Predicting a corporate financial crisis using letters to shareholders

• Published in: Soft computing (Berlin, Germany) Vol. 25; no. 5; pp. 3623 - 3636
• Main Authors: Chen, Yuh-Jen, Wu, Chia-Yen
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2021
• Subjects: Artificial Intelligence; Computational Intelligence; Control; Engineering; Mathematical Logic and Foundations; Mechatronics; Methodologies and Application; Robotics; Support vector machine (SVM); Financial crisis prediction; Letter to shareholders; Queen genetic algorithm (QGA)
• ISSN: 1432-7643; 1433-7479
• DOI: 10.1007/s00500-020-05391-9

This study utilizes the textual financial information—letters to shareholders to propose a scheme for corporate financial crisis prediction instead of traditional numerical financial ratios. In the scheme, the letters to shareholders were first parsed and analyzed to establish a library of financial crisis feature terms. Based on the financial crisis feature term library, queen genetic algorithm and support vector machine were then used to classify letters to shareholders (i.e., financial crisis and non-financial crises). This scheme can effectively enhance the accuracy of corporate financial crisis detection and reduce the resulting capital damage to enterprises and investors. To achieve the above objective, the following tasks were performed: (1) a process for predicting corporate financial crises by using letters to shareholders was designed, (2) techniques involved in the process of financial crisis prediction were developed, and (3) the use of the proposed approach was demonstrated and evaluated.