The Study On Financial Crisis Early-warning System Based On The Cash Flow And BP Neural Network Of The Manufacturing Listed Companies In China

The goods called "Made in China" are so popular in the world for a long time in the past and it has always been our pride. However, the United States has exceeded China in the narrow manufacturing output by41trillion in2011.Today’s Chinese manufacturing is not the same as it looks like on the surface, it is in the throes of transformation and upgrading, facing with a variety of international and domestic difficulties. The winter of Chinese manufacturing has coming. Internationally, in recent years the United States and European countries frequently take various means to combat us through government intervention, trade barriers, anti-dumping investigations and so on. The overseas development of Chinese manufacturing enterprises encountered all kinds of difficulties. Simultaneously, the development of Mexican, Vietnamese, and Indian manufacture industry is so rapid, also forming a great threat to us. In domestic, due to the rising of labor costs, limited financing channels and the high capital cost, manufacturing enterprises develops at a slow pace. From2012annual reports of listed companies, we know that the top ten loss of listed companies are almost from manufacture industry, especially China Cosco, Chalco, MCC that total loss of14.7billion yuan. Overall, most listed manufacturing companies have a bad performance.Survival has already become the most important thing that every manufacturing enterprise has to face. The financial crisis is ultimately the crisis of cash flow. Cash is the lifeblood of the enterprise. Therefore, with the winter of Chinese manufacture industry coming, the financial crisis early-warning deeply relates to the survival of the enterprise and it has become the key to survival. In this paper, we make manufacturing listed companies as the study object, on the one hand, we make the cash flow as the basic theory, on the other hand, we use BP neural network as the main tool to build the early-warning model. Proved by the test of positive study, it’s feasible and effective to build the financial crisis early-warning model combining the theory of cash flow with BP neural network. This paper attempts to make a contribution for our listed manufacturing companies through the cold winter.The paper includes the following five parts:The first part is introduction. This part first expounds the study background and significance. Secondly, the domestic and foreign positive studies about financial crisis are reviewed and evaluated. Finally, it states the study methods, ideas, and the innovations of this paper.The second part states the study foundation of the financial crisis early-warning based on the theory of cash flow, including its basic theories, superiorities and so on.The third part introduces the application of the BP neural network in the study of financial crisis early-warning, including its basic concept, structure, calculation method, advantages and so on.The fourth part, positive study is the key part of this paper. In this paper, we make manufacturing listed companies as the study object and choose101ST and*ST companies and the other101matched normal companies (from January1,2009to May31,2013) as study samples, select23cash flow index from five categories including cash obtaining ability index, debt paying ability index, earnings quality index, development ability index and cash flow structure index which fully reflect the financial position of manufacturing listed companies. Then according to the results of the normal distribution test (K-S test) and significance test (paired T test and Mann-Whitney U test), we choose the cash flow index which is significantly different as the early-warning index. After that, according to the cash flow data of t-2year and t-3year (define the year in that the listed company is special treated as t year) of the normal companies and financial crisis companies, we build two BP models. Through testing and comparing the two BP models, we choose the one which is more accurate and stable as the early-warning model of this paper.The fifth part, finally it states the conclusions and limitations, and comes up with some suggestion aiming at making a contribution for our manufacturing listed companies through the cold winter.The innovations of this paper are the following two parts:1. In the past positive studies, the problem about the high degree of correlation and non-linear changes between the cash flow index can not be fundamentally solved. However, we use BP neural network that itself is a highly non-linear mapping model, and can solve this problem effectively. Although the study of financial crisis early-warning based the theory of cash flow has been relatively mature, the study and application of BP neural network has also developed, the study of combining the theory of cash flow with BP neural network never appears. Meanwhile, we build two BP models and choose the one which is more accurate and stable as the early-warning model of this paper.2. This paper simplifies and improves the cash flow index variables. It selects23representative cash flow index from five categories including cash obtaining ability index, debt paying ability index, earnings quality index, development ability index and cash flow structure index, through the normal distribution test (K-S test) and significance test (paired T test and Mann-Whitney U test) it builds a simple and efficient financial crisis early-warning system consisted of cash flow index.