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New Credibilistic Real Option Model Based on the Pessimism-Optimism Character of a Decision-Maker

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Data Management, Analytics and Innovation

Abstract

Fuzzy real options analysis has gained increasing attention among investment practitioners as well as investment theory-focused academics. The strength of the real option valuation (ROV) models, when compared to the more traditional net present value methods, is that they can account for flexibility, which is often available in long-term real investment opportunities. So called fuzzy pay-off methods (FPOMs) represent the most intuitive and easy-to-apply real options techniques published during the last decade. As part of the methodological FPOM family, the original credibilistic approach to real option valuation was published in 2012. In this paper, the credibilistic approach is extended by using the \({\mathrm{m}}_{\mathrm{\lambda }}\)-measure, which is built on a linear combination of necessity and possibility measures to deal with the problem of a decision-maker or, say, an expert analyst, neither being fully optimistic nor fully pessimistic; instead, the λ ∈ [0,1] parameter will represent the level of optimism of a decision-maker leading to a range of real option values estimated for an investment under analysis. The paper presents the new credibilistic ROV model with R code and compares it to the original credibilistic approach, as well as, to the recent center-of-gravity fuzzy pay-off model (CoG-FPOM) through a numerical example of valuing operational synergies developed during a corporate acquisition process. Finally, some future research opportunities are expressed.

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Acknowledgements

This research is supported by the Finnish Strategic Research Council at the Academy of Finland project Manufacturing 4.0 grants 313349 and 313396.

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Georgescu, I., Kinnunen, J., Collan, M. (2022). New Credibilistic Real Option Model Based on the Pessimism-Optimism Character of a Decision-Maker. In: Sharma, N., Chakrabarti, A., Balas, V.E., Bruckstein, A.M. (eds) Data Management, Analytics and Innovation. Lecture Notes on Data Engineering and Communications Technologies, vol 71. Springer, Singapore. https://doi.org/10.1007/978-981-16-2937-2_5

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