Abstract
Due to the inherent chaotic and fractal dynamics in the price series of Bitcoin, this paper proposes a two-stage Bitcoin price prediction model by combining the advantage of variational mode decomposition (VMD) and technical analysis. VMD eliminates the noise signals and stochastic volatility in the price data by decomposing the data into variational mode functions, while technical analysis uses statistical trends obtained from past trading activity and price changes to construct technical indicators. The support vector regression (SVR) accepts input from a hybrid of technical indicators (TI) and reconstructed variational mode functions (rVMF). The model is trained, validated, and tested in a period characterized by unprecedented economic turmoil due to the COVID-19 pandemic, allowing the evaluation of the model in the presence of the pandemic. The constructed hybrid model outperforms the single SVR model that uses only TI and rVMF as features. The ability to predict a minute intraday Bitcoin price has a huge propensity to reduce investors’ exposure to risk and provides better assurances of annualized returns.
Highlights
Bitcoin, which is considered the largest cryptocurrency with a market capitalization of about $125 billion [1], has experienced its largest-ever Bitcoin inflows and seen significant plunges in value during the COVID-19 pandemic period. is has caused an unstable intraday price leading to price uncertainties, threatening its potential to be used as currency, and regarded as a highly volatile digital currency [2]
Ese are the questions we seek to answer in this paper. e contribution of this paper is fourfold: (1) defining a new performance metric to evaluate the effectiveness of the reconstructed VMF in selecting an optimal mode (K) value called signal average absolute difference (SAAD), (2) evaluating the predictability of intraday price of Bitcoin out-ofsample in the midst of COVID-19 pandemic by using a hybrid of technical indicators (TI) and variational mode functions as features for support vector regression (SVR) prediction model, (3) evaluating and comparing the predictive performance of two features (TI and reconstructed variational mode functions (rVMF)) to the hybrid model in the midst of COVID-19, and (4) adding to scarce empirical evidence of hybrid model using SVR, TI, and rVMF in predicting oneminute intraday Bitcoin price
The theoretical concepts for the implementation of the SVR-TI-rVMF prediction model are described in detail. e methodology used for the proposed prediction model and the evaluation metrics are described
Summary
Bitcoin, which is considered the largest cryptocurrency with a market capitalization of about $125 billion [1], has experienced its largest-ever Bitcoin inflows and seen significant plunges in value during the COVID-19 pandemic period. is has caused an unstable intraday price leading to price uncertainties, threatening its potential to be used as currency, and regarded as a highly volatile digital currency [2]. Bitcoin, which is considered the largest cryptocurrency with a market capitalization of about $125 billion [1], has experienced its largest-ever Bitcoin inflows and seen significant plunges in value during the COVID-19 pandemic period. The daily average amount of Bitcoin that was sent to different Bitcoin exchange markets to be sold within March 12, 2020, to March 13, 2020, increased by nine times. Bitcoin market is an inefficient market; the market does not incorporate all available information to determine a fair price for Bitcoin [4,5,6]. Reference [4] concluded that Bitcoin returns do not satisfy the efficient market hypothesis. Using data at different frequencies, Complexity overlapping and nonoverlapping window analysis, [5] examined the dynamics of informational efficiency of Bitcoin and concluded that the Bitcoin market is an inefficient market. Reference [6] supported this claim by investigating the efficiency of the top 31 cryptocurrencies by market capitalization. is suggests that it is possible to uncover price predictability based on historical information
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