A Note on Selecting a Response Measure for Financial Distress

Since 1966, researchers have examined financial distress prediction models to determine the usefulness of accounting information to lenders. These researchers primarily used legal bankruptcy as the response variable for economic financial distress, or included legal bankruptcy with other events in dichotomous prediction models. However, theoretical models of financial distress normally define financial distress as an economic event, the inability to pay debts when due (insolvency). This study uses a loan default/accommodation response variable as a proxy for the inability to pay debts when due. The purpose of this note is to empirically test whether or not using the inability of a firm to pay debts when due, loan default/accommodation, as a response measure produces different results than using legal bankruptcy as the response measure. The study's empirical results show that legal bankruptcy and loan default/accommodation financial distress prediction models produce different statistical results, thus suggesting that the responses measure different constructs. A loan default/accommodation model also fits the data better than a bankrupt model. Our results suggest that a loan default/accommodation response may be a more appropriate measure to determine which accounting information is most useful to lenders in evaluating a firm's credit risk.     

Bankruptcy prediction of financially stressed firms: an examination of the predictive accuracy of artificial neural networks

This is an extension of prior studies that have used artificial neural networks to predict bankruptcy. The incremental contribution of this study is threefold. First, we use only financially stressed firms in our control sample. This enables the models to more closely approximate the actual decision processes of auditors and other interested parties. Second, we develop a more parsimonious model using qualitative ‘bad news’ variables that prior research indicates measure financial distress. Past research has focused on the ‘usefulness’ of accounting numbers and therefore often ignored non‐accounting variables that may contribute to the classification accuracy of the distress prediction models. In addition, rather than use multiple financial ratios, we include a single variable of financial distress using the Zmijewski distress score that incorporates ratios measuring profitability, liquidity, and solvency. Finally, we develop and test a genetic algorithm neural network model. We examine its predictive ability to that of a backpropagation neural network and a model using multiple discriminant analysis. The results indicate that the misclassification cost of the genetic algorithm‐based neural network was the lowest among the models. Copyright © 2001 John Wiley & Sons, Ltd.     

Simulation of Controlled Financial Statements

Many accounting and finance studies investigate the time-series properties of historical accounting records from corporate financial statements. Some of them have recognized the potential benefits of using disaggregated monthly accounting records. Disaggregated data are beneficial because one can use more data points within a relatively short period of time, thus reducing the chance of structural change. The added data points and reduction of the number of variables needed to accommodate potential structural changes can enhance the statistical power of any subsequent analysis. The use of disaggregated data may also improve the predictive ability of time-series analytic approaches. In order to systematically assess various financial indicators and investigate the effects of different organizational characteristics, a large number of monthly statements with certain predetermined characteristics are desirable. However, such statements are not readily available. At best, monthly statements can be obtained from a few volunteer companies. Under this circumstance, simulation of controlled financial statements seems to be a reasonable solution. This research explores a methodology for simulating complete monthly financial statements based on actual company quarterly financial statements. The methodology incorporates the interrelationships among accounting numbers and the effects of exogenous variables. To test the empirical validity and whether the monthly results derived from the quarterly data can accurately track the real monthly figures, we compare the results simulated by the proposed method and those generated by a naive random walk model. We test both complete financial statements for three companies and sales statistics from the retail industry. The results of both tests demonstrate the superiority of the method proposed by this study over a naive random walk model. The proposed simulation method provides an opportunity for researchers to examine the time-series properties of financial statement elements by using the monthly data of a large number of companies. In addition, the simulation approach allows researchers to perform cross sectional comparisons on companies with different characteristics (e.g., sales behavior patterns and degrees of stability) in their financial and economic activities. Moreover, it enables the researchers to manipulate some of these characteristics to test various hypotheses.     

The Time Series Properties of Financial Ratios: Lev Revisited

This paper re‐evaluates the time series properties of financial ratios. It presents new empirical analysis which explicitly allows for the possibility that financial ratios can be characterized as non‐linear mean‐reverting processes. Financial ratios are widely employed as explanatory variables in accounting and finance research with applications ranging from the determinants of auditors' compensation to explaining firms' investment decisions. An implicit assumption in this empirical work is that the ratios are stationary so that the postulated models can be estimated by classical regression methods. However, recent empirical work on the time series properties of corporate financial ratios has reported that the level of the majority of ratios is described by non‐stationary, I (1), integrated processes and that the ratio differences are parsimoniously described by random walks. We hypothesize that financial ratios may follow a random walk near their target level, but that the more distant a ratio is from target, the more likely the firm is to take remedial action to bring it back towards target. This behavior will result in a significant size distortion of the conventional stationarity tests and lead to frequent non‐rejection of the null hypothesis of non‐stationarity, a finding which undermines the use of these ratios as reliable conditioning variables for the explanation of firms' decisions.     

