How to cite this paper
Gandomi, A., Sadjadi, S & Amir, B. (2024). Stock price prediction portfolio optimization using different risk measures on application of genetic algorithm for machine learning regressions.Accounting, 10(4), 207-220.
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Xiao, H., Rasul, K., & Vollgraf, R. (2017). Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms. arXiv preprint arXiv:1708.07747.
Álvarez, F., Arena, M., Auteri, D., Binaglia, M., Castoldi, A. F., ... & Villamar‐Bouza, L. (2023). Peer review of the pesticide risk assessment of the active substance glyphosate. European Food Safety Authority (EFSA), 21(7), e08164.
Amihud, Y., Mendelson, H., & Pedersen, L. H. (2012). Market liquidity: asset pricing, risk, and crises. Cambridge University Press.
Brandhofer, S., Braun, D., Dehn, V., Hellstern, G., Hüls, M., Ji, Y., ... & Wellens, T. (2022). Benchmarking the performance of portfolio optimization with QAOA. Quantum Information Processing, 22(1), 25.
Chen, X., & Fan, Y. (2018). Machine learning techniques for stock market prediction: An empirical study. Journal of Applied Mathematics, 2018, 1-11.
Chen, Y., & Ge, Y. (2019). Portfolio optimization using machine learning. Journal of Risk and Financial Management, 12(2), 55.
Chen, Y., & Wang, Y. (2020). Machine learning techniques for asset allocation and portfolio optimization. Journal of Computational Finance, 23(3), 1-27.
Erwin, K., & Engelbrecht, A. (2023). Meta-heuristics for portfolio optimization. Soft Computing, 27(24), 19045-19073.
Karimov, E. Z., Myagkova, I. N., Shirokiy, V. R., Barinov, O. G., & Dolenko, S. A. (2023). The significance of input features for domain adaptation of spacecraft data. Cosmic Research, 61(6), 554-560.
Kreibich, H., Van Loon, A. F., Schröter, K., Ward, P. J., Mazzoleni, M., Sairam, N., ... & Di Baldassarre, G. (2022). The challenge of unprecedented floods and droughts in risk management. Nature, 608(7921), 80-86.
Lewellen, J. (2014). The cross section of expected stock returns. Forthcoming in Critical Finance Review, Tuck School of Business Working Paper, (2511246).
Markowitz, H. M. (1991). Foundations of portfolio theory. The journal of finance, 46(2), 469-477.
Metaxiotis, K., & Liagkouras, K. (2012). Multiobjective evolutionary algorithms for portfolio management: A comprehensive literature review. Expert systems with applications, 39(14), 11685-11698.
Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature machine intelligence, 1(5), 206-215.
Schuett, J. (2023). Risk management in the artificial intelligence act. European Journal of Risk Regulation, 1-19.
Song, Y., Zhao, G., Zhang, B., Chen, H., Deng, W., & Deng, W. (2023). An enhanced distributed differential evolution algorithm for portfolio optimization problems. Engineering Applications of Artificial Intelligence, 121, 106004.
Wadekar, S. N., & Chaurasia, A. (2022). Mobilevitv3: Mobile-friendly vision transformer with simple and effective fusion of local, global and input features. arXiv preprint arXiv:2209.15159.
Wen, H. T., Wu, H. Y., & Liao, K. C. (2022). Using XGBoost Regression to Analyze the Importance of Input Features Applied to an Artificial Intelligence Model for the Biomass Gasification System. Inventions, 7(4), 126.
Xiao, H., Rasul, K., & Vollgraf, R. (2017). Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms. arXiv preprint arXiv:1708.07747.