Real option analysis offers interesting insights on the value of assets and on the profitability of investments, which has made real options a growing field of academic research and practical application. Real option valuation is, however, often found to be difficult to understand and to implement due to the quite complex mathematics involved. Recent advances in modeling and analysis methods have made real option valuation easier to understand and to implement. This paper presents a new method (fuzzy pay-off method) for real option valuation using fuzzy numbers that is based on findings from earlier real option valuation methods and from fuzzy real option valuation. The method is intuitive to understand and far less complicated than any previous real option valuation model to date. The paper also presents the use of number of different types of fuzzy numbers with the method and an application of the new method in an industry setting.
Research and development (R&D) project ranking as investments is a well-known problem that is made difficult by incomplete and imprecise information about future project profitability. This paper shows how profitability results of R&D project evaluation with the fuzzy pay-off method can be ranked with four new variants of fuzzy TOPSIS each using a different fuzzy similarity measure. An overall project ranking that incorporates the four new variants' rankings with three different ideal solutions totaling 12 subrankings is presented. The implementation of the created methods is illustrated with a numerical example.
This paper presents a new method to enhance simulation-based analysis of complex investments that contain multi-variable uncertainty. The method is called "simulation decomposition". Typically the result of simulation-based investment analysis is in the form of histogram distributions-here we propose a method for first classifying the possible outcomes of selected uncertain variables into states and then using combinations of the created states in the decomposition of the simulated distribution into a number of sub-distributions. The sub-distributions that can be matched to state-combinations of the variables contain relevant actionable information that helps managers in decision-making with regards to the studied investments. A numerical illustration of a renewable energy investment is used to demonstrate the usability, the enhanced analytical power, and the intuitively understandable benefits that can be reached by using the simulation decomposition method. The proposed method is generally usable and can be utilized independent of the investment context.
Real option analysis offers interesting insights on the value of assets and on the profitability of investments, which has made real options a growing field of academic research and practical application. Real option valuation is, however, often found to be difficult to understand and to implement due to the quite complex mathematics involved. Recent advances in modeling and analysis methods have made real option valuation easier to understand and to implement. This paper presents a new method (fuzzy pay-off method) for real option valuation using fuzzy numbers that is based on findings from earlier real option valuation methods and from fuzzy real option valuation. The method is intuitive to understand and far less complicated than any previous real option valuation model to date. The paper also presents the use of number of different types of fuzzy numbers with the method and an application of the new method in an industry setting.
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