The Choice of Dynamic Portfolio based on the Learning Behavior
基于学习行为的动态资产组合选择

This paper studies the effects of parametric uncertainty of the first two moments about risky asset return on the choice of dynamic portfolio under incomplete information.In continuous-time framework,assuming that asset price follows stochastic diffusion process,it introduces parametric uncertainty,and applies stochastic dynamic programming to derive the closed-form solution of optimal portfolio choice,which maximizes the expected power utility of investor's terminal wealth;in discrete-time framework,continuous compounding monthly returns of risky asset are assumed to be normal i.i.d.,it applies the rule of Bayesian learning to do empirical study about two different sample of Shanghai Exchange Composite Index.Result shows,the uncertainty of parameter leads to negative(positive) investment horizon effects when investor's risk aversion is more(less) than that of logarithmic utility;the effects of parametric uncertainty will weaken when investor uses more past data in his estimation,or when his risk aversion increases;the effect of the first order moment's uncertainty is stronger than that of the second order moment's uncertainty.This study stresses the importance of parametric uncertainty in the context of dynamic portfolio choice.