Using Stochastic Frontier Analysis Algorithms to Study Corporate Capital Structure Optimization and Risk Management: A State-Owned Enterprise Research Perspective
DOI:
https://doi.org/10.56294/sctconf20251181Keywords:
Capital Structure Optimization, Risk Management, State-Owned Enterprise, Machine Learning, Mean EfficiencyAbstract
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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Copyright (c) 2025 Xiayi Zhang, Mohamed Hisham Dato Haji Yahya, Norhuda Abdul Rahim, Nazrul Hisyam Ab Razak (Author)
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