Full length articleA complete empirical ensemble mode decomposition and support vector machine-based approach to predict Bitcoin prices
Introduction
Blockchain technology and innovation in financial systems have led to the birth of an alternative mode of currency called cryptocurrencies. Cryptocurrencies have emerged as a digital currency, which offer a decentralized and transparent financial system, as opposed to the existing centralized traditional financial system. They provide an easy, fast, and seamless peer-to-peer transfer (Corbet et al., 2018a). One of the most popular cryptocurrencies is Bitcoin (Briere et al., 2015), which has emerged as a form of alternative investment for portfolio diversification as well. The frequent high fluctuations in Bitcoin prices have made it an extremely volatile investment. Price movements within an interval of 8000% have been observed in Bitcoin (Ciaian et al., 2016) and scholars have started emphasizing the need to examine its price behavior (Dyhrberg, 2016, Katsiampa, 2017).
Examining price fluctuations in traditional asset classes like equities, bonds, and commodities such as gold have garnered huge attention from scholars in the past (Balcilar et al., 2017). With the increasing popularity and adoption of Bitcoin as an asset class, it becomes imperative to understand the price fluctuations in Bitcoin. The mechanisms behind Bitcoin price fluctuations are very complicated and have been found to influence significant herding and contagion effect on other cryptocurrencies (da Gama Silva et al., 2019, Stavroyiannis and Babalos, 2019). Despite receiving tremendous research attention, Bitcoins are still surrounded by a lot of skepticism, as they are not backed by any state neither do their fundamental value reside on any underlying asset (Bariviera et al., 2017). Moreover, the price of Bitcoin today has increased by 5000% since its initial price in 2011 (Urquhart, 2016). Exploring the statistical properties of Bitcoin prices within the gamut of market efficiency (Fama, 1970), which is also known as the efficient market hypothesis (EMH), has become a debatable issue among scholars.
Bitcoin has become very speculative and sensitive to any fake or even real news dealing with economic or political events (Ramirez et al., 2018). Scholarly works in the field of finance, economics, and machine learning are shedding light on understanding price behavior of Bitcoins. However, compared to the traditional asset classes, the understanding of the potential of Bitcoins as an asset class has been relatively less explored. Hence, it becomes imperative to explore the nature of Bitcoin price fluctuations for reducing the downside investment risk in Bitcoins along with developing a better understanding of its asset returns potential. This study is a contribution in this direction and holds implications to develop trading strategies for Bitcoins.
Market efficiency implies that all the available information is fully reflected in the market prices, and hence, markets are subjected to a random walk model. While EMH makes room for short-term memory persistence in the market, it rejects the presence of long-term memory persistence in the financial time series of tradable assets. If there is long-term memory, it will imply that traders can develop a profitable trading strategy, as markets do not follow a random walk model (Briere et al., 2015). By exploring the linear and nonlinear stochastic movements in Bitcoins, scholars have attempted to understand the irregularity in Bitcoin prices, which can lead to better prediction of its price movement (Ramirez et al., 2018).
While few studies have shown that Bitcoins are subjected to market inefficiency as well as gradually moving toward becoming efficient (Kristoufek, 2018, Zargar and Kumar, 2019), none of these studies have examined the irregular movements in Bitcoin price series as a multiscale problem. A multiscale situation arises when a time series consists of components, which are at disparate scales and require analysis from different levels of resolution (e.g., hourly, weekly). The dynamics of the series at these resolutions also vary, thereby affecting the inferences of the underlying process. Hence, multiscale series warrants decomposition and aggregation of the original series at different time resolution. This study intends to investigate the multiscale nature of the Bitcoin price series. The exploration of multiscale movement features of Bitcoin price series can also lead to better prediction of Bitcoin prices.
The application of computationally intensive techniques using machine learning algorithms is extensively utilized to develop profitable stock trading strategies. Multiple studies have been conducted to examine whether machine learning tools can be leveraged to anticipate stock predictions in a consistent manner (Nardo et al., 2016, Patel et al., 2015). Studies have also explored machine learning algorithms to predict Bitcoin prices (Ciaian et al., 2016, McNally et al., 2018). However, there are limited studies on the multiscale property of Bitcoin prices to assess the prediction of the price series (Pichl and Kaizoji, 2017). Being a highly volatile currency and subject to lot of ambiguity, it is an interesting area of research to explore whether machine learning algorithms can be used to effectively predict Bitcoin prices.
The objective of this study is twofold. Firstly, it examines the nature of Bitcoin price series and assesses their multiscale properties. The decomposition of Bitcoin price series by using an algorithm is effective, if it enables the identification of different sub-series belonging to different scales. While the variants of auto-regressive conditional heteroscedasticity (ARCH) models and wavelet decomposition are widely used to identify multiscale properties of volatile series, scholars have subjected them to many shortcomings. Moreover, the ARCH models are highly model-specific and the decomposition scales are not adjustable in wavelet analysis (Wen et al., 2017). The empirical mode decomposition (EMD) techniques generate a more adaptable decomposition for nonstationary series (Huang et al., 1998). Hence, this study proposes to assess the multiscale properties of Bitcoin price series using EMD (Lahmiri, 2016a, Lahmiri, 2016b) and extended versions of EMD algorithms (Torres et al., 2011) like empirical ensemble mode decomposition (EEMD) and complete empirical ensemble mode decomposition (CEEMD).
