A key direction for studying the integration of analytical processing and ensemble learning of financial data is to accurately mine valuable information from a sea of real-time, incomplete and unbalanced data on China’s securities market. The objective of this article is to introduce different ensemble learning methods from the perspective of unbalanced classification to explore the ensemble classification model suitable for the corresponding data structure. There has been no research in using the after mentioned algorithm for financial warning prediction. The contribution of this paper is to build a relatively effective classification model to improve the forecasting ability of ST shares in financial data of listed companies, which has certain theoretical and practical significance for the construction and application of financial warning model under background of financial big data.
1.1. Theories Relating to Financial Warning for Listed Companies
Financial warning, as an important content of the management of a company’s financial risks, for a listed company, the financial warning means to make statistics of the company’s data such as financial statements and operating plans to predict its operational and financial activities as well as to find out operational and financial risks in its operation and management activities, so as to inform shareholders and managers with warning information.
A number of scholars have already made systematic studies on financial warning. Among them, Qin (2012) applied a Logistic regression analysis model to empirically study the selection of financial warning variables for companies listed on Shanghai and Shenzhen A-share markets whose stocks were, for the first time, prefixed with “ST”. Xiao and Yang (2018) constructed L1/2-regularized Logistic Regression model and took ST and non-ST manufacturing companies in Shanghai and Shenzhen stock markets as the research objects for comparative prediction. Song et al. (2019) used Cox regression to build financial crisis warning model for minor enterprises to achieve relatively accurate prediction of healthy enterprises. By using the annual reporting data of two sets of 37 pairs of “A” shares, Wang (2008) built a warning model for listed companies based on a multiple regression model and checked the accuracy as well, and he also gave his opinion and suggestions about the financial warning system. Li et al. (2013) adopt multiple discriminant analysis to modify a Z-Score baseline model, and establish a financial distress early warning model applicable to listed real estate companies in China. The findings indicate that the average accuracy of the financial distress early warning model reaches higher than 90%. Wang et al. (2018) combined PSO-BP and FOA-BP neural network model to select data of high-tech enterprises to effectively predict the financial crisis of enterprises. As indicated in the above studies, in most of the empirical studies about the financial warning for listed companies, a reasonable statistical analysis model is usually built based on financial data to check the validity and make a systematic analysis of the warning model. This paper has studied financial warning ensemble model for listed Companies based on Unbalanced Classification Perspective.