The Formation of Portfolio with Fuzzy Approach and Multi-objective Method
DOI:
https://doi.org/10.1285/i20705948v16n3p541Keywords:
Expected Return, Fuzzy Portfolio, Multi-objective Portfolio.Abstract
Forming a portfolio in the investment process is a crucial component. Itis because investors want maximum profi t while expecting a minimum levelof risk. The portfolio composition is inseparable from the weighting of eachobserved stock. In fact, mathematically, there are still problems when tryingto fulfi ll the preferences that investors want. The research objective was theformation of a portfolio using a fuzzy approach and a multi-objective method.This model simultaneously maximized the return and risk of the preparedportfolio. The result was the formation of a portfolio with two categories,namely risk-seeking and risk-averse, equipped with a λ value of each method,the weight of each stock, the expected return, and risk. Parameter λ wasthe value obtained from selecting the risk level determined by the investor.Parameter λ was used to assess the level of risk and the expected return onthe portfolio preparation. The last section compared the weights, expectedreturn, and risk values of the two methods. As a result, investors in therisk seeker category have the potential to get higher expected returns whenusing the multi-objective method. In contrast, the fuzzy approach producesthe possibility of a higher expected return for investors in the risk-aversecategory.References
Almahdi, S. and Yang, S. Y. (2017). An adaptive portfolio trading system: A risk-return
portfolio optimization using recurrent reinforcement learning with expected maximum
drawdown. Expert Systems with Applications, 87:267–279.
Bellman, R. E. and Zadeh, L. A. (1970). Decision-making in a fuzzy environment.
Management science, 17(4):B–141.
Bilbao-Terol, A., P´erez-Gladish, B., Arenas-Parra, M., and Rodr´ıguez-Ur´ıa, M. V.
(2006). Fuzzy compromise programming for portfolio selection. Applied Mathematics
and computation, 173(1):251–264.
Caviezel, V., Gambirasi, S., and Lozza, S. O. (2012). Risk profi le using pcm and rsm.
Electronic Journal of Applied Statistical Analysis, 5(3):327–332.
Chen, C. and Wei, Y. (2019). Robust multiobjective portfolio optimization: a set order
relations approach. Journal of Combinatorial Optimization, 38:21–49.
Ciavolino, E. and Calcagn`ı, A. (2016). A generalized maximum entropy (gme) estimation
approach to fuzzy regression model. Applied Soft Computing, 38:51–63.
Di Asih, I. M. and Purbowati, A. (2009). Pengukuran value at risk pada aset tunggal
dan portofolio dengan simulasi monte carlo. Media Statistika, 2(2):93–104.
Duan, Y. C. (2007). A multi-objective approach to portfolio optimization. Rose-Hulman
Undergraduate Mathematics Journal, 8(1):12.
Garc´ıa Garc´ıa, F., Gonz´alez-Bueno, J., Guijarro, F., and Oliver-Muncharaz, J. (2020).
A multiobjective credibilistic portfolio selection model. empirical study in the latin
american integrated market. Enterpreneurship and Sustainability Issues, 8(2):1027–
Goli, A., Zare, H. K., Tavakkoli-Moghaddam, R., and Sadeghieh, A. (2019). Hybrid
artifi cial intelligence and robust optimization for a multi-objective product portfolio
problem case study: The dairy products industry. Computers & industrial engineering,
:106090.
Gupta, P., Mehlawat, M. K., Inuiguchi, M., and Chandra, S. (2014). Fuzzy portfolio
optimization. Studies in fuzziness and soft computing, 316.
Hoyyi, A. and Ispriyanti, D. (2015). Optimisasi multiobjektif untuk pembentukan porto-
folio. Media Statistika, 8(1):31–39.
Huang, X. (2008). Mean-entropy models for fuzzy portfolio selection. IEEE Transactions
on Fuzzy Systems, 16(4):1096–1101.
Jreisat, A. (2018). Productivity growth of the australian real estate investment trusts.
Electronic Journal of Applied Statistical Analysis, 11(1):58–73.
Kalayci, C. B., Ertenlice, O., and Akbay, M. A. (2019). A comprehensive review of
deterministic models and applications for mean-variance portfolio optimization. Expert
Systems with Applications, 125:345–368.
