Research On The Automobile Manufacturing Listed Companies’ Financial Risk Prediction Based On BP Neural Network

Automobile manufacturing industry,which is one of pillar industries in China,occupies an important position in the national economy.Also with the characteristics of large investment amount and scale,long investment time,high technology,sustainable development and so on.Once there are financial risks,which will cause unpredictable losses to the enterprise.This year is the second year of "Made in China 2025".Both intelligent vehicle driving and green car are the strategic focus.China is both a large producer and seller of cars,also with great potential market.So the corporate financial risk operation becoming more important.There is full of uncertainty in business environment.The accuracy of financial risk early warning is not only a warning to the operators and investors,but also an important way to understand the problem during the enterprise operation.This paper adopts literature research method,qualitative researchand quantitativeresearchmethod,inter-disciplinaryresearch method,empirical analysis method and so on.The paper uses the rough set theory to extract the index and determine its weight to construct the BP neural network financial risk early warning model.Simulation experiment of the early warning model shows that the BP neural network that is optimized by genetic algorithm has better forecasting effect.It can meet the needs of stakeholders who related with automobile industry.This paper is divided into seven chapters,the main contents include:The first chapter mainly state the research purpose,research significance,overseas & domestic research status and research content & method.The second chapter discusses the theoretical basis of financial risk early warning model from four aspects: financial risk theory,rough set theory,BP neural network theory and genetic algorithm theory.The third chapter summarizes the form of financial risk of automobile manufacturing industry,and introduces the influencing factors of automobile manufacturing industry from two aspects: external factors and internal factors.The fourth chapter summarizes the combination of automobile industry characteristics and the form of financial risk,selected 18 financial indicators and 4 non-financial indicators,to build financial risk early warning index system.The fifth chapter,According to the previous chapters,40 listed companies of automobile manufacturing industry were selected as the research object,and the financial data of 2014 were used as the research samples.Firstly,rough set theory is used to simplify the data and extract the key indicators.The financial risk early warning model is constructed by combining the genetic algorithm and the neural network(Matlab R2014.a)The financial indicators are taken as input into the input layer,the neural network outputs the expected value,and calculates the accuracy of the early warning model,and verify the feasibility.At last,put forward the corresponding financial risk factors based on the index system.The sixth chapter analyzes the financial risk form of listed companies of automobile manufacturing industry,and combining the results of BP neural network model simulation experiment,and putting forward the financial risk control measures of external and internal risk factors.The seventh chapter: conclusion and prospect,based on the study of above chapters,coming to the conclusion of this paper,summarize the deficiency of research and direction of future research.The main innovations of this paper are as follows:(1)The financial early warning index system introduces the cash flow index and the non-financial index system,and filters the index through the rough set theory.(2)Considering the limitations of the BP neural network model,the genetic algorithm is used to optimize and the optimal weights and thresholds are selected.(3)Introduce the automobile manufacturing listed companies into the BP neural network model optimized by genetic algorithm for the first time.