Chinese companies distress prediction: an application of data envelopment analysis

Abstract

Bankruptcy prediction is a key part in corporate credit risk management. Traditional bankruptcy prediction models employ financial ratios or market prices to predict bankruptcy or financial distress prior to its occurrence. We investigate the predictive accuracy of corporate efficiency measures along with standard financial ratios in predicting corporate distress in Chinese companies. Data Envelopment Analysis (DEA) is used to measure corporate efficiency.In contrast to previous applications of DEA in credit risk modelling where it was used to generate a single efficiency -Technical Efficiency, we assume Variable Returns to Scale, and decompose Technical Efficiency into Pure Technical Efficiency and Scale Efficiency. These measures are introduced into Logistic Regression to predict the probability of distress, along with the levels of Returns to Scale. Effects of efficiency variables are allowed to vary across industries through the use of interaction terms, whilst the financial ratios are assumed to have the same effects across all sectors. The results show that the predictive power is improved by this corporate efficiency information.

Multi-criteria ranking of corporate distress prediction models: empirical evaluation and methodological contributions

“…According to the Basel Committee on Banking Supervision (BCBS), default in credit risk refers to a failure of a borrower or counterparty to meet its obligations in accordance with agreed terms (Basel Committee on Banking Supervision, 2000, p. 1). Corporate credit studies have used the same parametric and non-parametric frameworks to predict business failure events such as credit default (e.g., Beaver, 1996), bankruptcy (e.g., Barboza et al, 2017;Ouenniche and Tone, 2017;Liang et al, 2016, Kim et al, 2016, Bauer and Agarwal, 2014Tinoco and Wilson, 2013;Zhou, 2013;Hillegeist et al, 2004;Shumway, 2001;Wilson and Sharda, 1994;Ohlson, 1980), financial distress (e.g., Altman et al, 2017;Li et al, 2014Li et al, , 2017Sun et al, 2017;Zhou et al, 2015;Geng and Chen, 2015;Campbell et al, 2008;Bandyopadhyay, 2006), insolvency (e.g., Callejón et al, 2013;Jackson and Wood, 2013), and loan default (e.g., Jiang et al, 2017;Kou et al, 2014;Bhimani and Gulamhussen, 2013). Amongst the above-mentioned failure events, bankruptcy and distress events have been the subject of many prediction studies.…”

“…From a statistical point of view, a failure prediction model (FPM) is a typical classification problem, which uses the selected features; say accounting, market, and macroeconomic-based information, to classify firms into distress or non-distress categories or classes. During the last decades, numerous studies have employed different types of techniques from statistics, operational research (e.g., Li et al, 2017;Ouenniche and Tone, 2017;Avkiran and Cai, 2014;Li et al, 2014;Premachandra et al, 2011;Yeh et al, 2010;Premachandra et al, 2009), and artificial intelligence (e.g., Chen et al, 2016;Fethi and Pasiouras, 2010;Bahrammirzaee, 2010;Charalambous et al, 2000) fields to design new failure prediction models. Initial studies on failure prediction use statistical techniques such as univariate discriminant analysis (e.g., Beaver, 1966Beaver, , 1968, and multivariate discriminant analysis (e.g., Altman, 1968Altman, , 1973Altman, , 1983 as classification techniques.…”

Corporate Governance and Financial Distress in the Banking Industry: Nigerian Experience

“…However, despite the supervision and regulatory role of government, the industry has been periodically characterized by financial distress thereby resulting in huge loss of shareholders' funds and erosion of public confidence in the system (Lang and Schmidt, 2016). Financial distress in banking remains a significant issue for owners, managers and the public (Simpson and Gleason, 1999) and early warning signals have been advocated as essential to limit the potential adverse effect of financial distress on the economy (Li, Crooks, and Andreeva, 2014). Various models have been used in financial distress prediction starting with diverse statistical methods such as Altman's (1968) multiple discriminant analysis, Ohlson's (1980) logistic regression; Intelligent models such as neural network model, support vector machine, genetic algorithm, genetic programming and others.…”

A comparison of dynamic hazard models and static models for predicting the special treatment of stocks in China with comprehensive variables

“…In addition, most research for predicting financial distress of Chinese companies used only financial variables as well as characteristic variables (Wang and Li, 2007;Ding et al, 2008;Li and Sun, 2009;Cao et al, 2011;; however, Li et al (2014) investigated the prediction accuracy of corporate efficiency measures along with standard financial rations. They found that effects of efficiency variables are allowed to vary across industries through the use of interaction terms, while the financial ratios are assumed to have the same effects across all sectors.…”