A Study Of Optimal Dynamic Contracts Based On Investment Impact Divergence

In organizations such as financial institutions,economic agents with potential disagreements interact in the market.These disagreements have been proven to have a substantial impact on agent compensation and project financing arrangements.In this paper,we use principal-agent theory to develop a dynamic contract model by considering the different disagreements between principals and agents.The model in this paper consists of a principal hiring an agent to manage a project where the principal and the agent do not share a common belief about the potential development of the project.Principals and agents have different views on the probability distribution of stochastic innovations in the economy and different views on the expected unit return on the amount invested in the project.Finally,the HJB equation for optimal contract satisfaction is derived by the dynamic programming principle.By solving the HJB equation and numerical simulations,we get the following conclusions:(1)When the ongoing volatility of the project and the risk is higher,agents will reduce their level of effort due to risk aversion considerations;(2)When agents are pessimistic about the return per unit of investment in a project,the level of agent effort decreases as the investment increases;and vice versa;(3)When agents are more optimistic about investment returns and future innovation,principals will invest more money and give agents greater incentives to maximize the value of the contract,and agents' efforts will increase;(4)When the belief disagreement between the principal and the agent is small,the agent's optimal effort is smaller and decreases over time.When cognitive differences are large,the agent's optimal effort is larger and increases over time.Finally,by comparing the solutions of dynamic contracts considering different divergences,it is found that different divergences have to be taken into account to explain the actual financial activities more comprehensively.