An empirical comparison of bankruptcy models

Mossman, Charles E.; Bell, Geoffrey G.; Swartz, Lee Mick; Turtle, Harry J.

doi:10.1111/j.1540-6288.1998.tb01367.x

Cited by 145 publications

(72 citation statements)

References 21 publications

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“…Lastly, the estimated coefficients can be interpreted separately as the importance or significance of each of the independent variables in the explanation of the estimated PD. After the work of Ohlson (1980), most of the academic literature (Zavgren (1983), Gentry et al (1985), Keasey and Watson (1987), Aziz et al (1988), Platt and Platt (1990), Ooghe et al (1995), Mossman et al (1998), Charitou andTrigeorgis (2002), Lizal (2002), Becchetti and Sierra (2002)) used logit models to predict default. Despite the theoretic differences between MDA and logit analysis, studies (see for example Lo (1985)) show that empirical results are quite similar in terms of classification accuracy.…”

Section: Default Prediction Studiesmentioning

confidence: 99%

Modeling Credit Risk for SMEs: Evidence from the US Market

Altman

Sabato²

2005

SSRN Journal

123

217

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Considering the fundamental role played by small and medium sized enterprises (SMEs) in the economy of many countries and the considerable attention placed on SMEs in the new Basel Capital Accord, we develop a distress prediction model specifically for the SME sector and to analyze its effectiveness compared to a generic corporate model. The behaviour of financial measures for SMEs is analyzed and the most significant variables in predicting the entities' credit worthiness are selected in order to construct a default prediction model. Using a logit regression technique on panel data of over 2,000 US firms (with sales less than $65 million) over the period 1994-2002, we develop a one-year default prediction model. This model has an out-of-sample prediction power which is almost 30 percent higher than a generic corporate model. An associated objective is to observe our model's ability to lower bank capital requirements considering the new Basel Capital Accord's rules for SMEs. JEL classification: G21; G28

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Section: Default Prediction Studiesmentioning

confidence: 99%

Modeling Credit Risk for SMEs: Evidence from the US Market

Altman

Sabato²

2005

SSRN Journal

123

217

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“…Investigation on cash flows revealed that many authors (Carslaw & Mills 1991) (Mills & Yamamura 1998) (Mossman & Bell 1998) agree on the importance of cash flow information. Cash flow may be viewed as the lifeblood of a company and the essence of its very existence (Rujoub, Cook & Hay 1995).…”

Section: The Importance Of Cash Flow Informationmentioning

confidence: 99%

Cash flow ratios as a yardstick for evaluating financial performance in African businesses

Jooste¹

2006

Managerial Finance

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Purpose -The purpose of this paper is to compare companies in a developing country with those of a first-world country. For this purpose South African (SA) companies in the chemical, food and electronic industries are to be evaluated on the hand of cash flow ratios and compared with companies in the USA in similar industries. Design/methodology/approach -Giacomino and Mielke proposed nine cash flow ratios for performance evaluation. Ratios were calculated for companies in the USA in the chemical, food and electronic industries for 1986-1988. Industry norms were calculated for the period, indicating that the potential existed to develop benchmarks for the ratios by industry. Jooste calculated cash flow ratios for listed companies in SA, similar to those calculated by Giacomino and Mielke. The results of the SA companies were then compared with the US companies. Findings -The comparison revealed some similarities and differences. The cash flow sufficiency ratio showed that the SA industries had enough cash to pay primary obligations, whereas the US industries did not. At the levels of cash generated by SA industries the investments in assets and dividend payouts were more than for US industries. The cash flow generated by assets used in SA is also more than that of the USA but US industries retire long-term debt in a shorter period than SA industries.Research limitations/implications -The periods used in the comparison differ. Research using the same periods was not available. No information was available on the state of the economies in each country for those periods. Practical implications -The work done by Giacomino and Mielke is to be recommended. Further studies on the utility of cash flow data would be necessary to develop a set of cash flow-based ratios. Such ratios used in conjunction with traditional balance-sheet and income statement ratios should lead to a better understanding of the financial strengths and weaknesses of a company. Originality/value -By comparing industries of a developing country with those of a first-world country one may have an indication of the performance of SA companies in a global market.

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“…Further, when evaluating the discriminatory ability of each of the models, the authors found that while the cash-flow model produced consistent results as much as three years prior to bankruptcy, the Z-score model performed better in the final year prior to failure. As such, it would appear that cash-flow characteristics may provide advance warning of potential future problems, while ratio analysis is best at predicting imminent failure (Mossman, Bell, & Swartz, 1998).…”

Section: Implications For Causes Of Failurementioning

confidence: 99%

Corporate Bankruptcy: Assessment, Analysis and Prediction of Financial Distress, Insolvency, and Failure

Danilov

2014

SSRN Journal

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This paper is divided into three sections that address the various elements of understanding, predicting and analyzing corporate failure and bankruptcy. Part I covers the definitions of corporate failure, explains the bankruptcy process and then classifies various potential causes of failure into broad categories. The causes are bifurcated into company-specific versus external factors. The company-specific factors include an in depth discussion and analysis of business causes versus financial causes, as well as the interrelation between the two. The most important factors, such as financial and operating leverage, are explored in great detail to gain a better understanding of their implications and impact on corporate failure. Part II covers various approaches for analyzing corporate risk and predicting corporate failure. It first details the credit analysis process from the perspective of lenders and credit agencies as a method for credit evaluation and prediction of default. Then, it provides an in depth explanation of financial ratio analysis as a prediction method and provides an overview of the main statistical prediction models. It concludes with a discussion of the implications of the model findings with respect to the causes detailed in Part I. Lastly, Part III describes the outcome of the study performed to analyze the causes of failure as described in Part I by using a combination of the methods highlighted in Part II. The purpose of the study is to identify the sequence and magnitude of relative ratio deterioration in failed firms in order to establish the relative frequency of the various categories of causes of failure. Various ratios and metrics were used as proxies for relative liquidity complications, profitability issues, business problems, and leverage concerns. The impact of macroeconomic events is also evaluated by isolating the impact of the

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An empirical comparison of bankruptcy models

Cited by 145 publications

References 21 publications

Modeling Credit Risk for SMEs: Evidence from the US Market

Modeling Credit Risk for SMEs: Evidence from the US Market

Cash flow ratios as a yardstick for evaluating financial performance in African businesses

Corporate Bankruptcy: Assessment, Analysis and Prediction of Financial Distress, Insolvency, and Failure

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