Prediction of financial distress: An empirical study of listed Chinese companies using data mining

Geng, Ruibin; Bose, Indranil; Chen, Xi

doi:10.1016/j.ejor.2014.08.016

Cited by 376 publications

(261 citation statements)

References 45 publications

Supporting

Mentioning

234

Contrasting

Unclassified

Order By: Relevance

“…This result is consistent with Geng et al (2015) who stated that there is a high probability of financial distress if a firm has a lower profitability. This ratio gives significant information to investors, creditors and regulators, in determining the financial distress level.…”

Section: Resultssupporting

confidence: 82%

“…The higher the net profit margin, the higher the Altman Z-Score; thus, it reduces the possibility of a company turning into a financially distressed company. This is consistent with the research done by Geng, Bose, and Chen (2015), which indicates that the financial distress risk is high when the profitability is low. A low profitability is a signal of firm incapacity to convert revenue flow into profits.…”

Section: Discussionsupporting

confidence: 81%

“…Controlled By: Geng, Bose, and Chen (2015) indicates that the risk of financial distress is high when profitability is low. A low profitability is a signal of the firm's incapacity to convert the revenue flow into profits.…”

Section: Dependent Variable Independent Variables Financial Distressmentioning

confidence: 99%

See 2 more Smart Citations

Monitoring mechanisms and financial distress of public listed companies in Malaysia

Kazemian

Shauri

Sanusi³

et al. 2017

Journal of International Studies

View full text Add to dashboard Cite

Abstract. This study examines the relationships between financial distress and financial ratio (liquidity, leverage, profitability, firm's performance, and dividend) among public listed companies, using the Altman Z-Score to determine the financial distress levels among public listed companies in Malaysia. Five-year data has been collected (2010 to 2014) from the annual financial statements and from Data Stream of public listed companies in Malaysia. The findings indicate significant relationships between liquidity, leverage, profitability, firm's performance, and dividend with the financial distress levels among the companies in question. This study also examines the interaction effects of financial ratios and the year after implementation of the Malaysian Code on Soheil Kazemian, Noor Shauri, Zuraidah Sanusi, Amrizah Kamaluddin, Shuhaida Shuhdan Monitoring mechanisms and financial distress of public listed companies in Malaysia 93Corporate Governance (MCCG) in 2012 on financial distress levels. The results suggest that only liquidity and firm's performance have stronger effects on financial distress levels in two years after MCCG implementation. This indicates that after the implementation of the Code, liquidity and firms' performance ratios had strong and significant effect on financial distress levels. Overall, this study could help investors, creditors as well as external regulators in monitoring companies from being classified as financially distressed companies.

show abstract

Section: Resultssupporting

confidence: 82%

Section: Discussionsupporting

confidence: 81%

See 1 more Smart Citation

Monitoring mechanisms and financial distress of public listed companies in Malaysia

Kazemian

Shauri

Sanusi³

et al. 2017

Journal of International Studies

View full text Add to dashboard Cite

show abstract

“…14-17] employed special treatment (ST) regulation to define business failure. ST regulation is the early warning system to identify abnormalities in Chineselisted companies' financial status [17]. Ding et al [18] showed that the companies fell into business failure after receiving the ST and showed some signs of potential failure prior to receiving it.…”

Section: Data and Variable Selectionmentioning

confidence: 99%

Comparison of Single Classifier Models for Predicting Long-term Business Failure of Construction Companies Using Finance-based Definition of the Failure

Choi¹,

Son²,

Kim³

et al. 2017

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

View full text Add to dashboard Cite

This study compares the performance of six classifier models (ANN, KNN, C4.5, SVM, LR, and NB) for predicting the business failure of construction companies after three years from 2010 to 2012. Although previous studies have explored numerous business failure prediction models for construction companies, these models have focused on short-term failure, defined as failure occurring within one year, and have defined business failure based on companies' experiencing serious legal events, including bankruptcy, delisting, and default. However, the construction industry is typically characterized by projects with longer durations, usually exceeding one year. This implies that previous short-term models cannot predict the business failure of construction companies until the end of particular projects. Moreover, this problem is compounded by the fact that legal events can involve lengthy proceedings, which are often initiated much later than the actual moment of the business failure. Therefore, in this study, six classifier models will be used to predict the business failure of the construction companies within three years using a finance-based definition of failure. The results show that all six models' performances noticeably decrease when they predict more than one year. These results demonstrate that previous short-term prediction models with outstanding performance cannot be practical in predicting the long-term business failure of construction companies.

show abstract

“…With the further deepening of China's market economic reform and accelerate the integration of the global economy, companies are in a critical period of development, especially in increasing the financial risks that enterprises face [1]. In this competitive environment, how to help companies effectively disperse and avoid financial crisis has became the hot topic in theoretical exploration.…”

Section: Introductionmentioning

confidence: 99%

Research of the enterprise financial risk based on support vector machine

Lin¹

2015

Proceedings of the 2015 International Conference on Social Science and Technology Education

View full text Add to dashboard Cite

Prediction of financial distress: An empirical study of listed Chinese companies using data mining

Cited by 376 publications

References 45 publications

Monitoring mechanisms and financial distress of public listed companies in Malaysia

Monitoring mechanisms and financial distress of public listed companies in Malaysia

Comparison of Single Classifier Models for Predicting Long-term Business Failure of Construction Companies Using Finance-based Definition of the Failure

Research of the enterprise financial risk based on support vector machine

Contact Info

Product

Resources

About