Skip to main content
SpringerLink
Log in

A perceptual computer for hierarchical portfolio selection based on interval type-2 fuzzy sets

  • Original Paper
  • Published:
Granular Computing Aims and scope Submit manuscript

Abstract

Today’s advancements have made financial markets accessible to everyone; hence, portfolio selection has become an individualized decision-making problem without the need of being highly educated. Individual judgments, however, are subjective and are influenced by the individual’s background, experience, and views. Existing methods do not account for the personalized criteria and preferences or do not let people express their preferences and assessments using words or terms from natural languages. This paper proposes a framework for an individualized hierarchical portfolio selection system based on perceptual computing. The proposed method assists individuals to rank and select portfolios based on their personalized criteria and preferences and according to their subjective assessments. In this paper, words that are used to express one’s preferences, evaluations, and weights are modeled with interval type-2 fuzzy sets (IT2FS), which allows handling different levels of linguistic uncertainties with manageable computational complexities. The proposed method is applicable to any set of criteria and sub-criteria devised to evaluate portfolios. Moreover, it enables different individuals with different expertise levels to evaluate those criteria. The conducted experiments show that the proposed method, compared to other methods, is reliable and robust to the linguistic uncertainties and provide plausible recommendations.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Availability of data and materials (data transparency)

Available in the paper.

Code availability (software application or custom code)

Available upon request.

References

  • Akbaş S, Erbay Dalkiliç T (2021) A hybrid algorithm for portfolio selection: an application on the Dow Jones Index (DJI). J Comput Appl Math 398:113678

    Article  MathSciNet  MATH  Google Scholar 

  • Arnott D, Gao S (2019) Behavioral economics for decision support systems researchers. Decis Support Syst 122:113063

    Article  Google Scholar 

  • Bajestani NS, Zare A (2011) Forecasting TAIEX using improved type-2 fuzzy time series. Expert Syst Appl 38(5):5816–5821

    Article  Google Scholar 

  • Bengio Y (2009) Learning deep architectures for AI. Found Trends Mach Learn 2:1–127

    Article  MATH  Google Scholar 

  • Biswas A, De AK (2018) A unified method of defuzzification for type-2 fuzzy numbers with its application to multiobjective decision making. Granul Comput 3:301–318

    Article  Google Scholar 

  • Busseti E, Osband I, Wong S (2012) Deep learning for time series modeling. Tech report, Stanford Univ 5

  • Castillo O, Ochoa P, Soria J (2021) Differential evolution algorithm with type-2 fuzzy logic for dynamic parameter adaptation with application to intelligent control, 1st edn. Springer International Publishing

  • Chen S-M, Chen C-D (2011) TAIEX forecasting based on fuzzy time series and fuzzy variation groups. IEEE Trans Fuzzy Syst 19(1):1–12

    Article  Google Scholar 

  • Chen S-M, Hong J-A (2014) Fuzzy multiple attributes group decision-making based on ranking interval type-2 fuzzy sets and the TOPSIS method. IEEE Trans Syst Man Cybern Syst 44(12):1665–1673

    Article  Google Scholar 

  • Chen S-M, Lee L-W (2011) Fuzzy interpolative reasoning for sparse fuzzy rule-based systems based on interval type-2 fuzzy sets. Expert Syst Appl 38:9947–9957

    Article  Google Scholar 

  • Chen S-M, Chang Y-C, Pan J-S (2012) Fuzzy rules interpolation for sparse fuzzy rule-based systems based on interval type-2 Gaussian fuzzy sets and genetic algorithms. IEEE Trans Fuzzy Syst 21(3):412–425

    Article  Google Scholar 

  • Guo S, Ching W-K, Li W-K et al (2020) Fuzzy hidden Markov-switching portfolio selection with capital gain tax. Expert Syst Appl 149:113304

    Article  Google Scholar 

  • Gupta PK, Muhuri PK (2019) Computing with words for student strategy evaluation in an examination. Granul Comput 4:167–184

    Article  Google Scholar 

  • Han S, Mendel JM (2012) A new method for managing the uncertainties in evaluating multi-person multi-criteria location choices, using a perceptual computer. Ann Oper Res 195:277–309

