A proposed real options method for assessing investments

ORIGINAL ARTICLE

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.

Keywords

Investment analysis Real options (RO) Automobile industry System dynamics Monte Carlo simulation Fuzzy logic 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Myers S (1977) Determinants of corporate borrowing. J Financ Econ 5:147–175CrossRefGoogle Scholar
  2. 2.
    Kulatilaka N (1993) The value of flexibility: the case of a dual-fuel industrial steam boiler. Financ Manag 22(3)Google Scholar
  3. 3.
    Paddock JL, Siegel DR, Smith JL (1988) Option valuation of claims on real assets: the case of offshore petroleum leases. Q J Econ 103:479–508CrossRefGoogle Scholar
  4. 4.
    Tufano P, Moel A, Harvard Business School (1997) Bidding for Antamina. Harvard Business School Publishing, BostonGoogle Scholar
  5. 5.
    Amram M, Kulatilaka N (1999) Real options: managing strategic investment in an uncertain world. Harvard Business School Press, BostonGoogle Scholar
  6. 6.
    Dixit AK, Pindyck RS (1994) Investment under uncertainty. Princeton University Press, PrincetonGoogle Scholar
  7. 7.
    Trigeorgis L (1996) Real options: managerial flexibility and strategy in resource allocation. MIT Press, CambridgeGoogle Scholar
  8. 8.
    Hausman J, Myers S (2002) Regulating the United States railroads: the effects of sunk costs and asymmetric risk. J Regul Econ 22(3):287–310CrossRefGoogle Scholar
  9. 9.
    Childs PD, Riddiough TJ, Triantis AJ (1996) Mixed uses and the redevelopment option. Real Estate Econ 24(3):317–339CrossRefGoogle Scholar
  10. 10.
    Geltner D (1989) On the use of the financial option price model to value and explain vacant land. AREUEA J 17(2):142–158CrossRefGoogle Scholar
  11. 11.
    Markish J, Willcox K (2002) Multidisciplinary techniques for commercial aircraft system design, in 9th AIAA/ISSMO Symposium on Multidisciplinary Analysis and Optimization.Google Scholar
  12. 12.
    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 231–250Google Scholar
  13. 13.
    Greden L, Glicksman L (2005) A real options model for valuing flexible space. J Corp Real Estate 7(1):34–48CrossRefGoogle Scholar
  14. 14.
    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 25–40Google Scholar
  15. 15.
    Iran Khodro company. Available from: http://www.ikco.com.
  16. 16.
    Aracil J, Toro M (1992) Qualitative behavior associated to system dynamics influence diagrams. in Int. conf. of the system dynamics society.Google Scholar
  17. 17.
    Toro M, Riquelme J, Aracil J (1992) Classifying systems behavior modes by statistical search in the parameter space. Eurpoean Simulation Multiconference, York, pp 181–185Google Scholar
  18. 18.
    Sterman JD (1988) Deterministic chaos in models of human behavior: methodological issues and experimental results. Syst Dyn Rev 4(1–2):148–178CrossRefGoogle Scholar
  19. 19.
    Mosekilde E, Larsen ER (1988) Deterministic chaos in beer production-distribution model. Syst Dyn Rev 4(1–2):131–148CrossRefGoogle Scholar
  20. 20.
    Aracil J (1981) Further results on structural stability of urban dynamics models. in 6th International Conference on System Dynamics.Google Scholar
  21. 21.
    Aracil J (1981) Structural stability of low-order system dynamic models. Int J Syst Sci 12:423–441CrossRefMATHMathSciNetGoogle Scholar
  22. 22.
    Aracil J (1984) Qualitative analysis and bifurcations in system dynamics models. IEEE Trans Syst Man Cybern SMC 14(4):688–696CrossRefMATHMathSciNetGoogle Scholar
  23. 23.
    Aracil J (1986) Bifurcations and structural stability in the dynamical systems modeling process. Syst Res 3:242–252CrossRefGoogle Scholar
  24. 24.
    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, BerlinGoogle Scholar
  25. 25.
    Aracil J, Toro M (1991) Qualitative analysis of system dynamics models. Rev Int Syst 5(5):493–515MATHGoogle Scholar
  26. 26.
    Toro M, Aracil J (1988) Qualitative analysisof system dynamic ecological models. Syst Dyn Rev 4(1–2):56–60CrossRefGoogle Scholar
  27. 27.
    Toro M, Macil J (1988) Oscillations andchaos in ecological populations. Proceeding of the International Conference of the Systems Dynamics Society, La JollaGoogle Scholar
  28. 28.
    Richardson GP (1984) Loop polarity, loop dominance, and the concept of polarity dominance. Proceedings of The 1984 International system dynamics Conference, OsloGoogle Scholar
  29. 29.
    Richardson GP (1986) Dominant structure. Syst Dyn Rev 2(1):68–75CrossRefGoogle Scholar
  30. 30.
    Mosekilde E et al (1985) Chaotic behavior in a simple model of urban migration. Proceedings of The 1985 International System Dynamics Conference, KeystoneGoogle Scholar
  31. 31.
    Mosekilde E, Rasmussen S, Serensen TS (1983) Self-organization and stochastic recausalization in dynamic models. Proceedings of the 1983 International System Dynamics conference, BostonGoogle Scholar
  32. 32.
    Rasmussen SE, Mosekilde E, Sterman JD (1985) Bifurcations and chaotic behavior in a simple model of the economic long wave. Syst Dyn Rev 1:92–110CrossRefGoogle Scholar
  33. 33.
    Sturis J, Mosekilde E (1988) Bifurcation sequence in a simple model of migratory dynamics. Syst Dyn Rev 4(1–2):208–217CrossRefGoogle Scholar
  34. 34.
