<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In order to reduce the default rate of corporate bond market, the author proposes to use digital signal processing and multiple regression analysis to study the prediction system of financial distressed companies. First, design the research method, Logistic regression model is the most commonly used multivariate statistical method when modeling binary dependent variables, it can solve the problem of nonlinear classification, it has no specific requirements for the distribution of variables, and the accuracy of judgment is high. The author selects 32 financial ratios from the perspectives of solvency, operating ability, profitability, development ability, per share index, and risk level. Taking special treatment (ST) due to abnormal financial status as a sign of financial distress in listed companies, when selecting samples, the matching principle is adopted to select non-ST companies as matching samples. Two methods of logistic regression and support vector machine are used for empirical testing, and both in-sample testing and out-of-sample prediction are performed. The results show that when 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 is consistent with the in-sample test, this is mainly reflected in the reduction of the first type of error, that is, the probability of misjudging a financially distressed company as a normal company. Changes in the proportions have little effect on the relative importance of financial ratio variables when modeling with support vector machines, the propensity to default indicator (TTD) entered the top ten important variables in both ratios, and ranked fourth among all indicators when the ratio was 1:2, importance has increased significantly. From this it can be seen that, when using support vector machine to build a financial distress prediction model, the propensity to default indicator (TTD) has played an important role. In the case of using the support vector machine method, adding the default tendency indicator (TTD) reflected by the text information can also improve the accuracy of the financial distress prediction model.</p>
</abstract>

In order to reduce the default rate of corporate bond market, the author proposes to use digital signal processing and multiple regression analysis to study the prediction system of financial distressed companies. First, design the research method, Logistic regression model is the most commonly used multivariate statistical method when modeling binary dependent variables, it can solve the problem of nonlinear classification, it has no specific requirements for the distribution of variables, and the accuracy of judgment is high. The author selects 32 financial ratios from the perspectives of solvency, operating ability, profitability, development ability, per share index, and risk level. Taking special treatment (ST) due to abnormal financial status as a sign of financial distress in listed companies, when selecting samples, the matching principle is adopted to select non-ST companies as matching samples. Two methods of logistic regression and support vector machine are used for empirical testing, and both in-sample testing and out-of-sample prediction are performed. The results show that when 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 is consistent with the in-sample test, this is mainly reflected in the reduction of the first type of error, that is, the probability of misjudging a financially distressed company as a normal company. Changes in the proportions have little effect on the relative importance of financial ratio variables when modeling with support vector machines, the propensity to default indicator (TTD) entered the top ten important variables in both ratios, and ranked fourth among all indicators when the ratio was 1:2, importance has increased significantly. From this it can be seen that, when using support vector machine to build a financial distress prediction model, the propensity to default indicator (TTD) has played an important role. In the case of using the support vector machine method, adding the default tendency indicator (TTD) reflected by the text information can also improve the accuracy of the financial distress prediction model.

Prediction of corporate financial distress based on digital signal processing and multiple regression analysis

Shijiazhuang University of Applied Technology

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

In order to reduce the default rate of corporate bond market, the author proposes to use digital signal processing and multiple regression analysis to study...

Prediction of corporate financial distress based on digital signal processing and multiple regression analysis

Published Online: Jul 15, 2022

Page range: 2209 - 2220

Received: Mar 06, 2022

Accepted: May 24, 2022

DOI: https://doi.org/10.2478/amns.2022.2.0140

Keywords
Digital signal, multiple regression, corporate financial distress, logistic regression model, support vector machine

© 2023 Liyang Li et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Prediction of corporate financial distress based on digital signal processing and multiple regression analysis

Published Online: Jul 15, 2022

Page range: 2209 - 2220

Received: Mar 06, 2022

Accepted: May 24, 2022

DOI: https://doi.org/10.2478/amns.2022.2.0140

KeywordsDigital signal, multiple regression, corporate financial distress, logistic regression model, support vector machine

© 2023 Liyang Li et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Keywords
Digital signal, multiple regression, corporate financial distress, logistic regression model, support vector machine