Failure Prediction: Evidence from Korea

This study is an attempt to construct and test a distress classification model for Korean companies. Utilizing a sample of 34 distressed firms from the recent 1990-1993 period and a matched (by industry and year) sample of non-failed firms, we observe the classification accuracy of two models. Both models utilize measures of firm size, asset turnover, solvency and leverage with one model available for testing only on publicly traded companies and one model applicable to all public and private entities. We observe excellent classification accuracy based on data from the first two years prior to distress. And, although the accuracy drops off after t -2, the models still provide effective early warnings of distress in many cases. The results of this study are of particular relevance in the current financial market scenario of increased deregulation and greater individual financial institution decision making. It is somewhat ironic for us to be proposing the use of a financial distress early-warning model given the current robust economic growth and low bankruptcy rate in Korea. But, the financial problems in Japan are a sobering reminder that high growth can be followed by financial excesses, increased business failures and large loan losses.     

The liquidation/merger alternative some results for the UK corporate sector

Previous researchers have explicitly extended the definition of corporate failure to include distressed acquired firms when they are (mis) classified by statistical models as failing. We argue that this approach is erroneous, since the acquisition of a financially distressed firm is an entirely separate economic outcome from corporate failure. This paper reports some new evidence for the UK corporate sector where the acquisition of a distressed firm is modelled as a distinct alternative to corporate failure. Our empirical results suggest that it is feasible to develop statistical models which are able to discriminate, with a reasonably high degree of accuracy, between those distressed firms which fail and those where a timely merger appears to serve as a viable alternative to corporate bankruptcy.     

Does audit report information improve financial distress prediction over Altman's traditional Z‐Score model?

We analyze empirically the usefulness of combining accounting and auditing data in order to predict corporate financial distress. Concretely, we examine whether audit report information incrementally predicts distress over a traditional accounting model: the Altman's Z‐Score model. Although the audit report seems to play a critical part in financial distress prediction because auditors should warn investors about any default risks, this is the first study that uses audit report disclosures for predicting purposes. From a dataset of 1,821 Spanish distressed private firms, we analyze a sample of distressed and non‐distressed firms and develop logit prediction models. Our results show that while the only accounting model registers a classification accuracy of 77%, combined models of accounting and auditing data exhibit considerably higher accuracy (about 87%). Specifically, our findings indicate that the number of disclosures included in the audit report, as well as disclosures related to a firm's going concern status, firms’ assets, and firms’ recognition of revenues and expenses contribute the most to the prediction. Our empirical evidence has implications for financial distress practice. For managers, our study highlights the importance of audit report disclosures for anticipating a financial distress situation. For regulators and auditors, our study underscores the importance of recent changes in regulation worldwide intended to increase auditor's transparency through a more informative audit report.     

Do Country Specific Bankruptcy Codes Determine Long‐term Financial Performance? The Case of Metallgesellschaft AG and Columbia Gas System

In this paper, we examine the impact of financial distress, the bankruptcy code, and related procedures on the long-term performance of two companies engaged in similar businesses across two countries. Both the companies were driven into bankruptcy as a result of unanticipated changes in energy prices. Though the resolution of bankruptcy of the US firm took a longer time, the post-reorganization performance of the firm has been excellent. In contrast, the post-reorganization performance of the German firm, which emerged out of bankruptcy in 2 weeks, has been poor. These results are consistent with the view that one of the important determinants of post-bankruptcy performance of a firm is more likely to be the underlying economic fundamentals rather than the country specific bankruptcy code through which the firm reorganizes.     

