## 2 Related Studies
### Financial Crisis Early Warning Indicators and Methodological Techniques  
In terms of the selection of early warning indicators for financial crises, existing studies have mainly used traditional financial indicators as the basis for early warning of financial crises, with the indicators mainly reflecting solvency, operating performance and cash flow [8; 9; 10; 11; 12; 13; 14]. However, little literature considered other sources of information that interact with financial data, where textual information is an important form. There was a small proportion of literature that used the tone of texts such as company annual reports to predict corporate financial crises and confirmed that non-standardized financial information can be used for financial crisis early warning [15; 16; 17; 18]. Referring to existing literature [19; 20], this paper considers the following five perspectives which measure the performance of listed companies as profitability, solvency, asset operating efficiency, cash flow quality and development quality, choosing ten traditional financial indicators as benchmarks. For the textual information, this paper selects both internal (MD&A) and external (financial news) texts and calculates the emotional tone to complement traditional financial indicators. In terms of the use of early warning models, related research presents a transition from univariate analysis to multivariate analysis, and then to the machine learning method which is broadly used nowadays. At the very beginning, researchers mainly focused on univariate analysis methods, using two ratios, net income/shareholders' equity and shareholders' equity/debt, for early warning of the financial crisis of the company [21]. Some researchers selected fourteen financial ratios from company financial statements for comparative studies and found that the ratio of cash flow to total liabilities is a better predictor of financial crisis in a company [1]. For bridging the limitation of the univariate analysis method, researchers used the Z-score model as the introduction of multivariate discriminant analysis into financial crisis warning. Some researchers chose their five most significant indicators among the beginning twenty-two financial indicators to construct the Z-score model. They used the magnitude of the Z-value to reflect the bankruptcy risk a company faces and found it more accurate than the univariate warning model [2]. However, in practice, it was found that the Z-score model is especially suitable for short-term prediction, so the ZETA model was subsequently proposed as a complement to this model. The modified model had a significantly better long-term warning effect [22]. Other scholars used logistic models for financial crisis prediction. With the help of this model, they overcame the strict requirements for the distribution of independent variables in the analysis and confirmed its high accuracy in predicting studies of listed companies [23]. As a result, this method has gradually replaced discriminant analysis as the mainstream method in this field. Machine learning began to be introduced into the field of financial crisis early warning with the development of information technology. Some scholars applied neural network techniques to crisis early warning models and found that this method could better predict samples [24]. Random forest was also applied in the risk prediction of listed companies. Compared to the AdaBoost algorithm, the result of the random forest exercise showed a decreasing error rate [25]. Researchers also applied the model of support vector machine (SVM), which works well for nonlinear and high-dimensional samples. In the prediction results of 944 manufacturing companies, they found that the SVM has a better early warning effect than the Back Propagation (BP) neural network, logistic regression and multiple linear regression models [26]. Furthermore, some researchers conducted a comparative study, by using several methods to construct financial early warning models. [PERSON] et al.