Price Model with Generalized Wiener Process for Life Insurance Company Portfolio Optimization using Mean Absolute Deviation
Abstract
The Financial Services Authority (OJK) has issued Regulation of the Financial Services Authority of the Republic of Indonesia Number 5 Year 2023. Article 11 paragraph 1d explains the limitations of assets allowed in the form of investment, investment in the form of shares listed on the stock exchange for each issuer is a maximum of 10% of the total investment and a maximum of 40% of the total investment. The investment manager of a life insurance company needs to adjust its investment portfolio. In 1991, Konno and Yamazaki proposed an approach to the portfolio selection problem with Mean Absolute Deviation (MAD) model. This model can be solved using linear programming, effectively solving high-dimensional portfolio optimization problems. Another problem in stock portfolio formation is that the ever-changing financial markets demand the development of models to understand and forecast stock price behavior. One method that has been widely used to model stock price movements is the generalized Wiener Process. The generalized Wiener process provides a framework that can accommodate the stochastic nature of stock price changes, thus allowing portfolio managers to be more sensitive to unanticipated market fluctuations. The stock price change model using the Generalized Wiener Process is very good at predicting stock price changes. The results of this stock price prediction can then be used to find the optimal portfolio using the MAD model. The portfolio optimization problem with the MAD model can be solved using linear programming to obtain the optimal stock portfolio for life insurance companies.
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Aksaraylı, M. & Pala, O. (2018). A polynomial goal programming model for portfolio optimization based on entropy and higher moments. Expert Systems with Applications, 94, 185–192. https://doi.org/https://doi.org/10.1016/j.eswa.2017.10.056
Banihashemi, S. & Navidi, S. (2017). Portfolio performance evaluation in Mean-CVaR framework: A comparison with non-parametric methods value at risk in Mean-VaR analysis. Operations Research Perspectives, 4, 21–28. https://doi.org/https://doi.org/10.1016/j.orp.2017.02.001
Deng, G.-F., Lin, W.-T. & Lo, C.-C. (2012). Markowitz-based portfolio selection with cardinality constraints using improved particle swarm optimization. Expert Systems with Applications, 39(4), 4558–4566. https://doi.org/https://doi.org/10.1016/j.eswa.2011.09.129
Erwin, K. & Engelbrecht, A. (2023). Meta-heuristics for portfolio optimization. Soft Computing, 27(24), 19045–19073. https://doi.org/10.1007/s00500-023-08177-x
Grechuk, B. & Zabarankin, M. (2014). Inverse portfolio problem with mean-deviation model. European Journal of Operational Research, 234(2), 481–490. https://doi.org/https://doi.org/10.1016/j.ejor.2013.04.056
Gupta, P., Mehlawat, M. K., Inuiguchi, M. & Chandra, S. (2014). Portfolio Optimization: An Overview. In P. Gupta, M. K. Mehlawat, M. Inuiguchi & S. Chandra (Eds.), Fuzzy Portfolio Optimization: Advances in Hybrid Multi-criteria Methodologies (pp. 1–31). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-54652-5_1
Hosseini-Nodeh, Z., Khanjani-Shiraz, R. & Pardalos, P. M. (2023). Portfolio optimization using robust mean absolute deviation model: Wasserstein metric approach. Finance Research Letters, 54, 103735. https://doi.org/https://doi.org/10.1016/j.frl.2023.103735
Huang, X. & Yang, T. (2020). How does background risk affect portfolio choice: An analysis based on uncertain mean-variance model with background risk. Journal of Banking & Finance, 111, 105726. https://doi.org/https://doi.org/10.1016/j.jbankfin.2019.105726
Kalayci, C. B., Ertenlice, O. & Akbay, M. A. (2019). A comprehensive review of deterministic models and applications for mean-variance portfolio optimization. Expert Systems with Applications, 125, 345–368. https://doi.org/https://doi.org/10.1016/j.eswa.2019.02.011
Konno, H. & Yamazaki, H. (1991). Mean-Absolute Deviation Portfolio Optimization Model and Its Applications to Tokyo Stock. In Source: Management Science (Vol. 37, Issue 5). https://doi.org/10.1287/mnsc.37.5.519
Le Thi, H. A. & Moeini, M. (2014). Long-Short Portfolio Optimization Under Cardinality Constraints by Difference of Convex Functions Algorithm. Journal of Optimization Theory and Applications, 161(1), 199–224. https://doi.org/10.1007/s10957-012-0197-0
Li, B., Sun, Y., Aw, G. & Teo, K. L. (2019). Uncertain portfolio optimization problem under a minimax risk measure. Applied Mathematical Modelling, 76, 274–281. https://doi.org/https://doi.org/10.1016/j.apm.2019.06.019
Li, B. & Zhang, R. (2021). A new mean-variance-entropy model for uncertain portfolio optimization with liquidity and diversification. Chaos, Solitons & Fractals, 146, 110842. https://doi.org/https://doi.org/10.1016/j.chaos.2021.110842
Liu, S.-T. (2011). The mean-absolute deviation portfolio selection problem with interval-valued returns. Journal of Computational and Applied Mathematics, 235(14), 4149–4157. https://doi.org/https://doi.org/10.1016/j.cam.2011.03.008
Lv, S., Wu, Z. & Yu, Z. (2016). Continuous-time mean–variance portfolio selection with random horizon in an incomplete market. Automatica, 69, 176–180. https://doi.org/https://doi.org/10.1016/j.automatica.2016.02.017
Ma, Y., Wang, Y., Wang, W. & Zhang, C. (2023). Portfolios with return and volatility prediction for the energy stock market. Energy, 270, 126958. https://doi.org/https://doi.org/10.1016/j.energy.2023.126958
Markowitz, H. (1952). Portfolio Selection. The Journal of Finance, 7(1), 77–91. https://doi.org/10.2307/2975974
Qin, Z. (2017). Random fuzzy mean-absolute deviation models for portfolio optimization problem with hybrid uncertainty. Applied Soft Computing, 56, 597–603. https://doi.org/https://doi.org/10.1016/j.asoc.2016.06.017
Qin, Z., Kar, S. & Zheng, H. (2016). Uncertain portfolio adjusting model using semiabsolute deviation. Soft Computing, 20(2), 717–725. https://doi.org/10.1007/s00500-014-1535-y
Ramos, H. P., Righi, M. B., Guedes, P. C. & Müller, F. M. (2023). A comparison of risk measures for portfolio optimization with cardinality constraints. Expert Systems with Applications, 228, 120412. https://doi.org/https://doi.org/10.1016/j.eswa.2023.120412
Vanti, E. N. & Supandi, E. D. (2020). Pembentukan Portofolio Optimal dengan Menggunakan Mean Absolute Deviation dan Conditional Mean Variance. Jurnal Fourier, 9(1), 25–34. https://doi.org/10.14421/fourier.2020.91.25-34
Zhang, P. & Zhang, W.-G. (2014). Multiperiod mean absolute deviation fuzzy portfolio selection model with risk control and cardinality constraints. Fuzzy Sets and Systems, 255, 74–91. https://doi.org/https://doi.org/10.1016/j.fss.2014.07.018
DOI: https://doi.org/10.31764/jtam.v8i4.23093
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