Vol. 8 No. 1 (2020): Business & Management Studies: An International Journal
Articles

MODELING BITCOIN PRICES WITH K-STAR ALGORITHM

PDF (Turkish)
  • Cem KARTAL
Cem KARTAL
Asisst. Prof. Dr., Zonguldak Bülent Ecevit University

Published 2020-03-25

Keywords

  • Cryptocurrency, Lazy Learning, K-Star Algorithm, Classification
  • Kripto Para, Lazy Learning, K-Star Algoritması, Sınıflandırma

How to Cite

MODELING BITCOIN PRICES WITH K-STAR ALGORITHM. (2020). Business & Management Studies: An International Journal, 8(1), 213-231. https://doi.org/10.15295/bmij.v8i1.1380

How to Cite

MODELING BITCOIN PRICES WITH K-STAR ALGORITHM. (2020). Business & Management Studies: An International Journal, 8(1), 213-231. https://doi.org/10.15295/bmij.v8i1.1380

Abstract

Bitcoin is the most popular and widely used digital currency. Therefore, the prediction of the Bitcoin price movement is of great importance for the financial markets. In addition to econometric models, data mining methods are used in Bitcoin price estimation. With the help of the tools and methods used in data mining, the data is modeled and converted into information to be utilized. K-Star algorithm is an example based approach which is used in many fields such as data mining, object identification and control systems. In this study, the effect levels of Macroeconomic variables on Bitcoin prices are analyzed by using K-Star Algorithm based on Lazy Learning which is one of the Machine Learning Method. The data set of the study includes 510 observational values ​​of dependent and independent variables between 3 January 2017 - 30 January 2019. 474 (93%) of these observations are used for modeling (training) and 36 (7%) are used for classification (test). The success rate of the model is 61,1% on whether Bitcoin prices will “increase” or “decrease” in the next period, while the correct classification success rate on an increase rate is 71,42% and the correct classification success rate on a decrease rate is 46,66% on Bitcoin prices. As a result, it is found that Machine Learning Technique shows a certain performance but the predictability of Bitcoin prices is still below the expectations.

PDF (Turkish)

