KRİPTO PARA FİYATLARININ KLASİK VE YAPAY SİNİR AĞI MODELLERİ İLE TAHMİNİ

Günümüzde kripto para birimlerinin önemi gittikçe artmaktadır. Kripto para birimleri sanal oyun platformlarında kullanılırken, şu an pek çok kurum ve kuruluş tarafından ödeme aracı olarak kullanılmaktadır. Güvenlik risklerine karşı blockchain (Blok Zinciri) adı verilen algoritması ile üretimi sağlanmaktadır. Kripto para fiyatlarının doğru olarak tahmin edilmesi yatırımcı ve karar vericiler açısından büyük önem taşımaktadır. Bu çalışma kapsamında en çok kullanılan dört kripto para birimine (Bitcoin, Ethereum, Ripple, Litecoin) ait fiyat değerleri tahmin edilmiştir. Çoklu kırılma testinden yararlanılarak her seriye ait kırılmalar belirlenerek analiz genişletilmiştir. Ele alınan sanal para değerlerini doğru bir şekilde tahmin etmek amacıyla hem klasik zaman serisi modellerinden hem de üç farklı tür yapay sinir ağı modelinden faydalanılmıştır. Ayrıca elde edilen tahminler üzerinde basit birleştirilme teknikleri uygulanmıştır. Rassal yürüyüşün egemen olduğu bu seriler arasından, özellikle işlem hacmi ve bilinilirliği en fazla olan Bitcoin sanal parasında rassal yürüyüş modelinden daha iyi sonuçlar elde edildiği gözlemlenmiştir.

FORECASTING THE CRYPTOCURRENCY PRICE USING THE CLASSICAL AND ARTIFICIAL NEURAL NETWORKS MODELS

Cryptocurrencies are increasing in importance. While to start with they were used only in virtual reality platforms for games, nowadays they are being used by many institutions and organisations as payment instruments. Against security risks, they are produced by an algorithm called blockchain. Predicting cryptocurrencies as accurately as possible is of great importance for investors and decision makers. In the scope of this study, the prices of the most-used four cryptocurrencies (Bitcoin, Ethereum, Ripple, Litecoin) are forecast. The analysis conducted is expanded by determining the break points of each series under investigation via the multiple break point test. For the purpose of predicting the concerned series, both classical time series models and three different artificial neural networks models are employed. In addition, the combining methods are carried out to improve the results. Among these random walk-dominated series, the best results are obtained with Bitcoin, which is the most widely known and has the highest trading volume, compared with other cryptocurrencies in the study.

