Abstract
Real options analysis is being increasingly used for assessing investments under uncertainty; however, traditional real options methods have some characteristics that restrict their use, such as modeling the value of the underlying asset using geometric Brownian motion and assuming a fixed cost in exercising the options. In this paper, another real options method is expounded that mitigates some of the difficulties posed by traditional methods. Another important aspect that we analyzed in this paper is considering the fuzzy aspects of real options theory. In this section, we are trying to use fuzzy logic concepts integrated with system dynamics to assessing real options in investment projects and we examine dynamic versions of fuzzy logic systems. System dynamics (SD) is an effective method for studying dynamic conditions and changes in complex systems. In this paper, a new dynamic model of real-world systems is designed based on the concepts of system dynamic and fuzzy logic approach. The method is explained with an example from aviation. The analysis offers obvious proof that the integrated fuzzy–SD model could help investors to decide how they should choose an investment program, that managers can use the same results to restructure the program to improve the financial feasibility of the project, and that both investors and managers can define minimum needs to ensure program success.
Similar content being viewed by others
References
Myers S (1977) Determinants of corporate borrowing. J Financ Econ 5:147–175
Kulatilaka N (1993) The value of flexibility: the case of a dual-fuel industrial steam boiler. Financ Manag 22(3)
Paddock JL, Siegel DR, Smith JL (1988) Option valuation of claims on real assets: the case of offshore petroleum leases. Q J Econ 103:479–508
Tufano P, Moel A, Harvard Business School (1997) Bidding for Antamina. Harvard Business School Publishing, Boston
Amram M, Kulatilaka N (1999) Real options: managing strategic investment in an uncertain world. Harvard Business School Press, Boston
Dixit AK, Pindyck RS (1994) Investment under uncertainty. Princeton University Press, Princeton
Trigeorgis L (1996) Real options: managerial flexibility and strategy in resource allocation. MIT Press, Cambridge
Hausman J, Myers S (2002) Regulating the United States railroads: the effects of sunk costs and asymmetric risk. J Regul Econ 22(3):287–310
Childs PD, Riddiough TJ, Triantis AJ (1996) Mixed uses and the redevelopment option. Real Estate Econ 24(3):317–339
Geltner D (1989) On the use of the financial option price model to value and explain vacant land. AREUEA J 17(2):142–158
Markish J, Willcox K (2002) Multidisciplinary techniques for commercial aircraft system design, in 9th AIAA/ISSMO Symposium on Multidisciplinary Analysis and Optimization.
Stonier JE (1999) What is an aircraft purchase option worth? Quantifying asset flexibility created through manufacturer lead-time reductions and product commonality. In: Butler GF, Keller MR (eds) Handbook of airline finance. McGraw-Hill, New York, pp 231–250
Greden L, Glicksman L (2005) A real options model for valuing flexible space. J Corp Real Estate 7(1):34–48
Skinner S, Diechter A, Langley P, Sabert H (1999) Managing growth and profitability across peaks and troughs of the airline industry cycle—an industry dynamics approach. In: Butler G, Keller M (eds) Handbook of airline finance. McGraw-Hill, New York, pp 25–40
Iran Khodro company. Available from: http://www.ikco.com.
Aracil J, Toro M (1992) Qualitative behavior associated to system dynamics influence diagrams. in Int. conf. of the system dynamics society.
Toro M, Riquelme J, Aracil J (1992) Classifying systems behavior modes by statistical search in the parameter space. Eurpoean Simulation Multiconference, York, pp 181–185
Sterman JD (1988) Deterministic chaos in models of human behavior: methodological issues and experimental results. Syst Dyn Rev 4(1–2):148–178
Mosekilde E, Larsen ER (1988) Deterministic chaos in beer production-distribution model. Syst Dyn Rev 4(1–2):131–148
Aracil J (1981) Further results on structural stability of urban dynamics models. in 6th International Conference on System Dynamics.
Aracil J (1981) Structural stability of low-order system dynamic models. Int J Syst Sci 12:423–441
Aracil J (1984) Qualitative analysis and bifurcations in system dynamics models. IEEE Trans Syst Man Cybern SMC 14(4):688–696
Aracil J (1986) Bifurcations and structural stability in the dynamical systems modeling process. Syst Res 3:242–252
Aracil J, Toro M (1989) Generic qualitative behavior of elementary system dynamics structure. In proceeding of the 1989 International Conference of the Systems Dynamics Society. Springer, Berlin
Aracil J, Toro M (1991) Qualitative analysis of system dynamics models. Rev Int Syst 5(5):493–515
Toro M, Aracil J (1988) Qualitative analysisof system dynamic ecological models. Syst Dyn Rev 4(1–2):56–60
Toro M, Macil J (1988) Oscillations andchaos in ecological populations. Proceeding of the International Conference of the Systems Dynamics Society, La Jolla
Richardson GP (1984) Loop polarity, loop dominance, and the concept of polarity dominance. Proceedings of The 1984 International system dynamics Conference, Oslo
Richardson GP (1986) Dominant structure. Syst Dyn Rev 2(1):68–75
Mosekilde E et al (1985) Chaotic behavior in a simple model of urban migration. Proceedings of The 1985 International System Dynamics Conference, Keystone
Mosekilde E, Rasmussen S, Serensen TS (1983) Self-organization and stochastic recausalization in dynamic models. Proceedings of the 1983 International System Dynamics conference, Boston
Rasmussen SE, Mosekilde E, Sterman JD (1985) Bifurcations and chaotic behavior in a simple model of the economic long wave. Syst Dyn Rev 1:92–110
Sturis J, Mosekilde E (1988) Bifurcation sequence in a simple model of migratory dynamics. Syst Dyn Rev 4(1–2):208–217
Toro M, Arrabal JJ, Romero L (1992) Piecewise linear analysis of an influence diagram. In Int. conf. of the system dynamics society.
