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Review on Bitcoin Price Prediction Using Machine Learning and Statistical Methods

Year 2020, , 272 - 282, 30.12.2020

I.sibel Kervancı , Fatih Akay

https://doi.org/10.35377/saucis.03.03.774276
Cited By: 7

Abstract

Bitcoin is invented in 2009 by the pseudonymous Satoshi Nakamoto. Bitcoin is a decentralized digital currency system [1]. Bitcoin is the most acknowledged cryptocurrency in the world, which provide it interesting for financier. The cryptocurrency market capitalization on date 22nd July 2020 value represents roughly USD 277 billion of dollars, bitcoin representing 62% of it. However, a disadvantage for investors is the difficulty of predicting the price of bitcoin due to the high volatility of the bitcoin exchange rate. Measurement, estimation, and modeling of currency exchange rate volatility compose a significant research area. For this reason, a lot of studies done about bitcoin price prediction both Machine Learning (ML) and Statistical Methods. In comparison studies, ML methods perform better in general. This review is a comprehensive study on how we can better predict bitcoin prices by grouping previously done studies. The presentation of Bitcoin price prediction studies in groups reveals, the difference from other review studies. These are statistical methods, ML and statistical methods, ML-ML, frequency effect of selected time, effect of social media and web search engine, causality, optimization of hyperparameters methods.