PDF

___

Alessandretti, L., ElBahrawy, A., Aiello, L. M., & Baronchelli, A. (2018). Anticipating cryptocurrency prices using machine learning. Complexity, 2018.
Almeida, J., Tata, S., Moser, A., & Smit, V. (2015). Bitcoin prediciton using ANN. Neural networks, 1-12.
Andrews, D. W. (1993). Tests for parameter instability and structural change with unknown change point. Econometrica: Journal of the Econometric Society, 61(4),821-856.
Andrews, D. W., & Ploberger, W. (1994). Optimal tests when a nuisance parameter is present only under the alternative. Econometrica: Journal of the Econometric Society, 62(6), 1383-1414.
Aras, S., & Gülay, E. (2017). A new consensus between the mean and median combination methods to improve forecasting accuracy. Serbian Journal of Management, 12(2), 217-236.
Aras, S., & Kocakoç, İ. D. (2016). A new model selection strategy in time series forecasting with artificial neural networks: IHTS. Neurocomputing, 174, 974-987.
Aras, S., Deveci Kocakoç, İ., & Polat, C. (2017). Comparative study on retail sales forecasting between single and combination methods. Journal of Business Economics and Management, 18(5), 803-832.
Aras, S., Nguyen, A., White, A., & He, S. (2017). Comparing and Combining MLP and NEAT for Time Series Forecasting. İstanbul Üniversitesi İşletme Fakültesi Dergisi, 46(2), 147-160.
Armstrong, J. S. (Ed.). (2001). Principles of forecasting: a handbook for researchers and practitioners (Vol. 30). Springer Science & Business Media.
Baek, C., & Elbeck, M. (2015). Bitcoins as an investment or speculative vehicle? A first look. Applied Economics Letters, 22(1), 30-34.
Bai, J. (1997). Estimating multiple breaks one at a time. Econometric theory, 13(03), 315-352.
Bai, J., & Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 66(1), 47-78.
Bakar, N. A., & Rosbi, S. (2017). High Volatility Detection Method Using Statistical Process Control for Cryptocurrency Exchange Rate: A Case Study of Bitcoin. The International Journal of Engineering and Science, 6(11), 39-48.
Bates, J. M., & Granger, C. W. (1969). The combination of forecasts. Journal of the Operational Research Society, 20(4), 451-468.
Box, G. E. P., & Jenkins, G. (1970). Time Series Analysis, Forecasting and Control. Holden-Day, San Francisco, CA.
Boyacioglu, M. A., & Avci, D. (2010). An adaptive network-based fuzzy inference system (ANFIS) for the prediction of stock market return: the case of the Istanbul stock exchange. Expert Systems with Applications, 37(12), 7908- 7912.
Buchholz, M., Delaney, J., Warren, J., & Parker, J. (2012). Bits and Bets, Information, Price Volatility, and Demand for Bitcoin. Economics, 312, 2-48.
Catania, L., & Grassi, S. (2017). Modelling crypto-currencies financial time-series.. CEIS Working Paper, 15 Mayıs 2018 tarihinde https://ssrn.com/abstract=3084109 adresinden erişildi.
Chang, J. R., Wei, L. Y., & Cheng, C. H. (2011). A hybrid ANFIS model based on AR and volatility for TAIEX forecasting. Applied Soft Computing, 11(1), 1388-1395.
Chow, G. C. (1960). Tests of equality between sets of coefficients in two linear regressions. Econometrica: Journal of the Econometric Society, 28, 591-605.
Clemen, R. T. (1989). Combining forecasts: A review and annotated bibliography. International journal of forecasting, 5(4), 559-583.
Clements, M. P., & Hendry, D. F. (2001). Forecasting non-stationary economic time series. MIT pres.
FATF (Financial Action Task Force), (2014). Virtual Currencies: Key Definitions and Potential AML/CFT Risks. 10 Mart 2018 tarihinde www.fatfgafi.org/media/fatf/documents/reports/Virtual-currency-key-definitions-andpotential-aml-cft-risks.pdf adresinden erişildi.
Coinmarketcap (2018). 16 Nisan 2019 tarihinde www.coinmarketcap.com/currencies/bitcoin/historical-data adresinden erişildi.
Currencycalculate (2018). 16 Nisan 2019 tarihinde www.currencycalculate.com/tr/ethereum/ adresinden erişildi.
Granger, C. W. J., & Newbold, P. (2014). Forecasting economic time series. Academic Press.
Gronwald, M. (2014). The economics of Bitcoins: Market characteristics and price jumps. CESifo Working Paper Series No. 5121.
Hagan, M. T., Demuth, H. B., & Beale, M. H. (1996). Neural network design. Boston: PWS publishing company.
Harvey, A. (1997). Trends, cycles and autoregressions. The Economic Journal, 107(440), 192-201.
Hegazy, K., & Mumford, S. (2016). Comparitive Automated Bitcoin Trading Strategies. CS229 Project, 2016, 27 Nisan 2018 tarihinde www.divaportal.org/smash/get/diva2:1110776/FULLTEXT01.pdf adresinden erişildi.
Hsu, C. W., & Lin, C. J. (2002). A comparison of methods for multiclass support vector machines. IEEE transactions on Neural Networks, 13(2), 415- 425.
Hyndman, R., & Khandakar, Y. (2008). Automatic time series forecasting: The forecast package for R, Journal of Statistical Software, 27(3): 1–22.
Hyndman, R. J., Koehler, A. B., Snyder, R. D., & Grose, S. (2002). A state space framework for automatic forecasting using exponential smoothing methods, International Journal of Forecasting, 18(3): 439–454.
Hyndman, R. J., Koehler, A. B., Ord, J. K., & Snyder, R. D. (2008). Forecasting With Exponential Smoothing: The state Space Approach. Berlin: Springer.
Indera, N. I., Yassin, I. M., Zabidi, A., & Rizman, Z. I. (2017). Non-linear autoregressive with exogeneous input (NARX) Bitcoin price prediction model using PSO-optimized parameters and moving average technical indicators. Journal of Fundamental and Applied Sciences, 9(3S), 791-808.
Jang, J. S. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE transactions on systems, man, and cybernetics, 23(3), 665-685.
Jang, H., & Lee, J. (2017). An empirical study on modeling and prediction of bitcoin prices with bayesian neural networks based on blockchain information. Ieee Access, 6, 5427-5437.
