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Reseach Article

A Control System for Long Term Investor under Uncertainty based on Fuzzy Logic

by Hegazy Zaher, Nisren Hassanen Mohamed
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 118 - Number 9
Year of Publication: 2015
Authors: Hegazy Zaher, Nisren Hassanen Mohamed
10.5120/20777-3333

Hegazy Zaher, Nisren Hassanen Mohamed . A Control System for Long Term Investor under Uncertainty based on Fuzzy Logic. International Journal of Computer Applications. 118, 9 ( May 2015), 47-51. DOI=10.5120/20777-3333

@article{ 10.5120/20777-3333,
author = { Hegazy Zaher, Nisren Hassanen Mohamed },
title = { A Control System for Long Term Investor under Uncertainty based on Fuzzy Logic },
journal = { International Journal of Computer Applications },
issue_date = { May 2015 },
volume = { 118 },
number = { 9 },
month = { May },
year = { 2015 },
issn = { 0975-8887 },
pages = { 47-51 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume118/number9/20777-3333/ },
doi = { 10.5120/20777-3333 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:01:16.261460+05:30
%A Hegazy Zaher
%A Nisren Hassanen Mohamed
%T A Control System for Long Term Investor under Uncertainty based on Fuzzy Logic
%J International Journal of Computer Applications
%@ 0975-8887
%V 118
%N 9
%P 47-51
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper introduce a novel control model for long–term investment decision making that faces uncertainty based on fuzzy logic. The proposed control model has the ability to generate accurate recommendations that support long–term investor decisions. This paper presents a creative methodology for dealing with historical data through a deep analysis to explore the strong relation between the age, and the risk of investor. The core idea of this control system is to reduce the number of inputs used in the model. This process is called dimension reduction based on fuzzy techniques. The output of this model is a good allocation for financial resources among three channels: saving, income, and growth.

References
  1. Acar, M. , D. Karahoca and A. Karahoca (2011)"Designing an Early Warning System for Stock Market Crashes by Using ANFIS".
  2. George, Bojadziev. , Maria, Bojadziev. (2007). " fuzzy logic for business, finance, and management".
  3. George, Bojadziev. , Maria, Bojadziev. and Lotfi, A. Zadeh. (1997). "Applications of Fuzzy Logic Control. Fuzzy Logic for Business, Finance, and Management": pp. 157-185.
  4. Chen,p. , Pankaj,c. ,Claudia, R (2007), "Fuzzy MLS: An Experiment on Quantified Risk–Adaptive Access Control".
  5. Doyle J. C. , (1989)," Glover K. , Khargoneckar P. P. , Francis B. A. State space solutions to standard and control problems. IEEE Transactions on Automatic Control, , 34(8):831–847.
  6. Dostál, p. , Brož, Z. (2011). "Fuzzy logic investment support on the financial market".
  7. Hegazy, Z. , and naglaa, r. , and nisren, h. (2014), "Optimizing Local Portfolio Return under Risk In Terms Of International Stock Markets Indices".
  8. Hegazy, Z. , nisren, h. (2015),"Minimization of Portfolio Risk using Three Different Methods (A Comparative Study)".
  9. Lotfi, A. Zadeh (1965) , "Fuzzy Sets, information and control ", vol. 8, pp. 338-353.
  10. Ossama Y. Abul-Haggag1, Walied. (2013). " Application of Fuzzy Logic for Risk Assessment using Risk Matrix".
  11. Rajasekaran, s. , vijayalak, S. G. A. (2003)," neural networks, fuzzy logic and genetic algorithm: synthesis and applications " ISBN :978-81-203-2186-1.
Index Terms

Computer Science
Information Sciences

Keywords

Control system uncertainty fuzzy logic long term investment.