An Early Warning System of Financial Distress Using Multinomial Logit Models and a Bootstrapping Approach

This study adopts multinomial logit models to separately measure the extent to which financial ratios and corporate governance signal the likelihood of "slight distress events" and "reorganization and bankruptcy." The results show that corporate governance variables are closely related to the occurrence of "slight distress events." The estimated misclassification costs of the 1,000 resamples generated through bootstrapping procedures are statistically lower for a model that makes use of corporate governance (CG model) than one without corporate governance (non-CG model) at all cutoff points in 2009, and cutoff points from 0.11 to 0.27 in 2008. Since corporate governance is incrementally useful in predicting financial distress, the CG model's predictive ability improves as two corporate governance factors are considered: ownership ratio of insiders and pledge-ownership ratio of insiders.     

The Relationship between Changes in Fixed Plant Investment and the Likelihood of Emergence from Corporate Financial Distress

Companies experiencing financial distress can attempt to mitigate financial distress through changing the investment in the fixed asset base. Management may choose to expand the asset base in hopes of increasing sales. Alternatively, management may choose to contract the asset base in order to eliminate and/or reduce investment in unprofitable or risky ventures, improve liquidity, reduce earnings volatility, and reduce the need for operating capital.In this study, we examined how observed changes in the investment base affect the likelihood of emergence from a financially distressed condition. We find that, when management chooses to contract the investment in property, plant, and equipment, the likelihood of emergence from financial distress is significantly improved. On the other hand, when management chooses to expand property, plant, and equipment in the face of distress, the distress is only intensified. Our explanation is that companies that choose to contract their fixed asset base in times of trouble are taking steps that will most likely improve their financial condition—they are less likely to need working capital, and can better tolerate increased levels of long-term debt. Conversely, increasing the fixed asset base amplifies the need for working capital, and borrowing money to facilitate the expansion simply increases the necessary uses of that working capital because the debt must be serviced. As a result, companies descend even deeper into financial distress and decrease the likelihood that they will emerge therefrom.     

A re-evaluation of auditors’ opinions versus statistical models in bankruptcy prediction

Existent empirical evidence on the relative performance of auditors’ going concern opinions versus statistical models in predicting bankruptcy is mixed. This study attempts to add new reliable evidence on this important issue by conducting the comparison based upon an improved statistical model. The improved statistical model incorporates some new developments advocated by recent bankruptcy prediction research (e.g., Shumway, 2001). First, the following non-traditional variables are added: a composite measure of financial distress, industry failure rate, abnormal stock returns, and market capitalization. Secondly, a hazard model is employed. The prediction ability of the hazard model with incorporation of non-financial-ratio variables is superior to that of auditors’ going concern opinions in the holdout sample. This suggests that a well-developed statistical model could serve as a decision aid for auditors to better make going-concern judgments. Further analyses reveal some evidence that industry failure rate does not have a significant impact upon auditors’ going concern judgments as it should be; auditors could improve their going concern judgments by considering industry-level information in addition to firm-specific information. Finally, we find that auditors’ opinions do have incremental contribution beyond stock-market information and industry failure rate in predicting bankruptcy.

Credit Granting: A Comparative Analysis of Classification Procedures

Financial classification issues, and particularly the financial distress problem, continue to be subject to vigorous investigation. The corporate credit granting process has not received as much attention in the literature. This paper examines the relative effectiveness of parametric, nonparametric and judgemental classification procedures on a sample of corporate credit data. The judgemental model is based on the Analytic Hierarchy Process. Evidence indicates that (nonparametric) recursive partitioning methods provide greater information than simultaneous partitioning procedures. The judgemental model is found to perform as well as statistical models. A complementary relationship is proposed between the statistical and the judgemental models as an effective paradigm for granting credit.     

Valuing Debt in a Complex Capital Structure

This article extends previous bond valuation models to account for more realistic assumptions regarding financial distress. Realized value of an individual bond under severe financial distress will reflect the expected outcome of credit-event negotiations and the relative priority listing of the security. We explicitly represent the probability rate of credit-event occurrence as a function of firm value relative to the fixed overall debt obligations of the firm. Risk premiums generated under reasonable parameter value choices fall within the range of observed bond risk premiums. Our model also provides an explanation as to why observed bond risk premia are positive after adjustment for default.     

