Graphical analysis and financial classification: A case study

This paper illustrates the use of graphical analysis as a complementary diagnostic tool in financial classification problems. For more than two decades statistical models have been used frequently to understand the information content of multivariate data in the context of financial classification. These statistical classification models can be complemented by the use of computer-generated multidimensional data displays and graphical analysis. We illustrate the use of the scatterplot matrix, which is the simplest and very effective form of graphical analysis on a sample of bankrupt and non-bankrupt firms. The paper demonstrates the complementary nature of the scatterplot matrix for tree-structured classification models.     

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.     

Bankruptcy Prediction: Evidence from Korean Listed Companies during the IMF Crisis

This paper empirically studies the predictive model of business failure using the sample of listed companies that went bankrupt during the period from 1997 to 1998 when deep recession driven by the IMF crisis started in Korea. Logit maximum likelihood estimator is employed as the statistical technique. The model demonstrated decent prediction accuracy and robustness. The type I accuracy is 80.4 per cent and the Type II accuracy is 73.9 per cent. The accuracy remains almost at the same level when the model is applied to an independent holdout sample. In addition to building a bankruptcy prediction model this paper finds that most of firms that went bankrupt during the Korean economic crisis from 1997 to 1998 had shown signs of financial distress long before the crisis. Bankruptcy probabilities of the sample are consistently high during the period from 1991 to 1996. The evidence of this paper can be seen as complementary to the perspective that traces Asian economic crisis to the vulnerabilities of corporate governance of Asian countries.     

Predicting corporate financial distress: Reflections on choice-based sample bias

Financial distress precedes bankruptcy. Most financial distress models actually rely on bankruptcy data, which is easier to obtain. We obtained a dataset of financially distressed but not yet bankrupt companies supplying a major auto manufacturer. An early warning model successfully discriminated between these distressed companies and a second group of similar but healthy companies. Previous researchers argue the matched-sample design, on which some earlier models were built, causes bias. To test for bias, the dataset was partitioned into smaller samples that approach equal groupings. We statistically confirm the presence of a bias and describe its impact on estimated classification rates.     

The role of corporate governance in forecasting bankruptcy: Pre- and post-SOX enactment

This paper contributes to the literature by documenting the improved performance of bankruptcy prediction models after including corporate governance variables. The empirical results demonstrate better predictive power for financial bankruptcy than previous bankruptcy prediction models, particularly in the post-SOX period. Our theoretical argument emphasizes the urgent need for such improvements to the bankruptcy prediction model following the introduction of the SOX Act, with the empirical results providing intuitive economic meaning for all relevant market participants. Policymakers may consider enacting laws to include designs for corporate governance monitoring mechanisms, entrepreneurs may use this model to improve their own governance structures and compensation mechanisms to avoid financial bankruptcy, and investors may refer to it to ensure that ‘losers’ are excluded from their investment portfolios.     

The role of debt and bankruptcy statutes in facilitating tacit collusion

Using a two‐period model this paper examines the quantity decisions of leveraged duopolists that are vulnerable to bankruptcy in the first period. When the firms have symmetric costs, a bankrupt firm reorganizes under Chapter 11. If a Chapter 11 firm experiences marginal cost relief, each firm produces a collusive output in period one in order to prevent its rival's financial demise. When the firms have asymmetric costs, the less efficient firm is liquidated under Chapter 7 upon bankruptcy. A predatory equilibrium exists, whereby the inefficient firm is driven from the market. Copyright © 1999 John Wiley & Sons, Ltd.     

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.     

Assessing bankruptcy prediction models via information content of technical inefficiency

We use a stochastic frontier model with firm-specific technical inefficiency effects in a panel framework (Battese and Coelli in Empir Econ 20:325–332, 1995) to assess two popular probability of bankruptcy (PB) measures based on Merton model (Merton in J Financ 29:449–470, 1974) and discrete-time hazard model (DHM; Shumway in J Bus 74:101–124, 2001). Three important results based on our empirical studies are obtained. First, a firm with a higher PB generally has less technical efficiency. Second, for an ex-post bankrupt firm, its PB tends to increase and its technical efficiency of production tends to decrease, as the time to its bankruptcy draws near. Finally, the information content about firm’s technical inefficiency provided by PB based on DHM is significantly more than that based on Merton model. By the last result and the fact that economic-based efficiency measures are reasonable indicators of the long-term health and prospects of firms (Baek and Pagán in Q J Bus Econ 41:27–41, 2002), we conclude that PB based on DHM is a better credit risk proxy of firms.     