Maghyereh, A. and Abdoh, H. (2020). Tail dependence between bitcoin and fi nancial
assets: Evidence from a quantile cross-spectral approach. International Review of
Financial Analysis, 71:101545.
Manik, N. I. and Sukandar, A. R. (2021). Optimalisasi pemilihan portofolio saham
menggunakan fuzzy linear programming berbasis komputer. Jurnal Algoritma, Logika
dan Komputasi, 3(2).
Markowitz, H. (1952). Portfolio selection j. fi nance.
Mehlawat, M. K., Gupta, P., Kumar, A., Yadav, S., and Aggarwal, A. (2020). Multiob-
jective fuzzy portfolio performance evaluation using data envelopment analysis under
credibilistic framework. IEEE Transactions on Fuzzy Systems, 28(11):2726–2737.
Parra, M. A., Terol, A. B., and Urıa, M. R. (2001). A fuzzy goal programming approach
to portfolio selection. European Journal of Operational Research, 133(2):287–297.
Pradana, D. C., Di Asih, I. M., and Yasin, H. (2015). Penggunaan simulasi monte
carlo untuk pengukuran value at risk aset tunggal dan portofolio dengan pendekatan
capital asset pricing model sebagai penentu portofolio optimal (studi kasus: Index
saham kelompok sminfra18). Jurnal Gaussian, 4(4):765–774.
Pramono, E. S., Rudianto, D., Siboro, F., Baqi, M. P. A., and Julianingsih, D. (2022).
Analysis investor index indonesia with capital asset pricing model (capm). Aptisi
Transactions on Technopreneurship (ATT), 4(1):35–46.
Raihan, M. T. and Saepudin, D. (2018). Pemilihan portofolio fuzzy mean-semi variance
multi-periode dengan biaya transaksi dan jumlah transaksi minimum menggunakan
algoritma genetika. eProceedings of Engineering, 5(3).
Ramaswamy, S. (1998). Portfolio selection using fuzzy decision theory.
Ruiz-Torrubiano, R. and Su´arez, A. (2015). A memetic algorithm for cardinality-
constrained portfolio optimization with transaction costs. Applied Soft Computing,
:125–142.
Septiano, D. R., Syafriand, S., et al. (2019). Pembentukan portofolio optimal meng-
gunakan metode optimasi multiobjektif pada saham di bursa efek indonesia. UNP:
Journal of Mathematics, 4(2).
Seyedhosseini, S. M., Esfahani, M. J., and Ghaff ari, M. (2016). A novel hybrid algorithm
based on a harmony search and artifi cial bee colony for solving a portfolio optimization
problem using a mean-semi variance approach. Journal of Central South University,
:181–188.
Subekti, R., Abdurakhman, A., and Rosadi, D. (2022). Can zakat and purifi cation
be employed in portfolio modelling? Journal of Islamic Monetary Economics and
Finance, 8:1–16.
Subekti, R. and Kusumawati, R. (2015). Portfolio selection in indonesia stock market
with fuzzy bi-objective linear programming. In 2015 International Conference on
Research and Education in Mathematics (ICREM7), pages 97–102. IEEE.
Sumer, L. and Ozorhon, B. (2021). Investing in gold or reit index in turkey: evidence
from global fi nancial crisis, 2018 turkish currency crisis and covid-19 crisis. Journal
of European Real Estate Research, 14(1):84–99.
Ta, V.-D., Liu, C.-M., and Tadesse, D. A. (2020). Portfolio optimization-based stock
prediction using long-short term memory network in quantitative trading. Applied
Sciences, 10(2):437.
Yu, G.-F., Li, D.-F., Liang, D.-C., and Li, G.-X. (2021). An intuitionistic fuzzy multi-
objective goal programming approach to portfolio selection. International Journal of
Information Technology & Decision Making, 20(05):1477–1497.
Zhang, W.-G. and Nie, Z.-K. (2005). On admissible effi cient portfolio selection policy.
Applied mathematics and computation, 169(1):608–623.
Downloads
Published
Issue
Section
License
Authors who publish with EJASA agree to the Creative Commons Attribuzione - Non commerciale - Non opere derivate 3.0 Italia License.
