Presenting a Prediction Model for CEO Compensation Sensitivity using Meta-heuristic Algorithms (Genetics and Particle Swarm) - Saeed Khaljastani; Habib Piri; Reza Sotoudeh (مدیریت دولتی)
Objective To reduce the conflict of interests between managers and shareholders, it is crucial to focus on the sharing of benefits. Managerial remuneration is one way to address this conflict and serves as a tool to align managers' perspectives and performance with the goal of increasing shareholder wealth. Cash rewards for managers should be performance-based to ensure their alignment with shareholder interests. When designing the CEO's salary package in companies, the role of institutional investors is significant. Agency theory highlights the problems that arise when owners delegate the management of the company to managers. To mitigate agency conflicts, managerial rewards should be tied to the value created for shareholders. One of the primary methods for measuring managerial performance is through accounting reports, which act as tools for assessing and motivating managerial performance. Given these points, the aim of this research is to provide a model for predicting the sensitivity of CEO compensation using meta-heuristic algorithms, specifically genetic algorithms and particle swarm optimization. Methods The statistical population of this research comprises all companies listed on the Tehran Stock Exchange from 1390 to 1400. To select the sample, a systematic elimination method was employed, resulting in a sample of 110 companies. Based on the classification of research according to its purpose, the current study is applied in nature. Additionally, it is a quasi-experimental study within the domain of descriptive research (non-experimental survey). Data collection methods for this research include document analysis, internet research, and library study, depending on the specific requirements. In this research, 12 parameters influencing the sensitivity of CEO compensation were selected: institutional ownership, family ownership, comparability of financial statements, profit management, conditional conservatism, income and cost matching, market added value, corporate acquisition, debt contracts, and cost behavior (categorized into three types: changes in asset returns, changes in sales revenue, and changes in operating costs). These parameters were used as inputs for the data mining model. The sensitivity of CEO service compensation was chosen as the output parameter. Three data mining models were created by separating the cost behavior parameter, and for comparison, three linear regression models were also employed. Results The results demonstrate the superiority of the deep neural network model in terms of the coefficient of determination and MSE index. This superiority holds true for all three data mining models compared to the three linear regression models. Among the data mining models, the third model, which incorporates the cost behavior parameter of changes in operational costs, produced the best results. The second model, which includes the cost behavior parameter of changes in sales revenue, achieved the next best results. Finally, the first data mining model, which uses the cost behavior parameter of asset return changes, delivered the weakest results. Conclusion The application of deep neural networks, optimized by meta-heuristic algorithms, can create predictive models based on real data, which can be used for management decisions and enhancing service compensation processes in organizations. These methods offer the potential to improve CEO performance and the quality of services provided by organizations by leveraging existing data and artificial intelligence algorithms. Furthermore, this research can assist investors and economic decision-makers in more accurately analyzing and predicting the sensitivity of CEO compensation using deep neural networks and meta-heuristic algorithms.