Tóm tắt:
Mục đích – Bitcoin (BTC) có mối tương quan đáng kể với các tài sản tài chính toàn cầu như dầu thô, vàng và đô la Mỹ. BTC và tài sản tài chính toàn cầu đã trở nên liên quan chặt chẽ hơn, đặc biệt kể từ khi đại dịch COVID-19 bùng phát. Mục đích của bài viết này là đưa ra các quyết định đầu tư BTC với sự hỗ trợ của các tài sản tài chính toàn cầu.
Thiết kế/phương pháp/cách tiếp cận – Nghiên cứu này đề xuất một mô hình dự đoán chính xác hơn cho giao dịch BTC bằng cách kết hợp mô hình phương sai thay đổi có điều kiện tổng quát tương quan (DCC-GARCH) với mạng lưới thần kinh nhân tạo (ANN). Mô hình DCC-GARCH cung cấp thông tin đầu vào quan trọng, bao gồm cả mối tương quan động và độ biến động, cho ANN. Để phân tích dữ liệu một cách hiệu quả, nghiên cứu chia dữ liệu thành hai giai đoạn: trước và trong đợt bùng phát COVID-19. Mỗi giai đoạn sau đó được chia thành tập huấn luyện và tập dự đoán.
Những phát hiện mới – Kết quả thực nghiệm cho thấy BTC và vàng có mối tương quan tích cực cao nhất so với dầu thô và USD, trong khi BTC và USD có mối tương quan động và tiêu cực. Quan trọng hơn, mô hình ANN-DCC-GARCH có lợi nhuận tích lũy là 318% trước khi đại dịch COVID-19 bùng phát và có thể giảm tổn thất 50% trong đại dịch COVID-19. Hơn nữa, những người không thích rủi ro có thể biến lỗ thành lãi khoảng 20% vào năm 2022.
Tính mới / giá trị nguyên bản – Phân tích thực nghiệm cung cấp hỗ trợ kỹ thuật và tham khảo ra quyết định cho các nhà đầu tư và tổ chức tài chính để đưa ra quyết định đầu tư vào BTC.
Tài liệu tham khảo:
- Adcock, R. and Gradojevic, N. (2019), “Non-fundamental, non-parametric Bitcoin forecasting”, Physica A: Statistical Mechanics and its Applications, Vol. 531, 121727, doi: 10.1016/j.physa.2019.121727.
- Al Mamun, M., Uddin, G.S., Suleman, M.T. and Kang, S.H. (2020), “Geopolitical risk, uncertainty and Bitcoin investment”, Physica A: Statistical Mechanics and Its Applications, Vol. 540, 123107, doi: 10.1016/j.physa.2019.123107.
- Arouxet, M.B., Bariviera, A.F., Pastor, V.E. and Vampa, V. (2022), “COVID-19 impact on cryptocurrencies: evidence from a wavelet-based Hurst exponent”, Physica A: Statistical Mechanics and its Applications, Vol. 596, 127170, doi: 10.1016/j.physa.2022.127170.
- Atsalakis, G.S., Atsalaki, I.G., Pasiouras, F. and Zopounidis, C. (2019), “Bitcoin price forecasting with neuro-fuzzy techniques”, European Journal of Operational Research, Vol. 276 No. 2, pp. 770-780, doi: 10.1016/j.ejor.2019.01.040.
- Baek, C. and Elbeck, M. (2015), “Bitcoins as an investment or speculative vehicle? A first look”, Applied Economics Letters, Vol. 22 No. 1, pp. 30-34, doi: 10.1080/13504851.2014.916379.
- Bahrambeygi, B. and Moeinzadeh, H. (2017), “Comparison of support vector machine and neutral network classification method in hyperspectral mapping of ophiolite mélanges–A case study of east of Iran”, The Egyptian Journal of Remote Sensing and Space Science, Vol. 20 No. 1, pp. 1-10, doi: 10.1016/j.ejrs.2017.01.007.
