Dynamic Portfolio Choice under Uncertainty about Asset Return Model

The effect of uncertainty about stochastic diffusion model on dynamic portfolio choice of an investor who maximizes utility of terminal portfolio wealth was studied.It applied stochastic control method to obtain the closed-form solution of optimal dynamic portfolio,and used the Bayesian rule to estimate the model parameters to do an empirical study on two different samples of Shanghai Exchange Composite Index.Results show,model uncertainty results in positive or negative hedging demand of portfolio,which depends on investor's attitude toward risk;the effect of model uncertainty is more significant with the increasing of investment horizon,the decreasing of investor's risk-aversion degree,and the decreasing of information;predictability of risky asset return increases its allocation in portfolio,at the same time,the effect of model uncertainty also strengthens.

Corporate Tax Differences under the New Accounting Standards

The tax differences is the enterprise according to the provisions of accounting standards to calculate the total profit and tax calculated according to the enterprise income tax＇s tax base taxable income differences. Enterprise accounting standards and tax for the same subject for different purposes, leading to both the principle is different, as well as for the same matter measurement and confirmation of different, resulting in tax differences. In this paper, from the aspect of theory to the study of tax differences, from tax differences that the tax law and accounting purposes different proceed with, on the basis of theoretical analysis, design a regression model from empirical research on tax differences. According to the 2009,2010 and201 lthree annual sample data regression results, discussed in the new＂ accounting standards for business enterprises＂ of corporate tax difference change tendency, and according to the tax law and accounting standards caused by the different effects of tax differences for the main project correlation analysis, so as to verify the theoretical expectations.

Primary Research of EIT Inverse Problem Based on CS （Compressed Sensing） Technique

EIT （electrical impedance tomography） problem should be represented by a group of partial differential equation, in numerical calculation： the nonlinear problem should be linearization approximately, and then linear equations set is obtained, so EIT image reconstruct problem should be considered as a classical ill-posed, ill-conditioned, linear inverse problem. Its biggest problem is the number of unknown is much more than the number of the equations, this result in the low imaging quality. Especially, it can not imaging in center area. For this problem, we induce the CS technique into EIT image reconstruction algorithm. The main contributions in this paper are： firstly, built up the relationship between CS and EIT definitely; secondly, sparse reconstruction is a critical step in CS, built up a general sparse regularization model based on EIT; finally, gives out some EIT imaging models based on sparse regularization method. For different scenarios, compared with traditional Tikhonov regularization （smooth regularization） method, sparse reconstruction method is not only better at anti-noise, and imaging in center area, but also faster and better resolution.