    Article  MathSciNet  MATH  Google Scholar 

  • Hao M, Mendel JM (2012) Perceptual computer application in learning outcome evaluation. In: Fuzzy information processing society (NAFIPS), 2012 annual meeting of the North American, pp 1–6

  • Hao M, Mendel JM (2016) Encoding words into normal interval type-2 fuzzy sets: HM approach. IEEE Trans Fuzzy Syst 24(4):865–879

    Article  Google Scholar 

  • Ho W-RJ, Tsai C-L, Tzeng G-H, Fang S-K (2011) Combined DEMATEL technique with a novel MCDM model for exploring portfolio selection based on CAPM. Expert Syst Appl 38:16–25

    Article  Google Scholar 

  • Jeng DJ-F, Huang K-H (2015) Strategic project portfolio selection for national research institutes. J Bus Res 68:2305–2311

    Article  Google Scholar 

  • John R, Hagras H, Castillo O (2018) Type-2 fuzzy logic and systems. Springer, Berlin

    Book  MATH  Google Scholar 

  • Karnik NN, Mendel JM (2001) Centroid of a type-2 fuzzy set. Inf Sci (NY) 132:195–220

    Article  MathSciNet  MATH  Google Scholar 

  • Keller JM, Gader P (1995) Fuzzy logic and the principle of least commitment in computer vision. In: 1995 IEEE international conference on systems, man and cybernetics, intelligent systems for the 21st century, pp 4621–4625

  • Kodogiannis V, Lolis A (2002) Forecasting financial time series using neural network and fuzzy system-based techniques. Neural Comput Appl 11:90–102

    Article  Google Scholar 

  • Lai Z, Dai D, Ren C, Huang K (2018a) A peak price tracking-based learning system for portfolio selection. IEEE Trans Neural Netw Learn Syst 29:2823–2832

    MathSciNet  Google Scholar 

  • Lai Z, Dai D, Ren C, Huang K (2018b) Radial basis functions with adaptive input and composite trend representation for portfolio selection. IEEE Trans Neural Netw Learn Syst 29:6214–6226

    Article  Google Scholar 

  • Lashgari Z, Safari K (2014) Portfolio selection using fuzzy analytic hierarchy process (FAHP). J Account Financ Econ 4:68–85

    Google Scholar 

  • Lintner J (1965) The valuation of risk assets and the selection of risky investments in stock portfolios and capital budgets. Rev Econ Stat 47:13–37

    Article  Google Scholar 

  • Liu F, Mendel JM (2008) Encoding words into interval type-2 fuzzy sets using an interval approach. Fuzzy Syst IEEE Trans 16:1503–1521

    Article  Google Scholar 

  • Liu Y, Zhou S, Chen Q (2011) Discriminative deep belief networks for visual data classification. Pattern Recognit 44:2287–2296

    Article  MATH  Google Scholar 

  • Markowitz H (1952) Portfolio selection. J Finance 7:77–91

    Google Scholar 

  • Mashayekhi Z, Omrani H (2016) An integrated multi-objective Markowitz–DEA cross-efficiency model with fuzzy returns for portfolio selection problem. Appl Soft Comput 38:1–9

    Article  Google Scholar 

  • Mendel JM (2001) The perceptual computer: an architecture for computing with words. In: 10th IEEE international conference on fuzzy systems, pp 35–38

  • Mendel JM (2002) An architecture for making judgments using computing with words. Int J Appl Math Comput Sci 12:325–336

    MATH  Google Scholar 

  • Mendel JM (2007) Computing with words and its relationships with fuzzistics. Inf Sci (NY) 177:988–1006

    Article  MathSciNet  Google Scholar 

  • Mendel JM (2016) A comparison of three approaches for estimating (synthesizing) an interval type-2 fuzzy set model of a linguistic term for computing with words. Granul Comput 1:59–69

    Article  Google Scholar 

  • Mendel JM (2017) Uncertain rule-based fuzzy systems: introduction and new directions, 2nd edn. Springer, Cham