- Bani-Khalaf, O. and Taspinar, N. (2023), “The role of oil price in determining the relationship between cryptocurrencies and non-fungible assets”, Investment Analysts Journal, Vol. 52 No. 1, pp. 53-66, doi: 10.1080/10293523.2022.2155354.
- Basher, S.A. and Sadorsky, P. (2022), “Forecasting Bitcoin price direction with random forests: how important are interest rates, inflation, and market volatility?”, Machine Learning with Applications, Vol. 9, 100355, doi: 10.1016/j.mlwa.2022.100355.
- Baur, D.G. and Dimpfl, T. (2021), “The volatility of Bitcoin and its role as a medium of exchange and a store of value”, Empirical Economics, Vol. 61 No. 5, pp. 2663-2683, doi: 10.1007/s00181-020-01990-5.
- Bhuiyan, R.A., Husain, A. and Zhang, C. (2021), “A wavelet approach for causal relationship between bitcoin and conventional asset classes”, Resources Policy, Vol. 71, 101971, doi: 10.1016/j.resourpol.2020.101971.
- Bouri, E., Das, M., Gupta, R. and Roubaud, D. (2018), “Spillovers between Bitcoin and other assets during bear and bull markets”, Applied Economics, Vol. 50 No. 55, pp. 5935-5949, doi: 10.1080/00036846.2018.1488075.
- Cheah, J.E.-T., Luo, D., Zhang, Z. and Sung, M.C. (2022), “Predictability of bitcoin returns”, The European Journal of Finance, Vol. 28 No. 1, pp. 66-85, doi: 10.1080/1351847X.2020.1835685.
- Chen, J. (2023), “Analysis of bitcoin price prediction using machine learning”, Journal of Risk and Financial Management, Vol. 16 No. 1, p. 51, doi: 10.3390/jrfm16010051.
- Das, D., Le Roux, C.L., Jana, R. and Dutta, A. (2020), “Does Bitcoin hedge crude oil implied volatility and structural shocks? A comparison with gold, commodity and the US Dollar”, Finance Research Letters, Vol. 36, 101335, doi: 10.1016/j.frl.2019.101335.
- Dutta, A., Das, D., Jana, R. and Vo, X.V. (2020), “COVID-19 and oil market crash: revisiting the safe haven property of gold and Bitcoin”, Resources Policy, Vol. 69, 101816, doi: 10.1016/j.resourpol.2020.101816.
- Dyhrberg, A.H. (2016), “Bitcoin, gold and the dollar–A GARCH volatility analysis”, Finance Research Letters, Vol. 16, pp. 85-92, doi: 10.1016/j.frl.2015.10.008.
- Erdas, M.L. and Caglar, A.E. (2018), “Analysis of the relationships between Bitcoin and exchange rate, commodities and global indexes by asymmetric causality test”, Eastern Journal of European Studies, Vol. 9 No. 2, available at: https://www.ceeol.com/search/article-detail?id=730359 (accessed 4 December 2023).
- Gkillas, K., Bouri, E., Gupta, R. and Roubaud, D. (2022), “Spillovers in higher-order moments of crude oil, gold, and Bitcoin”, The Quarterly Review of Economics and Finance, Vol. 84, pp. 398-406, doi: 10.1016/j.qref.2020.08.004.
- Grobys, K. (2021), “When Bitcoin has the flu: on Bitcoin’s performance to hedge equity risk in the early wake of the COVID-19 outbreak”, Applied Economics Letters, Vol. 28 No. 10, pp. 860-865, doi: 10.1080/13504851.2020.1784380.
- Gupta, N. and Nigam, S. (2020), “Crude oil price prediction using artificial neural network”, Procedia Computer Science, Vol. 170, pp. 642-647, doi: 10.1016/j.procs.2020.03.136.
- Hau, L., Zhu, H., Shahbaz, M. and Sun, W. (2021), “Does transaction activity predict Bitcoin returns? Evidence from quantile-on-quantile analysis”, The North American Journal of Economics and Finance, Vol. 55, 101297, doi: 10.1016/j.najef.2020.101297.