法律制度、法律执行与上市公司破产重整效率——基于宝硕股份的案例研究

破产重整是危机企业解除财务危机的重要法定机制。本文基于执法经济学理论，以宝硕股份的破产重整事件为例，研究了新破产法实施后上市公司破产重整的经济效率。研究发现，虽然宝硕股份借助重整机制暂时避免了破产清算，但是重整活动却明显缺乏经济效率，新破产法在执行过程中偏离了原先的立法预期，相对落后的执法环境致使参与重整的利益相关方的目标函数和博弈行为发生异化，并最终导致重整的低效率。本文揭示了我国转型期相对落后的执法环境制约企业破产重整效率的典型作用路径。最后，本文提出了若干政策建议。     

Financial Distress and Optimal Capital Structure Adjustments

We show that asymmetric information may prevent firms with pure discount bonds from renegotiating their capital structure prior to the maturity of the debt, although this would increase the value of the firm when its prospects are poor. This inefficiency can be reduced if the firm issues debt with a risky intermediate debt payment, such as a coupon or a sinking fund payment. We also demonstrate that bankruptcy institutions leading to deviations from absolute priority can improve the timing of recapitalizations by financially distressed firms. Finally, we show that, under certain conditions, the optimal capital structure adjustment during financial distress consists of a convertible debt-for-straight debt swap.     

The effects of handling outliers on the performance of bankruptcy prediction models

Ratio type financial indicators are the most popular explanatory variables in bankruptcy prediction models. These measures often exhibit heavily skewed distribution because of the presence of outliers. In the absence of clear definition of outliers, ad hoc approaches can be found in the literature for identifying and handling extreme values. However, it is not clear how these different approaches can affect the predictive power of models. There seems to be consensus in the literature on the necessity of handling outliers, at the same time, it is not clear how to define extreme values to be handled in order to maximize the predictive power of models. There are two possible ways to reduce the bias originating from outliers: omission and winsorization. Since the first approach has been examined previously in the literature, we turn our attention to the latter. We applied the most popular classification methodologies in this field: discriminant analysis, logistic regression, decision trees (CHAID and CART) and neural networks (multilayer perceptron). We assessed the predictive power of models in the framework of tenfold stratified crossvalidation and area under the ROC curve. We analyzed the effect of winsorization at 1, 3 and 5% and at 2 and 3 standard deviations, furthermore we discretized the range of each variable by the CHAID method and used the ordinal measures so obtained instead of the original financial ratios. We found that this latter data preprocessing approach is the most effective in the case of our dataset. In order to check the robustness of our results, we carried out the same empirical research on the publicly available Polish bankruptcy dataset from the UCI Machine Learning Repository. We obtained very similar results on both datasets, which indicates that the CHAID-based categorization of financial ratios is an effective way of handling outliers with respect to the predictive performance of bankruptcy prediction models.     

The dynamic financial distress prediction method of EBW-VSTW-SVM

Financial distress prediction (FDP) takes important role in corporate financial risk management. Most of former researches in this field tried to construct effective static FDP (SFDP) models that are difficult to be embedded into enterprise information systems, because they are based on horizontal data-sets collected outside the modelling enterprise by defining the financial distress as the absolute conditions such as bankruptcy or insolvency. This paper attempts to propose an approach for dynamic evaluation and prediction of financial distress based on the entropy-based weighting (EBW), the support vector machine (SVM) and an enterprise’s vertical sliding time window (VSTW). The dynamic FDP (DFDP) method is named EBW-VSTW-SVM, which keeps updating the FDP model dynamically with time goes on and only needs the historic financial data of the modelling enterprise itself and thus is easier to be embedded into enterprise information systems. The DFDP method of EBW-VSTW-SVM consists of four steps, namely evaluation of vertical relative financial distress (VRFD) based on EBW, construction of training data-set for DFDP modelling according to VSTW, training of DFDP model based on SVM and DFDP for the future time point. We carry out case studies for two listed pharmaceutical companies and experimental analysis for some other companies to simulate the sliding of enterprise vertical time window. The results indicated that the proposed approach was feasible and efficient to help managers improve corporate financial management.     

Board of directors’ effects on financial distress evidence of family owned businesses in Lebanon

The objective of this paper is to determine the managerial governance characteristics related to financial distress companies. The boards failed to accomplish their monitoring duties, which seemed to be one of the main reasons behind the actual financial distress and bankruptcy that swept the companies across the planet. Through the analysis of a sample of 178 Lebanese non listed and owned family firms, the results showed that the boards (that have a higher proportion of outside directors) are less inclined to face a financial distress than the boards with a lower proportion. Besides, a different conclusion proves that the board’s size and financial distress are directly linked. The paper highlights the extent to which financial distress is associated with corporate governance from a Euro Mediterranean country. It would be a source of education to Lebanese investors who excessively go for short-term returns and of help for regulatory authorities in the framework of making policies on corporate governance reformation.     

