Models of Structure Capital Management Based on the Lag Determinants, Including the Cost of Debt and Tax

The question of the optimal capital structure existence has been a controversial field for scientists for more than 70 years. The development of capital structure theories has prompted scientists to analyze the factors that influence the decision-making on the equity/debt ratio. The applied methods of capital structure management depend on the goals of the company’s management and may include ensuring financial stability, profit maximization or ensuring the return on invested capital. At the same time, managerial decision-making regarding the capital structure is based, as a rule, on informal assessments due to the lack of specific tools that allow for making scientifically sound decisions in this area. The purpose of the study is to develop a formalized approach to capital structure management by varying lag determinants. A three-factor lag model allows us to determine the corporate capital structure based on the increment of three determinants — return on sales, resource efficiency, and equity multiplier. A five-factor lag model takes into account, in addition to the above, the costs of debt and taxes. The choice of a model for capital structure is based on the availability and objectivity of information for calculation. At the same time, both three-factor and five-factor lag models produce a very similar capital structure for the metallurgical industry. As calculations have shown, the difference in modeling arises due to an increase in the return on sales (ΔROS) and an interest burden (ΔIb). Moreover, if the increase in the return on sales exceeds ΔIb, then the share of equity capital will rise. In order to improve the quality of management decisions regarding the capital structure, it is advisable to carry out forecasting using two models, and in practical activity the obtained results should be used as a guideline for possible changes in the total capital structure. The empirical basis of the study was the reporting and forecast data from the largest companies of the metallurgical industry in the Russian Federation over 2019–2023.

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