- Hu, Y., Hou, Y.G. and Oxley, L. (2020), “What role do futures markets play in Bitcoin pricing? Causality, cointegration and price discovery from a time-varying perspective?”, International Review of Financial Analysis, Vol. 72, 101569, doi: 10.1016/j.irfa.2020.101569.
- Huang, W. and Gao, X. (2022), “LASSO-based high-frequency return predictors for profitable Bitcoin investment”, Applied Economics Letters, Vol. 29 No. 12, pp. 1079-1083, doi: 10.1080/13504851.2021.1908512.
- Huang, J.-Z., Huang, W. and Ni, J. (2019), “Predicting bitcoin returns using high-dimensional technical indicators”, The Journal of Finance and Data Science, Vol. 5 No. 3, pp. 140-155, doi: 10.1016/j.jfds.2018.10.001.
- Huang, Y., Duan, K. and Urquhart, A. (2023), “Time-varying dependence between Bitcoin and green financial assets: a comparison between pre-and post-COVID-19 periods”, Journal of International Financial Markets, Institutions and Money, Vol. 82, 101687, doi: 10.1016/j.intfin.2022.101687.
- Jana, R.K. and Das, D. (2020), “Did Bitcoin act as an antidote to the Chinese equity market and booster to Altcoins during the Novel Coronavirus outbreak?”, SSRN 3544794, available at: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3544794 (accessed 4 December 2023).
- Jareño, F., González, M.O., López, R. and Ramos, A.R. (2021), “Cryptocurrencies and oil price shocks: a NARDL analysis in the COVID-19 pandemic”, Resources Policy, Vol. 74, 102281, doi: 10.1016/j.resourpol.2021.102281.
- Kang, S.H., McIver, R.P. and Hernandez, J.A. (2019), “Co-movements between Bitcoin and Gold: a wavelet coherence analysis”, Physica A: Statistical Mechanics and its Applications, Vol. 536, 120888, doi: 10.1016/j.physa.2019.04.124.
- Kayal, P. and Rohilla, P. (2021), “Bitcoin in the economics and finance literature: a survey”, SN Business and Economics, Vol. 1 No. 7, p. 88, doi: 10.1007/s43546-021-00090-5.
- Khalfaoui, R., Hammoudeh, S. and Rehman, M.Z. (2023), “Spillovers and connectedness among BRICS stock markets, cryptocurrencies, and uncertainty: evidence from the quantile vector autoregression network”, Emerging Markets Review, Vol. 54, 101002, doi: 10.1016/j.ememar.2023.101002.
- Kulkarni, S. and Haidar, I. (2009), “Forecasting model for crude oil price using artificial neural networks and commodity futures prices”, arXiv, available at: http://arxiv.org/abs/0906.4838 (accessed 4 December 2023).
- Kumar, A.S. and Padakandla, S.R. (2022), “Testing the safe-haven properties of gold and bitcoin in the backdrop of COVID-19: a wavelet quantile correlation approach”, Finance Research Letters, Vol. 47, 102707, doi: 10.1016/j.frl.2022.102707.
- Kwon, J.H. (2020), “Tail behavior of Bitcoin, the dollar, gold and the stock market index”, Journal of International Financial Markets, Institutions and Money, Vol. 67, 101202, doi: 10.1016/j.intfin.2020.101202.
- Liu, Y., Naktnasukanjn, N., Tamprasirt, A. and Rattanadamrongaksorn, T. (2023), “Comparison of the asymmetric relationship between bitcoin and gold, crude oil, and the US dollar before and after the COVID-19 outbreak”, Journal of Risk and Financial Management, Vol. 16 No. 10, p. 455, doi: 10.3390/jrfm16100455.
- Long, S., Pei, H., Tian, H. and Lang, K. (2021), “Can both Bitcoin and gold serve as safe-haven assets? — a comparative analysis based on the NARDL model”, International Review of Financial Analysis, Vol. 78, 101914, doi: 10.1016/j.irfa.2021.101914.
