Policy analysis to maximize the firm value: performing firm valuation using system dynamics

IF 1.8 Q3 MANAGEMENT

Journal of Modelling in Management Pub Date : 2023-05-02 DOI:10.1108/jm2-10-2020-0272

Aima Khan, M. Qureshi

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Abstract

Purpose The purpose of this study is firm value management through corporate finance policy design and scenario analysis to maximize the firm value. Design/methodology/approach The study develops a system dynamics model for an oil firm and incorporates the financial and physical processes to perform the firm valuation. The model is simulated under the current and alternative investment, capital structure and dividend policies of the case firm, assuming different oil and gas price and tax rate scenarios to identify which combination of policies maximizes the firm value. Findings The simulation results suggest that lowering the volume of investments, increasing the debt ratio and reducing the dividend payments from the current level increases the share price, given increased oil and gas price expectations and lower tax rates. However, the total firm value outperforms with increased investments toward the end of the simulation period. In case of decreased oil and gas price expectations, lower volume of investments, lower debt ratio and lower dividend payments increase the share prices, given lower taxes. Originality/value This study entails significance as it provides a comprehensive financial planning model for an oil firm, which incorporates the complex interactions of key financial and physical processes of the firm. The study contributes to debates on corporate finance policies by integrating multiple theories, accounting for accumulation processes and feedback loops and their non-linear interactions. The study proposes the consideration of combined impact of policies for firm value management.

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企业价值最大化的政策分析:运用系统动力学进行企业估值

目的通过企业财务政策设计和情景分析，实现企业价值管理，实现企业价值最大化。设计/方法/途径本研究为一家石油公司开发了一个系统动力学模型，并结合了财务和物理过程来执行公司估值。该模型在案例公司的当前和替代投资、资本结构和股息政策下进行模拟，假设不同的石油和天然气价格和税率情景，以确定哪种政策组合使公司价值最大化。模拟结果表明，在油气价格预期上升和税率降低的情况下，降低投资量、提高负债率和减少股息支付会提高股价。然而，在模拟期结束时，随着投资的增加，公司的总价值表现得更好。在油气价格预期下降的情况下，由于税收降低，投资规模减少，负债率降低，股息支付减少，股价会上涨。原创性/价值本研究具有重要意义，因为它为石油公司提供了一个全面的财务规划模型，该模型包含了公司关键财务和物理过程的复杂相互作用。该研究通过整合多种理论，考虑积累过程和反馈循环及其非线性相互作用，有助于讨论公司融资政策。本研究建议考虑企业价值管理政策的综合影响。

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来源期刊

Journal of Modelling in Management MANAGEMENT-

CiteScore

5.50

自引率

12.50%

发文量

期刊介绍： Journal of Modelling in Management (JM2) provides a forum for academics and researchers with a strong interest in business and management modelling. The journal analyses the conceptual antecedents and theoretical underpinnings leading to research modelling processes which derive useful consequences in terms of management science, business and management implementation and applications. JM2 is focused on the utilization of management data, which is amenable to research modelling processes, and welcomes academic papers that not only encompass the whole research process (from conceptualization to managerial implications) but also make explicit the individual links between ''antecedents and modelling'' (how to tackle certain problems) and ''modelling and consequences'' (how to apply the models and draw appropriate conclusions). The journal is particularly interested in innovative methodological and statistical modelling processes and those models that result in clear and justified managerial decisions. JM2 specifically promotes and supports research writing, that engages in an academically rigorous manner, in areas related to research modelling such as: A priori theorizing conceptual models, Artificial intelligence, machine learning, Association rule mining, clustering, feature selection, Business analytics: Descriptive, Predictive, and Prescriptive Analytics, Causal analytics: structural equation modeling, partial least squares modeling, Computable general equilibrium models, Computer-based models, Data mining, data analytics with big data, Decision support systems and business intelligence, Econometric models, Fuzzy logic modeling, Generalized linear models, Multi-attribute decision-making models, Non-linear models, Optimization, Simulation models, Statistical decision models, Statistical inference making and probabilistic modeling, Text mining, web mining, and visual analytics, Uncertainty-based reasoning models.