IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v9y2021i9p1041-d548528.html
   My bibliography  Save this article

RETRACTED: 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. "RETRACTED: 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/9/9/1041/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/9/9/1041/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. Frederiksen, Tomas, 2018. "Corporate social responsibility, risk and development in the mining industry," Resources Policy, Elsevier, vol. 59(C), pages 495-505.
    4. Damien Ackerer & Damir Filipović, 2020. "Linear credit risk models," Finance and Stochastics, Springer, vol. 24(1), pages 169-214, January.
    5. 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.
    6. 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.
    7. Anna Jaśkiewicz & Andrzej S. Nowak, 2021. "Markov decision processes with quasi-hyperbolic discounting," Finance and Stochastics, Springer, vol. 25(2), pages 189-229, April.
    8. 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.
    9. 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.
    10. Samis, Michael & Steen, John, 2020. "Financial evaluation of mining innovation pilot projects and the value of information," Resources Policy, Elsevier, vol. 69(C).
    11. 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.
    12. 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.
    13. Foo, Nam & Bloch, Harry & Salim, Ruhul, 2018. "The optimisation rule for investment in mining projects," Resources Policy, Elsevier, vol. 55(C), pages 123-132.
    14. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    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. Ziyan Zheng & Fangdao Qiu & Xinlin Zhang, 2020. "Heterogeneity of correlation between the locational condition and industrial transformation of regenerative resource‐based cities in China," Growth and Change, Wiley Blackwell, vol. 51(2), pages 771-791, June.
    2. Yaozu Xue, 2022. "Evaluation analysis on industrial green total factor productivity and energy transition policy in resource-based region," Energy & Environment, , vol. 33(3), pages 419-434, May.
    3. Yujian Jin & Lihong Yu & Yan Wang, 2022. "Green Total Factor Productivity and Its Saving Effect on the Green Factor in China’s Strategic Minerals Industry from 1998–2017," IJERPH, MDPI, vol. 19(22), pages 1-20, November.
    4. Zhang, Yijun & Li, Xiaoping & Song, Yi & Jiang, Feitao, 2021. "Can green industrial policy improve total factor productivity? Firm-level evidence from China," Structural Change and Economic Dynamics, Elsevier, vol. 59(C), pages 51-62.
    5. Shuying Wang & Yifei Gao & Hongchang Zhou, 2022. "Research on Green Total Factor Productivity Enhancement Path from the Configurational Perspective—Based on the TOE Theoretical Framework," Sustainability, MDPI, vol. 14(21), pages 1-20, October.
    6. Xiangqian Wang & Shudong Wang & Yongqiu Xia, 2022. "Evaluation and Dynamic Evolution of the Total Factor Environmental Efficiency in China’s Mining Industry," Energies, MDPI, vol. 15(3), pages 1-19, February.
    7. Meiling Wang & Silu Pang & Ikram Hmani & Ilham Hmani & Cunfang Li & Zhengxia He, 2021. "Towards sustainable development: How does technological innovation drive the increase in green total factor productivity?," Sustainable Development, John Wiley & Sons, Ltd., vol. 29(1), pages 217-227, January.
    8. Zhang, Rui & Qie, Xiaotong & Hu, Yanyong & Chen, Xue, 2022. "Does de-capacity policy promote the efficient and green development of the coal industry? –Based on the evidence of China," Resources Policy, Elsevier, vol. 77(C).
    9. Chen, Xiang & Chen, Yong & Huang, Wenli & Zhang, Xuping, 2023. "A new Malmquist-type green total factor productivity measure: An application to China," Energy Economics, Elsevier, vol. 117(C).
    10. Wang, Miao & Feng, Chao, 2021. "The win-win ability of environmental protection and economic development during China's transition," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    11. Zhou, Anhua & Li, Jun, 2021. "Investigate the impact of market reforms on the improvement of manufacturing energy efficiency under China’s provincial-level data," Energy, Elsevier, vol. 228(C).
    12. Joshi, Shruti & Nath, Siddhartha & Ranjan, Abhishek, 2023. "Green Total Factor Productivity for India: Some Recent Estimates and Policy Directions," MPRA Paper 117717, University Library of Munich, Germany.
    13. Gao, Kang & Yuan, Yijun, 2022. "Spatiotemporal pattern assessment of China’s industrial green productivity and its spatial drivers: Evidence from city-level data over 2000–2017," Applied Energy, Elsevier, vol. 307(C).
    14. Huaping Zhang & Yue Dong, 2022. "Measurement and Spatial Correlations of Green Total Factor Productivities of Chinese Provinces," Sustainability, MDPI, vol. 14(9), pages 1-15, April.
    15. Qingshan Ma & Yuanmeng Zhang & Kexin Yang & Lingyun He, 2021. "Have China’s Pilot Free Trade Zones Improved Green Total Factor Productivity?," IJERPH, MDPI, vol. 18(21), pages 1-20, November.
    16. Ruomeng Zhou & Yunsheng Zhang, 2023. "Measurement of Urban Green Total Factor Productivity and Analysis of Its Temporal and Spatial Evolution in China," Sustainability, MDPI, vol. 15(12), pages 1-32, June.
    17. Cenjie Liu & Zhongbao Zhou & Qing Liu & Rui Xie & Ximei Zeng, 2020. "Can a low-carbon development path achieve win-win development: evidence from China’s low-carbon pilot policy," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 25(7), pages 1199-1219, October.
    18. Xi Qin & Xiaoling Wang & Yusen Xu & Yawen Wei, 2019. "Exploring Driving Forces of Green Growth: Empirical Analysis on China’s Iron and Steel Industry," Sustainability, MDPI, vol. 11(4), pages 1-11, February.
    19. Yang, Jun & Cheng, Jixin & Zou, Ran & Geng, Zhifei, 2021. "Industrial SO2 technical efficiency, reduction potential and technology heterogeneities of China's prefecture-level cities: A multi-hierarchy meta-frontier parametric approach," Energy Economics, Elsevier, vol. 104(C).
    20. Wu, Rongxin & Tan, Zhizhou & Lin, Boqiang, 2023. "Does carbon emission trading scheme really improve the CO2 emission efficiency? Evidence from China's iron and steel industry," Energy, Elsevier, vol. 277(C).

    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:gam:jmathe:v:9:y:2021:i:9:p:1041-:d:548528. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.