- Mahdiani, M.R. and Khamehchi, E. (2016), “A modified neural network model for predicting the crude oil price”, Intellectual Economics, Vol. 10 No. 2, pp. 71-77, doi: 10.1016/j.intele.2017.02.001.
- Mariana, C.D., Ekaputra, I.A. and Husodo, Z.A. (2021), “Are Bitcoin and Ethereum safe-havens for stocks during the COVID-19 pandemic?”, Finance Research Letters, Vol. 38, 101798, doi: 10.1016/j.frl.2020.101798.
- Nakano, M., Takahashi, A. and Takahashi, S. (2018), “Bitcoin technical trading with artificial neural network”, Physica A: Statistical Mechanics and its Applications, Vol. 510, pp. 587-609, doi: 10.1016/j.physa.2018.07.017.
- Nguyen, K.Q. (2022), “The correlation between the stock market and Bitcoin during COVID-19 and other uncertainty periods”, Finance Research Letters, Vol. 46, 102284, doi: 10.1016/j.frl.2021.102284.
- Okorie, D.I. and Lin, B. (2020), “Crude oil price and cryptocurrencies: evidence of volatility connectedness and hedging strategy”, Energy Economics, Vol. 87, 104703, doi: 10.1016/j.eneco.2020.104703.
- Pabuccu, H., Ongan, S. and Ongan, A. (2020), “Forecasting the movements of Bitcoin prices: an application of machine learning algorithms”, Quantitative Finance and Economics, Vol. 4 No. 4, pp. 679-692, doi: 10.3934/QFE.2020031.
- Palazzi, R.B., Júnior, G.D.S.R. and Klotzle, M.C. (2021), “The dynamic relationship between bitcoin and the foreign exchange market: a nonlinear approach to test causality between bitcoin and currencies”, Finance Research Letters, Vol. 42, 101893, doi: 10.1016/j.frl.2020.101893.
- Rehman, M.U. and Kang, S.H. (2021), “A time–frequency comovement and causality relationship between Bitcoin hashrate and energy commodity markets”, Global Finance Journal, Vol. 49, 100576, doi: 10.1016/j.gfj.2020.100576.
- Rehman, M.U., Katsiampa, P., Zeitun, R. and Vo, X.V. (2023), “Conditional dependence structure and risk spillovers between bitcoin and fiat currencies”, Emerging Markets Review, Vol. 55, 100966, doi: 10.1016/j.ememar.2022.100966.
- Rodriguez, M.R., Besteiro, R., Ortega, J.A., Fernandez, M.D. and Arango, T. (2022), “Evolution and neural network prediction of CO2 emissions in weaned piglet farms”, Sensors, Vol. 22 No. 8, p. 2910, doi: 10.3390/s22082910.
- Sarkodie, S.A., Ahmed, M.Y. and Owusu, P.A. (2022), “COVID-19 pandemic improves market signals of cryptocurrencies–evidence from Bitcoin, Bitcoin Cash, Ethereum, and Litecoin”, Finance Research Letters, Vol. 44, 102049, doi: 10.1016/j.frl.2021.102049.
- Selmi, R., Mensi, W., Hammoudeh, S. and Bouoiyour, J. (2018), “Is Bitcoin a hedge, a safe haven or a diversifier for oil price movements? A comparison with gold”, Energy Economics, Vol. 74, pp. 787-801, doi: 10.1016/j.eneco.2018.07.007.
- Sharma, G.D., Shahbaz, M., Singh, S., Chopra, R. and Cifuentes-Faura, J. (2023), “Investigating the nexus between green economy, sustainability, bitcoin and oil prices: contextual evidence from the United States”, Resources Policy, Vol. 80, 103168, doi: 10.1016/j.resourpol.2022.103168.
- Smales, L.A. (2019), “Bitcoin as a safe haven: is it even worth considering?”, Finance Research Letters, Vol. 30, pp. 385-393, doi: 10.1016/j.frl.2018.11.002.
- Tan, Z., Huang, Y. and Xiao, B. (2021), “Value at risk and returns of cryptocurrencies before and after the crash: long-run relations and fractional cointegration”, Research in International Business and Finance, Vol. 56, 101347, doi: 10.1016/j.ribaf.2020.101347.
