Based On Early Warning On Financial Listed Companies BP Network Neighborhood Rough Set And Particle Swarm Optimization

In2011, because of the weak capital market performance, the capital requirements and the difficulty of financing increased greatly than before in many listed companies. Enterprises are facing high risk in finance. Therefore, it is very necessary to make further research in the theory and practice of financial warning and to construct a financial crisis warning model that is suitable for Chinese national conditions for listed enterprises. Financial statements and the change of financial indicators can reflect the management of enterprise is normal or not. Meanwhile, more and more non-financial factors also play an important role in the development of enterprise in the21st century. So we need to monitor these indexes every moment and use feasible technology to predict the running state of enterprise according to the change of these indexes, and all these measures have very important significance for those listed enterprises themselves, investors, capital market and other stakeholders.Based on above, this paper mainly focuses on three aspects:firstly, the enterprise financial warning index system constructed in this paper includes not only traditional financial indexes, but also some non-financial indexes. Secondly, in order to solve the classical rough set (CRS) theory in processing numeric data insufficiency, this paper will introduce neighborhood rough set (NRS) theory into financial research area for attribute reduction. Thirdly, this paper also use particle swarm optimization (PSO) algorithm to make up the BP neural network algorithm which has some defects and then construct a model named PSOBP. We make NRS and PSOBP combined to construct a NRS-PSOBP financial crisis warning model so as to realize early-warning for listed enterprise financial status.The main research process is described as follows:First of all, we study and analysis the financial warning research situation in domestic and oversea, and then summarize various methods of index selection and model research.Second, we systematically introduce the NRS theory, PSO algorithm and BP neural network algorithm, and make a comparative analysis between the classical rough set and NRS theory, construct a model based on PSOBP.Third, according to many selection principles we get initial indicators, and then do attribute reduction for indicators in using CRS and NRS respectively, construct the listed enterprise financial early-warning index system.Finally, according to the new listed enterprise financial early-warning index system, we use the training sample data to train the PSOBP model, and then use the test sample data to examine the early-warning performance of the model. We compared this forecast results with other results that are got from the CRSBP model and the NRSBP model, and then find that it is feasible and effective to make NRS replace CRS and using PSOBP. The combination model provides a new method for enterprise financial early-warning research.