Using Stochastic Frontier Analysis Algorithms to Study Corporate Capital Structure Optimization and Risk Management: A State-Owned Enterprise Research Perspective

Authors

  • Xiayi Zhang School of Business and Economics, Universiti Putra Malaysia, UPM Serdang, Selangor Darul Ehsan, 43400, Malaysia Author
  • Mohamed Hisham Dato Haji Yahya School of Business and Economics, Universiti Putra Malaysia, UPM Serdang, Selangor Darul Ehsan, 43400, Malaysia Author
  • Norhuda Abdul Rahim School of Business and Economics, Universiti Putra Malaysia, UPM Serdang, Selangor Darul Ehsan, 43400, Malaysia Author
  • Nazrul Hisyam Ab Razak School of Business and Economics, Universiti Putra Malaysia, UPM Serdang, Selangor Darul Ehsan, 43400, Malaysia Author

DOI:

https://doi.org/10.56294/sctconf20251181

Keywords:

Capital Structure Optimization, Risk Management, State-Owned Enterprise, Machine Learning, Mean Efficiency

Abstract

For any industries, the measuring of Capital Structure Optimization (CSO) and Risk Management (RM) are essential aspect to improve performance and sustainability. State owned enterprise provide considerable challenges to perform the CSO and RM because of its inherent complexities and unique attributes. Further there are too little attempts were made to measure those attributes. This work is an attempt to study the influence of CSO and RM over the performance of State-Owned Enterprise (SOE). Particularly this study focuses on industries such as energy, utilities, telecommunications, transportation, manufacturing, financial services, real estate, healthcare, technology, and agriculture. The work study had employed a Translog Stochastic Frontier (TSF) model with Return on Assets (ROA) as the dependent variable and key financial ratios as independent variables. Using the data that was collected three years during the period from 2020 to 2023. The TSF model was optimized using goal programming approach based on set of constraints. The results from the findings have shown that the mean efficiency scores have improved across all industries after constraint applications

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Published

2025-01-01

How to Cite

1.
Zhang X, Dato Haji Yahya MH, Abdul Rahim N, Ab Razak NH. Using Stochastic Frontier Analysis Algorithms to Study Corporate Capital Structure Optimization and Risk Management: A State-Owned Enterprise Research Perspective. Salud, Ciencia y Tecnología - Serie de Conferencias [Internet]. 2025 Jan. 1 [cited 2024 Dec. 1];4:1181. Available from: https://conferencias.ageditor.ar/index.php/sctconf/article/view/1181