Stock portfolio optimization based on the combined model of omega ratio and mean-variance Markowitz based on two-level ensemble machine learning

Subject Areas : Financial Knowledge of Securities Analysis

sanaz faridi ¹ , Mahdi Madanchi Zaj ² , amir daneshvar ³ , shadi shahverdiani ⁴ , fereydoon rahnama ⁵

1 - Department of Financial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran Tehran, Iran
2 - Department of Financial Management, Electronic Branch, Islamic Azad University, Tehran, Iran (Corresponding Author)
3 - Department of Entrepreneurship and Business Management, Faculty of Management, Electronic Branch, Islamic Azad University, Tehran,
4 - Department of Financial Management, Human Sciences Faculty, Islamic Azad university, Shahr-e-Qods branch, Tehran, Iran.
5 - Department of Accounting, Islamic Azad University, Science and Research Branch, Tehran, Iran

Received: 2021-11-23 Accepted : 2022-08-14 Published : 2022-11-22

Keywords: Omega Ratio Model, Markowitz Mean-Variance Model, Two-Level Collective Learning , Ultra-Innovative Algorithm,

Abstract :

In this paper, the stock portfolio of active companies listed on the Tehran Stock Exchange is optimized based on the combined model of omega ratio and mean-variance Markowitz (MVOF). For this purpose, 480 companies listed on the Tehran Stock Exchange during the years 1390 to 1399 were selected and based on the input data, the companies were filtered. Hence a combined method consisting of trading rules optimization method based on technical analysis (6 indicators RSI, ROC, SMA, EMA, WMA and MACD) and two-level collective learning machine (SVM, RF, BN, MLP and KNN) for Data training and purchase signal presentation were addressed. Therefore, 85 companies were selected to optimize the stock portfolio. To teach the data, 85 companies filtered by the combined method were used and the number of different classes with 50 learners was used. The results show that using the OF model compared to the MVF model has the highest stock portfolio returns during the years 1395 to 1399. While the MVF model has the lowest investment risk. As a result, by combining the above models, the stock portfolio return in this method is much higher than other methods. While the investment risk was lower. Therefore, if the MVOF model is used, the return on the stock portfolio will increase and the investment risk in it will decrease.

References:

• Aboussalah, A. M., & Lee, C. G. (2020). Continuous control with stacked deep dynamic recurrent reinforcement learning for portfolio optimization. Expert Systems with Applications, 140, 112891.


• Al Janabi, M. A. (2021). Optimization algorithms and investment portfolio analytics with machine learning techniques under time-varying liquidity constraints. Journal of Modelling in Management.


• Alexander, G. J., & Baptista, A. M. (2002). Economic implications of using a mean-VaR
  model for portfolio selection: A comparison with mean–variance analysis. Journal
  of Economic Dynamics and Control, 26, 1159–1193.


• Awoye, O. A. (2016). Markowitz minimum variance portfolio optimization using new machine learning methods (Doctoral dissertation, (UCL) University College London).


• Ban, G. Y., El Karoui, N., & Lim, A. E. (2018). Machine learning and portfolio optimization. Management Science, 64(3), 1136-1154.


• Chang, K. H., & Young, M. N. (2019). Behavioral stock portfolio optimization considering holding periods of B-stocks with short-selling. Computers & Operations Research, 112, 104773.


• Chen, W., Zhang, H., Mehlawat, M. K., & Jia, L. (2021). Mean–variance portfolio optimization using machine learning-based stock price prediction. Applied Soft Computing, 100, 106943.


• Cheong, D., Kim, Y. M., Byun, H. W., Oh, K. J., & Kim, T. Y. (2017). Using genetic algorithm to support clustering-based portfolio optimization by investor information. Applied Soft Computing, 61, 593-602.


• Conlon, T., Cotter, J., & Kynigakis, I. (2021). Machine Learning and Factor-Based Portfolio Optimization. Available at SSRN 3889459.


• Freitas, F. D., De Souza, A. F., & de Almeida, A. R. (2009). Prediction-based portfolio optimization model using neural networks. Neurocomputing, 72(10-12), 2155-2170.


• Ghahremani-Nahr, J., Nozari, H., & Sadeghi, M. E. (2021). Investment modeling to study the performance of dynamic networks of insurance

 

companies in Iran. Modern Research in Performance Evaluation.