    Book  MATH  Google Scholar 

  • Mendel JM, John RI (2002) Type-2 fuzzy sets made simple. IEEE Trans Fuzzy Syst 10:117–127

    Article  Google Scholar 

  • Mendel JM, Wu D (2010) Perceptual computing: aiding people in making subjective judgments. Wiley, New York

    Book  Google Scholar 

  • Mendel JM, John RI, Liu F (2006) Interval type-2 fuzzy logic systems made simple. IEEE Trans Fuzzy Syst 14:808–821

    Article  Google Scholar 

  • Meniz B, Bas SA, Ozkok BA, Tiryaki F (2021) Multilevel AHP approach with interval type-2 fuzzy sets to portfolio selection problem. J Intell Fuzzy Syst 40:8819–8829

    Article  Google Scholar 

  • Moharrer M, Tahayori H, Livi L et al (2015) Interval type-2 fuzzy sets to model linguistic label perception in online services satisfaction. Soft Comput 19:237–250

    Article  Google Scholar 

  • Mossin J (1966) Equilibrium in a capital asset market. Econ J Econ Soc 34:768–783

    Google Scholar 

  • Muhuri PK, Gupta PK, Mendel JM (2018) User-satisfaction-aware power management in mobile devices based on perceptual computing. IEEE Trans Fuzzy Syst 26:2311–2323

    Article  Google Scholar 

  • Naderipour M, Fazel Zarandi MH, Bastani S (2020) A type-2 fuzzy community detection model in large-scale social networks considering two-layer graphs. Eng Appl Artif Intell 90:103206

    Article  Google Scholar 

  • Naimi M, Tahayori H (2020) Centroid of polygonal fuzzy sets. Appl Soft Comput 95(2):106519

    Article  Google Scholar 

  • Naimi M, Tahayori H, Sadeghian A (2021) A fast and accurate method for calculating the center of gravity of polygonal interval type-2 fuzzy sets. IEEE Trans Fuzzy Syst 29(6):1472–1483

    Article  Google Scholar 

  • Neto JC, Filipe JA, Caleiro AB (2019) Creativity and innovation: a contribution of behavioral economics. Int J Innov Stud 3:12–21

    Article  Google Scholar 

  • Ontiveros E, Melin P, Castillo O (2018) High order α-planes integration: a new approach to computational cost reduction of general type-2 fuzzy systems. Eng Appl Artif Intell 74:186–197

    Article  Google Scholar 

  • Ontiveros-Robles E, Melin P (2020) Toward a development of general type-2 fuzzy classifiers applied in diagnosis problems through embedded type-1 fuzzy classifiers. Soft Comput 24:83–99

    Article  Google Scholar 

  • Ontiveros-Robles E, Melin P, Castillo O (2021) An efficient high-order α-plane aggregation in general type-2 fuzzy systems using newton-cotes rules. Int J Fuzzy Syst 23:1102–1121

    Article  Google Scholar 

  • Paranjape-Voditel P, Deshpande U (2013) A stock market portfolio recommender system based on association rule mining. Appl Soft Comput 13:1055–1063

    Article  Google Scholar 

  • Pedrycz W (2013) Granular computing: analysis and design of intelligent systems, 1st edn. CRC Press, Boca Raton

    Book  Google Scholar 

  • Prokopczuk M (2011) Optimal portfolio choice in the presence of domestic systemic risk: empirical evidence from stock markets. Decis Econ Financ 34:141–168

    Article  MathSciNet  MATH  Google Scholar 

  • Rahiminezhad Galankashi M, Mokhatab Rafiei F, Ghezelbash M (2020) Portfolio selection: a fuzzy-ANP approach. Financ Innov 6:1–34

    Article  Google Scholar 

  • Saaty RW (1987) The analytic hierarchy process—what it is and how it is used. Math Model 9:161–176

    Article  MathSciNet  MATH  Google Scholar 

  • Safarzadegan Gilan S, Sebt MH, Shahhosseini V (2012) Computing with words for hierarchical competency based selection of personnel in construction companies. Appl Soft Comput 12:860–871

