Predictingcompanyfailure

1、Predicting company failureIs it possible to use financial data to predict company failure ?1Predicting company failureA univariate modelSome of the earliest analysis was carried out by Beaver (1966). His analysis involved the use of a single variable (financial ratio) to predict failure. His analysi

2、s indicated that the distribution of the variable for distressed firms will differ systematically from that in non-distressed firms. His contention was that this difference could be used to predict future company failure.2The following data relates to a number of companies in a particular industry.

3、The ratio used is that of interest cover.CompanyRatioStatusA4.0Non-failedB3.6Non-failedC3.4Non-failedD3.2Non-failedE2.8Non-failedF1.6Non-failedG1.4FailedH0.2FailedI0.1Non-failedJ0.1Failed3Predicting company failureThe average ratio for the failed group is 0.57. The average ratio for the non-failed g

4、roup is 2.67. There is a significant difference between the two groups. The main problem is to determine the appropriate cut-off point. If it is assumed that companies with a ratio of less than 1.6 will fail then there is only one error, for company I. However, if suppliers of capital are making the

5、 judgement it is likely that firm I will fail due to lack of support.4Predicting company failureBeaver extended his analysis over a period of 5 years prior to failure and noted that the ratios for the failed firms generally worsened significantly. It could of course be argued that this approach is a

6、 self-fulfilling predictive tool.5Predicting company failureBeaver took 79 failed firms and matched each one with a non-failed firm from the same industry and with a similar asset size. The arithmetic mean of 30 financial ratios were calculated for the two different groups over the 5 year period bef

7、ore failure. There was a significant difference between the two groups for all ratios. This difference became greater as failure became closer. The two most significant ratios were the cash flow to total debt ratio and the net income to total assets ratio.6Predicting company failureZ scores and disc

8、riminant analysis7Predicting company failureThis form of analysis assumes a linear and additive relationship between several variables which enables the result to be expressed as a single figure known as a Z score. This can be compared with a cut-off point to determine whether or not the company is

9、likely to fail. The result is expressed in the form of an equation:8Predicting company failureZ = aR1 + bR2 +xRnR1, R2 through to Rn are the financial ratios chosen for their ability to predict failure and a, b through to x are weightings reflecting their relative importance.9Predicting company fail

10、ureOn the basis of Z scores Taffler and Tisshaw developed the concept of the solvency thermometer. The Z model developed by Taffler and Tisshaw was as follows:10Predicting company failureZ = C0 + C1R1 + C2R2 + C3R3 + C4R4Where:R1=profit before tax/current liabilitiesR2=current assets/total liabiliti

11、esR3=current liabilities/total assetsR4=the no-credit interval11Predicting company failureThe no-credit interval was defined as immediate assets less current liabilities divided by operating costs excluding depreciation12Predicting company failureAlthough Taffler and Tisshaw disclosed the ratios use

12、d in their analysis they did not disclose the coefficients and so their findings cannot be verified.13Predicting company failureAnother approach to Z scores was that developed by Altman, where he devised the following model:Z = 1.2R1 +1.4R2 + 3.3R3 + 0.6R4 + 1.0R514Predicting company failureWhere:R1

13、=working capital/total assetsR2=retained earnings/total assetsR3=earnings before interest and tax/total assetsR4=market value of preference and ordinary shares/book value of total debtR5=sales/total assets15Predicting company failureAltmans sample comprised 33 US manufacturing firms which had gone i

14、nto liquidation. These were paired with firms of similar size and industry which did not go into liquidation. From his model he was able to forecast that a firm with a Z score above 3 would be safe whereas a firm with a Z score below 1.8 was a potential failure.16Predicting company failureThere are

15、of course non-financial indicators of possible failure.Clutterbuck and Kernaghan identify a totally subjective list of factors which, in their view, are significant indicators of potential failure, namely:17Predicting company failurepersonalised number platesnamed car parking and executive washrooms

16、annual report showing the chairman leaving in a helicopterfountain in the forecourtfish tank in the boardroomnew offices opened by the Prime Ministercompany yacht or aircraftfast talking managing directordirectors who use military titlescompany matchboxes and other disposable promotions18Predicting

17、company failureA less subjective approach was adopted by Argenti. He believes that the process leading to failure can take five or more years and comprises three stages:inherent defects in the companya major mistakethe appearance of financial and non-financial symptoms19Predicting company failureDEF

18、ECTS Management:autocratic chief executivechief executive is also chairmanunbalanced skill and knowledge on the boardpassive boardweak finance directorlack of professional managers below the board Accounting systems:budgetary controlcash flow planscosting systems Response to change:products, process

19、es, markets, employee practice, etc.84222133315Total possible (Danger mark = 10)4320Predicting company failureMISTAKES Overtrading: expanding faster than cash funding Gearing: bank overdrafts (loans) imprudently high Big project: project failure jeopardising company151515Total possible (Danger mark = 15)45SYMPTOMS Financial: deteriorat