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New Approaches to Project Risk Assessment Utilizing the Monte Carlo Method

Author

Listed:
  • Andrea Senova

    (Faculty of Mining, Ecology, Process Control and Geotechnologies, Technical University of Kosice, Letna 9, 042 00 Kosice, Slovakia)

  • Alica Tobisova

    (Faculty of Aeronautics, Technical University of Kosice, Rampova 7, 041 21 Kosice, Slovakia)

  • Robert Rozenberg

    (Faculty of Aeronautics, Technical University of Kosice, Rampova 7, 041 21 Kosice, Slovakia)

Abstract

An environment of turbulence in the market in recent years and increasing inflation, mainly as a result of the post-COVID period and the ongoing military operation in Ukraine, represents a significant financial risk factor for many companies, which has a negative impact on managerial decisions. A lot of enterprises are forced to look for ways to effectively assess the riskiness of the projects that they would like to implement in the future. The aim of the article is to present a new approach for companies with which to assess the riskiness of projects. The basis of this is the use of the new Crystal Ball software tool and the effective application of the Monte Carlo method. The article deals with the current issues of investment and financial planning, which are the basic pillars for effective management decisions with the goal of sustainability. The article has verified a methodology that allows companies to make effective investment decisions based on assessing the level of risk. For practical application, the Monte Carlo method was chosen, as it uses sensitivity analysis and simulations, which were evaluated for two types of projects. Both simulations were primarily carried out based on a deterministic approach through traditional mathematical models. Subsequently, stochastic modeling was performed using the Crystal Ball software tool. As a result of the sensitivity analysis, two tornado graphs were created, which display risk factors according to the degree of their influence on the criterion value. The output of this article is the presentation of these new approaches for financial decision-making within companies.

Suggested Citation

  • Andrea Senova & Alica Tobisova & Robert Rozenberg, 2023. "New Approaches to Project Risk Assessment Utilizing the Monte Carlo Method," Sustainability, MDPI, vol. 15(2), pages 1-19, January.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:2:p:1006-:d:1026298
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    References listed on IDEAS

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    1. Katarina Valaskova & Tomas Kliestik & Lucia Svabova & Peter Adamko, 2018. "Financial Risk Measurement and Prediction Modelling for Sustainable Development of Business Entities Using Regression Analysis," Sustainability, MDPI, vol. 10(7), pages 1-15, June.
    2. Yunyu Zhang, 2020. "The value of Monte Carlo model-based variance reduction technology in the pricing of financial derivatives," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-13, February.
    3. Silvana M. Pesenti, 2021. "Reverse Sensitivity Analysis for Risk Modelling," Papers 2107.01065, arXiv.org, revised May 2022.
    4. Kyoung-jae Kim & Kichun Lee & Hyunchul Ahn, 2018. "Predicting Corporate Financial Sustainability Using Novel Business Analytics," Sustainability, MDPI, vol. 11(1), pages 1-17, December.
    5. Georgios K. Koulinas & Olympia E. Demesouka & Konstantinos A. Sidas & Dimitrios E. Koulouriotis, 2021. "A TOPSIS—Risk Matrix and Monte Carlo Expert System for Risk Assessment in Engineering Projects," Sustainability, MDPI, vol. 13(20), pages 1-14, October.
    6. Jungmin An & Dong-Kwan Kim & Jinyeong Lee & Sung-Kwan Joo, 2021. "Least Squares Monte Carlo Simulation-Based Decision-Making Method for Photovoltaic Investment in Korea," Sustainability, MDPI, vol. 13(19), pages 1-14, September.
    7. Marco Nunes & António Abreu & Célia Saraiva, 2021. "A Model to Manage Cooperative Project Risks to Create Knowledge and Drive Sustainable Business," Sustainability, MDPI, vol. 13(11), pages 1-28, May.
    8. Alica Tobisova & Andrea Senova & Robert Rozenberg, 2022. "Model for Sustainable Financial Planning and Investment Financing Using Monte Carlo Method," Sustainability, MDPI, vol. 14(14), pages 1-18, July.
    9. Arkadiy Larionov & Ekaterina Nezhnikova & Elena Smirnova, 2021. "Risk Assessment Models to Improve Environmental Safety in the Field of the Economy and Organization of Construction: A Case Study of Russia," Sustainability, MDPI, vol. 13(24), pages 1-37, December.
    10. Silvana M. Pesenti, 2022. "Reverse Sensitivity Analysis for Risk Modelling," Risks, MDPI, vol. 10(7), pages 1-23, July.
    11. Li, Zhenghui & Yang, Cunyi & Huang, Zhehao, 2022. "How does the fintech sector react to signals from central bank digital currencies?," Finance Research Letters, Elsevier, vol. 50(C).
    12. Daniel J Arenas & Lanair A Lett & Heather Klusaritz & Anne M Teitelman, 2017. "A Monte Carlo simulation approach for estimating the health and economic impact of interventions provided at a student-run clinic," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-12, December.
    13. Rama Cont & Romain Deguest & Giacomo Scandolo, 2010. "Robustness and sensitivity analysis of risk measurement procedures," Quantitative Finance, Taylor & Francis Journals, vol. 10(6), pages 593-606.
    14. Borgonovo, Emanuele & Plischke, Elmar, 2016. "Sensitivity analysis: A review of recent advances," European Journal of Operational Research, Elsevier, vol. 248(3), pages 869-887.
    15. Jaroslava Janekova & Jana Fabianova & Andrea Rosova, 2016. "Environmental And Economic Aspects In Decision Making Of The Investment Project “Wind Park”," Polish Journal of Management Studies, Czestochowa Technical University, Department of Management, vol. 13(1), pages 90-100, June.
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