COVID-19 국면의 암호화폐 가격 예측: 네이버트렌드와 딥러닝의 융합 연구
Forecasting Cryptocurrency Prices in COVID-19 Phase: Convergence Study on Naver Trends and Deep Learning
0
조회
0
다운로드
1
인용
0
참고문헌
키워드
초록
본 연구의 목적은 COVID-19 팬데믹 국면에서 코로나 발생과 확산에 따른 투자자 불안심리가 암호화폐 가격에 영향을 미치는지를 분석하고, 딥러닝 모형에 기반하여 암호화폐의 가격 예측을 실험하는 것이다. 투자자 불안심리는 네이버의 코로나 검색지수와 코로나 확진자 정보를 결합하여 산출하며, 암호화폐 가격과의 그랜저 인과성을 분석하고 딥러닝모형을 이용하여 암호화폐 가격을 예측한다. 실험 결과는 다음과 같다. 첫째, CCI 지표는 비트코인, 이더리움, 라이트코인의 수익률에 유의적인 그랜저 인과성을 보여주었다. 둘째, CCI를 입력변수로 하는 LSTM은 높은 예측성과를 보여주었다. 셋째, 암호화폐 사이의 비교에서는 비트코인의 가격 예측 성과가 가장 높게 나타났다. 본 연구는 코로나 국면에서 네이버 코로나 검색 정보와 암호화폐 가격과의 관련성을 분석한 첫 시도라는 점에서 학술적 의의를 찾을 수 있다. 향후 연구에서는 가격 예측 정확성을 높이기 위하여 다양한 딥러닝 모형으로의 확장 연구가 필요하다.
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.
참고문헌 (34)
- F. Steinmetz, "Ownership, uses and perceptions of cryptocurrency: Results from a population survey", Technological Forecasting & Social Change, 173, pp.1~19 (2021)[DOI]
- B. Gaies, "Is Bitcoin rooted in confidence? Unraveling the determinants of globalized digital currencies", Technological Forecasting & Social Change, 172, pp.1~11 (2021)[DOI]
- L. Rognone, "News sentiment in the cryptocurrency market: An empirical comparison with Forex", International Review of Financial Analysis, 69, pp.1~17 (2020)[DOI]
- C. C. Wu, "The determinants of Bitcoin returns and volatility:perspectives on global and national economic policy uncertainty", Finance Research Letters, 102175, pp.1~7 (2021)[DOI]
- I. D. Raheem, "COVID-19 pandemic and the safe haven property of Bitcoin", The Quarterly Review of Economics and Finance, 81, pp.370~375 (2021)[DOI]
- "A peer-to-peer electronic cash system" (2008)[URL]
- E. Cheah, "Speculative bubbles in Bitcoin markets? An empirical investigation into the fundamental value of Bitcoin", Economics Letters, 130, pp.32~36 (2015)[DOI]
- L. Kristoufek., "What are the main drivers of the Bitcoin price? Evidence from wavelet coherence analysis", PLoS ONE, 10(4), pp.1~15 (2015)[DOI]
- F. Kjerland, "An analysis of bitcoin’s price dynamics", Journal of Risk and Financial Management, 11(63), pp.1~18 (2018)[DOI]
- N. Cachanosky., "Can Bitcoin become money? The monetary rule problem", Australian Economic, 58, pp.365~374 (2019)[DOI]
- S. Dastgir, "The causal relationship between Bitcoin attention and Bitcoin returns:Evidence from the Copular-based Granger causality test", Finance Research Letters, 28, pp.160~164 (2019)[DOI]
- L. Goczek, "What drives the Bitcoin price? A factor augmented error correction mechanism investigation", Applied Economics, 51(59), pp.6393~6410 (2019)[DOI]
- A. S. Hayes., "Cryptocurrency value formation: An empirical analysis leading to a cost of production model for valuing bitcoin", Telematics and Informatics, 34(7), pp.1308~1321 (2017)[DOI]
- E. Bouri, "Bitcoin and global financial stress: A copula-based approach to dependence and causality in the quantiles", The Quarterly Review of Economics and Finance, 69, pp.297~307 (2018)[DOI]
- J. Paule-Vianez, "Economic policy uncertainty and Bitcoin Is Bitcoin a safe-haven asset?", European Journal of Management and Business Economics, 29(3), pp.347~363 (2020)[DOI]
- E. Demir, "Does economic policy uncertainty predict the Bitcoin returns? An empirical investigation", Finance Research Letters, 26, pp.145~149 (2018)[DOI]
- S. Wu, "Does gold or Bitcoin hedge economic policy uncertainty?", Finance Research Letters, 31, pp.171~178 (2019)[DOI]
- S. A. Sarkodie, "COVID-19 pandemic improve market signals of cryptocurrencies-evidence from Bitcoin, Bitcoin Cash, Ethereum, and Litecoin", Finance Reasearch Letters, 44, pp.1~10 (2022)[DOI]
- E. Koo, "Prediction of Bitcoin price based on manipulating distribution strategy", Applied Soft Computing, 110, pp.107738~ (2021)[DOI]
- M. Liu, "Forecasting the price of Bitcoin using deep learning", Finance Research Letters, 40, pp.101755~ (2021)[DOI]
- G. S. Atsalakis, "Bitcoin price forecasting with neuro-fuzzy techniques", European Journal of Operational Research, 276, pp.770~780 (2019)[DOI]
- 김선웅, "딥러닝을 이용한 비트코인 투자전략의 성과 분석", 한국융합학회논문지, 12(4), pp.249~258 (2021), 한국융합학회[DOI]
- M. Nakano, "Bitcoin technical trading with artificial neural network", Physia A, 510, pp.587~609 (2018)[DOI]
- L. A. Smales., "Investor attention in cryptocurrency markets", International Review of Financial Analysis, 79, pp.101972~ (2022)[DOI]
- D. Philippas, "Media attention and Bitcoin prices", Finance Research Letters, 30, pp.37~43 (2019)[DOI]
- Q. Gu, "A novel recurrent neural network algorithm with long short-term memory model for futures trading", Journal of Intelligent & Fuzzy Systems, 37, pp.4477~4484 (2019)[DOI]
- A. Yadav, "Optimizing LSTM for time series prediction in Indian stock market", Procedia Computer Science, 167, pp.2091~2100 (2020)[DOI]
- S. Hochreiter, "Long short-term memory", Neural Computation, 9(8), pp.1735~1780 (1997)[DOI]
- F. Liu, "Bitcoin transaction strategy construction based on deep reinforcement learning", Applied Soft Computing, 113, pp.107952~ (2021)[DOI]
- P. Jaquart, "Short-term Bitcoin market prediction via machine learning", The Journal of Finance and Data Science, 7, pp.45~66 (2021)[DOI]
- M. Gang, "LSTM-based prediction of Bitcoin price fluctuation using sentiment analysis", Proceedings of Symposium of the Korean Institute of Communications and Information Sciences, pp.561~562 (2020)
- 김선웅, "COVID-19 공포지수와 주식시장", 융합정보논문지, 11(9), pp.84~93 (2021), 중소기업융합학회[DOI]
- W. Mensi, "Does volatility connectedness across major cryptocurrencies behave the same at different frequencies? A portfolio risk analysis", International Review of Economics and Finance, 76, pp.96~113 (2021)[DOI]
- S. Lahmiri, "The effect of COVID-19 on long memory in retyrns and volatility of cryptocurrency and stock markets", Chaos, Solitons and Fractals, 151, pp.111221~ (2021)[DOI]