Hamedinia, Hamed, Mohammadi Roudbari, Kiarash, Asadi, Behrang. (1404). Comparison of the Portfolio Optimization Methods Based on Historical Return Approach and Predicted Return Using ARIMA Model. , 59(1), 171-182. doi: 10.22059/aie.2024.381585.1911
Hamed Hamedinia; Kiarash Mohammadi Roudbari; Behrang Asadi. "Comparison of the Portfolio Optimization Methods Based on Historical Return Approach and Predicted Return Using ARIMA Model". , 59, 1, 1404, 171-182. doi: 10.22059/aie.2024.381585.1911
Hamedinia, Hamed, Mohammadi Roudbari, Kiarash, Asadi, Behrang. (1404). 'Comparison of the Portfolio Optimization Methods Based on Historical Return Approach and Predicted Return Using ARIMA Model', , 59(1), pp. 171-182. doi: 10.22059/aie.2024.381585.1911
Hamedinia, Hamed, Mohammadi Roudbari, Kiarash, Asadi, Behrang. Comparison of the Portfolio Optimization Methods Based on Historical Return Approach and Predicted Return Using ARIMA Model. , 1404; 59(1): 171-182. doi: 10.22059/aie.2024.381585.1911
Comparison of the Portfolio Optimization Methods Based on Historical Return Approach and Predicted Return Using ARIMA Model
1Ph.D., Department of Finance: Management Faculty, University of Tehran, Tehran, Iran.
2M.Sc., Department of MBA, School of Business Faculty, Amirkabir University of Technology, Tehran, Iran.
3Assistant Professor, Department of MBA, School of Business Faculty, Amirkabir University of Technology, Tehran, Iran.
چکیده
Risk assessment and the selection of an optimal portfolio selection are critical issues in financial research, investment firms, and among investors. Traditional optimization models like Mean-Variance, Value-at-Risk, Conditional Value-at-Risk, and Omega often rely on historical returns, which can be insufficient in optimizing return and reducing risk. Recently, researches have used predictive return models in the optimization process. This study uses ARIMA model to predict stock returns alongside a mean-variance optimization model (ARIMA_MV) using Iranian exchange market data from 1395 to 1401. First, stock returns have been predicted using ARIMA model and error criteria such as MSE, MAD, HR, HR+ and HR- are estimated. The results show that despite its simplicity, the ARIMA model has a relatively good performance in predicting the return of stock. Then, using the predicted return and the mean variance model, the optimal portfolio has been calculated in the sliding window process. The results show that portfolios calculated by ARIMA_MV model outperform the Tehran Exchange Index (TEDPIX), risk-adjusted criterion like Sharpe and Jensen's alpha ratios and mean_variance model with historical data (HMV). With the model developed in this project (ARIMA_MV), investment companies can offer shareholders higher returns and lower risks, potentially increasing company value in the capital market and boosting shareholder wealth.
Adebiyi AA, Adewumi AO, Ayo CK. Comparison of ARIMA and artificial neural networks models for stock price prediction. Journal of Applied Mathematics. 2014;2014(1):614342. doi:10.1155/2014/614342
Ghasemi A, Zahediasl S. Normality tests for statistical analysis: a guide for non-statisticians. International journal of endocrinology and metabolism. 2012;10(2):486. doi: 10.5812/ijem.3505.
Esmaeili B, Souri A, Mirlohi SM. Higher moments portfolio Optimization with unequal weights based on Generalized Capital Asset pricing model with independent and identically asymmetric Power Distribution. Advances in Mathematical Finance and Applications. 2021 Apr 1;6(2):263-83. DOI: https://doi.org/10.22034/amfa.2020.1909590.1484
Rom BM, Ferguson KW. Post-modern portfolio theory comes of age. Journal of investing. 1994 Aug 31;3(3):11-7. Doi: 10.3905/joi.2.4.27
Weng B, Lu L, Wang X, Megahed FM, Martinez W. Predicting short-term stock prices using ensemble methods and online data sources. Expert Systems with Applications. 2018 Dec 1; 112:258-73. doi: 10.1016/j.eswa.2018.06.016
Huang CF. A hybrid stock selection model using genetic algorithms and support vector regression. Applied soft computing. 2012 Feb 1;12(2):807-18. doi:10.1016/j.asoc.2011.10.009
Kharoubi-Rakotomalala C, Maurer F. Copulas in finance ten years later. Journal of Applied Business Research. 2013 Sep 1;29(5):1555. doi:10.19030/jabr.v29i5.8036
Lu CJ, Lee TS, Chiu CC. Financial time series forecasting using independent component analysis and support vector regression. Decision support systems. 2009 May 1;47(2):115-25. doi: 10.1016/j.dss.2009.02.001
Lin CM, Huang JJ, Gen M, Tzeng GH. Recurrent neural network for dynamic portfolio selection. Applied Mathematics and Computation. 2006 Apr 15;175(2):1139-46. doi:10.1016/j.amc.2005.08.031
Gandhmal DP, Kumar K. Systematic analysis and review of stock market prediction techniques. Computer Science Review. 2019 Nov 1;34:100190. doi:10.1016/j.cosrev.2019.08.001
Jondeau E. Asymmetry in tail dependence in equity portfolios. Computational Statistics & Data Analysis. 2016 Aug 1;100:351-68. Doi: 10.1016/j.csda.2015.02.014
Lakzaie F, Bahiraie A. Visualized Portfolio Optimization of stock market: Case of TSE. Advances in Mathematical Finance and Applications. 2024 Feb;2(2):707.
