A Hybrid Model for Portfolio Optimization Based on Stock Price Forecasting with LSTM Recurrent Neural Network using Multi-Criteria Decision Making Techniques and Mean-CVaR

Document Type : Original Article

Authors

1 Ph.D. Candidate in Financial Engineering, Department of Management, Karaj Branch, Islamic Azad University, Karaj, Iran

2 Associate Professor, Department of Accounting, Karaj Branch, Islamic Azad University, Karaj, Iran

3 Assistant professor Department of Accounting, Karaj Branch, Islamic Azad University, Karaj, Iran

4 Assistant professor, Department of Management, Karaj Branch, Islamic Azad University, Karaj, Iran

10.30495/ijfma.2022.67311.1849

Abstract

The importance of a price forecasting issue due to the market volatility is very substantial. Investors have no desire to tolerate the high risk and invest in such a market due to avoiding ambiguity and mainly looking for a suitable solution for investing with high returns and low risk. The purpose of the research is to combine decision-making techniques with recurrent neural networks to create and develop a mathematical model for stock portfolio optimization due to different time horizons. Therefore, the top ten industries were selected using the Fuzzy Analytical Hierarchy Process (FAHP), according to effective criteria on the active industries in the stock market and using the opinions of active industries' experts, between May 2016 and May 2021. Then the price of stocks was forecasted in intended time periods using Long Short Term Memory RNN. In the next step, three stock portfolios with the short-term, mid-term, and long-term time horizons were created using a Combined Compromise Solution method, and then the optimized weights of each stock in the different portfolios were defined, and an efficient frontier was drawn by using Conditional Value at Risk (CVaR). The results showed that the provided model has high efficiency in stock portfolio optimization.