    Article  Google Scholar 

  • Sharpe WF (1964) Capital asset prices: a theory of market equilibrium under conditions of risk. J Finance 19:425–442

    Google Scholar 

  • Srivastava PR, Eachempati P (2020) A hybrid portfolio selection model: multi-criteria approach in the Indian stock market. Int J Intell Inf Technol 16:100–116

    Article  Google Scholar 

  • Sun X, Cai C, Pan S et al (2021) A university teachers’ teaching performance evaluation method based on type-II fuzzy sets. Mathematics 9:2126

    Article  Google Scholar 

  • Tahayori H, Sadeghian A (2012) Median interval approach to model words with interval type-2 fuzzy sets. Int J Adv Intell Paradig 4:313–336

    Google Scholar 

  • Tahayori H, Sadeghian A (2015) A new fuzzy disjointing difference operator to calculate union and intersection of type-2 fuzzy sets. In: Sadeghian A, Tahayori H (eds) Frontiers of higher order fuzzy sets. Springer, New York, pp 1–17

    MATH  Google Scholar 

  • Tahayori H, Livi L, Sadeghian A, Rizzi A (2015) Interval type-2 fuzzy set reconstruction based on fuzzy information-theoretic kernels. IEEE Trans Fuzzy Syst 23(4):1014–1029

    Article  Google Scholar 

  • Tavrov D, Kovalchuk-Khymiuk L, Temnikova O, Kaminskyi N-M (2019) Perceptual computer for grading mathematics tests within bilingual education program. In: Hu Z, Petoukhov S, Dychka I, He M (eds) Advances in computer science for engineering and education. Springer, Cham, pp 724–734

    Chapter  Google Scholar 

  • Wang T (2012) The interactive trade decision-making research: an application case of novel hybrid MCDM model. Econ Model 29:926–935

    Article  Google Scholar 

  • Wang L-X (2015) Dynamical models of stock prices based on technical trading rules part I: the models. Fuzzy Syst IEEE Trans 23:787–801

    Article  Google Scholar 

  • Wu D, Mendel JM, Coupland S (2012) Enhanced interval approach for encoding words into interval type-2 fuzzy sets and its convergence analysis. IEEE Trans Fuzzy Syst 20:499–513

    Article  Google Scholar 

  • Wu D, Mendel JM (2010a) Social judgment advisor: an application of the perceptual computer. In: 2010 IEEE international conference on fuzzy systems (FUZZ). IEEE, Barcelona, pp 1–8

  • Wu D, Mendel JM (2010b) Computing with words for hierarchical decision making applied to evaluating a weapon system. IEEE Trans Fuzzy Syst 18:441–460

    Article  Google Scholar 

  • Yoshida Y (2019) Portfolio optimization with perception-based risk measures in dynamic fuzzy asset management. Granul Comput 4:615–627

    Article  Google Scholar 

  • Zadeh LA (1996) Fuzzy logic = computing with words. IEEE Trans Fuzzy Syst 4:103–111

    Article  Google Scholar 

  • Zhou W, Xu Z (2018) Portfolio selection and risk investment under the hesitant fuzzy environment. Knowl Based Syst 144:21–31

    Article  Google Scholar 

Download references

Funding

Authors have not received any funding for this research.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering and IT, Shiraz University, Shiraz, Iran

    Mohsen Karimi & Hooman Tahayori

  2. Department of Computer Science, Ryerson University, Toronto, ON, M5B 2K3, Canada

    Kayvan Tirdad & Alireza Sadeghian

Authors
  1. Mohsen Karimi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hooman Tahayori
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kayvan Tirdad
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Alireza Sadeghian
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hooman Tahayori.

Ethics declarations

Conflict of interest

Authors declare that they have no conflict of interests.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Karimi, M., Tahayori, H., Tirdad, K. et al. A perceptual computer for hierarchical portfolio selection based on interval type-2 fuzzy sets. Granul. Comput. 8, 23–43 (2023). https://doi.org/10.1007/s41066-021-00311-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41066-021-00311-0

Keywords

Search

Navigation