Stock price prediction portfolio optimization using different risk measures on application of genetic algorithm for machine learning regressions

Gandomi, A.; Sadjadi, S.; Amiri, B.

Growing Science » Accounting » Stock price prediction portfolio optimization using different risk measures on application of genetic algorithm for machine learning regressions

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Accounting

ISSN 2369-7407 (Online) - ISSN 2369-7393 (Print) Quarterly Publication Volume 10 Issue 4 pp. 207-220 , 2024

Stock price prediction portfolio optimization using different risk measures on application of genetic algorithm for machine learning regressions Pages 207-220 Download PDF

Authors: Amir Hossein Gandomi, Seyed Jafar Sadjadi, Babak Amiri

DOI: 10.5267/j.ac.2024.7.002

Keywords: Portfolio optimization, Stock market performance, Risk measures, Machine learning, Regression algorithms, Genetic algorithm

Abstract: This research aims to enhance portfolio selection by integrating machine learning regression algorithms for predicting stock returns with various risk measures. These measures include mean-value-at-risk (VaR) variance (Var), semi-variance mean-absolute-deviation (MAD) and conditional value-at-risk (C-VaR). Addressing gaps in existing literature. Traditional methods lack adaptability to dynamic market conditions. We propose a hybrid approach optimized by genetic algorithms. The study employs multiple machine learning models. These include Random Forest, AdaBoost XGBoost, Support Vector Machine Regression (SVR) K-Nearest Neighbors (KNN) and Artificial Neural Network (ANN). These models are used to forecast stock returns. Utilizing monthly data from the Tehran Stock Exchange, the results indicate that the genetic algorithm prediction model combined with mean-VaR, Var semi-variance and MAD, produces the most efficient portfolios. These portfolios offer superior returns with minimized risk compared to other models. This hybrid strategy provides a robust and efficient method for investors aiming to optimize returns while managing risk effectively. To implement this approach successfully it is crucial to balance investments. This involves both traditional and alternative asset classes, ensuring diversification. It also capitalizes on market opportunities. Regular review and adjustment of fund allocation are essential. Maintain an optimized strategy for maximum returns and minimal risk.

How to cite this paper

 Gandomi, A., Sadjadi, S & Amiri, 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.

Refrences

Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., & Zhang, L. (2016, October). Deep learning with differential privacy. In Proceedings of the 2016 ACM SIGSAC conference on computer and communications security (pp. 308-318). 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.