The Web Information-driven Research Of Listed Companies' Financial Crisis Early Warning

With the development of market economy, the competition among companies becomes more and more intense. The global economic integration, not only brings the development opportunities, but also the endless crises and risks. Due to financial crises, listed companies have to be treated specially or forced to delist, which not only affects their own survival and development, but also brings enormous economic loss to investors and creditors. Therefore, accurate, timely and effective early warning of listed companies'financial crisis will promote the development of the capital market and the national economy and meanwhile maintain social stability.Existing works of financial crisis early warning focus on two aspects:early warning indicators and model. For the first one, previous studies used to select financial indicators. However, these indicators have the inherent shortcomings, such as lag, easily manipulated. So, such non-financial indicators like macroeconomic variables, corporate governance variables, industry variables, etc are presented. However, non-financial indicators of diversity, non-available of data and hard index quantification, all lead to be difficult to introduce non-financial indicators. With development of network technology, it has rapidly evolved to an emerging a large number of Web financial information. The characteristics of real-time, diversity, comprehensiveness and accessibility is just to compensate the defects of non-finance indicators and provides a new way to get non-financial indicators for financial crisis early warning.For the model of financial crisis early warning, existing works mainly build static warning models based on one-period cross-sectional data by using traditional statistical models or artificial intelligence models. As we are seen that the listed company's financial crisis will not be happened suddenly and will be last a gradual process of evolution. However, in static warning model, the timing characteristics of early warning indicators have not been taken into account and the historical data's the impact on the results have been ignored as well. Thus, it is less effective for the models'early warning and hard to popularize in practical applications.This thesis focuses on two parts of work. On the one hand, we study how to introduce Web Financial Information into indicator system of listed companies' financial crisis early warning, and its role in early warning. On the other hand, the dynamic warning of listed companies'financial crisis is presented.The following problems are addressed in this thesis.(1) We initiate the problem of quantification about Web Financial Information. Due to the unstructured text, Web Financial Information must be quantified reasonably can we introduce it into indicator system of the financial crisis early warning. Calculation the text sentiment tendencies value is a common means of text messages quantification. We construct the sentiment dictionary of financial domain, and propose a calculation method of sentiment tendencies values based on evaluation scores of morpheme.(2) We analyze the relationship between the Web Financial Information and listed companies'financial situation. In this thesis, two aspects are studied. At first, we study the relationship between indicators of Web Financial Information (information heat and emotional value) and financial indicators through correlation analysis. Subsequently, we utilize the Logistic regression analysis to study whether the indicators of Web Financial Information will affect the early warning.(3) We verified the impact of indicators of Web Financial Information on the model of financial crisis early warning. By LIBSVM, an early warning model of pure financial indicators, and an early warning model of mixed indicators with the Web financial information indicator are constructed respectively. The empirical comparative analysis show that, the model of mixed indicators is better than the model of pure financial indicators in the validity, stability and advancing of early warning.(4) We also research into the problem of the dynamic warning of listed companies'financial crisis. Firstly, we use the econometrics ARMA model to fit the Timing Characteristics of the listed companies'financial position. Secondly, learned from the thought of control chart in the quality management, a dynamic early warning model for listed companies'financial cricis(S-EWMA)is constructed by adding the emotional inclination value of Web financial information to EWMA. Finally, the comparison between EWMA and S-EWMA is performed through empirical analysis.The contribution of this thesis can be summarized as follows:(1) We propose an approach of sentiment tendencies calculation based on morpheme value for web financial text information. The research community of text sentiment tendencies calculation has been made significant progresses, however, the existing reported solution are still far from perfect. The main issue is that the current methods are limited by the seed opinion lexicon selection and coverage of it. Furthermore, the findings on text sentiment tendencies calculation of financial text have not been found yet. The proposed method in this thesis could fully meet the demand of text sentiment tendencies analysis to financial information. Firstly, a sentiment dictionary of financial-domain is constructed, in which sentiment words for financial-domain are identified and the sentiment tendencies classification(calculation) approach solves the problem of coverage. Secondly, many effect factors are considered in the sentiment tendencies of the whole document, such as the modification funcition of negative word and degree-averb, different position of sentence, and turning, parallel, progressive mode.The experimental results have proved that the proposed method is feasible and effective.(2) We analyze the relationship between the web financial information and listed companies'financial situation. We found that the opinion orientation of Web financial information contains some information of listed companies'financial situation, which have not been included in the financial indicators. Thus, the opinion orientation of Web financial information could be used as important information supplement of listed companies'financial indicators.(3) Combing web financial information indicator with financial indicators, an early warning model is constructed. The experiment results show that the model of mixed indicators outperforms the pure financial indicators in the validity, stability and advancing of prediction.(4) An dynamic early warning model-S-EWMA for listed companies' financial crisis is built, in which the opinion orientation of web financial information is added. The model has the following the advantages:firstly, by the dynamic panel data of financial indicators, the model could reflect sequential variation characteristics of financial indicators. Secondly, due to adding the opinion orientation of web financial information in the Exponentially Weighted Moving Average model(EWMA), the proposed model could make up financial indicators'lag and other inherent defects and meanwhile reflect the gradual evolution and evolution trend of listed companies' financial crisis. Finally, it could give the time point of financial crisis of early warning effectively. Empirical analysis shows that the model is superior to other models.