Secondly, it uses support vector machine (SVM) learning algorithm to predict the Bitcoin prices. The widespread application of SVM learning, such as addressing real-life engineering issues (Ahmadi and Mahmoudi, 2016, Ahmadi, 2016, Ahmadi and Chen, 2019) substantiates its ability to analyze complex time-series data. Comparing the myriad financial time series prediction methods, scholars have shown SVM based regression models as a promising alternative because it gives more optimal results (Lahmiri, 2013, Peng et al., 2018).
Section snippets
Background
With over 90% of the investor being actively involved in cryptocurrency trading (Hasso et al., 2019), cryptocurrencies have received phenomenal attention and growth during the last few years. Being the most popular cryptocurrency, different studies have explored the nature of Bitcoin price series from multiple perspectives. One set of studies have investigated the market efficiency of Bitcoin prices and another set of studies have tried to examine the relation of Bitcoin prices with other asset
Methodology
The volatile price series like gold price and crude oil prices are too complicated to process because of its nonlinear and nonstationary characteristics. Bitcoin prices like other asset classes are characterized by non-linear dynamics, which include non-periodicity, randomness, and presence of spectrum of scaling components in the series (El Alaoui et al., 2019). The complexity of the dynamics of such series are best analyzed using the non-linear models. Research has shown improved forecasting
Data analysis
For the study, daily Bitcoin price data from to is sourced from https://in.investing.com. Fig. 1 shows the Bitcoin price series and the log of Bitcoin prices’ graph. From the graph, it can be seen that though the Bitcoin prices have shown an upward trend, the series has been highly volatile and nonlinear in nature.
From the Bitcoin price and logarithm price graph, it can be observed that the fluctuations in prices are rapid and varying in level. Hence, such price series are
Results and discussion
To better understand the forecasting performance, the CEEMDAN-SVM is compared with EMD-SVM. Table 3, Table 4 give the MSE and MAPE of CEEMDAN series and EMD series.
In light of the fact that MAPE is a more stable error metric than MSE, the prediction error of CEEMDAN is less than the prediction error of EMD for MAPE. This is because the CEEMDAN extracts more effective features from the original data. Relatively, EMD-SVM is better in the case of short-term forecasting with regard to MSE measure
Conclusion
Cryptocurrencies have created disruption in the financial markets and pose huge challenges for monetary policy makers. This has also raised questions related to understanding the qualities of Bitcoin as an asset. Being a highly volatile currency and a speculative asset, it is imperative to understand the price behavior of Bitcoins. The study applies the CEEMDAN analysis to examine the multiscale nature of Bitcoin prices. The application of CEEMDAN sheds light on the distinct nature and price
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Divya Aggarwal has done her Ph.D. in the area of behavioral finance from XLRI - Xavier School of Management, Jamshedpur, India. Her research interests include financial decision making and behavioral biases. She has worked with Mckinsey Knowledge Centre, KPMG India and Avendus capital before starting an academic career. She is a recipient of UK Case Centre scholarship and has won academic awards of national and international repute. She won the best paper award at 11th ISDSI Conference by
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Divya Aggarwal has done her Ph.D. in the area of behavioral finance from XLRI - Xavier School of Management, Jamshedpur, India. Her research interests include financial decision making and behavioral biases. She has worked with Mckinsey Knowledge Centre, KPMG India and Avendus capital before starting an academic career. She is a recipient of UK Case Centre scholarship and has won academic awards of national and international repute. She won the best paper award at 11th ISDSI Conference by Decision Science Institute. In 2014, she won the Global Peter Drucker essay challenge under the student’s category and was invited to Vienna, Austria.
Shabana Chandrasekaran is an Assistant Professor of Decision Sciences area at Xavier Institute of Management, Bhubaneshwar, India. She received her Ph.D. in Information Systems from XLRI - Xavier School of Management, Jamshedpur, India, and was awarded the XLRI medal for outstanding student of the year. She facilitates courses on Business Analytics & Data Mining, Business Statistics, and Research Methods. Her research interests include machine learning, text mining, and natural language processing and social media analytics. Her research work has been published in refereed journals and international conferences.
Balamurugan Annamalai is an Assistant Professor in the area of Marketing at Indian Institute of Management (IIM), Sambalpur, India. He holds a doctorate (FPM) in marketing from XLRI - Xavier School of Management, Jamshedpur, India. His research interest is in the area of customer engagement on social media. He is an engineering graduate in computer science from IIT (BHU), Varanasi, and a management postgraduate from IIM Calcutta. He has consulting and industry experience in brand management and marketing research.