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2020 | OriginalPaper | Chapter

Bankruptcy Forecasting for Small and Medium-Sized Enterprises Using Cash Flow Data

Authors : Yong Xu, Gang Kou, Yi Peng, Fawaz E. Alsaadi

Published in: Data Science

Publisher: Springer Singapore

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Abstract

Credit rating has long been a topic of interest in academic research. There are lots of studies about credit rating methods for large and listed companies. However, due to the lack of financial data and information asymmetry, developing credit ratings for small and medium-sized enterprises (SMEs) is difficult. To alleviate this problem, this paper adopts a novel approach, using SMEs’ cash flow data to make bankruptcy predictions and improve the accuracy of bankruptcy prediction for SMEs through feature extraction of cash flow data. We validate the prediction performance after adding features extracted from cash flow data on six supervised learning algorithms. The results show that using cash flow data can improve the performance of bankruptcy prediction for SMEs.

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Footnotes
1
After discussing with the staff of Shandong City Commercial Bank Alliance Co., Ltd., we conclude that bankruptcy prediction plays an important role in bank lending decisions and credit risk warnings.
 
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Metadata
Title
Bankruptcy Forecasting for Small and Medium-Sized Enterprises Using Cash Flow Data
Authors
Yong Xu
Gang Kou
Yi Peng
Fawaz E. Alsaadi
Copyright Year
2020
Publisher
Springer Singapore
Book
Data Science

Print ISBN: 978-981-15-2809-5

Electronic ISBN: 978-981-15-2810-1

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-981-15-2810-1_44

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