A Comparative Study of Bitcoin Price Prediction Using Deep Learning

mathematics Article A Comparative Study of Bitcoin Price Prediction Using Deep Learning Suhwan Ji, Jongmin Kim and Hyeonseung Im * Department of Computer Science, Kangwon National University, Chuncheon-si, Gangwon-do 24341, Korea; (S.J.); (J.K.) * Correspondence: ; Tel.: +82-33-250-8441 Received: 12 July 2019; Accepted: 23 September 2019; Published: 25 September 2019



 Abstract: Bitcoin has recently received a lot of attention from the media and the public due to its recent price surge and crash. Correspondingly, many researchers have investigated various factors that affect the Bitcoin price and the patterns behind its fluctuations, in particular, using various machine learning methods. In this paper, we study and compare various state-of-the-art deep learning methods such as a deep neural network (DNN), a long short-term memory (LSTM) model, a convolutional neural network, a deep residual network, and their combinations for Bitcoin price prediction. Experimental results showed that although LSTM-based prediction models slightly outperformed the other prediction models for Bitcoin price prediction (regression), DNN-based models performed the best for price ups and downs prediction (classification). In addition, a simple profitability analysis showed that classification models were more effective than regression models for algorithmic trading. Overall, the performances of the proposed deep learning-based prediction models were comparable. Keywords: bitcoin; blockchain; cryptocurrency; deep learning; predictive model; time series analysis 1. Introduction Bitcoin [1] has recently received a lot of attention from the media and the public due to its recent price surge and crash. Figure 1 shows the Bitcoin daily prices from 29 November 2011 to 31 December 2018 on Bitstamp (https://www.bitstamp.net/), which is the longest-running cryptocurrency exchange. On Bitstamp, the Bitcoin price reached the highest price (19, 187.78 USD) on 16 December 2017 and has fallen up to 3179.54 USD (16.57% of the highest price) on 15 December 2018. Then, it has again increased with some fluctuations since April 2019. Although the Bitcoin price seems to follow a random walk [2], some recurring patterns seem to exist in the price fluctuations when considering the log value of the Bitcoin price, as shown in Figure 1. As Bitcoin has been considered to be a financial asset and is traded through many cryptocurrency exchanges like a stock market, many researchers have investigated various factors that affect the Bitcoin price and the patterns behind its fluctuations using various analytical and experimental methods; for example, see the works by the authors of [3,4] and references therein. In particular, due to the recent advances in machine learning, many deep learning-based prediction models for the Bitcoin price have been proposed [5–11]. Although so far several deep learning methods were studied and compared for the Bitcoin price prediction, most previous work considered only a few deep learning methods, mostly based on a deep neural network (DNN) or a recurrent neural network (RNN) [12]. For example, a convolutional neural network (CNN) [13,14] and its variants, such as a deep residual network (ResNet) [15], have gained little attention for the Bitcoin price prediction, even though they were shown to be very effective for many applications, including long sequence data analysis [16]. Moreover, most previous work addressed only Mathematics 2019, 7, 898; doi:10.3390/math7100898 www.mdpi.com/journal/mathematics Mathematics 2019, 7, 898 2 of 20 a regression problem, where the prediction model predicts the next Bitcoin price based on the previous prices, but not a classification problem, where the prediction model predicts if the next price will go up or down with respect to the previous prices. More precisely, for a regression problem, the performance of prediction models is often measured in terms of the root-mean-square error (RMSE) or the mean absolute percentage error (MAPE) between the predicted values and the actual values, but a low RMSE or MAPE value does not necessarily mean that the prediction model is indeed effective; for instance, for Bitcoin trading, as it might not perform well for a classification problem, as shown in Section 4. In this paper, we study and compare various state-of-the-art deep learning methods, such as DNNs, long short-term memory (LSTM) models [17], CNNs, ResNets, a combination of CNNs and RNNs (CRNN) [18], and their ensemble models for Bitcoin price prediction. In particular, we developed both regression and classification models by exploiting the Bitcoin blockchain information and compared their prediction performance under various settings. Experimental results showed that although LSTM-based prediction models slightly outperformed the other prediction models for regression problems, DNN-based prediction models performed the best for classification problems. In addition, to determine the applicability of the proposed prediction models to algorithmic trading, we compared the profitability of the proposed models by using a simple trading strategy. More specifically, for regression models, if the predicted price is higher than or equal to the current price, then we buy Bitcoin with all funds or hold it if we already spent all funds. Otherwise, we sell all Bitcoin or wait if we did not buy Bitcoin yet. Similarly, for classification models, we buy or hold if the prediction model predicts a price rise and otherwise sell or wait. The analysis result showed that classification models were more effective than regression models. Overall, the performance of the deep learning-based prediction models studied in this paper was comparable to each other. Figure 1. Bitcoin daily prices on Bitstamp (USD) from 29 November 2011 to 31 December 2018. The upper line shows log prices, whereas the lower line shows plain prices. Some recurring patterns seem to exist when considering the log value of the Bitcoin price. 1.1. Related Work This section briefly reviews previous studies on Bitcoin price prediction using deep learning. For other statistical analysis, we refer the reader to the works by the authors of [3,4] and references therein. McNally et al. [5] proposed two prediction models based on recurrent neural networks (RNNs) and long short-term memory (LSTM), and compared them with an autoregressive integrated moving average (ARIMA) model [19], which is a traditionally widely used time series forecasting model. They developed classification models using the Bitcoin price information, which predict if the next Bitcoin price will go up or down based on the previous prices. In the work by the authors of [5], the RNN and LSTM models were shown to be better than the ARIMA model. In addition to the price information, Saad and Mohaisen [6] analyzed the Bitcoin blockchain information, such as the number of Bitcoin wallets and unique addresses, block mining dif (...truncated)