Asl, M. G., Jabeur, S. B. (2024). Could the Russia-Ukraine war stir up the persistent memory of interconnectivity among Islamic equity markets, energy commodities, and environmental factors?. Research in International Business and Finance, 69, 102260.
Barkoulas, J. T., Baum, C. F. (1996). Long-term dependence in stock returns. Economics Letters, 53(3), 253-259.
Bayoude, K., Ouassit, Y., Ardchir, S., Azouazi, M. (2018). How machine learning potentials are transforming the practice of digital marketing: State of the art. Periodicals of Engineering and Natural Sciences, 6(2), 373-379.
Garcia-Jorcano, L., Benito, S. (2020). Studying the properties of the Bitcoin as a diversifying and hedging asset through a copula analysis: Constant and time-varying. Research in International Business and Finance, 54, 101300.
Graves, T., Gramacy, R., Watkins, N., Franzke, C. (2017). A brief history of long memory: Hurst, Mandelbrot and the road to ARFIMA, 1951–1980. Entropy, 19(9), 437.
Greene, M., Fielitz. B. (1977). Long Term Dependence in Common Stock Returns. Journal of Financial Economics, 5(4), 339-349.
Green, L., Myerson, J. (2003). Discounting delayedband probabilistic rewards. Journal of Economis Psychology, 24(5), 619-635.
Han, C., Wang, Y., Ning, Y. (2019). Comparative analysis of the multifractality and efficiency of exchange markets: Evidence from exchange rates dynamics of major world currencies. Physica A: Statistical Mechanics and its Applications, 535, 122365.
Khairunnisa, D. A., Rodoni, A., Rama, A. (2024). Predicting Shariah Stock Market Indices with Machine Learning: A Cross-Country Case Study. Jurnal Ekonomi Syariah Teori dan Terapan, 11(1).
Kim, J. M., Kim, S. T., Kim, S. (2020). On the relationship of cryptocurrency price with us stock and gold price using copula models. Mathematics, 8(11), 1859.
Mahmood, T., Ahmad, I., Ansar, M. M. Z., Darwish, J. A., Sherwani, R. A. K. (2024). Comparative Study of Long Short-Term Memory (LSTM) and Quantum Long Short-Term Memory (QLSTM): Prediction of Stock Market Movement. arXiv preprint. https://doi.org/10.48550/arXiv.2409.08297.
Mandelbrot, B. B. (1971). A fast fractional Gaussian noise generator, Water Resources Research, 7(3), 543-553.
Stosic, D., Stosic, D., de Mattos Neto, P. S., Stosic, T. (2019). Multifractal characterization of Brazilian market sectors. Physica A: Statistical Mechanics and its Applications, 525, 956-964.
Yajima, Y. (1985). On estimation of long‐memory time series models. Australian Journal of Statistics, 27(3), 303-320.
Yuan, Y., Zhang, T. (2020). Forecasting stock market in high and low volatility periods: A modified multifractal volatility approach. Chaos. Solitons & Fractals, 140, 110252.