Resumen de Prediction of corporate financial distress based on digital signal processing and multiple regression analysis
Liang Li, Mohamed F. Yousif, Nasser El Kanj
In order to reduce the default rate of corporate bond market, the author proposes to use digital signal processingand multiple regression analysis to study the prediction system of financial distressed companies. First, designthe research method, Logistic regression model is the most commonly used multivariate statistical method whenmodeling binary dependent variables, it can solve the problem of nonlinear classification, it has no specificrequirements for the distribution of variables, and the accuracy of judgment is high. The author selects 32financial ratios from the perspectives of solvency, operating ability, profitability, development ability, per shareindex, and risk level. Taking special treatment (ST) due to abnormal financial status as a sign of financialdistress in listed companies, when selecting samples, the matching principle is adopted to select non-STcompanies as matching samples. Two methods of logistic regression and support vector machine are used forempirical testing, and both in-sample testing and out-of-sample prediction are performed. The results show thatwhen using the logistic regression method, the propensity to default indicator (TTD) reflected in the text content,it can indeed improve the out-of-sample prediction accuracy of the financial distress prediction model, and it isconsistent with the in-sample test, this is mainly reflected in the reduction of the first type of error, that is, theprobability of misjudging a financially distressed company as a normal company. Changes in the proportionshave little effect on the relative importance of financial ratio variables when modeling with support vectormachines, the propensity to default indicator (TTD) entered the top ten important variables in both ratios, andranked fourth among all indicators when the ratio was 1:2, importance has increased significantly. From this itcan be seen that, when using support vector machine to build a financial distress prediction model, thepropensity to default indicator (TTD) has played an important role. In the case of using the support vectormachine method, adding the default tendency indicator (TTD) reflected by the text information can also improvethe accuracy of the financial distress prediction mode