Zeigler BP (1976) Theory of modelling and simulation. John Wiley, New York
Kaufmann A, Gupta MM (1991) Introduction to fuzzy arithmetic, Theory and Applications. I. Van Nostrand Reinhold, New York
Abe S, Lan M (1995) Fuzzy rules extraction directly from numerical data for function approximation. IEEE Trans Syst Man Cybern 25(1):119–129
Horikawa S, Furuhashi T, Uchikawa Y (1992) On fuzzy modeling using fuzzy neural networks with the back-propagation algorithm. IEEE Trans Neural Netw 3(5):801–814
Jang J (1993) Anfis: adaptive network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23:665–685
Pomares H et al (2002) Structure identification in complete rule-based fuzzy systems. IEEE Trans Fuzzy Syst 10(3):349–359
Takagi T, Sugeno M (1985) Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions of System. Man Cybern 15(1):116–132
Wang L, Mendel J (1992) Generating fuzzy rules by learning from examples. IEEE Trans Syst Man Cybern 22(6):1414–1427
Wang L (1994) Adaptive fuzzy systems and control: design and stability analysis. Prentice-Hall: University of California at Berkeley, Englewood Cliff
Ljung L (1987) System identification—theory for the user. Prentice-Hall, Englewood Cliffs
Kuipers BJ (1994) Qualitative reasoning: modeling and simulation with incomplete knowledge. MIT Press, Cambridge
Tsoukalas L, Uhrig R (1997) Fuzzy and Neural Applications in Engineering. John Wiley.
Al-Najjar B, Alsyouf I (2003) Selecting the most efficient maintenance approach using fuzzy multiple criteria decision making. Int J Prod Econ 84(1):85–100
Jang JSR, Sun CT (1997) Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence. Prentice Hall.
Mamdani EH, Assilian S (1975) An experiment in linguistic synthesis with a fuzzy logic controller. Int J Man Mach Stud 7(1):1–13
Matlab, Fuzzy inference diagram, in Matlab. 2012, Mathworks.
Collan M, Fullér R, Mezei J (2009) A fuzzy pay-off method for real option valuation. J Appl Math Decis Sci. doi:10.1155/2009/238196
Sterman J (2000) Business dynamics: systems thinking and modeling for a complex world. McGraw-Hill, New York
Shen LY, Wu YZ (2005) Risk concession model for build operate transfer contract projects. J Constr Eng Manag 131(2):211–220
Khanzadi M, Nasirzadeh F, Alipour M (2010) Using Fuzzy-Delphi technique to determine the concession period in BOT projects. IEEE p. 442–446.
Liou F-M, Huang C-P (2008) Automated approach to negotiations of BOT contracts with the consideration of project risk. J Constr Eng Manag 134(1):18–24
Ng TS et al (2007) A simulation model for optimizing the concession period of public private partnerships schemes. Int J Proj Manag 25:791–798
Shen LY, Li H, Li QM (2002) Alternative concession model for build operate transfer contract projects. J Constr Eng Manag 128(4):326–330
Ng TS, Xie J, Skitmore M, Cheung YK (2007) A fuzzy simulation model for evaluating the concession items of public private partnership schemes. J Autom Constr 17(1):22–29
Shen LY, Bao HJ, Wu YZ, Lu WS (2007) Using bargaining-game theory for negotiating concession period for BOT-type contract. J Constr Eng Manag 133(5):385–392
Nasirzadeh F et al (2008) Integrating system dynamics and fuzzy logic modeling for construction risk management. J Constr Manag Econ 26(11):1197–1212
Zimmermann HJ (2001) Fuzzy set theory and its application, 4th edn. Kluwer, Boston
Zhang H, Xing F (2010) Fuzzy-multi-objective particle swarmoptimization for time–cost–quality tradeoff in construction. J Autom Constr 19:1065–1075
Maier HR, Dandy GC (2000) Neural network for the prediction and forecasting of water resource variables: a review of modeling issues and applications. Environ Model Software 15:101–124
Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, MI
Joliffe IT (1986) Principal component analysis. Springer Verlag, New York
Goldberg DE (1989) Genetic algorithm in search, optimization and machine learning. Addison-Wesley Publishing Company, MA
Salski A (1999) Ecological modeling and data analysis. In: Zimmermann H-J (ed) Ecological modeling and data analysis, vol 6, The handbook of fuzzy sets series. Springer, New York
Sugeno M (1974) Theory of fuzzy integrals and its applications. Tokyo Institute of Technology, Tokyo
Ralescu D, Adams G (1980) The fuzzy integral. J Math Anal Appl 75(2):562–570
Congxin W, Ming M (1990) On the integrals, series and integral equations of fuzzy set-valued functions. J Harbin Inst Technol 21:9–11
Friedman M, Ma M, Kandel A (1999) Numerical solutions of fuzzy differential and integral equations. Fuzzy Set Syst 106:35–48
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Arasteh, A., Aliahmadi, A. A proposed real options method for assessing investments. Int J Adv Manuf Technol 70, 1377–1393 (2014). https://doi.org/10.1007/s00170-013-5390-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00170-013-5390-2