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Research on Financial Crisis Warning of Listed Company in Chemical Industry

Author: ChenChao
Tutor: LiJun
School: West China University
Course: Business management
Keywords: chemical industry financial crisis multi-period warning financialindicators BP artificial neural network model
CLC: F275
Type: Master's thesis
Year: 2013
Downloads: 1
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Abstract


China is a great power in the chemical manufacture and consumption in the world, andthere are mainly three categories which are chemical technology, chemical industry andchemical engineering. There are a great number of specific categories ranging from steel, oiland cement to textile, pesticides and paper-making. As a branch of industrial manufacture,chemical industry plays an important role. According to official statistics, by2012December, there have been more than138domestically listed chemical enterprises. As thecompetition keeps increasing, business environment in chemical industry becomes tougherand tougher. Therefore, preventing and managing financial crisis rationally and effectivelywill increase its vitality and anti-risk ability, but also enhance the operation efficiency. So,financial crisis management is an essential topic which will influence a company’s operationand even survival.As the research on financial warning has just started in China, there is a considerable gapbetween china and other countries in the aspect of the research method, achievement andatmosphere. However, it has acquired many achievements after domestic scholars’ studyingintensively on the basis of research results in foreign countries, and improved them tointegrate with Chinese market. From the aspect of research thoughts, it can be generallydivided into two categories. One tends to promotion of financial warning. It no longer sticksto a single mathematical model, but adds more qualitative indicators to the model tocomprehensively reflect a company’s financial position and apply to all types of enterprises.On one hand, the thought explores new research aspect so as to make financial warning morecomprehensive. On the other hand, it limits the development of financial warning. For it alldepends on users’ subjective judgment, users must have rich experience and sound judgment.Massive research results prove it unsatisfactory on the precision of their forecasts. The othercategory is focusing on the study of models. In order to ensure the accuracy of the model andits applicability, a large number of scholars choose to perfect the screening of financialindicators. They adopt one or more methods in the classification and screening of financialindicators. Such models can ensure the accuracy of the results, but they often only apply toone industry and their application is limited obviously. Therefore, it becomes a hot topic onhow to evaluate their advantages and disadvantages of financial warning in the study.The author attempted to overcome the defects of previous studies in order to make a breakthrough in terms of guiding theory, research methods and research system. However,due to limited capacity, the author chose chemical industry as the research object, which hasvarious categories and complexity. It narrowed down the scope of the research, and meet thecomplexity of the sample to some degree.Different from research findings in China, this thesis focuses on the study from thefollowing two aspects.Firstly, study objects. At present, the study of the financial crisis warning models of thedomestic listed companies is mainly classified by trades and professions. Especially,traditional manufacturing industry is mostly studied. However, chemical industry is playing avery important role in economy, but it is too complicated to be described by one specificmathematic model. This thesis studied listed chemical enterprises.Secondly, research method. The establishment of the research methods lies in the choiceof the model. Up to now, multivariate discriminate analysis and factor-principal componentanalysis have developed as mature models. Although such linear models have a higher levelof accuracy, a lot of the accuracy of the study relies on the financial data of the year before thecorporation’s crisis. This only applies to the industry of the same type, and this is not onlyinconsistent with the quality of the object of study but also difficult to realize. The reason isthat listed companies in China publish their annual report in April the following year. Theinformation of the corporation of the year before the crisis is gained, it is certain that thecorporation will receive special treatment or not, according to its abnormal financial condition.Therefore, the chosen model needs not only to meet the diverse needs of the industry, but alsoto be able to make stable long-terms forecasts.In this paper, considering the features of listed companies in chemical industry,combined the traditional financial indicators with cash flow information, built a financialwarning indicator system which tried to fully represent the listed companies’ financialcondition. As a simple linear analysis can hardly match the complexity of the object of study,a non-linear analysis of BP neural network system analysis is adopted. In order to improve themodel’s long-term predicting ability to realize multi-period warning, in the process ofmodeling, it is advisable to adopt multi-period financial data with model-training the fouryears’ data before the financial crisis in the learning sample. In order to adapt to theapplicability and complexity of chemical industry, the study used artificial neural networkswhich can be continued to trial and error, and correct the mode of the analysis, fully embodiedthe advantages of the neural network technology in the warning areas, thereby, improved the significance of the early warning model in practice.

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CLC: > Economic > Economic planning and management > Enterprise economy > Corporate Financial Management
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