- Tiwari, A.K., Abakah, E.J.A., Rehman, M.Z. and Lee, C.C. (2024), “Quantile dependence of Bitcoin with clean and renewable energy stocks: new global evidence”, Applied Economics, Vol. 56 No. 3, pp. 286-300, doi: 10.1080/00036846.2023.2167921.
- Tripathi, B. and Sharma, R.K. (2023), “Modeling bitcoin prices using signal processing methods, Bayesian optimization, and deep neural networks”, Computational Economics, Vol. 62 No. 4, pp. 1919-1945, doi: 10.1007/s10614-022-10325-8.
- Wang, G. and Hausken, K. (2022), “A Bitcoin price prediction model assuming oscillatory growth and lengthening cycles”, Cogent Economics and Finance, Vol. 10 No. 1, 2087287, doi: 10.1080/23322039.2022.2087287.
- Wang, G., Tang, Y., Xie, C. and Chen, S. (2019a), “Is bitcoin a safe haven or a hedging asset? Evidence from China”, Journal of Management Science and Engineering, Vol. 4 No. 3, pp. 173-188, doi: 10.1016/j.jmse.2019.09.001.
- Wang, J.-N., Liu, H.C., Chiang, S.M. and Hsu, Y.T. (2019b), “On the predictive power of ARJI volatility forecasts for Bitcoin”, Applied Economics, Vol. 51 No. 44, pp. 4849-4855, doi: 10.1080/00036846.2019.1602714.
- Wang, P., Zhang, H., Yang, C. and Guo, Y. (2021), “Time and frequency dynamics of connectedness and hedging performance in global stock markets: bitcoin versus conventional hedges”, Research in International Business and Finance, Vol. 58, 101479, doi: 10.1016/j.ribaf.2021.101479.
- Yıldırım, D.Ç., Esen, Ö. and Ertuğrul, H.M. (2022), “Impact of the COVID-19 pandemic on return and risk transmission between oil and precious metals: evidence from DCC-GARCH model”, Resources Policy, Vol. 79, 102939, doi: 10.1016/j.resourpol.2022.102939.
- Zeng, L., Li, L. and Jiang, Sh. (2023), “Stock price prediction based on BP neural network”, Journal of Yunnan Minzu University, Vol. 5, pp. 658-665 (in Chinese) (Natural Sciences Edition).
- Zhang, S. and Mani, G. (2021), “Popular cryptoassets (Bitcoin, Ethereum, and Dogecoin), Gold, and their relationships: volatility and correlation modeling”, Data Science and Management, Vol. 4, pp. 30-39, (in Chinese), doi: 10.1016/j.dsm.2021.11.001.
Abstract:
Purpose – Bitcoin (BTC) is significantly correlated with global financial assets such as crude oil, gold and the US dollar. BTC and global financial assets have become more closely related, particularly since the outbreak of the COVID-19 pandemic. The purpose of this paper is to formulate BTC investment decisions with the aid of global financial assets.
Design/methodology/approach – This study suggests a more accurate prediction model for BTC trading by combining the dynamic conditional correlation generalized autoregressive conditional heteroscedasticity (DCC-GARCH) model with the artificial neural network (ANN). The DCC-GARCH model offers significant input information, including dynamic correlation and volatility, to the ANN. To analyze the data effectively, the study divides it into two periods: before and during the COVID-19 outbreak. Each period is then further divided into a training set and a prediction set.
Findings – The empirical results show that BTC and gold have the highest positive correlation compared with crude oil and the USD, while BTC and the USD have a dynamic and negative correlation. More importantly, the ANN-DCC-GARCH model had a cumulative return of 318% before the outbreak of the COVID-19 pandemic and can decrease loss by 50% during the COVID-19 pandemic. Moreover, the risk-averse can turn a loss into a profit of about 20% in 2022.
Originality/value – The empirical analysis provides technical support and decision-making reference for investors and financial institutions to make investment decisions on BTC.