利益相关者与资本结构决策

传统权衡理论中的破产成本主要考虑的是,由重组或清算所带来的法律成本和管理成本之类的直接破产成本,以及由股东和债权人之间的矛盾而引起的在企业财务危机期间发生的间接破产成本。现代权衡理论基于利益相关者的考虑拓展了以前的间接破产成本内涵,主要是考虑了企业财务危机或破产清算给其他利益相关者带来的危害。这不但使企业的资本结构决策渗入了更多的战略因素,而且可以解释现实中许多企业的看似保守的融资行为。     

Auditor changes,information quality and bankruptcy prediction

In periods of financial distress management may attempt to suppress unfavorable information from creditors and investors through the use of undisclosed changes in accounting methods, estimates and procedures, thus reducing the quality of the information contained in the firm's financial statements. The auditor's role in this context is to ensure that such compromise does not take place. If the auditor does not permit such accounting treatments, the company may choose to switch to another auditor who will. Empirical evidence relating auditor-change behavior to the quality of comparative bankruptcy prediction models provides support for the notion that auditor changes before bankruptcy may be at least partially due to lack of success at suppressing unfavorable information with the current auditor. Conversely, non-auditor switching companies appear to enjoy greater success at suppressing negative income and leverage information.     

Analyzing the financial distress of Chinese public companies using probabilistic neural networks and multivariate discriminate analysis

Many studies have applied backpropagation feedforward neural networks (BPNNs) as an alternative to multivariate discriminant analysis (MDA) in attempts to predict business distress using relatively small data sets. Although these studies have generally reported the superiority of BPNNs vs. MDA, they seem to ignore the fact that the former suffers from overfitting if the data set is too small compared to the free parameters of the network. We thus suggest an alternative approach that involves use of a probabilistic neural network (PNN). From our study of financially distressed Chinese public companies, we found that both the PNN and MDA algorithms provide good classifications. Relative to MDA, however, the PNN method provides better prediction, and, at the same time, does not require multivariate normality of the data. Our results appear to offer an improvement from those of earlier efforts that employ MDA, BPNN, and other models. In particular, PNN was here able to predict company distress with greater than 87.5% short-term accuracy, and 81.3% medium-term accuracy.     

Bankruptcy and steel plant shutdowns

The bankruptcies resulting from the American steel industry downturn in the period, 1999–2002, raise the question of whether the bankruptcy process itself led to permanent plant shutdowns and job losses. With information on 110 of the steel plants operating in the United States in 1994, this paper develops empirical models of steel plant closure and firm bankruptcy to see if the latter impacts on the former. Based on survival models, the results provide support for the hypothesis that the bankruptcy of steel companies could have led to viable steel plants closing, and thus, the bankruptcies in themselves may have caused permanent inefficient employment loss.     

The recurrence of financial distress: A survival analysis

Companies often suffer periods of financial distress before filing for bankruptcy. Unlike one-off bankruptcies, financial distress can occur repeatedly within the same individual firm. This paper is focused on the recurrence of financial distress and studies the Chinese stock market, where Special Treatment – an official indicator of financial distress – can be repeatedly applied to a listed company. We employ a stratified hazard model to predict the probability of subsequent distress with variables, including duration dependency, event-based factors, institutional variables, financial ratios, market-based variables and macroeconomic conditions. Our empirical results show that accounting and market-based variables have limited power in predicting the recurrence of distress, whereas the duration of recovery, restructuring events and their interaction terms with the accounting and macroeconomic factors affect the recurrent risk significantly. Tested on out-of-time samples, our proposed hazard models show a robust performance in the prediction of recurrent risk over time.     

