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Implementation of Stochastic Analysis in Corporate Decision-Making Models

Author

Listed:
  • Jin-Biao Lu

    (School of Business, Huanggang Normal University, Huanggang 438000, China)

  • Zhi-Jiang Liu

    (School of Economics and Management, GuangXi Normal University, Guilin 541000, China)

  • Dmitry Tulenty

    (Department of Insurance and Economy of Social Sphere, Financial University under the Government of the Russian Federation, 125993 Moscow, Russia)

  • Liudmila Tsvetkova

    (Department of Risk Management and Insurance, MGIMO University, 119435 Moscow, Russia)

  • Sebastian Kot

    (The Management Faculty, Czestochowa University of Technology, 42-201 Częstochowa, Poland
    Department of Business Management, College of Business and Economics, University of Johannesburg, Johannesburg 1809, South Africa)

Abstract

The stochastic approach as a method for modeling factor systems of interrelationships of economic activity aspects allows minimizing managerial errors against the background of company growth and expansion of operating activities. The purpose of this study is to form a decision-making model to ensure the financial competitiveness of enterprises in the context of stochastic analysis. This study demonstrates stochastic analysis implementation in companies of the 2nd and 3rd degrees of internationalization based on multiple regression and factorial analysis of variance. The practical basis of the study was Chinese and Russian mining enterprises that enter highly competitive markets and therefore should avoid mistakes in decision-making as much as possible. The model of financial competitiveness proposed in the article demonstrates the best ways to introduce stochastics in companies to optimize their overall productivity, regardless of the country of origin. In a practical sense, research on reducing managerial mistakes allows enterprises to have financial success even in the turbulent conditions of today’s global market, regardless of the company’s jurisdiction.

Suggested Citation

  • Jin-Biao Lu & Zhi-Jiang Liu & Dmitry Tulenty & Liudmila Tsvetkova & Sebastian Kot, 2021. "Implementation of Stochastic Analysis in Corporate Decision-Making Models," Mathematics, MDPI, vol. 9(9), pages 1-16, May.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:9:p:1041-:d:548528
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    References listed on IDEAS

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    1. Dariush Khezrimotlagh & Yao Chen, 2018. "Data Envelopment Analysis," International Series in Operations Research & Management Science, in: Decision Making and Performance Evaluation Using Data Envelopment Analysis, chapter 0, pages 217-234, Springer.
    2. Samis, Michael & Steen, John, 2020. "Financial evaluation of mining innovation pilot projects and the value of information," Resources Policy, Elsevier, vol. 69(C).
    3. Mai, Ngoc Luan & Topal, Erkan & Erten, Oktay & Sommerville, Bruce, 2019. "A new risk-based optimisation method for the iron ore production scheduling using stochastic integer programming," Resources Policy, Elsevier, vol. 62(C), pages 571-579.
    4. Foo, Nam & Bloch, Harry & Salim, Ruhul, 2018. "The optimisation rule for investment in mining projects," Resources Policy, Elsevier, vol. 55(C), pages 123-132.
    5. Jonek-Kowalska, Izabela, 2019. "Consolidation as a risk management method in the lifecycle of a mining company: A novel methodological approach and evidence from the coal industry in Poland," Resources Policy, Elsevier, vol. 60(C), pages 169-177.
    6. Oksana Hoshovska & Zhanna Poplavska & Natalia Kryvinska & Natalia Horbal, 2020. "Considering Random Factors in Modeling Complex Microeconomic Systems," Mathematics, MDPI, vol. 8(8), pages 1-18, July.
    7. Frederiksen, Tomas, 2018. "Corporate social responsibility, risk and development in the mining industry," Resources Policy, Elsevier, vol. 59(C), pages 495-505.
    8. Damien Ackerer & Damir Filipović, 2020. "Linear credit risk models," Finance and Stochastics, Springer, vol. 24(1), pages 169-214, January.
    9. Escudero, Laureano F. & Garín, M. Araceli & Monge, Juan F. & Unzueta, Aitziber, 2020. "Some matheuristic algorithms for multistage stochastic optimization models with endogenous uncertainty and risk management," European Journal of Operational Research, Elsevier, vol. 285(3), pages 988-1001.
    10. Ioannis Karatzas & Donghan Kim, 2020. "Trading strategies generated pathwise by functions of market weights," Finance and Stochastics, Springer, vol. 24(2), pages 423-463, April.
    11. Anna Jaśkiewicz & Andrzej S. Nowak, 2021. "Markov decision processes with quasi-hyperbolic discounting," Finance and Stochastics, Springer, vol. 25(2), pages 189-229, April.
    12. Adrien Rimélé, M. & Dimitrakopoulos, Roussos & Gamache, Michel, 2018. "A stochastic optimization method with in-pit waste and tailings disposal for open pit life-of-mine production planning," Resources Policy, Elsevier, vol. 57(C), pages 112-121.
    13. Ruodu Wang & Zuo Quan Xu & Xun Yu Zhou, 2019. "Dual utilities on risk aggregation under dependence uncertainty," Finance and Stochastics, Springer, vol. 23(4), pages 1025-1048, October.
    14. Zhu, Xuehong & Chen, Ying & Feng, Chao, 2018. "Green total factor productivity of China's mining and quarrying industry: A global data envelopment analysis," Resources Policy, Elsevier, vol. 57(C), pages 1-9.
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    Cited by:

    1. Vladyslav Sotnyk & Artem Kupchyn & Viktor Trynchuk & Vladimer Glonti & Larisa Belinskaja, 2022. "Fuzzy Logic Decision-Making Model for Technology Foresight," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 1, pages 139-159.
    2. Octavian Dospinescu, 2022. "Business and Economics Mathematics," Mathematics, MDPI, vol. 10(20), pages 1-3, October.

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