Do adjustment costs explain investment-cash flow insensitivity?

In this paper, I explain two “puzzles” that have been observed in firm level data. First, firms that display a high sensitivity of investment to cash flow (commonly believed to be an indicator of liquidity constraints) usually have large unutilized lines of credit which, presumably, could be used to overcome the shortage of funds. Second, firms that are perceived to be extremely liquidity constrained actually show very little sensitivity of investment to cash flow.I show how a dynamic model of firm investment with liquidity constraints and non-convex costs of adjustment of capital can explain these facts. These two features together imply that firms need to have a certain threshold level of financial resources before they can afford to increase investment. Once they cross this threshold, firms’ investment will be positively correlated with their financial resources until they reach their desired level of capital stock. However, even if investment is sensitive to cash flow, firms may borrow below their credit limit to guard against future bankruptcy or binding liquidity constraints.     

Estimating the structural credit risk model when equity prices are contaminated by trading noises

The transformed-data maximum likelihood estimation (MLE) method for structural credit risk models developed by Duan [Duan, J.-C., 1994. Maximum likelihood estimation using price data of the derivative contract. Mathematical Finance 4, 155–167] is extended to account for the fact that observed equity prices may have been contaminated by trading noises. With the presence of trading noises, the likelihood function based on the observed equity prices can only be evaluated via some nonlinear filtering scheme. We devise a particle filtering algorithm that is practical for conducting the MLE estimation of the structural credit risk model of Merton [Merton, R.C., 1974. On the pricing of corporate debt: The risk structure of interest rates. Journal of Finance 29, 449–470]. We implement the method on the Dow Jones 30 firms and on 100 randomly selected firms, and find that ignoring trading noises can lead to significantly over-estimating the firm’s asset volatility. The estimated magnitude of trading noise is in line with the direction that a firm’s liquidity will predict based on three common liquidity proxies. A simulation study is then conducted to ascertain the performance of the estimation method.     

Forecasting distress in cooperative banks: The role of asset quality

This paper analyzes the drivers of financial distress that were experienced by small Italian cooperative banks during the latest deep recession, focusing mainly on the importance of bank capital as a predictor of bankruptcy for Italian nonprofit banks. The analysis aims to build an early-warning model that is suitable for this type of bank.The results reveal non-monotonic effects of bank capital on the probability of failure. In contrast to distress models for for-profit banks, non-performing loans, profitability, liquidity, and management quality have a negligible predictive value. The findings also show that unreserved impaired loans have an important impact on the probability of bank distress. Moreover, the loan–loss ratio provision on substandard loans constitutes a suitable antibody against bank distress. Overall, the results are robust in terms of both the methodology (i.e., frequentist and Bayesian approaches) and the sample used (i.e., cooperative banks in Italy and euro-area countries).     

FASB 95, Cash Flow and bankruptcy

In this paper, the authors evaluate the effectiveness of Statement of Cash Flows measures in the classification and prediction of bankruptcy. The problems of biased estimators and bankruptcy probabilities and of optimal cut-off rates are addressed by using a large random sample and conditional marginal probability density functions. It is found that cashflow variables provide statistically better classification and prediction rates when used with traditional accounting variables when bankruptcy is defined as a Chapter 11 filing.     

Institutional Investment Patterns in Troubled Corporations: A Sociological analysis

A bstract . Classical or neo-classical economic theories do not adequately explain institutional investment patterns in troubled corporations. Macroeconomic perspectives contend that abstract market forces direct investment capital away from troubled companies, and that bankruptcy weeds weak firms out of the economy for the general good. Microeconomic perspectives focus on the seemingly autonomous decisions of firms and their managers, where bad management leads to troubled and bankrupt firms and a corresponding loss of investment in these companies. Neither perspective is useful for understanding recent patterns of institutional investment. A more critical, sociological perspective for understanding these investment patterns has two main threads. First, investment activity is embedded in more general social relationships and cannot be understood strictly on "economic" grounds and with "economic" ideas. Second, social power—rather than abstract market forces —is critical in fostering specific investment patterns. More specifically, the organizational power of large financial firms may be the pivotal factor shaping investment patterns in troubled companies. Recent case studies of troubled and bankrupt corporations demonstrate the usefulness of this more sociological perspective, and suggest areas for future research.     

