Forecasting Cryptocurrency Prices Using ARIMA and Neural Network: A Comparative Study


  • Saurabh Kumar Indian Institute of Management Indore



ARIMA, Neural Network, Forecasting, Cryptocurrency, Market Efficiency


The prices of cryptocurrencies are very volatile and forecasting them is a challenging task for the researchers across the world. The present study examines the accuracy of forecasted returns of the two most popular cryptocurrencies (Bitcoin and Ethereum) for the sample period spanning from October 1, 2013, to November 30, 2018. Auto-regressive integrated moving average (ARIMA) and Neural Network models have been used to forecast the returns of the cryptocurrencies. The forecasting results for different time-horizons indicate that for a shorter time-horizon, ARIMA model is better for forecasting the returns of cryptocurrencies, whereas, for a longer time-horizon, Neural Network model is better for forecasting the returns of cryptocurrencies. These results have implications for traders, investors, regulators, policymakers and academia.


Atiya, A. F. (2001). Bankruptcy prediction for credit risk using neural networks: A survey and new results. IEEE Transactions on Neural Networks, 12(4), 929–935.

Atiya, A. F. 2001. ‘Bankruptcy Prediction for Credit Risk Using Neural Networks: A Survey and New Results’. IEEE Transactions on Neural Networks 12 (4): 929–35.

Balcilar, Mehmet, Elie Bouri, Rangan Gupta, and David Roubaud. 2017. ‘Can Volume Predict Bitcoin Returns and Volatility? A Quantiles-Based Approach’. Economic Modelling 64 (August): 74–81.

Bariviera, Aurelio F., María José Basgall, Waldo Hasperué, and Marcelo Naiouf. 2017. ‘Some Stylized Facts of the Bitcoin Market’. Physica A: Statistical Mechanics and Its Applications 484 (October): 82–90.

Bildirici, Melike, Elçin A. Alp, and Özgür Ö. Ersin. 2010. ‘TAR-Cointegration Neural Network Model: An Empirical Analysis of Exchange Rates and Stock Returns’. Expert Systems with Applications 37 (1): 2–11.

Bildirici, Melike, and Özgür Ömer Ersin. 2009. ‘Improving Forecasts of GARCH Family Models with the Artificial Neural Networks: An Application to the Daily Returns in Istanbul Stock Exchange’. Expert Systems with Applications 36 (4): 7355–62.

Box, George EP, and Gwilym M. Jenkins. 1976. ‘Time Series Analysis, Control, and Forecasting’. Holden-Day Inc., San Francisco, CA.

Chai, Tianfeng, and Roland R. Draxler. "Root mean square error (RMSE) or mean absolute error (MAE)?–Arguments against avoiding RMSE in the literature." Geoscientific model development 7, no. 3 (2014): 1247-1250.

Chen, An-Sing, Mark T. Leung, and Hazem Daouk. 2003. ‘Application of Neural Networks to an Emerging Financial Market: Forecasting and Trading the Taiwan Stock Index’. Computers & Operations Research, Operation Research in Emerging Economics, 30 (6): 901–23.

Dickey, David A., and Wayne A. Fuller. 1981. ‘Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root’. Econometrica: Journal of the Econometric Society 49 (4): 1057–1072.

Dunis, Christian L., Jason Laws, and Ulrike Schilling. 2012. ‘Currency Trading in Volatile Markets: Did Neural Networks Outperform for the EUR/USD during the Financial Crisis 2007–2009?’ Journal of Derivatives & Hedge Funds 18 (1): 2–41.

Fama, Eugene F. 1970. ‘Efficient Capital Markets: A Review of Theory and Empirical Work*’. The Journal of Finance 25 (2): 383–417.

Feng, Wenjun, Yiming Wang, and Zhengjun Zhang. 2018. ‘Informed Trading in the Bitcoin Market’. Finance Research Letters 26 (September): 63–70.

Fry, John, and Eng-Tuck Cheah. 2016. ‘Negative Bubbles and Shocks in Cryptocurrency Markets’. International Review of Financial Analysis 47 (October): 343–52.

Grudnitski, Gary, and Larry Osburn. 1993. ‘Forecasting S&P and Gold Futures Prices: An Application of Neural Networks’. Journal of Futures Markets 13 (6): 631–43.

