[1] N. Amanifard, N. Nariman-Zadeh, M. Borji, A. Khalkhali, A. Habibdoust, Modeling and Pareto
optimization of heat transfer and flow coefficients in microchannels using GMDH type neural net-
works and genetic algorithms, Energy Convers. Manag. 49 (2008) 311–325.
[2] A.F. Bariviera, The inefficiency of Bitcoin revisited: A dynamic approach, Econ. Lett. 161 (2017)
1–4.
[3] S. Basu, Investment performance of common stocks in relation to their priceearnings ratios: A test
of the efficient market hypothesis, J. Finance. 32 (1977) 663-682.
[4] E. Bouri, S.J.H. Shahzad, D. Roubaud, L. Kristoufek, B. Lucey, Bitcoin, gold, and commodities
as safe havens for stocks: New insight through wavelet analysis, Q Rev Econ Finance. 77 (2020)
156–164.
[5] W. Chen, H. Xu, L. Jia, Y. Gao, Machine learning model for Bitcoin exchange rate prediction using
economic and technology determinants, IIF. 37 (2021) 28-43.
[6] P.M. Crowley, A guide to wavelets for economists, J. Econ. Surv. 21 (2007) 207–267.
[7] P.M. Crowley, A. Habibdoust, Assessing the exchange rate exposure of US multinationals, Research
Discussion Papers 34/2013, Bank of Finland.
[8] A.H. Dyhrberg, Bitcoin, gold and the dollarA GARCH volatility analysis, Finance Res. Lett. 16
(2016) 85–92.
[9] M. Ferreira, S. Rodrigues, C.I. Reis, M. Maximiano, Blockchain: A tale of two applications, Appl.
Sci. 8 (2018) 1506.
[10] N. Gradojevic, D. Kukolj, R. Adcock, V. Djakovic, Forecasting Bitcoin with technical analysis: A
not-so-random forest?, Int. J. Forecast. 39 (2023) 1–17.
[11] J. Green, J.R.M. Hand, X.F. Zhang, The supraview of return predictive signals, Rev. Account. Stud.
18 (2013) 692–730.
[12] S. J. Grossman, J. E. Stiglitz, On the impossibility of informationally efficient markets, AER. 70
(1980) 393–408.
[13] F. E. Grubbs, Sample criteria for testing outlying observations, Ann. Math. Statist. (1950) 27–58.
[14] R. Hakim das Neves, Bitcoin pricing: impact of attractiveness variables, Financ. Innov. 6 (2020)
1–18.
[15] I.A. Hashish, F. Forni, G. Andreotti, T. Facchinetti, S. Darjani, A hybrid model for bitcoin prices
prediction using hidden Markov models and optimized LSTM networks, IEEE, ETFA. (2019) 721–
728.
[16] A. G. Ivakhnenko, Polynomial theory of complex systems, IEEE, Trans. Syst. Man Cybern. Syst. 4
(1971) 364–378.
[17] P. Jaquart, D. Dann, C. Weinhardt, Short-term bitcoin market prediction via machine learning,
JFDS. 7 (2021) 45–66.
[18] S.H. Kang, R. P. McIver, J. A. Hernandez, Co-movements between Bitcoin and Gold: A wavelet
coherence analysis, Phys. A: Stat. Mech. Appl. 536 (2019).
[19] S. Khuntia, J.K. Pattanayak, Adaptive market hypothesis and evolving predictability of bitcoin,
Econ. Lett. 167 (2018) 26–28.
[20] T. Klein, H.P. Thu, T. Walther, Bitcoin is not the New GoldA comparison of volatility, correlation,
and portfolio performance, Int. Rev. Financ. Anal. 59 (2018) 105–116.
[21] I.E. Livieris, N. Kiriakidou, S. Stavroyiannis, P. Pintelas, An Advanced CNN-LSTM Model for
Cryptocurrency Forecasting, Electronics. 10 (2021) 287.
[22] A.W. Lo, The adaptive markets hypothesis, J. Portf. Manag. 30 (2004) 15–29.
[23] S.G. Mallat, Multiresolution approximations and wavelet orthonormal bases of L(R), Trans. Am.
Math. Soc. 315 (1989) 69–87.
[24] R.S. Mariano, D. Preve, Statistical tests for multiple forecast comparison, J. Econom. 169 (2012),
123–130.
[25] S.C. Nayak, Bitcoin closing price movement prediction with optimal functional link neural net-
works, Evol. Intell. 15 (2022) 1825–1839.
[26] N. Parvini, M. Abdollahi, S. Seifollahi, D. Ahmadian, Forecasting Bitcoin returns with long short-
term memory networks and wavelet decomposition: A comparison of several market determinants,
Appl. Soft Comput. 121 (2022) 108707.
[27] M.M. Patel, S. Tanwar, R. Gupta, N. Kumar, A deep learning-based cryptocurrency price prediction
scheme for financial institutions, JISA. 55 (2020) 102583.
[28] G. Strang, Wavelets and dilation equations: A brief introduction, SIAM Review. 31 (1989) 614–
627.
[29] A. Urquhart, The inefficiency of Bitcoin, Econ. Lett. 148 (2016) 80–82.
[30] F. Valencia, A. Gmez-Espinosa, B. Valds-Aguirre, Price movement prediction of cryptocurrencies
using sentiment analysis and machine learning, Entropy. 21 (2019) 589.
[31] A.D. Vo, Q.P. Nguyen, C.Y. Ock, Sentiment Analysis of News for Effective Cryptocurrency Price
Prediction , IJKE. 5 (2019) 47–52.
[32] Y. Zhu, D. Dickinson, J. Li, Analysis on the influence factors of Bitcoins price based on VEC model,
FIN. 3 (2017) 1–13.