee & Nasir, Muhammad Ali & Huynh, Toan Luu Duc, 2020. "Asymmetric correlation and hedging effectiveness of gold & cryptocurrencies: From pre-industrial to the 4th industrial revolution✰," Technological Forecasting and Social Change, Elsevier, vol. 159(C).
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    Cited by:

    1. David L. John & Sebastian Binnewies & Bela Stantic, 2024. "Cryptocurrency Price Prediction Algorithms: A Survey and Future Directions," Forecasting, MDPI, vol. 6(3), pages 1-35, August.
    2. Andrés García-Medina & Ester Aguayo-Moreno, 2024. "LSTM–GARCH Hybrid Model for the Prediction of Volatility in Cryptocurrency Portfolios," Computational Economics, Springer;Society for Computational Economics, vol. 63(4), pages 1511-1542, April.
    3. Demiralay, Sercan & Gencer, Hatice Gaye & Bayraci, Selcuk, 2022. "Carbon credit futures as an emerging asset: Hedging, diversification and downside risks," Energy Economics, Elsevier, vol. 113(C).
    4. Lee, Donghyun & Kim, Mingyu & Lee, Beomhui & Chae, Sangwon & Kwon, Sungjun & Kang, Sungwon, 2022. "Integrated explainable deep learning prediction of harmful algal blooms," Technological Forecasting and Social Change, Elsevier, vol. 185(C).

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