Keywords

References

  • AmirHossini, Z., . Masoumeh Ghobadi. (2016). Fuzzy MCDM Approach of Portfolio Evaluation and Selection. 7(27), 1-16. http://fej.iauctb.ac.ir/article_522039_1597745c523a7959cb6faf179fea810f.pdf
  • Centeno, V., Georgiev, I. R., Mihova, V., & Pavlov, V. (2019). Price forecasting and risk portfolio optimization. AIP Conference Proceedings, 2164(1), 060006. https://doi.org/10.1063/1.5130808
  • Chang, D.-Y. (1996). Applications of the extent analysis method on fuzzy AHP. European journal of operational research, 95(3), 649-655.
  • Chen, K., Zhou, Y., & Dai, F. (2015, 29 Oct.-1 Nov. 2015). A LSTM-based method for stock returns prediction: A case study of China stock market. 2015 IEEE International Conference on Big Data (Big Data),
  • 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. https://doi.org/https://doi.org/10.1016/j.asoc.2020.106943
  • Cornuejols, G., & Tütüncü, R. (2006). Optimization methods in finance (Vol. 5). Cambridge University Press.
  • Dezsi, E., & Nistor, I. A. (2016). Can deep machine learning outsmart the market? a comparison between econometric modelling and long-short term memory. Romanian Economic and Business Review.
  • Fekri, M., & Barazandeh, B. (2019). Designing an Optimal Portfolio for Iran's Stock Market with Genetic Algorithm using Neural Network Prediction of Risk and Return Stocks.
  • Freitas, F. D., De Souza, A. F., & de Almeida, A. R. (2009). Prediction-based portfolio optimization model using neural networks. Neurocomputing, 72(10), 2155-2170. https://doi.org/https://doi.org/10.1016/j.neucom.2008.08.019
  • Gârleanu, N., & Pedersen, L. H. (2013). Dynamic trading with predictable returns and transaction costs. The Journal of Finance, 68(6), 2309-2340.
  • Ghaffari-Nasab, N., Ahari, S., & Makui, A. (2011). A portfolio selection using fuzzy analytic hierarchy process: A case study of Iranian pharmaceutical industry. International Journal of Industrial Engineering Computations, 2(2), 225-236.
  • Haddad, M. F. C. (2019). Sphere-sphere intersection for investment portfolio diversification—A new data-driven cluster analysis. MethodsX, 6, 1261-1278.
  • Haddadi, M. r., Nademi, Y., & Tafi, F. (2021). Stock Portfolio Optimization with MAD and CVaR Criteria by Comparing Classical and Metaheuristic Methods. 12(47), 514-533. http://fej.iauctb.ac.ir/article_682742_947038fd1f3098f532a60f22128c92b8.pdf
  • Haykin, S., & Network, N. (2004). A comprehensive foundation. Neural networks, 2(2004), 41.
  • Karimi, A., & goodarzi dahrizi, s. (2020). Stock portfolio optimization using Imperialist Competitive Algorithm (ICA) and Particle Swarm Optimization (PSO) under Conditional Value at Risk (CVaR). 11(45), 423-444. http://fej.iauctb.ac.ir/article_679100_3e11579c3e410c1be3005d85278e9d69.pdf
  • Lashgari, Z., & Safari, K. (2012). Portfolio selection using fuzzy analytic hierarchy process (FAHP). European Business Research Conference 2012 Proceedings,
  • Lee, S. I., & Yoo, S. J. (2020). Threshold-based portfolio: the role of the threshold and its applications. The Journal of Supercomputing, 76(10), 8040-8057. https://doi.org/10.1007/s11227-018-2577-1
  • LI, G.-c., & XIAO, Q.-x. (2013). Hybrid meta-heuristic algorithm for solving cardinality constrained portfolio optimization [J]. Application Research of Computers, 30(8), 2292-2297.
  • Lwin, K. T., Qu, R., & MacCarthy, B. L. (2017). Mean-VaR portfolio optimization: A nonparametric approach. European Journal of Operational Research, 260(2), 751-766.
  • Ma, Y., Han, R., & Wang, W. (2021). Portfolio optimization with return prediction using deep learning and machine learning. Expert Systems with Applications, 165, 113973. https://doi.org/https://doi.org/10.1016/j.eswa.2020.113973
  • Markowitz, H. (1952). PORTFOLIO SELECTION*. The Journal of Finance, 7(1), 77-91. https://doi.org/10.1111/j.1540-6261.1952.tb01525.x
  • Nasini, S., Labbé, M., & Brotcorne, L. (2021). Multi-market portfolio optimization with conditional value at risk. European Journal of Operational Research. https://doi.org/https://doi.org/10.1016/j.ejor.2021.10.010
  • Navidi, S., Banihashemi, s., & Sanei, M. (2016). Three steps method for portfolio optimization by using Conditional Value at Risk measure. Journal of New Researches in Mathematics, 2(5), 43-60. https://jnrm.srbiau.ac.ir/article_9368_135d27e70ea09f03c8e2fd1a422feb59.pdf
  • Patterson, J., & Gibson, A. (2017). Deep learning: A practitioner's approach. " O'Reilly Media, Inc.".
  • Peng, X., & Luo, Z. (2021). Decision-making model for China’s stock market bubble warning: the CoCoSo with picture fuzzy information. Artificial Intelligence Review, 1-23.
  • Raei, R., Basakha, H., & Mahdikhah, H. (2020). Equity Portfolio Optimization Using Mean-CVaR Method Considering Symmetric and Asymmetric Autoregressive Conditional Heteroscedasticity. Financial Research Journal, 22(2), 149-159. https://doi.org/10.22059/frj.2019.205531.1006186
  • Rahnama, H. (2016). A Portfolio Optimization Model Ecole Polytechnique, Montreal (Canada)].
  • Rahnama Roodposhti, F., Sadeh, E., Fallahshams, M., Ehteshamrasi, r., & Jalilian, j. (2018). A Portfolio Optimization Model for a Private Equity Investment Company under Data Insufficiency Condition with an Artificial Bee Colony Meta-heuristic Approach. 9(35), 77-104. http://fej.iauctb.ac.ir/article_541829_8be8b16fdfc1d3c4771684f084b2bde2.pdf
  • RAHNAMAY ROODPOSHTI, F., NIKOOMARAM, H., TOLOIE ESHLAGHI, A., HOSSEINZADEH LOTFI, F., & BAYAT, M. (2015). PORTFOLIO OPTIMIZATION MODEL TO OPTIMIZE THE PERFORMANCES OF CLASSICAL FORECASTING STABLE PORTFOLIO RISK AND RETURN. FINANCIAL ENGINEERING AND SECURITIES MANAGEMENT (PORTFOLIO MANAGEMENT), 6(22), -. https://www.sid.ir/en/journal/ViewPaper.aspx?ID=433760
  • Samarawickrama, A. J. P., & Fernando, T. G. I. (2017, 15-16 Dec. 2017). A recurrent neural network approach in predicting daily stock prices an application to the Sri Lankan stock market. 2017 IEEE International Conference on Industrial and Information Systems (ICIIS),
  • Sivam, S., & Rajendran, R. (2020). On the Modelling of Integrated AHP and CoCoSo Approach for Robust Design of Multi-objective Optimization of thinning Parameters for Maximum thinning Rate and Determine Optimum Locations for Directionally-rolled Deep-drawn Cups using Scaling Laws.
  • Uryasev, S. (2000, 28-28 March 2000). Conditional value-at-risk: optimization algorithms and applications. Proceedings of the IEEE/IAFE/INFORMS 2000 Conference on Computational Intelligence for Financial Engineering (CIFEr) (Cat. No.00TH8520),
  • Yazdani, M., Zarate, P., Kazimieras Zavadskas, E., & Turskis, Z. (2019). A combined compromise solution (CoCoSo) method for multi-criteria decision-making problems. Management Decision, 57(9), 2501-2519. https://doi.org/10.1108/MD-05-2017-0458
  • Zeleny, M. (1973). Compromise programming. Multiple criteria decision making.

 

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