Corporate distress prediction in China: a machine learning approach

Yi Jiang and Stewart Jones

Accounting and Finance, 2018, vol. 58, issue 4, 1063-1109

Abstract: Rapid growth and transformation of the Chinese economy and financial markets coupled with escalating default rates, rising corporate debt and poor regulatory oversight motivates the need for more accurate distress prediction modelling in China. Given China's historical, social and cultural intolerance towards corporate failure, this study examines the Special Treatment system introduced by Chinese regulators in 1998. Regulators can assign Special Treatment status to listed Chinese companies for poor financial performance, financial abnormality and other events. Using an advanced machine learning model known as TreeNet® we model more than 90 predictor variables, including financial ratios, market returns, macro‐economic indicators, valuation multiples, audit quality factors, shareholder ownership/control, executive compensation variables, corporate social responsibility metrics and other variables. Based on out‐of‐sample tests, our TreeNet® model is 93.74 percent accurate in predicting distress (a Type I error rate of 6.26 percent) and 94.81 percent accurate in predicting active/healthy companies (a Type II error rate of 5.19 percent). Variables with the strongest predictive value in the TreeNet® model includes market capitalization and annual market returns, macro‐economic variables such as gross domestic product growth, financial ratios such as retained earnings to total assets and return on assets; and certain non‐traditional variables such as executive compensation.

Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (19)

Downloads: (external link)
https://doi.org/10.1111/acfi.12432

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:acctfi:v:58:y:2018:i:4:p:1063-1109

Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=0810-5391

Access Statistics for this article

Accounting and Finance is currently edited by Robert Faff

More articles in Accounting and Finance from Accounting and Finance Association of Australia and New Zealand Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().