Have Financial Statements Become Less Informative? Evidence from the Ability of Financial Ratios to Predict Bankruptcy

Using a hazard model, we examine secular changes in the ability of financial statement data to predict bankruptcy from 1962 to 2002. We identify three trends in financial reporting that could influence predictive ability with respect to bankruptcy: FASB standards, the perceived increase in discretionary financial reporting behavior, and the increase in unrecognized assets and obligations. A parsimonious three-variable model provides significant explanatory power throughout the time period, with only a slight deterioration in predictive power from the first to the second time period. The striking feature of the results is the robustness of the predictive models over a forty-year period.JEL Classification: M41, G14, G33, C41     

Predicting Private Company Failures in Italy Using Financial and Non‐financial Information

The study assesses the use of non‐financial information in predicting financial distress in private companies by developing credit risk models tailored to Italian private companies. The in‐sample and out‐of‐sample prediction test results are indicative of the incremental predictive ability of the two new non‐financial variables, that is, number of shareholders and number of subsidiaries, over accounting ratios and other widely used non‐financial information, including firm age and industry dummies. To be more specific, number of shareholders and number of subsidiaries are negatively associated with private company failures, and the models augmented by the two non‐financial variables improve forecasting performance from acceptable discrimination to excellent discrimination over one‐ to three‐year time horizons.     

Stationarity and Cointegration in Systems with Real Estate and Financial Assets

This study examines the properties of wealth indices for investments in several asset classes (real estate, stocks, bonds, and Treasury bills), for several types of real estate (office, retail, research and development office, and warehouse), and by region (East, Midwest, South, and West). The series representing the value of investments in real estate and financial assets are not stationary; therefore, ordinary statistical procedures cannot be applied. Since many of the properties that are included in the real estate series have outside appraisals on an annual basis, especially in the fourth quarter, the real estate series may show seasonal influences. Hence, the appropriate test for cointegration is the Johansen's test, which is formulated in such a way as to allow for deterministic seasonality by the inclusion of seasonal dummy variables. The finding of cointegration implies that there is a long-run relationship between the series in the cointegrated system. When the CPI (or a proxy for inflation) is included in the three systems, the number of common factors increase to two, implying that inflation plays an important role in creating a linkage between these time series. These findings also have implications for developing portfolios comprising financial assets and real estate. The findings also have implications for developing a model to forecast real estate prices.     

Determinants of Financial Distress Costs

This paper provides international evidence on financial distress costs. To achieve this aim, we have developed a model where financial distress costs are determined, on the one hand, by making use of a more accurate indicator of the probability of financial distress and, on the other, by a set of variables that, according to financial theory, explain the magnitude of the costs borne by a firm in the case of financial distress. Our results reveal the relevance of our improved indicator of the probability of financial distress, since it positively affects financial distress costs in all the countries analyzed. Furthermore, since our model controls for the probability of financial distress, we can test the trade-off between the benefits and costs of debt. This allows us to verify that the benefits debt outweigh the costs. Our results also indicate that distress costs are negatively related to liquid assets; hence, their benefits more than offset their opportunity costs.     

Twenty‐five years of the Taffler z‐score model: Does it really have predictive ability?

Although copious statistical failure prediction models are described in the literature, appropriate tests of whether such methodologies really work in practice are lacking. Validation exercises typically use small samples of non‐failed firms and are not true tests of ex ante predictive ability, the key issue of relevance to model users. This paper provides the operating characteristics of the well‐known Taffler (1983) UK‐based z‐score model for the first time and evaluates its performance over the 25‐year period since it was originally developed. The model is shown to have clear predictive ability over this extended time period and dominates more naïve prediction approaches. This study also illustrates the economic value to a bank of using such methodologies for default risk assessment purposes. Prima facie, such results also demonstrate the predictive ability of the published accounting numbers and associated financial ratios used in the z‐score model calculation.     

股市波动、金融政策和宏观经济关系研究——基于因子VAR模型

通过因子分析从诸宏观经济变量中提取了金融政策因子和宏观经济状态因子,建立了基于VAR的股价波动、金融政策和宏观经济三变量回归模型。研究表明：金融政策影响股价的表现,而宏观经济状态对股价、股价对金融政策和宏观经济状态的影响均不显著;基于标准差的VAR（5）模型相对于基于收益率的VAR（3）模型能更好地刻画股市波动与金融政策、宏观经济三者之间的关系。     

The Application of Corporate Governance Indicators With XBRL Technology to Financial Crisis Prediction

ABSTRACT

The widespread adoption of eXtensible Business Reporting Language (XBRL) suggests that intelligent software agents can now use financial information disseminated on the Web with high accuracy. Financial data have been widely used by researchers to predict financial crises; however, few studies have considered corporate governance indicators in building prediction models. This article presents a financial crisis prediction model that involves using a genetic algorithm for determining the optimal feature set and support vector machines (SVMs) to be used with XBRL. The experimental results show that the proposed model outperforms models based on only one type of information, either financial or corporate governance. Compared with conventional statistical methods, the proposed SVM model forecasts financial crises more accurately.     