Jose, V.R.R., & Winkler, R.L. (2008). Simple robust averages of forecasts: some empirical results. International Journal of Forecasting, 24, 163–169.
Kaastra, I., & Boyd, M. (1996). Designing a neural network for forecasting financial and economic time series. Neurocomputing, 10(3), 215-236.
Karakoyun, E. S., & Cibikdiken, A. O. (2018, May). Comparison of ARIMA Time Series Model and LSTM Deep Learning Algorithm for Bitcoin Price Forecasting. In The 13th Multidisciplinary Academic Conference in Prague 2018 (The 13th MAC 2018) (pp. 171-180).
Kim, K. J. (2003). Financial time series forecasting using support vector machines. Neurocomputing, 55(1-2), 307-319.
Kriptoparapiyasası (2018). 16 Nisan 2018 tarihinde www.kriptoparapiyasasi.com adresinden erişildi.
Larreche, J.C., & Moinpour, R. (1983). Managerial judgment in marketing: the concept of expertise. Journal of Marketing Research, 20, 110-121.
Liu, J., Wu, S., & Zidek, J. V. (1997). On segmented multivariate regression. Statistica Sinica, 7, 497-525.
Makridakis, S., Andersen, A., Carbone, R., Fildes, R., Hibon, M., Lewandowski, R., ... & Winkler, R. (1982). The accuracy of extrapolation (time series) methods: Results of a forecasting competition. Journal of forecasting, 1(2), 111-153.
Mercer, B. A. (1909). XVI. Functions of positive and negative type, and their connection the theory of integral equations. Phil. Trans. R. Soc. Lond. A, 209(441-458), 415-446.
Montgomery, D. C., Jennings, C. L., & Kulahci, M. (2015). Introduction to time series analysis and forecasting. John Wiley & Sons.
Munim, Z. H., Shakil, M. H., & Alon, I. (2019). Next-Day Bitcoin Price Forecast. Journal of Risk and Financial Management, 12(2), 103.
Müller, K. R., Smola, A. J., Rätsch, G., Schölkopf, B., Kohlmorgen, J., & Vapnik, V. (1997, October). Predicting time series with support vector machines. In International Conference on Artificial Neural Networks (pp. 999-1004). Springer, Berlin, Heidelberg.
Nakamoto, S. (2008). Bitcoin: A Peer-to-Peer Electronic Cash System. The Cryptography Mailing List. 10.03.2018 tarihinde www.bitcoin.org/bitcoin.pdf adresinden erişildi.
Newbold, P., & Granger, C. W. (1974). Experience with forecasting univariate time series and the combination of forecasts. Journal of the Royal Statistical Society. Series A (General), 131-165.
Ord, J. K., Koehler, A. B., & Snyder, R. D. (1997). Estimation and prediction for a class of dynamic nonlinear statistical models, Journal of the American Statistical Association, 92(440): 1621–1629.
Palm, F. C., & Zellner, A. (1992). To combine or not to combine? Issues of combining forecasts. Journal of Forecasting, 11(8), 687-701.
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors, Nature, 323, 533–536.
Stock, J. H., & Watson, M. W. (1999). Cointegration, causality and forecasting, in R. F. Engle, H. White (Eds.). A comparison of linear and nonlinear models for forecasting macroeconomic time series. Oxford: Oxford University Press, 1–44.
Stock, J. H., & Watson, M. W. (2004). Combination forecasts of output growth in a seven‐country data set. Journal of Forecasting, 23(6), 405-430.
Suykens, J. A., & Vandewalle, J. (1999). Least squares support vector machine classifiers. Neural processing letters, 9(3), 293-300.
Tay, F. E., & Cao, L. (2001). Application of support vector machines in financial time series forecasting. Omega, 29(4), 309-317.
Timmermann, A. (2006). Forecast combinations, in: G.Elliott, C.Granger, A. Timmermann (Eds.), pp. 135–196, Handbook of Economic Forecasting, Elsevier.
Valencia, F., Gómez-Espinosa, A., & Valdés-Aguirre, B. (2019). Price Movement Prediction of Cryptocurrencies Using Sentiment Analysis and Machine Learning. Entropy, 21(6), 589.
Vapnik, V. N. (1995). The Nature of Statistical Learning Theory, Springer Verlag, Berlin.
Walczak, S. (2001). An empirical analysis of data requirements for financial forecasting with neural networks. Journal of management information systems, 17(4), 203-222.
Wallis, K. F. (2011). Combining forecasts–forty years later. Applied Financial Economics, 21(1-2), 33-41.
Wang, H., & Hu, D. (2005, October). Comparison of SVM and LS-SVM for regression. In Neural Networks and Brain, 2005. ICNN&B'05. International Conference on (Vol. 1, pp. 279-283). IEEE.
Wilcox, R. R. (2012). Introduction to robust estimation and hypothesis testing. Academic Press.
Yao, Y. C. (1988). Estimating the number of change-points via Schwarz' criterion. Statistics & Probability Letters, 6(3), 181-189.
Yazdani-Chamzini, A., Yakhchali, S. H., Volungevičienė, D., & Zavadskas, E. K. (2012). Forecasting gold price changes by using adaptive network fuzzy inference system. Journal of Business Economics and Management, 13(5), 994-1010.
Yelowitz, A., & Wilson, M. (2015). Characteristics of Bitcoin users: an analysis of Google search data. Applied Economics Letters, 22(13), 1030-1036.
Yermack, D. (2015). Is Bitcoin a real currency? An economic appraisal. In Handbook of digital currency (pp. 31-43).
Zeileis, A., Kleiber, C., Krämer, W., & Hornik, K. (2003). Testing and dating of structural changes in practice. Computational Statistics & Data Analysis, 44(1-2), 109-123.
Zhang, G. P. (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159-175.
Zhang, G.P., Patuwo, B.E., & Hu, M.Y. (1998). Forecasting with Artificial Neural Networks: The State of The Art. International Journal of Forecasting, 14, 35-62.
Zhang, G. P., Patuwo, B. E., & Hu, M. Y. (2001). A simulation study of artificial neural networks for nonlinear time-series forecasting. Computers & Operations Research, 28(4), 381-396.
Zou, H., & Yang, Y. (2004). Combining time series models for forecasting. International journal of Forecasting, 20(1), 69-84.