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A Neural Network Approach to Value R&D Compound American Exchange Option

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  • Giovanni Villani

    (University of Bari)

Abstract

In this paper we show as the neural network methodology, coupled with the Least Squares Monte Carlo approach, can be very helpful in valuing R&D investment opportunities. As it is well known, R&D projects are made in a phased manner, with the commencement of subsequent phase being dependent on the successful completion of the preceding phase. This is known as a sequential investment and therefore R&D projects can be considered as compound options. In addition, R&D investments often involve considerable cost uncertainty so that they can be viewed as an exchange option, i.e. a swap of an uncertain investment cost for an uncertain gross project value. Finally, the production investment can be realized at any time before the maturity date, after that the effects of R&D disappear. Consequently, an R&D project can be considered as a compound American exchange option. In this context, the Least Squares Monte Carlo method is a powerful and flexible tool for capital budgeting decisions and for valuing American-type options. But, using the simulated values as “targets”, the implementation of a neural network allows to extend the results for any R&D valuation and to abate the waiting time of Least Squares Monte Carlo simulation.

Suggested Citation

  • Giovanni Villani, 2022. "A Neural Network Approach to Value R&D Compound American Exchange Option," Computational Economics, Springer;Society for Computational Economics, vol. 60(1), pages 305-324, June.
    • Handle: RePEc:kap:compec:v:60:y:2022:i:1:d:10.1007_s10614-021-10150-5
    DOI: 10.1007/s10614-021-10150-5
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    References listed on IDEAS

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    1. Carr, Peter P, 1988. " The Valuation of Sequential Exchange Opportunities," Journal of Finance, American Finance Association, vol. 43(5), pages 1235-1256, December.
    2. Armada, Manuel Rocha & Kryzanowski, Lawrence & Pereira, Paulo Jorge, 2007. "A modified finite-lived American exchange option methodology applied to real options valuation," Global Finance Journal, Elsevier, vol. 17(3), pages 419-438, March.
    3. Flavia Cortelezzi & Giovanni Villani, 2009. "Valuation of R&D Sequential Exchange Options Using Monte Carlo Approach," Computational Economics, Springer;Society for Computational Economics, vol. 33(3), pages 209-236, April.
    4. Longstaff, Francis A & Schwartz, Eduardo S, 2001. "Valuing American Options by Simulation: A Simple Least-Squares Approach," University of California at Los Angeles, Anderson Graduate School of Management qt43n1k4jb, Anderson Graduate School of Management, UCLA.
    5. Longstaff, Francis A & Schwartz, Eduardo S, 2001. "Valuing American Options by Simulation: A Simple Least-Squares Approach," The Review of Financial Studies, Society for Financial Studies, vol. 14(1), pages 113-147.
    6. Myers, Stewart C., 1977. "Determinants of corporate borrowing," Journal of Financial Economics, Elsevier, vol. 5(2), pages 147-175, November.
    7. Margrabe, William, 1978. "The Value of an Option to Exchange One Asset for Another," Journal of Finance, American Finance Association, vol. 33(1), pages 177-186, March.
    8. Alfred Taudes & Martin Natter & Michael Trcka, 1998. "Real option valuation with neural networks," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 7(1), pages 43-52, March.
    9. Broadie, Mark & Glasserman, Paul, 1997. "Pricing American-style securities using simulation," Journal of Economic Dynamics and Control, Elsevier, vol. 21(8-9), pages 1323-1352, June.
    10. McDonald, Robert L & Siegel, Daniel R, 1985. "Investment and the Valuation of Firms When There Is an Option to Shut Down," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 26(2), pages 331-349, June.
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    Cited by:

    1. Chinonso Nwankwo & Nneka Umeorah & Tony Ware & Weizhong Dai, 2022. "Deep learning and American options via free boundary framework," Papers 2211.11803, arXiv.org, revised Dec 2022.

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    More about this item

    Keywords

    Monte Carlo simulation; Neural network; Real options; R&D investments;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D

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