Hamedinia H, Raei R, Bajalan S, Rouhani S. Analysis of Stock Market Manipulation using Generative Adversarial Nets and Denoising Auto-Encode Models. Advances in Mathematical Finance and Applications. 2022 Jan 1;7(1):133-51.DOI: https://doi.org/10.22034/amfa.2021.1933112.1608
HM M. Portfolio selection: efficient diversification of investments.
Yu JR, Chiou WJ, Lee WY, Lin SJ. Portfolio models with return forecasting and transaction costs. International Review of Economics & Finance. 2020 Mar 1;66:118-30.doi:10.1016/j.iref.2019.11.002.
Viebig J, Poddig T. Modeling extreme returns and asymmetric dependence structures of hedge fund strategies using extreme value theory and copula theory. Journal of Risk. 2011;13(2):23. Doi: 10.21314/JOR.2010.220
Yu JR, Chiou WJ, Lee WY, Lin SJ. Portfolio models with return forecasting and transaction costs. International Review of Economics & Finance. 2020 Mar 1; 66:118-30. doi: https://doi.org/10.1016/j.iref.2019.11.002.
Wang JZ, Wang JJ, Zhang ZG, Guo SP. Forecasting stock indices with back propagation neural network. Expert Systems with Applications. 2011 Oct 1;38(11):14346-55. doi:10.1016/j.eswa.2011.04.222
Ma Y, Han R, Wang W. Portfolio optimization with return prediction using deep learning and machine learning. Expert Systems with Applications. 2021 Mar 1; 165:113973. Doi: 10.1016/j.eswa.2020.113973
Kapsos M, Christofides N, Rustem B. Worst-case robust Omega ratio. European Journal of Operational Research. 2014 Apr 16;234(2): 499-507.DOI: 10.1016/j.ejor.2013.04.025
Zanjirdar M. Overview of portfolio optimization models. Advances in mathematical finance and applications. 2020 Oct 1;5(4): 419-35.DOI: https://doi.org/10.22034/amfa.2020.674941
Ustun O, Kasimbeyli R. Combined forecasts in portfolio optimization: a generalized approach. Computers & Operations Research. 2012 Apr 1;39(4): 805-19.doi:10.1016/j.cor.2010.09.008
Taleblou R, Davoudi MM. Estimation of Optimal Investment Portfolio Using Value at Risk (VaR) and Expected Shortfall (ES) Models: GARCH-EVT-Copula Approach. Economics Research. 2018 Dec 22;18(71):91-125. (in persian)
Fallahpour S, Ahmadi E. Estimating value at risk of portfolio of oil and gold by Copula-GARCH method. Financial Research Journal. 2014 Sep 23;16(2):309-26. (in persian)
Lee SI, Yoo SJ. Threshold-based portfolio: the role of the threshold and its applications. The Journal of Supercomputing. 2020 Oct;76(10): 8040-57.doi:48550/arXiv.1709.09822
Deng S, Min X. Applied optimization in global efficient portfolio construction using earning forecasts. The Journal of Investing. 2013 Nov 30;22(4):104-14. DOI:10.3905/joi.2013.22.4.104
Ta VD, Liu CM, Tadesse DA. Portfolio optimization-based stock prediction using long-short term memory network in quantitative trading. Applied Sciences. 2020 Jan 7;10(2):437. Doi:10.3390/app10020437
Wang W, Li W, Zhang N, Liu K. Portfolio formation with preselection using deep learning from long-term financial data. Expert Systems with Applications. 2020 Apr 1; 143:113042. doi: 10.1016/j.eswa.2019.113042
Hong Y, Tu J, Zhou G. Asymmetries in stock returns: Statistical tests and economic evaluation. The Review of Financial Studies. 2007 Sep 1;20(5):1547-81. DOI:10.1093/rfs/hhl037
Zhang Y, Li X, Guo S. Portfolio selection problems with Markowitz’s mean–variance framework: a review of literature. Fuzzy Optimization and Decision Making. 2018 Jun; 17:125-58. DOI: 10.1007/s10700-017-9266-z
Eghtesad A, Mohammadi Ů. Portfolio optimization with return prediction using LSTM, Random forest, and ARIMA. Financial Management Perspective. 2023 Dec 22;13(43):9-28. https://doi.org/10.48308/jfmp.2024.104191
