Browse > Article
http://dx.doi.org/10.22156/CS4SMB.2022.12.03.116

Forecasting Cryptocurrency Prices in COVID-19 Phase: Convergence Study on Naver Trends and Deep Learning  

Kim, Sun-Woong (Graduate School of Business IT, Kookmin University)
Publication Information
Journal of Convergence for Information Technology / v.12, no.3, 2022 , pp. 116-125 More about this Journal
Abstract
The purpose of this study is to analyze whether investor anxiety caused by COVID-19 affects cryptocurrency prices in the COVID-19 pandemic, and to experiment with cryptocurrency price prediction based on a deep learning model. Investor anxiety is calculated by combining Naver's Corona search index and Corona confirmed information, analyzing Granger causality with cryptocurrency prices, and predicting cryptocurrency prices using deep learning models. The experimental results are as follows. First, CCI indicators showed significant Granger causality in the returns of Bitcoin, Ethereum, and Lightcoin. Second, LSTM with CCI as an input variable showed high predictive performance. Third, Bitcoin's price prediction performance was the highest in comparison between cryptocurrencies. This study is of academic significance in that it is the first attempt to analyze the relationship between Naver's Corona search information and cryptocurrency prices in the Corona phase. In future studies, extended studies into various deep learning models are needed to increase price prediction accuracy.
Keywords
Covid-19; Cryptocurrency; Naver Search Index; CCI; Deep Learning Convergence Study;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 S. A. Sarkodie, M. Y. Ahmed & P. A. Owusu. (2022). COVID-19 pandemic improve market signals of cryptocurrencies-evidence from Bitcoin, Bitcoin Cash, Ethereum, and Litecoin. Finance Reasearch Letters, 44, 1-10. DOI : 10.1016/j.qref.2021.102049   DOI
2 P. Jaquart, D. Dann & C. Weinhardt. (2021). Short-term Bitcoin market prediction via machine learning. The Journal of Finance and Data Science, 7, 45-66. DOI : 10.1016/j.jfds.2021.03.001   DOI
3 W. Mensi, K. H. Al-Yahyaee, I. M. W. Al-Jarrah, X. V. Vo & S. H. Kang. (2021). Does volatility connectedness across major cryptocurrencies behave the same at different frequencies? A portfolio risk analysis. International Review of Economics and Finance, 76, 96-113. DOI : 10.1016/j.iref.2021.05.009   DOI
4 L. Kristoufek. (2015). What are the main drivers of the Bitcoin price? Evidence from wavelet coherence analysis. PLoS ONE, 10(4), 1-15. DOI : 10.1371/journal.pone.0123923   DOI
5 B. Gaies, M. S. Nakhli, J. M. Sahut & K. Guesmi. (2021). Is Bitcoin rooted in confidence? Unraveling the determinants of globalized digital currencies. Technological Forecasting & Social Change, 172, 1-11. DOI : 10.1016/j.techfore.2021.121038   DOI
6 F. Steinmetz, M. von Meduna, L. Ante & I. Fiedler. (2021). Ownership, uses and perceptions of cryptocurrency: Results from a population survey. Technological Forecasting & Social Change, 173, 1-19. DOI : 10.1016/j.techfore.2021.121073   DOI
7 L. Rognone, S. Hyde & S. S. Zhang. (2020). News sentiment in the cryptocurrency market: An empirical comparison with Forex. International Review of Financial Analysis, 69, 1-17. DOI : 10.1016/j.irfa.2020.101462   DOI
8 C. C. Wu, S. L. Ho & C. C. Wu. (2021). The determinants of Bitcoin returns and volatility: perspectives on global and national economic policy uncertainty. Finance Research Letters, 102175, 1-7. DOI : 10.1016/j.frl.2021.102175   DOI
9 I. D. Raheem. (2021). COVID-19 pandemic and the safe haven property of Bitcoin. The Quarterly Review of Economics and Finance, 81, 370-375. DOI : 10.1016/j.qref.2021.06.004   DOI
10 S. Nakamoto. (2008). A peer-to-peer electronic cash system. (Online). http://www.lopp.net/pdf/bitcoin.pdf
11 N. Cachanosky. (2019). Can Bitcoin become money? The monetary rule problem. Australian Economic Papers, 58, 365-374. DOI : 10.1111/1467-8454.12158   DOI
12 F. Kjerland, A. Khazal, E. A. Krogstad, F. B. G. Nordstrom & A. Oust. (2018). An analysis of bitcoin's price dynamics. Journal of Risk and Financial Management, 11, 63, 1-18. DOI : 10.3390/jrfm11040063   DOI
13 E. Cheah & J. Fry. (2015). Speculative bubbles in Bitcoin markets? An empirical investigation into the fundamental value of Bitcoin. Economics Letters, 130, 32-36. DOI : 10.1016/j.econlet.2015.02.029   DOI
