Predicting COVID-19 impact on demand and supply of cryptocurrency using machine learning

Year 2021, , 52 - 66, 30.09.2021

David Oyewola , Emmanuel Dada , Juliana Ndunagu , Daniel Eneojo Emmanuel

https://doi.org/10.18100/ijamec.958160

Cited By: 1

Abstract

In the wake of recent pandemic of COVID-19, we explore its unprecedented impact on the demand and supply of cryptocurrencies’market using machine learning such as Naïve Bayes (NB), Decision Trees (C5), Decision Trees Bagging (BG), Support Vector Machine (SVM), Random Forest (RF), Multinomial Logistic Regression (MLR), Recurrent Neural Network (RNN), Long Short Term Memory and Noise Bagging (NBG). The study employed Noise filters to enhance the performance of Decision Trees Bagging named NBG. Dataset utilized for this analysis were obtained from the website of Coin Market Cap, including: Binance Coin (BCN), BitCoin Cash (BCH), BitCoin (BTC), BitCoinSV (BSV), Cardano (CDO), Chainlink (CLK), CryptoCoin (CCN), EOS (EOS), Ethereum (ETH), LiteCoin (LTC), Monero (MNO), Stellar (SLR), Tether (TTR), Tezos (TZS), XRP (XRP), and daily data collected from exchange markets platforms spans from 2nd January 2018 to 7th July 2020. Auto encoder was utilized for the labelling of the trading strategies buy-hold-sell.

Keywords

Bitcoin, Cryptocurrency, Support vector machine, Bagging

References

• [1] Conway L. “The 10 most important cyryptocurrencies other than bitcoin, Retrieved from https://www.investopedia.com/tech/most-important- cryptocurrencies-other-than-bitcoin/, 2021”
• [2] Tran V. and Leirvik, T. “Efficiency in the markets of cryptocurrencies” Finance Research Letters, 35 (2020) 101382.
• [3] Giudici, G., Milne, A., and Vinogradov, D. (2020). “Cryptocurrencies: market analysis and perspectives”, Journal of Industrial and Business Economics, 47:1–18, 2020.
• [4] Oyewola, D.O, Augustine, F.E, Dada, E.G and Ibrahim, A. “Predicting Impact of COVID-19 on Crude Oil Price Image with Directed Acyclic Graph Deep Convolutional Neural Network”, Journal of Robotics and Control (JRC), 2(2): 103-109, 2021.
• [5] Demir, E., Mehmet Huseyin Bilgin, M.H, Karabulut, G. and Doker, A.C. “The relationship between cryptocurrencies and COVID 19 Pandemic”, Eurasian Economic Review, 10:349–360, 2020.
• [6] Emna Mnif Assistant Professor , Anis Jarboui Professor, Khaireddine Mouakhar Professor , “How the cryptocurrency market has performed during COVID 19?A multifractal analysis”, Finance Research Letter, 2020 doi: https://doi.org/10.1016/j.frl.2020.101647.
• [7] Najaf, I., Fareed, Z., Wan, G. and Shahzad, F. “Asymmetric nexus between COVID-19 outbreak in the world and cryptocurrency market”, International Review of financial Analysis, 73, 101613, 2021.
• [8] Aysan, Ahmet Faruk, Asad UI Islam Khan, and Humeyra Topuz. “Bitcoin and Altcoins Price Dependency: Resilience and Portfolio Allocation in COVID-19”. Outbreak.Risks 9: 74, 2020. https://doi.org/10.3390/risks9040074
• [9] Helder Sebastião and Pedro Godinho. “Forecasting and trading cryptocurrencies with machine learning under changing market conditions”, Financ Innov, 7(3):1-10, 2021.
• [10] Laura Alessandretti , Abeer ElBahrawy , Luca Maria Aiello ,and Andrea Baronchelli, “Anticipating Cryptocurrency Prices Using Machine Learning”, Hindawi Complexity,1-16, 2018.
• [11] Thomas E. Koker and Dimitrios Koutmos. “Cryptocurrency Trading Using Machine Learning”, Journal of Risk and Financial Management, 13 (178): 1-7, 2020.
• [12] David O. Oyewola , Asabe Ibrahim, Joshua.A. Kwanamu, Emmanuel Gbenga Dada (2021). A new auditory algorithm in stock market prediction on oil and gas sector in Nigerian stock exchange. Soft computing letters, 3 (2021) 100013
• [13] WHO. Novel Coronavirus–China. https://www.who.int/csr/don/12-january-2020-novel-coronavirus-china/en/. (Accessed 29 March 2020).
• [14] https://www.worldometers.info/coronavirus/ (Accessed on 14 May 2020)
• [15] Han, J., Kamber, M., and Pei, J. “Data Mining Concepts and Techniques” (3rd ed). USA: Elsevier Inc, 2012.
• [16] Pang, S., and Gong, J. “C5.0 Classification Algorithm and Application on Individual Credit Evaluation of Banks”. Systems Engineering - Theory & Practice, 29(12), 94–104.
• [17] David .O. Oyewola, Emmanuel Gbenga Dada, Oluwatosin Temidayo Omotehinwa and Isa.A.Ibrahim. “Comparative Analysis of Linear, Non Linear and Ensemble Machine Learning Algorithms for Credit Worthiness of Consumers”, Computational Intelligence & Wireless Networks, 1(1), 1-11, 2019.
• [18] José A. Sáez, M. Galar, J. Luengo, F. Herrera, “An iterative class noise filter based on the fusion of classifiers with noise sensitivity control”, Information Fusion, 27 19-32, 2016. doi: 10.1016/j.inffus.2015.04.002.
• [19] Ferhat Ozgur Catak, “Robust Ensemble Classifier Combination Based On Noise Removal with One-Class SVM”, ICONIP 2015 Springer International Publishing Switzerland 2015, Part II, LNCS 9490, Pp. 10–17, 2015.
• [20] Ronaldo C. Prati et al. “Emerging Topics and Challenges of Learning from Noisy Data in Nonstandard Classification: A
• Survey Beyond Binary Class Noise”, Knowledge and Information Systems, Springer-Verlag London Ltd., Part Of Springer Nature, 2018.
• [21] X. Wu and X. Zhu “Mining with Noise Knowledge: Error-Aware Data Mining”, IEEE Transactions on Systems, Man, And Cybernetics 38, 917-932, 2008.
• [22] Diego García-Gil , Julián Luengo , Salvador García and Francisco Herrera. “Enabling Smart Data: Noise filtering in Big Data classification”, Information Sciences 479 135-152, 2019.
• [23] Tomek I. “An Experiment with the Edited Nearest-Neighbor Rule, in Systems, Man and Cybernetics”, IEEE Transactions on, vol.SMC-6, no.6, pp. 448-452, 1976.
• [24] Erdinc Akyildirim, Oguzhan Cepni, Shaen Corbet and Gazi Salah Uddin. “Forecasting mid-price movement of Bitcoin futures using machine learning”, Annals of Operations Research, 2021. https://doi.org/10.1007/s10479-021-04205-x.
• [25] Mohammed Mudassir, Shada Bennbaia, Devrim Unal and Mohammad Hammoudeh.”Time-series forecasting of Bitcoin prices using high-dimensional features: a machine learning approach”, Neural Computing and Applications, 2020.https://doi.org/10.1007/s00521-020-05129-6.