Based On The Efficiency Coefficient-BP Neural Network A Research On Risk Early Warning Of China Shipbuilding Industry

In this century, China's shipbuilding industry is facing the real risk of delivery, order financing difficulty after rapid growth and continual prosperity. From January to August in 2009, China's shipbuilding capacity, industrial output continued to maintain two digit growth, but received new ship orders continued to decline, the amount of handheld shipbuilding orders declined for 11 consecutive months, the impact of the financial crisis on the shipbuilding industry further demonstrated. Uncertainties faced by China's shipbuilding industry in the future have increased, and industry risk has increased. Therefore, we urgently need to study risk identification, risk warning and risk-response for shipbuilding industry.This paper focuses on three areas of risk identification, risk warning and risk-coping strategies for China's shipbuilding industry. Firstly, this paper conducts Chinese shipbuilding industral risk identification by using a combination of quantitative and qualitative methods. Secondly, this paper conducts risk early-warning by using a combination of efficiency coefficient method and the neural network method. Based on results of risk early warning, this paper proposes coping strategies.For aspect of risk identification, this paper conducts a qualitative analysis of risk factors of Chinese shipbuilding industry. Risks of shipbuilding industry mainly come from external and intra-industry. Based on qualitative analysis, combined with a questionnaire survey and field research methods, this paper identifies shipbuilding industrial risks. According to analysis result, this paper establishes risk indicator system. Then, this paper study shipbuilding industrial risks from eight aspects of interest rates, exchange rates, export tax rebates, the composite steel price index, industry average wages of the workers , equipment prices, new boat price index and BDI index. Finally, this paper measures the eight indicator weight by using principal component analysis.For aspect of risk early warning, this paper firstly gave a qualitative description of shipbuilding industrial risk degree, which is divided into five degree level. What's more, 3δtheory in statistical theory and methods is used in the degree division of early warning process. Then this paper discusses the idea and method of the risk early warning respectively with the combination of efficacy coefficient method and BP neural network, and proposes EC-BP warning method with shipbuilding industrial characteristics. Finally, this paper uses .NET programming languages (C #) to compile the early warning procedure, and uses the program to conduct an empirical research on China's shipbuilding industry risk early-warning. This paper introduces three different cycle early warning method of month early warning, quarter early warning and annual early warning in empirical study, and the different early warning results could meet different needs.For aspect of risk response, this paper proposes risk response strategies from the macro, meso and micro perspective respectively, analyze the feasibility and importance of each strategy, and analyze the more feasible and important strategies deeply.