Employee Relations and the Likelihood of Occurrence of Corporate Financial Distress

Abstract:   In this paper, we investigate the association of employee relations with the occurrence of onset of financial distress. We argue that if adverse economic conditions arise, firms that have maintained good employee relations will be more effective in obtaining temporary labor concessions. As a result, firms with good employee relations, to the extent they are dependent on labor in the conduct of business operations, should be more likely to avoid the onset of future financial distress. The empirical findings we document support this prior.     

The Determinants of Foreign Currency Hedging: Does Foreign Currency Debt Induce a Bias?

In this paper we use UK data to present strong empirical evidence that explains the mixed results in previous studies with respect to the effect of financial distress on the demand for corporate hedging. We build on recent studies that have identified a strong link between foreign currency (FC) debt use and leverage. Given this relationship, we show that using leverage variables as proxies for financial distress and the failure to distinguish between FC debt users and non‐users causes misleading inference. More specifically, when we partition our sample of FC hedgers into firms that use and do not use foreign debt, we show that leverage variables are significantly related to the FC hedging decision for firms that use FC debt either in isolation or in combination with FC derivatives but not for firms that only use FC derivatives. This suggests that FC debt users are influencing these results. However, we also find that other financial distress cost proxies with no obvious link to FC debt use are significant determinants in the corporate demand for FC hedging, including derivatives use.     

The Limitations of Bankruptcy Prediction Models: Some Cautions for the Researcher

The purpose of this study is to demonstrate potential problems associated with the use of bankruptcy prediction models in current research. The tests in this study demonstrate the problems that may arise when bankruptcy prediction models are inappropriately applied. This analysis evaluated the Zmijewski (1984) and Ohlson (1980) models using time periods, industries, and financial distress situations other than those used to originally develop the models. The findings indicated that both models were sensitive to time periods. That is, the accuracy of the models declined when applied to time periods different from those used to develop the models. The findings also suggest that the accuracy of each model continues to decline moving from the 1988–1991 to the 1992–1999 sample period. Additionally, Ohlson's (Zmijewski's) model was (was not) sensitive to industry classifications. The findings of this study also suggest that the Ohlson and Zmijewski models are not sensitive to financial distress situations other than those used to develop the models. Thus, the models appear to be more generally useful for predicting financial distress, not just bankruptcy.In sum, the results of this study suggest that researchers should use bankruptcy prediction models cautiously. Applying the models to time periods and industries other than those used to develop the models may result in a significant decline in the models' accuracies. Additionally, some bankruptcy prediction models may be more appropriate for evaluating various forms of financial distress as opposed to just bankruptcy. To avoid erroneous applications of bankruptcy prediction models in the future, it is necessary for researchers not only to understand the uses of prediction models, but also to understand the limitations of the models.     

Explanators of Local Government Distress

This article develops a statistical model to explain sources of distress in local government. Whereas ‘financial distress’ in the private sector has been equated with a failure to meet financial commitments, here ‘distress’ is interpreted as an inability to maintain pre-existing levels of services to the community. Since the late 1990s local councils in an Australian state (New South Wales) have been required to estimate the cost of restoring infrastructure assets to a satisfactory condition (a requirement which predates that form of reporting on infrastructure condition introduced as an option in U.S. GASB 34). Information regarding the cost of restoring infrastructure is used in this study as a proxy for levels of distress (in contrast to the binary classification that characterizes much of prior private sector financial distress research). Data regarding service levels for a sample of 161 councils for 2001 and 2002 were used and a multiple regression model was estimated and interpreted. The main findings were that the degree of distress in local councils is positively associated with the size of the population they serve and the size and composition of their revenues. Road maintenance costs featured prominently in results, as higher road program costs were associated with higher levels of distress (particularly when interacted with other variables). However, the revenue generating capacity of councils had the strongest statistical impact on local government distress. Councils with lower percentages of rates revenue to total revenue and lower ordinary revenue levels to total assets were typically identified as more distressed. However, no systematic evidence was found that rural councils have higher distress levels than urban councils (i.e., both rural and urban councils serving larger populations were relatively more distressed than councils serving smaller populations). It is suggested that the model (or modifications thereof) may serve as an early warning system for those monitoring the circumstances and performance of local governments.