Predicting bankruptcy in the Texas nursing facility industry

Approximately 50% of nursing facilities in Texas petitioned for bankruptcy during the 1998–2004 period. Using a logit regression model tested for robustness, we find nursing facilities that are profit-seekers, chain members, pay higher than average wage rates, accept more intensive-care residents and obtain a larger than average portion of their funding from public sources are highly vulnerable to negative changes in regulatory policy decisions on Medicare and Medicaid reimbursement. Larger facilities having higher than average occupancy rates and quality of care are less susceptible to adverse decisions. The model correctly classifies a facility as either bankrupt or solvent in about 75% of cases. We also examine the duration of bankruptcy using accelerated failure-time models. It appears that the duration of bankruptcy depends on location, out-of-state ownership, length of ownership, volume of resident days supplied, total cost and proportion of revenues from Medicaid.     

A note on CEO compensation, elimination tournaments and bankruptcy risk

We investigate an economy in which firms have different risks to go bankrupt. We observe two things: first, workers in firms with higher bankruptcy risk (bad firms) always work less than workers in good firms. Second, the CEOs of bad firms may nonetheless receive larger wages.JEL Classification: J40, L20, M50Alexander Matros: We are grateful to Karl Wärneryd and an anonymous referee for helpful suggestions. Much of this paper was written while Matros was a Research Fellow at University College London and Friebel was at SITE, Stockholm. We are grateful for their research environmennt. The support of the Economic and Social Research Council (ESRC) is gratefully acknowledged. This work was part of the programme of the ESRC Research Center for Economic Learning and Social Evolution.     

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.     

Influence of earnings management on forecasting corporate failure

This paper studies the relationship between corporate failure forecasting and earnings management variables. Using a new threshold model approach that separates samples into different regimes according to a threshold variable, the authors examine regimes to evaluate the prediction capacities of earnings management variables. By proposing this threshold model and applying it innovatively, this research reveals boundaries within which earnings management variables can yield superior corporate failure forecasting. The inclusion of earnings management variables in corporate failure models improves failure prediction capacities for firms that manipulate substantial earnings. Furthermore, an accruals-based variable improves predictions of failed firms, but the real activities-based variable improves predictions of non-failed firms. These findings highlight the importance of indicators of the magnitude of earnings management and the tools used to improve the performance of corporate failure models. The proposed model can determine the predictive power of particular explanatory variables to forecast corporate failure.     

Forecasting corporate default risk in China

Default risk prediction can not only provide forward-looking and timely risk measures for regulators and investors, but also improve the stability of the financial system. However, the determinants of corporate default risk in China have not been well-identified. An empirical analysis was conducted using a unique dataset of default events in the Chinese market to fill this gap. First, we demonstrated that the default probability estimated by a structural model, which is widely used in the literature, do not fully reveal the default risk of firms in China. Second, we classified default events into minor and major defaults for empirical analysis. We found that firms that survive minor defaults behave differently from other bankrupt firms. Our results suggest that the determinants of corporate default risk in China and the United States differ. We also found that a firm’s continued increase in cash holdings is one of the most important signs of default. Overall, our study significantly improves the accuracy of forecasting corporate default risk in China.     

Forecasting short-term defaults of firms in a commercial network via Bayesian spatial and spatio-temporal methods

To protect financial institutions from unexpected credit losses, during the monitoring phase of granted loans it is of primary importance to foresee any evidence of a contagion of liquidity distress across a network of firms. This term indicates a situation of lack of solvency of a firm (e.g., a customer) that propagates to other firms (e.g, its suppliers), which could consequently face challenges in repaying their own granted loans. In this paper, we look for the evidence of contagion of liquidity distress on an Intesa Sanpaolo proprietary dataset by means of Bayesian spatial and spatio-temporal models. Our results indicate that such models can detect cases of distress not yet apparent from covariate information collected on the firms by instead borrowing information from the network, leading to improved forecasting performance on the prediction of short-term default with respect to state-of-the-art methods.     

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.