Financial distress and bankruptcy prediction: An appropriate model for listed firms in Vietnam

This paper aims to develop a comprehensive model, the first of its kind in Vietnam, for the purpose of predicting financial distress and bankruptcy at Vietnamese listed firms. The period 2003–2016 is used to study the likelihood of financial distress in different scenarios. Various factors are utilized, including (1) accounting factors in the emerging market score model; (2) market factors in the distance-to-default model; and (3) macroeconomic indicators. The area under the receiver operating characteristics (AUC) curve is used to compare the usefulness of various models that predict financial distress and bankruptcy. Empirical findings from this study show that accounting and market factors, together with macroeconomic fundamental factors, both affect financial distress when they are considered in isolation. However, in a comprehensive model, the effects from accounting factors appear to be more significant than those from market-based factors. The default prediction model, which includes accounting factors with macroeconomic indicators, appears to perform much better than the model comprising market-based factors with macroeconomic fundamentals.     

Financial Distress Prediction in an International Context: A Review and Empirical Analysis of Altman's Z‐Score Model

This paper assesses the classification performance of the Z‐Score model in predicting bankruptcy and other types of firm distress, with the goal of examining the model's usefulness for all parties, especially banks that operate internationally and need to assess the failure risk of firms. We analyze the performance of the Z‐Score model for firms from 31 European and three non‐European countries using different modifications of the original model. This study is the first to offer such a comprehensive international analysis. Except for the United States and China, the firms in the sample are primarily private, and include non‐financial companies across all industrial sectors. We use the original Z′′‐Score model developed by Altman, Corporate Financial Distress: A Complete Guide to Predicting, Avoiding, and Dealing with Bankruptcy (1983) for private and public manufacturing and non‐manufacturing firms. While there is some evidence that Z‐Score models of bankruptcy prediction have been outperformed by competing market‐based or hazard models, in other studies, Z‐Score models perform very well. Without a comprehensive international comparison, however, the results of competing models are difficult to generalize. This study offers evidence that the general Z‐Score model works reasonably well for most countries (the prediction accuracy is approximately 0.75) and classification accuracy can be improved further (above 0.90) by using country‐specific estimation that incorporates additional variables.     

Heterogeneous beliefs and the choice between private restructuring and formal bankruptcy

We present a theory for the puzzling issue regarding why certain firms in financial distress, prefer a costlier formal bankruptcy procedure over direct renegotiations. We show that claimholders’ heterogeneous beliefs about the results of a formal plan and about judicial discretion may lead to such a preference. The proposed model predicts which resolution would be chosen under claimholders’ beliefs about the determinants driving the outcome of a formal procedure, such as the extent to which firm value is affected by bankruptcy, the likelihood of deviation from the absolute priority rule, and the probability of the court adopting a reorganization plan.     

Why We Should Change Our Attitude Towards Distressed Firms

This article surveys ‘creditor‐friendly’ and ‘enterprise‐friendly’ bankruptcy regimes with a focus on the methodology underlying the filter test in distress, as reflected both in its academic treatment and in legal practice. I find that the test exhibits pro‐liquidation bias in designating liquidation of a firm with recovery potential as the Type II error, and in underplaying the benefits of a possible turnaround. Further influences militating against continuation include the power conferred on creditors through the balance sheet criterion and the undervaluation of intangible assets. I make the case for reversing such biases to establish a presumption in favour of continuation.     

非财务信息与财务危机预警——来自我国上市公司的证据

本文从我国上市公司中,选取30家财务危机公司,与30家财务健康公司作为样本。首先对两组公司的13个财务变量,进行均值的t检验和Wilcoxon秩检验。选择合适的财务变量集,再将此财务变量集分别与董事会高管人员持股比例、股权集中度、股票价格变动趋势,及是否更换会计师事务所等,四个非财务变量一起作为解释变量,建立Logistic回归模型。实证研究结果表明,股票价格变动趋势与财务危机风险显著相关,加入非财务变量的预测模型效果更优。     

随机利率下的复合Poisson-Geometric风险模型及破产概率

本文主要研究的是在随机利率下保费收入为复合Poisson-Geometric过程的风险模型,在随机利率为levy过程的情况下,得到了破产概率满足的积分方程,以及得到最终破产概率的上下界所满足的积分不等式,以此作为保险公司经营的预警信号更具有现实意义。