IDEAS home Printed from https://ideas.repec.org/a/mje/mjejnl/v15y2019i4161-171.html
   My bibliography  Save this article

The Normal Distribution Formalization for Investment Economic Project Evaluation Using the Monte Carlo Method

Author

Listed:
  • Daria Bilenko
  • Ruslan Lavrov
  • Natalia Onyshchuk
  • Borys Poliakov
  • Yuliia Kabenok

Abstract

Investment plays a very important role in the economy, ensures its sustainable growth, contributes to the improvement of the living standards of the population. The most common mistake of planning investment projects is the insufficient development of risks that may affect the profitability of projects. The purpose of the paper is the formalizing the normal distribution for investment project evaluation using the Monte Carlo method. Such formalizing should allow to present normal distribution in a form that is understandable for nonspecialists in mathematical statistics. A user can easily calculate the standard deviation value and determine the limits of the confidence interval and the range of deviation from the mean value. Such mistakes can lead to incorrect investment decisions and significant losses. The desire to minimize risk requires developing a risk model. One of the risk assessment tools is the Monte Carlo method, which combines and develops both methods of sensitivity analysis and scenario analysis.

Suggested Citation

  • Daria Bilenko & Ruslan Lavrov & Natalia Onyshchuk & Borys Poliakov & Yuliia Kabenok, 2019. "The Normal Distribution Formalization for Investment Economic Project Evaluation Using the Monte Carlo Method," Montenegrin Journal of Economics, Economic Laboratory for Transition Research (ELIT), vol. 15(4), pages 161-171.
    • Handle: RePEc:mje:mjejnl:v:15:y:2019:i:4:161-171
    as

    Download full text from publisher

    File URL: http://repec.mnje.com/mje/2019/v15-n04/mje_2019_v15-n04-a22.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Dangl, Thomas, 1999. "Investment and capacity choice under uncertain demand," European Journal of Operational Research, Elsevier, vol. 117(3), pages 415-428, September.
    2. Avinash K. Dixit & Robert S. Pindyck, 1994. "Investment under Uncertainty," Economics Books, Princeton University Press, edition 1, number 5474.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Balter, Anne G. & Huisman, Kuno J.M. & Kort, Peter M., 2022. "Effects of creative destruction on the size and timing of an investment," International Journal of Production Economics, Elsevier, vol. 252(C).
    2. Alain Bensoussan & Benoit Chevalier-Roignant & Alejandro Rivera, 2022. "A model for wind farm management with option interactions," Post-Print hal-04325553, HAL.
    3. Qin, Ruwen & Nembhard, David A., 2012. "Demand modeling of stochastic product diffusion over the life cycle," International Journal of Production Economics, Elsevier, vol. 137(2), pages 201-210.
    4. Kort, Peter M. & Murto, Pauli & Pawlina, Grzegorz, 2010. "Uncertainty and stepwise investment," European Journal of Operational Research, Elsevier, vol. 202(1), pages 196-203, April.
    5. d'Halluin, Y. & Forsyth, P.A. & Vetzal, K.R., 2007. "Wireless network capacity management: A real options approach," European Journal of Operational Research, Elsevier, vol. 176(1), pages 584-609, January.
    6. Xingang Wen & Peter M. Kort & Dolf Talman, 2017. "Volume flexibility and capacity investment: a real options approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(12), pages 1633-1646, December.
    7. Bøckman, Thor & Fleten, Stein-Erik & Juliussen, Erik & Langhammer, Håvard J. & Revdal, Ingemar, 2008. "Investment timing and optimal capacity choice for small hydropower projects," European Journal of Operational Research, Elsevier, vol. 190(1), pages 255-267, October.
    8. Di Corato, Luca & Moretto, Michele & Vergalli, Sergio, 2014. "Long-run investment under uncertain demand," Economic Modelling, Elsevier, vol. 41(C), pages 80-89.
    9. Ketelaars, Martijn & Kort, Peter M., 2022. "Investments in R&D and Production Capacity with Uncertain Breakthrough Time : Private versus Social Incentives," Discussion Paper 2022-010, Tilburg University, Center for Economic Research.
    10. Michail Chronopoulos & Verena Hagspiel & Stein-Erik Fleten, 2017. "Stepwise investment and capacity sizing under uncertainty," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 39(2), pages 447-472, March.
    11. Huberts, Nick F.D. & Rossi Silveira, Rafael, 2023. "How economic depreciation shapes the relationship of uncertainty with investments’ size & timing," International Journal of Production Economics, Elsevier, vol. 260(C).
    12. Charalambides, Marios & Koussis, Nicos, 2018. "A stochastic model with interacting managerial operating options and debt rescheduling," European Journal of Operational Research, Elsevier, vol. 267(1), pages 236-249.
    13. Castellini, Marta & Menoncin, Francesco & Moretto, Michele & Vergalli, Sergio, 2021. "Photovoltaic Smart Grids in the prosumers investment decisions: a real option model," Journal of Economic Dynamics and Control, Elsevier, vol. 126(C).
    14. Della Seta, Marco & Gryglewicz, Sebastian & Kort, Peter M., 2012. "Optimal investment in learning-curve technologies," Journal of Economic Dynamics and Control, Elsevier, vol. 36(10), pages 1462-1476.
    15. Jouvet, Pierre-André & Le Cadre, Elodie & Orset, Caroline, 2012. "Irreversible investment, uncertainty, and ambiguity: The case of bioenergy sector," Energy Economics, Elsevier, vol. 34(1), pages 45-53.
    16. Sarkar, Sudipto, 2021. "The uncertainty-investment relationship with endogenous capacity," Omega, Elsevier, vol. 98(C).
    17. Sarkar, Sudipto, 2018. "Optimal DOL (degree of operating leverage) with investment and production flexibility," International Journal of Production Economics, Elsevier, vol. 202(C), pages 172-181.
    18. Koussis, Nicos & Martzoukos, Spiros H., 2022. "Credit line pricing under heterogeneous risk beliefs," International Journal of Production Economics, Elsevier, vol. 243(C).
    19. Sarkar, Sudipto, 2011. "Optimal size, optimal timing and optimal financing of an investment," Journal of Macroeconomics, Elsevier, vol. 33(4), pages 681-689.
    20. Murto, Pauli & Nasakkala, Erkka & Keppo, Jussi, 2004. "Timing of investments in oligopoly under uncertainty: A framework for numerical analysis," European Journal of Operational Research, Elsevier, vol. 157(2), pages 486-500, September.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:mje:mjejnl:v:15:y:2019:i:4:161-171. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Nikola Draskovic Jelcic (email available below). General contact details of provider: http://www.mnje.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.