Investment Decision Support System Using Credibility Analysis With Fuzzy Interest Rate

Year 2023, , 1596 - 1604, 30.06.2023

Gülçin Canbulut

https://doi.org/10.56554/jtom.1182260

Abstract

Today, with a fluctuating course of the economy, it is inevitable that the interest method used by people for investment will also fluctuate. There may be serious inconsistency between the current interest rate and the interest rate at the time of the investment. Therefore, in order to eliminate these inconsistent situation, fuzzy set theory is used and the case where the interest rate parameter is fuzzy variable is examined. In this way, it is provided that the uncertain interest rate is close to the real-life interest rate.

Keywords

Fuzzy Logic, Credibility Theory, Compound Interest Rate, Investment

References

• 1. S.Jaimungal and T. Wang, “Catastrophe Options With Stochastic İnterest Rates And Compound Poisson Losses”, Insurance Mathematics And Economics, 2006, vol.38, pp. 469-483.
• 2. R.F. Hespos and P.A. Strassmann, “Stochastic Decision Trees for the Analysis of Investment Decisions”, Management Science, Vol. 11, No. 10, Series B, Managerial (Aug., 1965), pp. B244-B259.
• 3. J.M. Mulvey and H Vladimirou, “Stochastic Network Optimization Models for Investment Planning”, Annals of Operations Research, 1989, vol.20, pp. 187-217.
• 4. C. Kahraman and I. Kaya, “ Investment Analyses Using Fuzzy Probability Concept” , Technological and Economic Development of Economy, 2010, vol. 16, pp.43-57.
• 5. X. Bi and XF. Wang, “The Application of Fuzzy-Real Option Theory in BOT Project Investment Decision-Making”, 2009, IEEE 16th International Conference on Industrial Engineering and Engineering Management.
• 6. İ.Uçal and C. Kahraman, “ Fuzzy Real Options Valuation for Oil Investments”, Technological and Economic Development of Economy:Baltic Journal on Sustainability,2009, vol.15, pp. 646-669.
• 7. L. Dimova, P. Sevastianov and D. Sevastianov, “ MCDM in a Fuzzy Setting: Investment Projects Assessment Application”, International Journal of Production Economics, 2006, vol. 100, pp.10-29.
• 8. A. Üstündağ, M.S. Kılınç and E. Çevikcan, “Fuzzy Rule-based System for the Economic Analysis of RFID Investments”, Expert Systems with Applications,2010, vol. 37, pp. 5300-5306.
• 9. C. Kahraman, M. Gülbay and Z. Ulukan, “Fuzzy Applications in Industrial Engineering: Applications of Fuzzy Capital Budgeting Techniques”, 2006, vol.201, pp. 177-203.
• 10. E.E. Karsak and E. Tolga, “Fuzzy Multi-criteria Decision-Making Procedure for Evaluating Advanced Manufacturing System Investments”, International Journal of Production Economics, 2001, vol. 69, pp. 49-64.
• 11. F. Xue , W. Tang and R. Zhao, “The Expected Value of a Function of a Fuzzy Variable With A Contiuous Membership Function”, Computers and Mathematics with Applications, 2008, vol.55, pp. 1215-1224.
• 12. B.Liu and Y.K. Liu, “ Expected Value of Fuzzy Variable and Fuzzy Expected Value Model” , IEEE Transactions on Fuzzy Systems, 2002, vol. 10, pp.445-450.
• 13. L. A. Zadeh, “Fuzzy Sets,” Information And Control, no. 8, pp. 338-353, 1965.
• 14. G.J. Klir and B. Yuan, “Fuzzy Sets and Fuzzy Logic: Theory and Applications”, Prentice Hall PTR, 1995.
• 15. X. Huang, “Portfolio Analysis: From Probabilistic to Credibilistic and Uncertain Approaches”, Studies in Fuzziness and Soft Computing, 2010, vol. 250.
• 16. B. Liu, “Uncertainty Theory, 2007, Springer, 2nd edn.
• 17. S. Heilpern, “The expected value of a fuzzy number”, Fuzzy Sets And Systems, no. 47, pp. 81-86, 1992.
• 18. R. R. Yager, “A procedure for ordering fuzzy subsets of the unit interval”, Information Sciences, cilt 2, no. 24, pp. 143-161, 1981.
• 19. L. Campos ve A. Gonzalez, “A subjective approach for ranking fuzzy numbers”, Fuzzy Sets And Systems, no. 29, pp. 145-153, 1989.
• 20. A. Gonzalez, “A study of ranking function approach through mean values”, Fuzzy Sets And Systems, no. 35, pp. 29-41, 1990.