References

  1. Adetiloye, T., ve Awasthi, A. (2017). “Predicting Short-Term Congested Traffic Flow on Urban Motorway Networks”, In Handbook of Neural Computation, Academic Press, 145-165.
  2. Atik, M., Köse, Y., Yılmaz, B. ve Sağlam, F. (2015). “Kripto para: Bitcoin ve Döviz Kurları Üzerine Etkileri”, Bartın Üniversitesi İİBF Dergisi, 6(11), 247-261.
  3. Baek, C. ve Elbeck, M. (2015). “Bitcoin as An Investment or Speculative Vehicle? A First Look”, Applied Economics Letters 22, No. 1, 30–34.
  4. Basarır, C., ve Bayramoğlu, M. F. (2018). “Global Macroeconomic Determinants of the Domestic Commodity Derivatives”. In Global Approaches in Financial Economics, Banking and Finance, Springer, Cham, 331-349.
  5. Bayramoğlu, A. T. ve Başarır, Ç. (2019). “The Linkage Between Cryptocurrencies and Macro-Financial Parameters: A Data Mining Approach. In Blockchain Economics and Financial Market Innovation”, Springer, Cham 249-269.
  6. Birattari, M., Bontempi, G. ve Bersini, H. (1999). “Lazy Learning Meets The Recursive Least Squares Algorithm. in Advances in Neural Information”, Processing Systems, 375-381.
  7. Bontempi, G., Birattari, M. ve Bersini, H. (1999). “Lazy Learning For Local Modelling and Control Design”, International Journal of Control, 72(7-8), 643-658.
  8. Bozic, N., Guy, P. ve Stefano, S. (2016). “A Tutorial on Blockchain and Applications to Secure Network Control-Planes’’, 3rd Smart Cloud Networks and Systems (SCNS), 1-8.
  9. Briere, M., Oosterlinck, K. ve Szafarz, A. (2015). “Virtual Currency, Tangible Return: Portfolio Diversification with Bitcoin”, Journal of Asset Management, 16(6), 365-373.
  10. Ceyhan, K., Kurtulmaz, E., Sert, O. C. ve Özyer, T. (2018). “Bitcoin Movement Prediction With Text Mining”. 2018 26th Signal Processing and Communications Applications Conference (SIU), IEEE, 1-4.
  11. Cheah, E. T., ve Fry, J. (2015). “Speculative Bubbles in Bitcoin Markets? An Empirical Investigation into the Fundamental Value of Bitcoin”, Economics Letters, 32-36.
  12. Cheung, A., Eduardo R. ve Jen-Je S. (2015). "Crypto-Currency Bubbles: An Application of The Phillips–Shi–Yu (2013) Methodology On Mt. Gox Bitcoin Prices", Applied Economics, Cilt 47, Sayı 23, s. 2348-2358.
  13. Chu, J., Nadarajah, S. ve Chan, S. (2015). “Statistical Analysis of The Exchange Rate of Bitcoin. PLoS One”, 10(7), 1–27.
  14. Cleary, J. G., ve Trigg, L. E. (1995). “K*: An Instance-Based Learner Using An Entropic Distance Measure”, In Machine Learning Proceedings, 108-114.
  15. Coşkun, C. ve Baykal, A. (2011). “Veri Madenciliğinde Sınıflandırma Algoritmalarının Bir Örnek Üzerinde Karşılaştırılması”, Akademik Bilişim, 1-8.
  16. Crosby, M., Nachiappan, P., Sanjeev V. ve Vignesh K. (2016). “Blockchain Technology: Beyond Bitcoin,”, Applied Innovation Reiew, Sayı 2, 6-19.
  17. Ferdiansyah, F. (2019). “A Study of Bitcoin Stock Market Prediction: Methods”, Techniques and Tools, 1-5.
  18. Greaves, A. ve Au, B. (2015). “Using The Bitcoin Transaction Graph to Predict The Price of Bitcoin”, No Data, Computer Science, 1-8.
  19. Hormozi, H., Hormozi, E., ve Nohooji, H. R. (2012). “The Classification of The Applicable Machine Learning Methods in Robot Manipulators”. International Journal of Machine Learning and Computing, 2(5), 560–563.
  20. Investing, 2019a, https://tr.investing.com/crypto/currencies, Erişim tarihi: 18.12.2019.
  21. Investing, 2019b, https://tr.investing.com/crypto/Bitcoin/historical-data, Erişim tarihi: 10.12.2019.
  22. İçellioğlu, C. Ş., ve Öztürk, M. B. E. (2018). “Bitcoin ile Seçili Döviz Kurları Arasındaki İlişkinin Araştırılması: 2013-2017 Dönemi İçin Johansen Testi ve Granger Nedensellik Testi”, Maliye ve Finans Yazıları, 1(109), 51-70.
  23. Jang H. ve Lee J. (2018). “An Empirical Study On Modeling and Prediction of Bitcoin Prices with Bayesian Neural Networks Based on Blockchain”, Information IEEE Access, 6, 5427-5437.
  24. Joshi, A. P., Han, M., ve Wang, Y. (2018). “A Survey on Security And Privacy Issues of Blockchain Technology”, Mathematical Foundations of Computing, 1(2), 121-147.
  25. Kaplanov, N. M. (2012). “Nerdy Money: Bitcoin, The Private Digital Currency, and The Case Against Its Regulation”, Temple Law Review, 111-157.
  26. Karasu, S., Altan, A., Saraç, Z., ve Hacioğlu, R. (2018). “Prediction of Bitcoin Prices with Machine Learning Methods Using Time Series Data. 26”, Signal Processing and Communications Applications Conference (SIU) IEEE, 1-4.
  27. Kartal, C. ve Bayramoğlu M.F. (2019). “Blockchain Economics and Financial Market Innovation: Forecasting the Prices of Cryptocurrencies Using GM(1,1) Rolling Model”, Springer, 2019, 201-230.
  28. Khan, M. A. ve Salah, K. (2018). “Iot Security: Review, Blockchain Solutions, and Open Challenges”, Future Generation Computer Systems, 82, 1-32.
  29. Mallqui, D. C., ve Fernandes, R. A. (2019). “Predicting The Direction, Maximum, Minimum and Closing Prices Of Daily Bitcoin Exchange Rate Using Machine Learning Techniques”, Applied Soft Computing, 75, 596-606.
  30. Painuli, S., Elangovan, M. ve Sugumaran, V. (2014). “Tool Condition Monitoring Using K-Star Algorithm”, Expert Systems with Applications, 41(6), 2638-2643.
  31. Radityo, A., Munajat Q. ve Budi, I (2017). “Prediction of Bitcoin Exchange Rate to American Dollar Using Artificial Neural Network Methods”, Advanced Computer Science and Information Systems (ICACSIS), International Conference, 433–438.
  32. Swan, M. (2015). Blockchain: Blueprint for a New Economy. O'Reilly Media, Inc.
  33. Şahin, E. E. (2018). “Kripto Para Bitcoin: ARIMA ve Yapay Sinir Ağları ile Fiyat Tahmini”, Fiscaoeconomia, 2(2), 74-92.
  34. Tschorsch, F. ve Björn, S. (2016); “Bitcoin and Beyond: A Technical Survey on Decentralized Digital Currencies”, IEEE Communications Surveys & Tutorials’’, Cilt 18, sayı 3, 2084-2123.
  35. Underwood, S. (2016).” Blockchain Beyond Bitcoin”, Communications of The ACM, 59(11), 15-17.
  36. Usta, A. ve Doğantekin, S. (2017). Blokchain 101, MediaCat Kitapları. İstanbul.
  37. Viera, A. J. ve Garrett, J. M. (2005). “Understanding Interobserver Agreement: The Kappa Statistic”. Fam med, 37(5), 360-363.
  38. Vijayarani, S. ve Muthulakshmi, M. (2013). “Comparative Analysis of Bayes And Lazy Classification Algorithms”, International Journal of Advanced Research in Computer and Communication Engineering, 2(8), 3118–3124.
  39. Zheng, Z., Xie, S., Dai, H., Chen, X., ve Wang, H. (2017). “An Overview of Blockchain Technology: Architecture, Consensus, and Future Trends”, In Big Data (BigData Congress), IEEE Internationa

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