14 S. Dastgir, E. Demir, G. Downing, G. Gozgor & C. K. M. Lau. (2019). The causal relationship between Bitcoin attention and Bitcoin returns: Evidence from the Copular-based Granger causality test. Finance Research Letters, 28, 160-164. DOI : 10.1016/j,frl.2018.04.019   DOI
15 S. Wu, M. Tong, Z. Yang & A. Derbali. (2019). Does gold or Bitcoin hedge economic policy uncertainty?. Finance Research Letters, 31, 171-178. DOI : 10.1016/j.frl.2019.04.001   DOI
16 L. Goczek & I. Skliarov. (2019). What drives the Bitcoin price? A factor augmented error correction mechanism investigation. Applied Economics, 51(59), 6393-6410. DOI : 10.1080/00036846.2019.1619021   DOI
17 J. Paule-Vianez, C. Prado-Roman & R. Gomez-Martinez. (2020). Economic policy uncertainty and Bitcoin. Is Bitcoin a safe-haven asset?. European Journal of Management and Business Economics, 29(3), 347-363. DOI : 10.1108/EJMBE-07-2019-0116   DOI
18 E. Demir, G. Gozgor, C. K. M. Lau.& S. A. Vigne. (2018). Does economic policy uncertainty predict the Bitcoin returns? An empirical investigation. Finance Research Letters, 26, 145-149. DOI : 10.1016/j.frl.2018.01.005   DOI
19 M. Liu, G. Li, J. Li, X. Zhu & Y. Yao. (2021). Forecasting the price of Bitcoin using deep learning. Finance Research Letters, 40, 101755, 1-8. DOI : 10.1016/j.frl.2020.101755   DOI
20 G. S. Atsalakis, I. G. Atsalaki, F. Pasiouras & C. Zopounidis. (2019). Bitcoin price forecasting with neuro-fuzzy techniques. European Journal of Operational Research, 276, 770-780. DOI : 10.1016/j.ejor.2019.01.040   DOI
21 S. W. Kim. (2021). Performance analysis of Bitcoin investment strategy using deep learning. Journal of the Korean Convergence Society, 12(4), 249-258. DOI : 10.15207/JKCS.2021.12.4.249   DOI
22 M. Nakano, A. Takahashi & S. Takahashi. (2018). Bitcoin technical trading with artificial neural network. Physia A, 510, 587-609. DOI : 10.1016/j.physa.2018.07.017   DOI
23 E. Koo & G. Kim. (2021). Prediction of Bitcoin price based on manipulating distribution strategy. Applied Soft Computing, 110, 107738, 1-10. DOI : 10.1016/j.asoc.2021.107738   DOI
24 A. Yadav, C. K. Jha & A. Sharan. (2020). Optimizing LSTM for time series prediction in Indian stock market. Procedia Computer Science, 167, 2091-2100. DOI : 10.1016/j.procs.2020.03.257   DOI
25 L. A. Smales. (2022). Investor attention in cryptocurrency markets. International Review of Financial Analysis, 79, 101972, 1-17. DOI : 10.1016/j.irfa.2021.101972   DOI
26 D. Philippas, H. Rjiba, K. Guesmi & S. Goutte. (2019). Media attention and Bitcoin prices. Finance Research Letters, 30, 37-43. DOI : 10.1016/j.frl.2019.03.031   DOI
27 Q. Gu, N. Lu & L. Liu. (2019). A novel recurrent neural network algorithm with long short-term memory model for futures trading. Journal of Intelligent & Fuzzy Systems, 37, 4477-4484. DOI : 10.3233/JIFS-179280   DOI
28 S. Hochreiter & J. Schmidhuber. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780. DOI : 10.1162/neco.1997.9.8.1735   DOI
29 F. Liu, Y. Li, B. Li, J. Li & H. Xie. (2021). Bitcoin transaction strategy construction based on deep reinforcement learning. Applied Soft Computing, 113, 107952, 1-8. DOI : 10.1016/j.asoc.2021.107952   DOI
30 M. Gang, B. Kim, M. G. Shin, U. J. Baek & M. S. Kim. (2020). LSTM-based prediction of Bitcoin price fluctuation using sentiment analysis. Proceedings of Symposium of the Korean Institute of Communications and Information Sciences, 561-562.
31 S. W. Kim. (2021). COVID-19 fear index and stock market. Journal of Convergence for Information Technology, 11(9), 84-93. DOI : 10.22156/CS4SMB.2021.11.09.084   DOI
32 S. Lahmiri & S. Bekiros. (2021). The effect of COVID-19 on long memory in retyrns and volatility of cryptocurrency and stock markets. Chaos, Solitons and Fractals, 151, 111221, 1-8. DOI : 10.1016/j.chaos.2021.111221   DOI
33 E. Bouri, R. Gupta, C. K. M. Lau, D. Roubaud & S. Wang. (2018). Bitcoin and global financial stress: A copula-based approach to dependence and causality in the quantiles. The Quarterly Review of Economics and Finance, 69, 297-307. DOI : 10.1016/j.qref.2018.04.003   DOI
34 A. S. Hayes. (2017). Cryptocurrency value formation: An empirical analysis leading to a cost of production model for valuing bitcoin. Telematics and Informatics, 34(7), 1308-1321. DOI : 10.1016/j.tele.2016.05.005   DOI