Hassani, Hossein, Emmanuel Sirimal Silva, Rangan Gupta, and Mawuli K. Segnon. 2015. ‘Forecasting the Price of Gold’. Applied Economics 47 (39): 4141–52.

Hotz‐Behofsits, Christian, Florian Huber, and Thomas Otto Zörner. 2018. ‘Predicting Crypto-Currencies Using Sparse Non-Gaussian State Space Models’. Journal of Forecasting 37 (6): 627–40.

Huang, Zan, Hsinchun Chen, Chia-Jung Hsu, Wun-Hwa Chen, and Soushan Wu. 2004. ‘Credit Rating Analysis with Support Vector Machines and Neural Networks: A Market Comparative Study’. Decision Support Systems, Data mining for financial decision making, 37 (4): 543–58.

Inani, Sarveshwar Kumar, Manas Tripathi, and Saurabh Kumar. 2016. ‘Does Artificial Neural Network Forecast Better for Excessively Volatile Currency Pairs?’ Journal of Prediction Markets 10 (2).

Jiang, Yonghong, He Nie, and Weihua Ruan. 2018. ‘Time-Varying Long-Term Memory in Bitcoin Market’. Finance Research Letters 25 (June): 280–84.

Kapar, Burcu, and Jose Olmo. 2019. ‘An Analysis of Price Discovery between Bitcoin Futures and Spot Markets’. Economics Letters 174 (January): 62–64.

Khuntia, Sashikanta, and J. K. Pattanayak. 2018. ‘Adaptive Market Hypothesis and Evolving Predictability of Bitcoin’. Economics Letters 167 (June): 26–28.

Kristoufek, Ladislav. 2018. ‘On Bitcoin Markets (in)Efficiency and Its Evolution’. Physica A: Statistical Mechanics and Its Applications 503 (August): 257–62.

Kumar, P. Ravi, and V. Ravi. 2007. ‘Bankruptcy Prediction in Banks and Firms via Statistical and Intelligent Techniques – A Review’. European Journal of Operational Research 180 (1): 1–28.

Lahmiri, Salim, and Stelios Bekiros. 2019. ‘Cryptocurrency Forecasting with Deep Learning Chaotic Neural Networks’. Chaos, Solitons & Fractals 118 (January): 35–40.

Li, Xin, and Chong Alex Wang. 2017. ‘The Technology and Economic Determinants of Cryptocurrency Exchange Rates: The Case of Bitcoin’. Decision Support Systems 95 (March): 49–60.

Nadarajah, Saralees, and Jeffrey Chu. 2017. ‘On the Inefficiency of Bitcoin’. Economics Letters 150 (January): 6–9.

Nakamoto, Satoshi. 2008. ‘Bitcoin: A Peer-to-Peer Electronic Cash System’. Working Paper.

Özkan, Filiz. 2013. ‘Comparing the Forecasting Performance of Neural Network and Purchasing Power Parity: The Case of Turkey’. Economic Modelling 31 (March): 752–58.

Tkáč, Michal, and Robert Verner. 2016. ‘Artificial Neural Networks in Business: Two Decades of Research’. Applied Soft Computing 38 (January): 788–804.

Urquhart, Andrew. 2016. ‘The Inefficiency of Bitcoin’. Economics Letters 148 (November): 80–82.

Wei, Wang Chun. 2018. ‘Liquidity and Market Efficiency in Cryptocurrencies’. Economics Letters 168 (July): 21–24.

Wilson, Rick L., and Ramesh Sharda. 1994. ‘Bankruptcy Prediction Using Neural Networks’. Decision Support Systems 11 (5): 545–57.

Zhang, G. Peter, and Min Qi. 2005. ‘Neural Network Forecasting for Seasonal and Trend Time Series’. European Journal of Operational Research, Decision Support Systems in the Internet Age, 160 (2): 501–14.

Zhu, Xiaotian, Hong Wang, Li Xu, and Huaizu Li. 2008. ‘Predicting Stock Index Increments by Neural Networks: The Role of Trading Volume under Different Horizons’. Expert Systems with Applications 34 (4): 3043–54.