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Modelling Optimal Capital Structure in Gas and Oil Sector by Applying Simulation Theory and Programming Language of Python (Qatar Gas Transport Company)

Author

Listed:
  • Andrey Kulikov

    (Department of Organization of Medical Provision and Pharmacoeconomics, I.M. Sechenov First Moscow State Medical University (Sechenov University), 119991 Moscow, Russia)

  • Naief Alabed Alkader

    (Department of Sustainable Development Finance, Plekhanov Russian University of Economics, 117997 Moscow, Russia)

  • Galina Panaedova

    (Department of Tax Policy and Customs Affairs, North-Caucasus Federal University, 355017 Stavropol, Russia)

  • Aleksandr Ogorodnikov

    (Department of Philosophy and Sociology, Kutafin Moscow State Law University (MSAL), 125993 Moscow, Russia)

  • Evgenii Rebeka

    (Department of Sustainable Development Finance, Plekhanov Russian University of Economics, 117997 Moscow, Russia)

Abstract

The goal of this study is to optimize a company’s capital structure by modelling and programming the required equations in the components of weighted average cost of capital. This process will be carried out in the programming language Python, and then Monte Carlo simulation will be applied to increase the number of combinations of debt and equity in the capital structure and obtain more accurate results on the Qatar gas transport company. Throughout various scenarios and outcomes, the study seeks to determine the ideal capital structure that reduces both the firm’s weighted average cost of capital and the risk to which the Qatar gas transport company will be exposed. The suggested method offers a practical instrument for managers and investors to make well-informed decisions when they apply financial strategies to optimize the company’s financial performance. Debt and equity are considered the main parts in the capital structure in every company, and if we can solve the problems connected to these two parts, we can optimize performance. The code of the programming language (Python) used in this study depends on logical discussion to compare different results. This research also will pay attention to the whole probabilities of debt and equity ratios in the Qatar Gas Transport Company. This research considers more variables in the analysis to obtain more accurate results about the firm’s financial status. The results in this research show that the cost of capital in the Qatar Gas Transport Company will be reduced to 5% (currently 6%) if the percentage of debt and equity in the capital structure is changed to 79% and 21%, respectively. In comparison with the other 9999 components of capital structure, this ratio (79%, 21%) will be the optimal one. The scatterplot reflects that the more repeated values of weighted average cost of capital were located around the value of 5%. This fact can enable us to conclude that the minimum value of WACC is 5%, even though there are lower values of WACC than 5%, but at a lower frequency than that of values of 5%. The study’s results are anticipated to introduce additional findings to help other authors to understand the mechanism of optimizing the capital structure and reduce the risk in the gas and oil sector.

Suggested Citation

  • Andrey Kulikov & Naief Alabed Alkader & Galina Panaedova & Aleksandr Ogorodnikov & Evgenii Rebeka, 2023. "Modelling Optimal Capital Structure in Gas and Oil Sector by Applying Simulation Theory and Programming Language of Python (Qatar Gas Transport Company)," Energies, MDPI, vol. 16(10), pages 1-15, May.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:10:p:4067-:d:1146020
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    References listed on IDEAS

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    1. 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.
    2. Black, Fischer & Scholes, Myron S, 1972. "The Valuation of Option Contracts and a Test of Market Efficiency," Journal of Finance, American Finance Association, vol. 27(2), pages 399-417, May.
    3. Alex Borodin & Galina Panaedova & Irina Ilyina & Mustafa Harputlu & Natalia Kiseleva, 2023. "Overview of the Russian Oil and Petroleum Products Market in Crisis Conditions: Economic Aspects, Technology and Problems," Energies, MDPI, vol. 16(4), pages 1-18, February.
    4. Fama, Eugene F. & French, Kenneth R., 1993. "Common risk factors in the returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 33(1), pages 3-56, February.
    5. 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.
    6. Myers, Stewart C, 1984. "The Capital Structure Puzzle," Journal of Finance, American Finance Association, vol. 39(3), pages 575-592, July.
    7. Black, Fischer & Scholes, Myron S, 1973. "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 637-654, May-June.
    8. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    9. Kim, Hyunseob, 2020. "How does labor market size affect firm capital structure? Evidence from large plant openings," Journal of Financial Economics, Elsevier, vol. 138(1), pages 277-294.
    10. Elmina Homapour & Larry Su & Fabio Caraffini & Francisco Chiclana, 2022. "Regression Analysis of Macroeconomic Conditions and Capital Structures of Publicly Listed British Firms," Mathematics, MDPI, vol. 10(7), pages 1-28, March.
    11. Liu, Huimin & Song, Shuang & Hu, Yi & Yan, Xue, 2020. "Monte-Carlo optimization model for dynamic capital structure adjustment in Chinese public-private partnerships under revenue uncertainty," Transportation Research Part A: Policy and Practice, Elsevier, vol. 142(C), pages 115-128.
    12. B Espen Eckbo & Michael Kisser, 2021. "Tradeoff Theory and Leverage Dynamics of High-Frequency Debt Issuers [The leverage ratchet effect]," Review of Finance, European Finance Association, vol. 25(2), pages 275-324.
    13. Xin Chang & Sudipto Dasgupta, 2011. "Monte Carlo Simulations and Capital Structure Research," International Review of Finance, International Review of Finance Ltd., vol. 11(1), pages 19-55, March.
    14. repec:cdl:anderf:qt43n1k4jb is not listed on IDEAS
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    Cited by:

    1. Galina Panaedova & Alex Borodin & Cemal Zehir & Sergey Laptev & Andrey Kulikov, 2023. "Overview of the Russian Coal Market in the Context of Geopolitical and Economic Turbulence: The European Embargo and New Markets," Energies, MDPI, vol. 16(19), pages 1-29, September.
    2. Afolabi, Chukwudi Segun & Ogunleye, Joshua Kehinde & Akinleye, Bilikis Olayem & Odetayo, Tajudeen Adewale & Oladeji, Felicia Oluremi, 2025. "Exploring the Effect of Capital Structure on Firm Value in Information and Communication Technology Listed on the Nigeria Exchange Group (Ngx)," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 9(3), pages 4188-4201, March.

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