Keywords

Bitcoin price prediction machine learning statistical methods

References

  • M. Rahouti, K. Xiong and N. Ghani, "Bitcoin Concepts, Threats, and Machine-Learning Security Solutions," in IEEE Access, vol 6, pp. 67189 - 67205, 9 November 2018.
  • S. Abboushi, "Global Virtual Currency – Brief Overview.19(6), 10-118.," The Journal of Applied Business and Economics, vol 19, no. 6, 10 2017
  • O. D. Juarez and H. E. Manzanilla , "Forecasting Bitcoin Pricing with Hybrid Models A Review of The Literature," International Journal of Advanced Engineering Research and Science (IJAERS), vol 6, no. 9, pp. 161-164, Sept 2019.
  • H. Garrick and M. Rauchs, "Global cryptocurrency benchmarking study," Cambridge Centre for Alternative Finance, 2017.
  • H. Ince and T. B. Trafalis, "A Hybrid Model for Exchange Rate Prediction," Decision Support Systems, vol 42, p. 1054–1062, 2006.
  • I. N. Indera, I. M. Yassin, A. Zabidi and Z. I. Rizman, "Non-Linear Autoregressive with Exogeneous Input (NARX) Bitcoin Price Prediction Model Using PSO-Optimized Parameters Optimized Parameters," Journal of fundamental and applied sciences, vol 9, no. 3S, p. 791, 10 September 2017
  • S. Roy , S. Nanjiba and A. Chakrabarty, "Bitcoin Price Forecasting Using Time Series Analysis," ICCIT, Dhaka, Bangladesh, 2018
  • A.Zeba; F.Jinan ; S.Ahmer ; A.M. Anwer, "Bitcoin Price Prediction using ARIMA Model," Canada, 2020.
  • P. Katsiampa, "Volatility Estimation for Bitcoin: A Comparison of GARCH Models," Economics Letters, vol 158, pp. 3-6, 2017.
  • N. Mangla, A. Bhat, G. Avabratha and N. Bhat, "Bitcoin Price Prediction Using Machine Learning," International Joutnal of Information And Computing Science, vol 6, no. 5, pp. 318-320, May 2019.
  • S. Ze, W. Qing and L. J. David, "Model, Bitcoin Return Volatility Forecasting: A Comparative Study of GARCH Model and Machine Learning,"Agricultural & Applied Economics Association, Atlanta, GA, 2019.
  • M. Sean, R. Jason and C. Simon, "Predicting the Price of Bitcoin Using Machine Learning," 26th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, 2018.
  • P. Thearasak and N. Thanisa, "Machine Learning Models Comparison for Bitcoin Price Prediction," 2018 10th International Conference on Information Technology and Electrical Engineering (ICITEE), pp. 506-511, 2018.
  • T. Shintate and . L. Pichl, "Trend Prediction Classification for High Frequency Bitcoin Time Series with Deep Learning," Risk and Financial Management, 2019.
  • S. Devavrat and Z. Kang , "Bayesian regression and Bitcoin," Fifty-second Annual Allerton Conference, Monticello, IL, USA, 2014.
  • I. Madan, S. Saluja and A. Zhao,"Automated Bitcoin Trading via Machine Learning Algorithms," Google Scholar, 2014.
  • M. Martina , L. Ilaria and M. Michele , "Bitcoin Spread Prediction Using Social And Web Search Media," UMAP Workshops, 2015, 2015
  • J. C. Kaminski and M. M. Lab, "Nowcasting the Bitcoin Market with Twitter Signals," Cornell University, Jan 2016.
  • D. Garcia, C. J. Tessone, P. Mavrodiev and N. Perony, "Feedback Cycles Between Socio-Economic Signals in the Bitcoin Economy," The Journal of The Royal Society, Oct 2014.
  • D. Shen, A. Urquhart and P. Wanga, "Does Twitter Predict Bitcoin?," Economics Letters, vol 174, p. 118–122, 2019.
  • D. G. Baur, T. Dimpfl and K. Kuck, "Bitcoin, Gold and the US Dollar – A Replication and Extension," Finance Research Letters, vol 25, pp. 103-110, 2018.
  • A. H. Dyhrberg, "Bitcoin, Gold and The Dollar –A GARCH Volatility Analysis," Finance Research Letters, vol 16, pp. 85-92, 2016.
  • E. Bouri, R. Gupta, A. Lahiani and M. Shahbaz, "Testing for Asymmetric Nonlinear Short- and Long-Run Relationships Between Bitcoin, Aggregate Commodity and Gold Prices," Elsevier, vol 57, p. 224–235, 2018.
  • J. Bouoiyour and R. Selmi, "What Does Bitcoin Look Like?," Annals of Economics and Finance, vol 16, no. 2, pp. 449-492, 2015.
  • M. Balcilar, E. Bouri, R. Gupta and D. Roubaud, "Can Volume Predict Bitcoin Returns and Volatility? A Quantiles-Based Approach.," Economic Modelling, 2017.
  • A. Aggarwal, I. Gupta, N. Garg and A. Goel, "Deep Learning Approach to Determine the Impact of Socio Economic Factors on Bitcoin Price Prediction," Twelfth International Conference on Contemporary Computing (IC3), India, 2019 .
  • M. Polasik, A. I. Piotrowska, T. P. Wisniewski, R. Kotkowski and G. Lightfoot, "Price Fluctuations and the Use of Bitcoin: An Empirical Inquiry," International Journal of Electronic Commerce, vol 20, no. 1, pp. 9-49, 2015.
  • S. Karasu, A. Altan, Z. Saraç and R. Hacioğlu, "Prediction of Bitcoin Prices with Machine Learning Methods Using Time Series Data," 2018 26th Signal Processing and Communications Applications Conference (SIU), Izmir, Turkey, May 2018.
  • A. Dutta, S. Kumar and M. Basu, "A Gated Recurrent Unit Approach to Bitcoin Price Prediction," Journal of Risk and Financial Management, vol 13, no. 2, Feb 2020
  • R. Chowdhury, M. A. Rahman, M. S. Rahman and M. Mahdy, "An approach to predict and forecast the price of constituents and index of cryptocurrency using machine learning,» Physica A: Statistical Mechanics and its Applications, vol 551, April 2020.
  • J. H. Friedman, "Stochastic Gradient Boosting," Computational Statistics & Data Analysis, vol 38, pp. 367-378, 2002.
  • S. Lahmiri and S. Bekiros, "Cryptocurrency Forecasting with Deep Learning Chaotic Neural Networks," Chaos,Solitons and Fractals, vol 118, pp. 35-40, 2019.
  • F. A. Narudin, A. Feizollah, N. B. Anuar and A. Gani, "Evaluation of Machine Learning Classifiers for Mobile Malware Detection," Soft Computing, no. 20, p. 343–357, 2014.
  • F. Long, K. Zhou and W. Ou, "Sentiment Analysis of Text Based on Bidirectional LSTM With Multi-Head Attention," IEEE Access, vol 7, pp. 141960 - 141969, Sep 2019.
  • C. Zheshi, L. Chunhong and S. Wenjun, "Bitcoin Price Prediction Using Machine Learning: An Approach to Sample Dimension Engineering," Journal of Computational and Applied, vol 365, July 2019.
  • S. Corbet, A. Meegan, C. Larkin, B. Lucey and L. Yarovaya, "Exploring the Dynamic Relationships Between CryptoCurrencies and Other Financial Assets," Economics Letters, vol 165, pp. 28-34, 2018.
  • C. Yu, X. Qi, H. Ma, X. He, C. Wang and Y. Zhao, "LLR: Learning Learning Rates by LSTM for Training neural Networks," Neurocomputing, pp. 22-45, Feb 2020.
  • T. M. Breuel, "Benchmarking of LSTM Networks," Cornell University, 2015.
  • Y. Bengio, A. Courville and P. Vincent, "Representation Learning a Review and Perspectives," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol 35, no. 8, August 2013.
  • M. Nakano, A. Takahashi and S. Takahashi, "Bitcoin Technical Trading with Artificial Neural Network," Physica A, vol 510, p. 587–609, 2018.
  • B. Spilak, "Deep Neural Networks For Cryptocurrencies Price Prediction," Thesis Master in Humboldt University , Berlin, 2018.

