Abstract
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.
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Acknowledgements
The authors would like to thank the Ministry of Science and Technology R.O.C, Taiwan, for financially supporting this research under Contract Nos. MOST105-2410-H-327-014 and MOST106-2410-H-327-005-MY3. Additionally, we deeply appreciate the editor and reviewers for their constructive comments and suggestions on the work.
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This study was funded by Grant Numbers MOST105-2410-H-327-014 and MOST106-2410-H-327-005-MY3.
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Chen, YJ., Wu, CY. Predicting a corporate financial crisis using letters to shareholders. Soft Comput 25, 3623–3636 (2021). https://doi.org/10.1007/s00500-020-05391-9
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DOI: https://doi.org/10.1007/s00500-020-05391-9