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Optimal Executive Compensation Based On Intertemporal Discrete Time Model

Posted on:2017-05-19Degree:MasterType:Thesis
Country:ChinaCandidate:M TuFull Text:PDF
GTID:2349330512459947Subject:Financial engineering
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Since 1988 our listed companies began to disclose the executive compensation and shareholdings, this is the first time that our listed companies to do this, later on, society began to focus on executive compensation and incentive problems, and in recent years People has been discuss listed company executives sky-high compensation problem. Some companies have profit loss but executives still have high salaries, this phenomenon is really strange.The fundamental of executive compensation problem is the principal-agent problem. Modern enterprise has a very important feature is the separation of ownership and managerial authority which causes the principal (shareholders) and agent (executive) form the principal-agent relationship, shareholders and executives do not have the same target utility. Information asymmetry caused the principal-agent problems. So through reasonable construction of models this paper wants discuss inter-temporal effects between executives and shareholders dynamic optimization process as well as the objective function.In the standard principal-agent model, there is a principal and an agent, each action for their own interests, if the principal can fully observe agent's effort level, information is completely symmetrical, the principle will require the agent to achieve a particular effort level and pay the agent only fixed amount compensation (monetary compensation) can achieve a goal, this time the agent meet the individual rationality constraint, but if the principle cannot fully observe agent's effort level, because of information asymmetry, the principal cannot clear agent need to achieve how much effort level, and because they have different interests in the case of a given compensation, agent will take action to maximize his own interests, then the agent not only meets the individual rationality constraint but also meets the incentive compatibility constraint, and agent requires not only monetary compensation, but also requires restricted stocks or stock options, this part is called incentive compensation, the role of incentive pay is to motivate executives receive valuable projects (net present value>0) which will let the company to take more risk. When we achieve first best outcome, there's principal and the agent's overall welfare maximization. When we achieve second best outcome, there's agent's utility maximization.Based on the above theory, this paper develop a discrete time model (binary tree model) framework, explore the principal-agent model multiphase model under the agent path independent (path-independent) decisions, explore the game and equilibrium between the principal and agent. In the framework of principal-agent model, this paper wants to get the numerical solution of the optimal compensation contract. Given the importance of executives, the volatility of the company,the risk aversion of the agent, this paper wants to investigate what's the optimal executive pay and on different stages how's manager's decision-making. Even more this paper wants to know how people can derive first best outcome. Also a random shock is put in to consideration of influencing the value of the company. Shareholders need to know how to achieve their own utility maximization, inspire and restrain executive action at the same time. Through the analysis of the models, with the help of empirical model, this paper hopes to provide theoretical support for the later research, help people better understand the game between shareholders and executives.This article summarizes the theory of classic principal-agent problems of literature and on the basis of those literatures this paper further innovates the model which contribute to the determination of the optimal compensation mechanism and incentive plan.
Keywords/Search Tags:executive compensation, restricted stock, executive stock option, first best outcome, second best outcome
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