Sains Malaysiana 52(10)(2023): 2999-3011
http://doi.org/10.17576/jsm-2023-5210-20
The Analysis Level of Optimism that Influence
Investor’s Risk Tolerance in Asset Allocation
(Analisis Tahap Optimisme yang Mempengaruhi
Toleransi Risiko Pelabur dalam Peruntukan Saham)
SITI
NAZIFAH ZAINOL ABIDIN1,2, SAIFUL HAFIZAH JAAMAN2,* &
AHMAD SYAFADHLI ABU BAKAR3
1Faculty of Computer and Mathematical Sciences,
Universiti Teknologi MARA Negeri Sembilan, Seremban Campus, 70300 Seremban,
Negeri Sembilan, Malaysia
2Department of Mathematical Sciences, Faculty of
Science and Technology, Universiti Kebangsaan Malaysia, 43600 UKM Bangi,
Selangor, Malaysia
3Mathematics Division Centre for Foundation Studies in
Sciences, University of Malaya, 50603 Kuala Lumpur, Malaysia
Diserahkan: 12 Jun 2023/Diterima: 10 Oktober
2023
Abstract
Investor’s risk of tolerance level has been
widely categorized into three types, namely, risk averse, risk seeker and risk neutral. Nevertheless, in assessing the risk of a
particular asset, investors that fall under the same risk tolerance
classification may have different levels of optimism. It is thus beneficial to
complement types of investor’s risk of tolerance with level of optimism. In
this study, a fuzzy asset allocation model that satisfy heterogeneous
investor’s risk of tolerance with regards to investor’s level of optimism is
proposed. Enhancing Fuzzy Inferences System (FIS) with cooperation of optimism
level, this study obtains a flexible fuzzy allocation model which is based on
heterogeneous types of investor’s risk of tolerance combined with various level
of optimism. Empirical evidence on 30 Malaysian shares employing the model
developed shows that the proposed model successfully able to differentiate
various combinations of investor’s risk of tolerance level and investor’s level
of optimism. Furthermore, model is able to determine asset allocation and
priority shares for each combination accordingly. In conclusion, it is shown
that employing the proposed model allows investor to make beneficial investment
decision according to his combined risk tolerance and level of optimism.
Keywords: Fuzzy asset allocation; fuzzy inference system;
heterogeneous investor’s risk of tolerance; investor’s level of optimism
Abstrak
Tahap
toleransi risiko pelabur sering dikategorikan kepada tiga jenis iaitu,
kehindaran risiko, pencari risiko dan risiko neutral. Walau bagaimanapun, dalam
menilai risiko sesuatu saham tertentu, pelabur yang tergolong dalam pengelasan
toleransi risiko yang sama mungkin mempunyai tahap optimisme yang berbeza. Oleh
itu, adalah penting untuk melengkapkan jenis toleransi risiko pelabur dengan
tahap optimisme. Dalam kajian ini, satu model peruntukan saham kabur yang
memenuhi tahap toleransi risiko pelabur yang heterogen berdasarkan tahap
optimisme pelabur dicadangkan. Dengan meningkatkan Sistem Penaakulan Kabur
(FIS) dengan kerjasama tahap optimisme, kajian ini memperoleh model peruntukan
kabur yang fleksibel berdasarkan jenis toleransi risiko pelabur yang berbeza
digabungkan dengan pelbagai tahap optimisme. Bukti empirik terhadap 30 saham
Malaysia menggunakan model yang dibangunkan menunjukkan bahawa model yang
dicadangkan berjaya membezakan pelbagai gabungan tahap toleransi risiko pelabur
dan tahap optimisme pelabur. Tambahan pula, model ini mampu menentukan
peruntukan saham dan saham keutamaan bagi setiap gabungan. Kesimpulannya, telah
ditunjukkan bahawa penggunaan model yang dicadangkan membolehkan pelabur
membuat keputusan yang bermanfaat berdasarkan gabungan antara toleransi risiko
dan tahap optimisme pelabur.
Kata kunci: Peruntukan saham kabur; sistem penaakulan kabur; toleransi risiko pelabur heterogen; tahap optimisme pelabur
RUJUKAN
Ahn,
D., Choi, S., Gale, D. & Kariv, S. 2014. Estimating
ambiguity aversion in a portfolio choice experiment. Quantitative Economics 5(2): 195-223.
Andani, S.R. 2013. Fuzzy mamdani dalam menentukan tingkat keberhasilan dosen mengajar. Seminar Nasional Informatika, UPN
‘Veteran’ Yogyakarta, 18 Mei.
Bhattacharjee, S. 2017. A comparative analysis of impact of asset allocation on
portfolio performance as medium term investments. Journal of Management and Research 11(3/4): 1-15.
Chen, L.H. & Huang, L.
2009. Portfolio optimization of equity mutual funds with fuzzy return rates and
risks. Expert Systems with Applications 36(2): 3720-3727.
Febriany, N., Agustina,
F. & Marwati, R. 2017. Aplikasi metode fuzzy mamdani dalam penentuan status gizi dan kebutuhan kalori harian balita menggunakan software
MATLAB. Jurnal Eureka Matika 5(1): 84-96.
Gong, X., Min, L. & Yu, C. 2022. Multi-period portfolio
selection under the coherent fuzzy environment with dynamic risk-tolerance and expected-return
levels. Applied Soft Computing 114: 108104.
