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International Journal of Fuzzy Systems

, Volume 20, Issue 6, pp 1888–1900 | Cite as

Optimal Investment Timing with Investment Propensity Using Fuzzy Real Options Valuation

  • Yonggu Kim
  • Eul-Bum Lee
Article
  • 48 Downloads

Abstract

This study focuses on optimal investment timing using real options valuation with fuzzy logic applied to support decision making for investors with diverse investment propensity. This study also describes an optional pricing model that uses the Black–Scholes–Merton model applied to numerical examples for the steel plant project to understand model performance. Previous studies of real options valuation focused only on the volatility of net cash flow, but did not consider the volatility of cash inflows and cash outflows separately. To overcome this issue, fuzzy real options valuation can consider not only the volatility of the cash inflow but also the volatility of the cash outflow. The fuzzy real options valuation can also be considered with the inherent risks of the real option valuation. This paper expands the selections of investment decisions for the project using scenario analysis for various investors. Through the Black’s approximation, optimal investment timing is derived by the fuzzy real options valuation using the annual options to defer the projects for 20 years. The contribution of this research is that it confirms the optimal investment timing by applying the strong condition applied by fuzzy logic through numerical examples whereby the results are variable according to the investors’ investment propensities.

Keywords

Project valuation Asymmetric triangular fuzzy number Fuzzy real options valuation (FROV) Two Monte Carlo simulations (MCSs) Possibility distributions Optimal investment timing 

Notes

Acknowledgements

This work was supported by the Technology Innovation Program (10077606, To Develop an Intelligent Integrated Management Support System for Engineering Project) funded By the Ministry of Trade, Industry and Energy (MOTIE, Korea).

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Copyright information

© Taiwan Fuzzy Systems Association and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Graduate Institute of Ferrous TechnologyPohang University of Science and TechnologyPohang-siRepublic of Korea
  2. 2.Graduate Institute of Ferrous Technology & Graduate School of Engineering MastershipPohang University of Science and TechnologyPohang-siRepublic of Korea

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