Research On Improvement And Application Of Financial Crisis Early Warning Of Internet Industry Based On Big Data And GBM Model

Along with the rapid development of the global economy,market competition has intensified,and the situations faced by various enterprises are increasingly complicated and dangerous.Corporate conditions are often only disclosed as the tip of the iceberg,because of technical constraints and accounting distortions.When the crisis comes,investors are like a bird of surprise,and they urgently need to improve their initiative.If we can improve the predictive accuracy rate of the crisis,enterprises and investors can make behavior adjustments in advance and stop losses in time.Therefore,establishing a model for effectively alerting corporate crises is significant.At present,the research on corporate financial crisis is mainly based on the establishment of financial indicators,but in today’s world economic linkage,the traditional warning system is not accurate enough.In response to the integration and development of Internet technology,this paper uses big data to mine emotional information,overcomes the incompleteness and time lag of traditional data,and provides new ideas for breaking the predicament of financial crisis warning effect.This paper studies the financial crisis warning based on big data and GBM model.First of all,this paper analyzes the feasibility and value of applying big data to the financial crisis early warning model through behavioral economics theory,information asymmetry theory,principal-agent theory and risk management theory.Secondly,this paper uses Python to crawl the relevant information transmitted by investors in the network,judges the emotional tendency of investor reviews through emotional dictionaries and combines the heat degree of communications as big data indicators.Then establish a financial crisis early warning model by integrating financial and non-financial indicators which are based on the characteristics of the Internet industry and big data indicators.In this paper,the dimensionality reduction of the indicators is first realized by the LASSO method,puts the filtered variables into the GBM model which has strong nonlinear fitting ability.Some improvements have been made in sample matching,index selection,and method optimization.Finally,through the application of warning model,the causes and manifestations of the financial crisis are analyzed from the industry and specific enterprises.Based on this research,this paper obtains the following conclusions: The introduction of big data indicators can make enterprise information more comprehensive and opportune,and is an important way to improve the early warning effect;comparing the financial crisis early warning model,it is found that the financial crisis early warning effect has been greatly improved after the introduction of big data indicators;by apply financial crisis early warning model on a case study of OTMC,it is found that the annual data can alert the financial crisis before the negative profit disclosure,which reflects the early warning value and accuracy of the model.Based on the perspectives of regulators,enterprises,and investors,this article puts forward corresponding suggestions on financial crisis early warning and financial crisis response under big data.The innovations are as follows: 1.This paper extends the research of existing big data to the analysis of the impact on business operations and financial status,introduces big data and specific text content in the research of traditional enterprise financial crisis,and explore the early warning value of stock market platform information.2.In the financial crisis early warning research,GBM which is a kind of intelligent machine learning model is used to observe the prediction accuracy.3.Unlike a lot of previous researches on manufacturing industries,this paper focuses on internet companies that are closely watched by social changes and have large financial risks,and study the early warning value of financial crisis of internet companies.