Year 2020, , 272 - 282, 30.12.2020

I.sibel Kervancı , Fatih Akay

https://doi.org/10.35377/saucis.03.03.774276
Cited By: 7

Abstract

Bitcoin, 2009 yılında kod adı Satoshi Nakamoto olan kişi tarafından icat edilmiştir. Bitcoin merkezi olmayan ve dünyanın en çok kabul gören kripto para birimidir bu da onu yatırımcılar için oldukça cazip kılmaktadır [1]. 22 Temmuz 2020 tarihindeki verilere göre kripto para piyasası kabaca 292 milyar dolardır ve bunun % 66'sını bitcoin'in oluşturmaktadır. Bununla birlikte, bitcoin döviz kurunu tahmin etmedeki zorluk, yüksek oynaklığıdır. Bitcoin in döviz fiyatı oynaklığının ölçülmesi, tahmini ve modellenmesi önemli bir araştırma alanını oluşturmaktadır. Bu nedenle, son zamanlarda Bitcoin fiyat tahmini hakkında hem Makine Öğrenimi hem de İstatistiksel Yöntemler hakkında birçok çalışma yapılmıştır. Makine öğrenimi ve istatistiksel yöntemlerin karşılaştırılmasında, makine öğrenim yöntemleri genel olarak daha iyi performans göstermektedir. Bu çalışma, yapılmış çalışmaları gruplandırarak bitcoin fiyatının nasıl daha iyi tahmin edebileceği üzerine kapsamlı bir çalışmadır. Bitcoin fiyat tahmini çalışmalarının gruplar halinde sunumu, diğer inceleme çalışmalarından farkını ortaya koymaktadır. Bunlar istatistiksel yöntemler, makine öğrenimi-istatistiksel yöntemler, makine Öğrenmesi-makine öğrenmesi, seçilen zamanın frekansı etkisi, sosyal media ve Web Arama Motoru etkisi, nedensellik ve hiperparametrelerin optimizasyonu yöntemlerinin etkileri olarak gruplandırılarak incelenmiştir.

