Forecasting Cryptocurrency Markets Through the Use of Time Series Models

• Autorzy: Kiril Desev , Stanimir Kabaivanov , Desislav Desev
• Języki publikacji: EN

Abstrakty

EN

This paper analyses the efficiency of cryptocurrency markets by applying econometric models to different short-term investment horizons. A number of experiments are carried out to demonstrate that small training sets can still be used to build efficient and useful forecasts, which in turn can be transformed into straight-forward investment strategies. It also compares the application of selected models on cryptocurrency and mature stock markets. The forecasting accuracy of the models is explored using different error metrics and different horizons. The results suggest that the variation of the error estimates doesn't appear to be tightly related to the maturity of the markets, but rather depends on the intrinsic characteristics of the analyzed time series. (original abstract)

Słowa kluczowe

EN

Cryptocurrencies; Autoregressive integrated moving average (ARIMA) models; Market effectiveness; Forecasting

PL

Kryptowaluty; Modele ARIMA; Efektywność rynku; Prognozowanie

• Czasopismo: Business and Economic Horizons
• Rocznik: 2019
• Numer: vol. 15, iss. 2
• Strony: 242--253

Twórcy

autor

Kiril Desev

• University of Plovdiv "Paisii Hilendarski", Republic of Bulgaria

autor

Stanimir Kabaivanov

• University of Plovdiv "Paisii Hilendarski", Republic of Bulgaria

autor

Desislav Desev

• Morgan Stanley & Co. International plc., London, United Kingdom

Bibliografia

• Babu, C. N., & Reddy, B. E. (2014). A moving-average filter based hybrid ARIMA-ANN model for forecasting time series data. Applied Soft Computing, 23, 27-38.
• Benartzi, S., & Thaler, R. H. (1995). Myopic loss aversion and the equity premium puzzle. The Quarterly Journal of Economics, 110(1), 73-92.
• Burbine, A., Fryer, D., & Sturtevant, J. (2015). Akaike information criterion to select well-fit resist models. Proceedings Volume 9427, Design-Process-Technology Co-optimization for Manufacturability IX. doi:https://doi.org/10.1117/12.2085770
• Busse, J. A., & Green, C. T. (2002). Market efficiency in real time. Journal of Financial Economics, 65(3), 415-437.
• Cajueiro, D. O., & Tabak, B. M. (2004). The Hurst exponent over time: testing the assertion that emerging markets are becoming more efficient. Physica A: Statistical Mechanics and its Applications, 3(4), 521-537.
• Catania, L., & Grassi, S. (2017). Modelling crypto-currencies financial time-series. Available at SSRN: https://ssrn.com/abstract=3028486
• Chu, J., Chan, S., Nadarajah, S., & Osterrieder, J. (2017). GARCH modelling of cryptocurrencies. Journal of Risk and Financial Management, 10(17), 1-15.
• Constantinou, E., Georgiades, R., Kazandjian, A., & Kouretas, G. P. (2006). Regime switching and artificial neural network forecasting of the Cyprus Stock Exchange daily returns. International Journal of Finance and Economics, 11(4), 371-383.
• de Oliveira, F. A., Nobre, C. N., & Zárate, L. E. (2013). Applying artificial neural networks to prediction of stock price and improvement of the directional prediction index - Case study of PETR4, Petrobras, Brazil. Expert Systems with Applications, 40, 7596-7606.
• Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The Journal of Finance, 25(2), 383-417.
• Fildes, R., & Makridakis, S. (1995). The impact of empirical accuracy studies on time series analysis and forecasting. International Statistical Review, 63(3), 289-308.
• Hsu, M.-W., Lessmann, S., Sung, M.-C., Ma, T., & Johnson, J. E. (2016). Bridging the divide in financial market forecasting: machine learners vs. financial economists. Expert Systems With Applications(61), 215-234.
• Kang, Y., Hyndman, R. J., & Smith-Miles, K. (2017). Visualising forecasting algorithm performance using time series instance spaces. International Journal of Forecasting, 33, 345-358.
• Kayal, P., & Maheswaran, S. (2018). Speed of price adjustment towards market efficiency: Evidence from emerging countries. Journal of Emerging Market Finance, S112-S135. doi:https://doi.org/10.1177/0972652717751542
• Khashei, M., & Bijari, M. (2011). A novel hybridization of artificial neural networks and ARIMA models for time series forecasting. Applied Soft Computing, 1, 2664-2675.
• Koehler, A. B., & Murphree, E. S. (1988). A comparison of the Akaike and Schwarz criteria for selecting model order. Applied Statistics, 37(2), 157-321.
• Kristoufek, L. (2018). On Bitcoin markets (in)efficiency and its evolution. Physica A: Statistical Mechanics and its Applications, 503, 257-262.
• Lee, C. K., Sehwan, Y., & Jongdae, J. (2007). Neural network model versus SARIMA model in forecasting Korean stock price index (KOSPI). Issues in Information System, 8(2), 372-378.
• Lee, M.-C. (2009). Using support vector machine with a hybrid feature selection method to the stock trend prediction. Expert Systems with Applications, 36, 10896-10904.
• Makridakis, S. (1993). Accuracy measures: theoretical and practical concerns. International Journal of Forecasting, 9(4), 527-529.
• Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2018). Statistical and machine learning forecasting methods: Concerns and ways forward. PLoS ONE, 13(3), e0194889.
• Milev, M., Georgieva, S., & Markovska, V. (2013). Valuation of exotic options in the framework of Levy processes. Paper presented at the 39th International conference Applications of Mathematics in Engineering and Economics, AMEE13. AIP Conference Proceedings 1570, 65.
• Patel, J., Shah, S., Thakkar, P., & Kotecha, K. (2015). Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques. Expert Systems with Applications, 42, 259-268.
• S&P Dow Jones Indices. (n.d.). Dow Jones Industrial Average. Retrieved from https://us.spindices.com
• Sanjiv, R. D., Mokashi, K., & Culkin, R. (2018). Are markets truly efficient? Experiments using deep learning algorithms for market movement prediction. Algorithms, 11(9), 138.
• Satis Group Crypto Research. (2018). Cryptoasset market coverage initiation: Network creation. Satis Group / Bloomberg. Retrieved from https://research.bloomberg.com
• Schaller, H., & Van Norden, S. (1997). Regime switching in stock market returns. Applied Financial Economics, 7(2), 177-191.
• Shen, J., & Long, W. (2016). The regime characteristics of Chinese stock market industry sectors. Procedia Computer Science, 91, 512-518.
• Shynkevich, Y., McGinnity, T. M., Coleman, S. A., Belatreche, A., & Li, Y. (2017). Forecasting price movements using technical indicators: Investigating the impact of varying input window length. Neurocomputing, 264, 71-88.
• Wang, P., Zhang, H., Qin, Z., & Zhang, G. (2017). A novel hybrid-Garch model based on ARIMA and SVM for PM2.5 concentrations forecasting. Atmospheric Pollution Research, 8(5), 850-860.
• Wang, Q., Song, X., & Li, R. (2018). A novel hybridization of nonlinear grey model and linear ARIMA residual correction for forecasting U.S. shale oil production. Energy, 165, 1320-1331.
• Xiaming, L., Haiyan, S., & Romilly, P. (1997). Are Chinese stock markets efficient? A cointegration and causality analysis. Applied Economics Letters, 4(8), 511-515.

Identyfikatory

DOI

10.15208/beh.2019.14