    Toro M, Arrabal JJ, Romero L (1992) Piecewise linear analysis of an influence diagram. In Int. conf. of the system dynamics society.Google Scholar
  35. 35.
    Zeigler BP (1976) Theory of modelling and simulation. John Wiley, New YorkMATHGoogle Scholar
  36. 36.
    Kaufmann A, Gupta MM (1991) Introduction to fuzzy arithmetic, Theory and Applications. I. Van Nostrand Reinhold, New YorkMATHGoogle Scholar
  37. 37.
    Abe S, Lan M (1995) Fuzzy rules extraction directly from numerical data for function approximation. IEEE Trans Syst Man Cybern 25(1):119–129CrossRefMathSciNetGoogle Scholar
  38. 38.
    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):801–814CrossRefGoogle Scholar
  39. 39.
    Jang J (1993) Anfis: adaptive network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23:665–685CrossRefGoogle Scholar
  40. 40.
    Pomares H et al (2002) Structure identification in complete rule-based fuzzy systems. IEEE Trans Fuzzy Syst 10(3):349–359CrossRefGoogle Scholar
  41. 41.
    Takagi T, Sugeno M (1985) Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions of System. Man Cybern 15(1):116–132CrossRefMATHGoogle Scholar
  42. 42.
    Wang L, Mendel J (1992) Generating fuzzy rules by learning from examples. IEEE Trans Syst Man Cybern 22(6):1414–1427CrossRefMathSciNetGoogle Scholar
  43. 43.
    Wang L (1994) Adaptive fuzzy systems and control: design and stability analysis. Prentice-Hall: University of California at Berkeley, Englewood CliffGoogle Scholar
  44. 44.
    Ljung L (1987) System identification—theory for the user. Prentice-Hall, Englewood CliffsMATHGoogle Scholar
  45. 45.
    Kuipers BJ (1994) Qualitative reasoning: modeling and simulation with incomplete knowledge. MIT Press, CambridgeGoogle Scholar
  46. 46.
    Tsoukalas L, Uhrig R (1997) Fuzzy and Neural Applications in Engineering. John Wiley.Google Scholar
  47. 47.
    Al-Najjar B, Alsyouf I (2003) Selecting the most efficient maintenance approach using fuzzy multiple criteria decision making. Int J Prod Econ 84(1):85–100CrossRefGoogle Scholar
  48. 48.
    Jang JSR, Sun CT (1997) Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence. Prentice Hall.Google Scholar
  49. 49.
    Mamdani EH, Assilian S (1975) An experiment in linguistic synthesis with a fuzzy logic controller. Int J Man Mach Stud 7(1):1–13CrossRefMATHGoogle Scholar
  50. 50.
    Matlab, Fuzzy inference diagram, in Matlab. 2012, Mathworks.Google Scholar
  51. 51.
    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
  52. 52.
    Sterman J (2000) Business dynamics: systems thinking and modeling for a complex world. McGraw-Hill, New YorkGoogle Scholar
  53. 53.
    Shen LY, Wu YZ (2005) Risk concession model for build operate transfer contract projects. J Constr Eng Manag 131(2):211–220CrossRefMathSciNetGoogle Scholar
  54. 54.
    Khanzadi M, Nasirzadeh F, Alipour M (2010) Using Fuzzy-Delphi technique to determine the concession period in BOT projects. IEEE p. 442–446.Google Scholar
  55. 55.
    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):18–24CrossRefGoogle Scholar
  56. 56.
    Ng TS et al (2007) A simulation model for optimizing the concession period of public private partnerships schemes. Int J Proj Manag 25:791–798CrossRefGoogle Scholar
  57. 57.
    Shen LY, Li H, Li QM (2002) Alternative concession model for build operate transfer contract projects. J Constr Eng Manag 128(4):326–330CrossRefGoogle Scholar
  58. 58.
    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):22–29CrossRefGoogle Scholar
  59. 59.
    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):385–392CrossRefGoogle Scholar
  60. 60.
    Nasirzadeh F et al (2008) Integrating system dynamics and fuzzy logic modeling for construction risk management. J Constr Manag Econ 26(11):1197–1212CrossRefGoogle Scholar
  61. 61.
    Zimmermann HJ (2001) Fuzzy set theory and its application, 4th edn. Kluwer, BostonCrossRefGoogle Scholar
  62. 62.
    Zhang H, Xing F (2010) Fuzzy-multi-objective particle swarmoptimization for time–cost–quality tradeoff in construction. J Autom Constr 19:1065–1075MathSciNetGoogle Scholar
  63. 63.
    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 15:101–124CrossRefGoogle Scholar
  64. 64.
    Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, MIGoogle Scholar
  65. 65.
    Joliffe IT (1986) Principal component analysis. Springer Verlag, New YorkCrossRefGoogle Scholar
  66. 66.
    Goldberg DE (1989) Genetic algorithm in search, optimization and machine learning. Addison-Wesley Publishing Company, MAGoogle Scholar
  67. 67.
    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 YorkGoogle Scholar
  68. 68.
    Sugeno M (1974) Theory of fuzzy integrals and its applications. Tokyo Institute of Technology, TokyoGoogle Scholar
  69. 69.
    Ralescu D, Adams G (1980) The fuzzy integral. J Math Anal Appl 75(2):562–570Google Scholar
  70. 70.
    Congxin W, Ming M (1990) On the integrals, series and integral equations of fuzzy set-valued functions. J Harbin Inst Technol 21:9–11Google Scholar
  71. 71.
    Friedman M, Ma M, Kandel A (1999) Numerical solutions of fuzzy differential and integral equations. Fuzzy Set Syst 106:35–48CrossRefMATHMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  1. 1.Industrial Engineering DepartmentIran University of Science and TechnologyTehranIran

Personalised recommendations