Keywords

Bitcoin fiyat tahmini istatistiksel methodlar makine öğrenmesi

References

  • M. Rahouti, K. Xiong and N. Ghani, "Bitcoin Concepts, Threats, and Machine-Learning Security Solutions," in IEEE Access, vol 6, pp. 67189 - 67205, 9 November 2018.
  • S. Abboushi, "Global Virtual Currency – Brief Overview.19(6), 10-118.," The Journal of Applied Business and Economics, vol 19, no. 6, 10 2017
  • O. D. Juarez and H. E. Manzanilla , "Forecasting Bitcoin Pricing with Hybrid Models A Review of The Literature," International Journal of Advanced Engineering Research and Science (IJAERS), vol 6, no. 9, pp. 161-164, Sept 2019.
  • H. Garrick and M. Rauchs, "Global cryptocurrency benchmarking study," Cambridge Centre for Alternative Finance, 2017.
  • H. Ince and T. B. Trafalis, "A Hybrid Model for Exchange Rate Prediction," Decision Support Systems, vol 42, p. 1054–1062, 2006.
  • I. N. Indera, I. M. Yassin, A. Zabidi and Z. I. Rizman, "Non-Linear Autoregressive with Exogeneous Input (NARX) Bitcoin Price Prediction Model Using PSO-Optimized Parameters Optimized Parameters," Journal of fundamental and applied sciences, vol 9, no. 3S, p. 791, 10 September 2017
  • S. Roy , S. Nanjiba and A. Chakrabarty, "Bitcoin Price Forecasting Using Time Series Analysis," ICCIT, Dhaka, Bangladesh, 2018
  • A.Zeba; F.Jinan ; S.Ahmer ; A.M. Anwer, "Bitcoin Price Prediction using ARIMA Model," Canada, 2020.
  • P. Katsiampa, "Volatility Estimation for Bitcoin: A Comparison of GARCH Models," Economics Letters, vol 158, pp. 3-6, 2017.
  • N. Mangla, A. Bhat, G. Avabratha and N. Bhat, "Bitcoin Price Prediction Using Machine Learning," International Joutnal of Information And Computing Science, vol 6, no. 5, pp. 318-320, May 2019.
  • S. Ze, W. Qing and L. J. David, "Model, Bitcoin Return Volatility Forecasting: A Comparative Study of GARCH Model and Machine Learning,"Agricultural & Applied Economics Association, Atlanta, GA, 2019.
  • M. Sean, R. Jason and C. Simon, "Predicting the Price of Bitcoin Using Machine Learning," 26th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, 2018.
  • P. Thearasak and N. Thanisa, "Machine Learning Models Comparison for Bitcoin Price Prediction," 2018 10th International Conference on Information Technology and Electrical Engineering (ICITEE), pp. 506-511, 2018.
  • T. Shintate and . L. Pichl, "Trend Prediction Classification for High Frequency Bitcoin Time Series with Deep Learning," Risk and Financial Management, 2019.
  • S. Devavrat and Z. Kang , "Bayesian regression and Bitcoin," Fifty-second Annual Allerton Conference, Monticello, IL, USA, 2014.
  • I. Madan, S. Saluja and A. Zhao,"Automated Bitcoin Trading via Machine Learning Algorithms," Google Scholar, 2014.
  • M. Martina , L. Ilaria and M. Michele , "Bitcoin Spread Prediction Using Social And Web Search Media," UMAP Workshops, 2015, 2015
  • J. C. Kaminski and M. M. Lab, "Nowcasting the Bitcoin Market with Twitter Signals," Cornell University, Jan 2016.
  • D. Garcia, C. J. Tessone, P. Mavrodiev and N. Perony, "Feedback Cycles Between Socio-Economic Signals in the Bitcoin Economy," The Journal of The Royal Society, Oct 2014.
  • D. Shen, A. Urquhart and P. Wanga, "Does Twitter Predict Bitcoin?," Economics Letters, vol 174, p. 118–122, 2019.
  • D. G. Baur, T. Dimpfl and K. Kuck, "Bitcoin, Gold and the US Dollar – A Replication and Extension," Finance Research Letters, vol 25, pp. 103-110, 2018.
  • A. H. Dyhrberg, "Bitcoin, Gold and The Dollar –A GARCH Volatility Analysis," Finance Research Letters, vol 16, pp. 85-92, 2016.
  • E. Bouri, R. Gupta, A. Lahiani and M. Shahbaz, "Testing for Asymmetric Nonlinear Short- and Long-Run Relationships Between Bitcoin, Aggregate Commodity and Gold Prices," Elsevier, vol 57, p. 224–235, 2018.