Huang, X., Jiang, G., Gupta, P. & Mehlawat, M.K. 2021. A risk index model for uncertain
portfolio selection with background risk. Computers & Operations
Research 2021: 105331.
Jaaman,
S.H., Weng, H.L. & Isa, Z. 2013. Risk measures
and portfolio construction in different economic scenarios. Sains Malaysiana 42(6):
875-880.
Jouini,
E. & Napp, C. 2007. Are more risk averse agents
more optimistic? Insights from a rational expectations model. Economics
Letters 101(1): 73-76.
Kiliçman, A. & Sivalingam, J. 2010. Portfolio
optimization of equity mutual funds: Malaysian case study. Adv. Fuzzy Syst. 2010: 879453.
Kocadağlı,
O. & Keskin, R. 2015. A novel portfolio selection
model based on fuzzy goal programming with different importance and
priorities. Expert Systems with Applications 42(20):
6898-6912.
Lam, W.S., Jaaman, S.H. & Ismail, H. 2015. The impact of human
behaviour towards portfolio selection in Malaysia. Procedia-Social and Behavioral Sciences 172: 674-678.
Li, H.Q. & Yi, Z.H. 2019. Portfolio selection with
coherent Investor’s expectations under uncertainty. Expert Systems with
Applications 133: 49-58.
Lin, P. C., Watada, J., & Wu, B. 2013. Risk assessment of a portfolio selection model based on a fuzzy statistical test. IEICE Transactions on Information and Systems 96(3): 579-588.
Leungo,
E.A. 2010. Fuzzy mean-variance portfolio selection problems. Advanced Modelling and Optimization 12(3): 399-410.
Markowitz, H. 1952. Portfolio selection. Journal of Finance 7(1): 77-91.
Mirnoori, S.M. & Shariati, A. 2012. Fuzzy
portfolio optimization using Chen and Huang model: Evidence from Iranian mutual
funds. Afr. J. Bus. Manag. 6: 6608-6616.
Mohamed, Z., Mohamad, D. & Samat,
O. 2009. A fuzzy approach to portfolio selection. Sains Malaysiana 38(6): 895-899.
Mohd Amin, F.A. & Jaaman, S.H. 2023. Pemeringkatan saham patuh syariah menggunakan pembuatan keputusan berbilang-kriterium: TOPSIS dan GRA. Sains Malaysiana 52(6): 1865-1877.
Princy,
S. & Dhenakaran, S.S. 2016. Comparison of
triangular and trapezoidal fuzzy membership function. Journal of Computer Science and Engineering 2(8): 46-51.
Ramli,
N. & Mohamad, D. 2010. Fuzzy Jaccard with degree
of optimism ranking index based on function principle approach. Majlesi Journal of Electrical Engineering 4(4): 9-15.
Rinandiyana, L.R., Fahmi, A.N. & Kusnandar,
D.L. 2020. Experienced regret dan risk tolerance dalam membentuk perilaku perdagangan saham. Forum Ekonomi22(1): 44-48.
Robiyanto, R. 2018. Performance evaluation of stock price indexes in the
Indonesia stock exchange. International
Research Journal of Business Studies 10(3): 173-182.
Safdari, C. & Scannell, N.J. 2005. Investment risk
profiling utilizing business resource slack. Journal of Business & Economics Research 3(8). https://doi.org/10.19030/jber.v3i8.2794
Shyamal, A.K. & Pal, M. 2007. Triangular fuzzy matrices. Iranian Journal of Fuzzy System 4(1):
75-87.
Sutara, B. & Kuswanto, H. 2019. Analisa perbandingan fuzzy logic metode Tsukamoto, Sugeno, Mamdani dalam penentuan keluarga miskin. Infotekmesin 10(2): 75-86.
Turan,
H.H., Atmis, M., Kosanoglu,
F., Elsawah, S. & Ryan, M.J. 2020. A risk-averse
simulation-based approach for a joint optimization of workforce capacity, spare
part stocks and scheduling priorities in maintenance planning. Reliability Engineering & System Safety 204:
107199.
Tsaur,
R.C. 2013. Fuzzy portfolio model with different investor risk attitudes. European
Journal of Operational Research 227(2): 385-390.
Van Staden, P.M., Dang, D.M. & Forsyth,
P.A. 2021. The surprising robustness of dynamic mean-variance portfolio
optimization to model misspecification errors. European Journal of Operational Research 289(2): 774-792.
Vlad, C. & Surlaru, A.C.P. 2020. Empirical check of the return-risk
tandem. Ovidius University Annals, Economic Sciences Series 20(1):
1070-1073.
Wen, F., He, Z. & Chen,
X. 2014. Investors’ risk preference characteristics and conditional
skewness. Mathematical Problems in
Engineering 2014: 814965.
Xing, F.Z., Cambria, E.
& Welsch, R.E. 2018. Intelligent asset allocation
via market sentiment views. IEEE
Computational Intelligence Magazine 13(4): 25-34.
Yao, Z. & Rabbani, A.G.
2021. Association between investment risk tolerance and portfolio risk: The
role of confidence level. Journal of Behavioral and Experimental Finance 30:
100482.
Zainol Abidin, S.N., Jaaman, S.H.,
Ismail, M. & Abu Bakar, A.S. 2020. Clustering stock performance considering
investor preferences using a fuzzy inference system. Symmetry 12(7):
1148.
*Pengarang untuk surat-menyurat; email: shj@ukm.edu.my
|