• Kaczmarek, T., & Perez, K. (2021). Building portfolios based on machine learning predictions. Economic Research-Ekonomska Istraživanja, 1-19.


• Kapsos, M., Christofides, N., & Rustem, B. (2014). Worst-case robust Omega ratio. European Journal of Operational Research, 234, 499–507.


• Keating, C., & Shadwick, W. F. (2002). A universal performance measure. Journal of Performance Measurement, 6, 59–84.


• Konno, H., & Yamazaki, H. (1991). Mean-absolute Deviation Portfolio Optimization model and its applications to Tokyo Stock Market. Management Science, 37(5), 519–531.


• Ma, Y., Han, R., & Wang, W. (2021). Portfolio optimization with return prediction using deep learning and machine learning. Expert Systems with Applications, 165, 113973.


• Markowitz, H. M. (1959). Portfolio selection: Efficient diversification of investments. New
  York: John Wiley Sons Inc


• Min, L., Dong, J., Liu, J., & Gong, X. (2021). Robust mean-risk portfolio optimization using machine learning-based trade-off parameter. Applied Soft Computing, 113, 107948.


• Naik, N., & Mohan, B. R. (2019, February). Optimal feature selection of technical indicator and stock prediction using machine learning technique. In International Conference on Emerging Technologies in Computer Engineering (pp. 261-268). Springer, Singapore.


• Paiva, F. D., Cardoso, R. T. N., Hanaoka, G. P., & Duarte, W. M. (2019). Decision-making for financial trading: A fusion approach of machine learning and portfolio selection. Expert Systems with Applications, 115, 635-655.


• Perold, A. F. (1984). Large-scale portfolio optimization. Management science, 30(10), 1143-1160.


• Perrin, S., & Roncalli, T. (2020). Machine learning optimization algorithms & portfolio allocation. Machine Learning for Asset Management: New Developments and Financial Applications, 261-328.


• Prasad, P. C., Jaiswal, A., Shakya, S., & Singh, S. (2021). Portfolio Optimization: A Study of Nepal Stock Exchange. In Proceedings of International Conference on Sustainable Expert Systems (pp. 659-672). Springer, Singapore.


• Sami, H. M. (2021). Portfolio Construction Using Financial Ratio Indicators and Classification through Machine Learning. Int. J. Manag. Account, 3(4), 83-90.


• Schwendner, P., Papenbrock, J., Jaeger, M., & Krügel, S. (2021). Adaptive Seriational Risk Parity and Other Extensions for Heuristic Portfolio Construction Using Machine Learning and Graph Theory. The Journal of Financial Data Science, 3(4), 65-83.


• Sen, J., Dutta, A., & Mehtab, S. (2021). Stock Portfolio Optimization Using a Deep Learning LSTM Model. arXiv preprint arXiv: 2111.04709.


• Solin, M. M., Alamsyah, A., Rikumahu, B., & Saputra, M. A. A. (2019, July). Forecasting Portfolio Optimization using Artificial Neural Network and Genetic Algorithm. In 2019 7th International Conference on Information and Communication Technology (ICoICT) (pp. 1-7). IEEE.


• Ta, V. D., Liu, C. M., & Addis, D. (2018, December). Prediction and portfolio optimization in quantitative trading using machine learning techniques. In Proceedings of the Ninth International Symposium on Information and Communication Technology (pp. 98-105).


• Thakkar, A., & Chaudhari, K. (2021). A comprehensive survey on portfolio optimization, stock price and trend prediction using particle swarm optimization. Archives of Computational Methods in Engineering, 28(4), 2133-2164.


• Wang, W., Li, W., Zhang, N., & Liu, K. (2020). Portfolio formation with preselection using deep learning from long-term financial data. Expert Systems with Applications, 143, 113042.


• Yu, J. R., Chiou, W. J. P., Lee, W. Y., & Lin, S. J. (2020). Portfolio models with return
  forecasting and transaction costs. International Review of Economics & Finance, 66,
  118–130, 2020.


• Zhang, Y., Li, X., & Guo, S. (2018). Portfolio selection problems with Markowitz’s mean–variance framework: a review of literature. Fuzzy Optimization and Decision Making, 17(2), 125-158.

Zhang, Z., Zohren, S., & Roberts, S. (2020). Deep learning for portfolio optimization. The Journal of Financial Data Science, 2(4), 8-20