  • J. Bouoiyour and R. Selmi, "What Does Bitcoin Look Like?," Annals of Economics and Finance, vol 16, no. 2, pp. 449-492, 2015.
  • M. Balcilar, E. Bouri, R. Gupta and D. Roubaud, "Can Volume Predict Bitcoin Returns and Volatility? A Quantiles-Based Approach.," Economic Modelling, 2017.
  • A. Aggarwal, I. Gupta, N. Garg and A. Goel, "Deep Learning Approach to Determine the Impact of Socio Economic Factors on Bitcoin Price Prediction," Twelfth International Conference on Contemporary Computing (IC3), India, 2019 .
  • M. Polasik, A. I. Piotrowska, T. P. Wisniewski, R. Kotkowski and G. Lightfoot, "Price Fluctuations and the Use of Bitcoin: An Empirical Inquiry," International Journal of Electronic Commerce, vol 20, no. 1, pp. 9-49, 2015.
  • S. Karasu, A. Altan, Z. Saraç and R. Hacioğlu, "Prediction of Bitcoin Prices with Machine Learning Methods Using Time Series Data," 2018 26th Signal Processing and Communications Applications Conference (SIU), Izmir, Turkey, May 2018.
  • A. Dutta, S. Kumar and M. Basu, "A Gated Recurrent Unit Approach to Bitcoin Price Prediction," Journal of Risk and Financial Management, vol 13, no. 2, Feb 2020
  • R. Chowdhury, M. A. Rahman, M. S. Rahman and M. Mahdy, "An approach to predict and forecast the price of constituents and index of cryptocurrency using machine learning,» Physica A: Statistical Mechanics and its Applications, vol 551, April 2020.
  • J. H. Friedman, "Stochastic Gradient Boosting," Computational Statistics & Data Analysis, vol 38, pp. 367-378, 2002.
  • S. Lahmiri and S. Bekiros, "Cryptocurrency Forecasting with Deep Learning Chaotic Neural Networks," Chaos,Solitons and Fractals, vol 118, pp. 35-40, 2019.
  • F. A. Narudin, A. Feizollah, N. B. Anuar and A. Gani, "Evaluation of Machine Learning Classifiers for Mobile Malware Detection," Soft Computing, no. 20, p. 343–357, 2014.
  • F. Long, K. Zhou and W. Ou, "Sentiment Analysis of Text Based on Bidirectional LSTM With Multi-Head Attention," IEEE Access, vol 7, pp. 141960 - 141969, Sep 2019.
  • C. Zheshi, L. Chunhong and S. Wenjun, "Bitcoin Price Prediction Using Machine Learning: An Approach to Sample Dimension Engineering," Journal of Computational and Applied, vol 365, July 2019.
  • S. Corbet, A. Meegan, C. Larkin, B. Lucey and L. Yarovaya, "Exploring the Dynamic Relationships Between CryptoCurrencies and Other Financial Assets," Economics Letters, vol 165, pp. 28-34, 2018.
  • C. Yu, X. Qi, H. Ma, X. He, C. Wang and Y. Zhao, "LLR: Learning Learning Rates by LSTM for Training neural Networks," Neurocomputing, pp. 22-45, Feb 2020.
  • T. M. Breuel, "Benchmarking of LSTM Networks," Cornell University, 2015.
  • Y. Bengio, A. Courville and P. Vincent, "Representation Learning a Review and Perspectives," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol 35, no. 8, August 2013.
  • M. Nakano, A. Takahashi and S. Takahashi, "Bitcoin Technical Trading with Artificial Neural Network," Physica A, vol 510, p. 587–609, 2018.
  • B. Spilak, "Deep Neural Networks For Cryptocurrencies Price Prediction," Thesis Master in Humboldt University , Berlin, 2018.
There are 41 citations in total.

Details

Primary Language English
Subjects Artificial Intelligence
Journal Section Articles
Authors

I.sibel Kervancı 0000-0001-5547-1860

Fatih Akay 0000-0003-0780-0679

Publication Date December 30, 2020
Submission Date July 27, 2020
Acceptance Date December 2, 2020
Published in Issue Year 2020

Cite

IEEE I. Kervancı and F. Akay, “Review on Bitcoin Price Prediction Using Machine Learning and Statistical Methods”, SAUCIS, vol. 3, no. 3, pp. 272–282, 2020, doi: 10.35377/saucis.03.03.774276.

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