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

نویسندگان

چکیده

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

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ژورنال

عنوان ژورنال: Applied mathematics and nonlinear sciences

سال: 2022

ISSN: ['2444-8656']

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