Freeze: Company information
Freeze is listed on the small Kayland stock exchange and supplies construction services to the oil exploration industry in Kayland. Demand for Freeze’s services broadly relates directly to the world oil price. A recent fall in the world oil price has led to several corporate failures in the oil exploration industry as the exploration of new oil fields becomes unprofitable. In April 20X8, a major oil spill led to widespread environmental damage in Kayland. An initial investigation has indicated that the cause of the oil spill may be due to the incorrect installation of machinery by Freeze.
Quantitative failure model: the K Score
The K Score is a quantitative model used for predicting whether Freeze is at risk of corporate failure. The K Score model was developed by correlating recent historic data on financial ratios of all companies listed on the Kayland stock exchange with the incidence of subsequent corporate failure. The method of calculation of the K Score is shown in Appendix 1.
Corporate failure indicators and comparator information
A colleague of yours, who is an academic working in a Kayland university, has suggested that operational gearing and financial gearing1 are the two most important indicators of corporate failure in the industry in which Freeze operates. To demonstrate this, she has introduced you to Thor, which is a privately-owned company based in Jayland. Thor provides similar services to Freeze in both the Kayland and Jayland oil exploration industries. Thor reports its financial results in Jayland dollars (J$). An economic recession has recently begun in Jayland.
Extracts from the publicly available accounts of Freeze and Thor for the year ended 31 December 20X7 are both shown in Appendix 2. For comparison purposes, both extracts are in K$.
Financial gearing is defined as (preference share capital + long-term debt)/total equity
Required:
Appendix 1
Calculation of the K Score1
K Score = 2.5K1 + 5.0K2 + 0.1K3 + 1.9K4
Score Definition
K1 Net current assets/total assets
K2 Profit before interest and tax/total assets
K3 Market value of ordinary shares2/book value of non-current liabilities
K4 Retained earnings/total assets
Notes:
1A K Score of 2 or less indicates that corporate failure is highly likely, whereas a score of 5 or above indicates that corporate failure is unlikely. Scores of between 2 and 5 are in the ‘grey area’, where further analysis is required to determine the likelihood of corporate failure.
2 The average price of Freeze ordinary shares on the Kayland stock exchange during the year ended 31 December 20X7 was $10·60.
Appendix 2
Extracts from the management accounts of Freeze and Thor
Extract from the statement of profit or loss for the year ended 31 December 20X7
Evaluate the usefulness of the quantitative K Score model in predicting the corporate failure of Freeze and Thor.
Objectivity
The K Score can be calculated easily from readily available financial data. Both Freeze and Thor will publish their financial results, so the financial data will be readily available. There is no subjectivity required in calculating the score which can be easily compared between different companies, at different points in time, or against the likelihood of corporate failure. However, the translation of Thor’s results into K$ from its reporting currency of J$ may make it inappropriate to compare their K Scores.
Uses historical data
Historical financial data is used to calculate the K Score, which is therefore backwards looking. This data may be out of date and significant events or market changes may have occurred since the period to which the data relates.
The data given to calculate Freeze’s K Score is from the year ended 31 December 20X7 and is already almost a year out of date. In April 20X8, an oil spill led to widespread environmental damage in Kayland. The oil spill was thought to have been caused by the incorrect installation of machinery by Freeze. This will probably have a major impact on Freeze’s performance during 20X8, for example, due to fines or loss of customer trust. This is not reflected in the historical financial data given, which was approved by the board on 31 March 20X8.
Data may be unavailable or unreliable
Financial data needed to calculate the K Score may be unavailable. For example, as a privately owned business, Thor will not have a market value for equity, which is required to calculate the K Score.
The data may also be unreliable as a basis for calculating the score, though as a listed business Freeze will be subject to audit and listing regulations, so its published data is probably reliable.
Appropriateness of the K Score model
A quantitative model, such as the K Score, identifies financial ratios which significantly differ in value between surviving and failing companies. Statistical analysis is then used to choose the weightings for these ratios in a formula for the score, which can be used to identify companies which exhibit the features of previously failing companies. The company being analysed must be similar to those being used to build the model for the results to be relevant.
The K Score model is based on recent data for all Kayland listed companies and Freeze is a construction company in the oil exploration industry. It may, therefore, be too dissimilar to other industries on the small Kayland stock exchange, for example, in the markets it serves. Also, as the Kayland stock exchange is small, there may be insufficient data from failing companies on which to base the model.
Thor is based in Jayland, so the K Score model, which is based on data from the Kayland stock exchange, is unlikely to applicable.
K Score may not give a clear indication of corporate failure
The K Score may not always give a clear indication of whether corporate failure is likely. K Scores of between 2 and 5 lie in the ‘grey area’, where further analysis is needed in order to reach a clear conclusion.
The K Score is only a measurement at a single point in time, in this case 31 December 20X7, and without undertaking measures at different times, it will not indicate whether a company is becoming more or less likely to fail. As the K Score is based on statistical correlations of financial ratios with subsequent failure, it does not give any suggestions on how to reduce the likelihood of corporate failure.
Using only Freeze’s K Score for the year ended 31 December 20X7, assess whether Freeze is at risk of corporate failure.
Calculation of Freeze’s K Score at 31 December 20X7
With a K Score of 4·367, Freeze is in the grey area and further analysis is required in order to determine if corporate failure is likely
K Score = 2·5K1 + 5·0K2 + 0·1K3 + 1·9K4
= (2·5 x 0·367) + (5·0 x 0·180) + (0·1 x 14·930) + (1·9 x 0·556)
= 0·918 + 0·900 + 1·493 + 1·056 = 4·367
Workings
K1 Net current assets/total assets = (2,164 – 645)/4,135 = 0·367
K2 Profit before interest and tax/total assets = 745/4,135 = 0·180
K3 Market value of ordinary shares/book value of non-current liabilities = ($10·60 x 500)/355 = 14·930
K4 Retained earnings/total assets = 2,300/4,135 = 0·556
Evaluate your colleague’s comments on the most important indicators of corporate failure in Freeze’s industry, and using these indicators assess whether Freeze is more or less likely to suffer corporate failure than Thor.
Operational gearing
Operational gearing indicates the level of business risk which companies face by measuring the relative amount of fixed costs. Companies with high operational gearing have high business risk. They are less able to cover their fixed costs if contribution falls due to a reduction in revenue or if there is an increase in variable costs. Highly geared businesses are therefore more likely to fail than those with lower operational gearing.
The recent recession in Jayland may adversely affect Thor’s contribution, as might movements in exchange rates between the Kayland dollar and Thor’s home currency.
The demand for services in the oil exploration industry varies directly with the world oil price. The recent fall in world oil prices will result in a reduction in oil exploration activity. Companies with high fixed costs are therefore more likely to fail in these circumstances.
The operational gearing ratio is defined as contribution/PBIT. Freeze’s operational gearing ratio is 2·48 (1,845/745), whilst Thor’s is 5·48 (4,960/905). Thor is more highly geared and therefore more likely to suffer corporate failure.
Financial gearing
The financial gearing ratio measures financial risk and reflects the company’s ability to service its long-term debt. Similarly to operational gearing, high financial gearing makes companies more likely to suffer corporate failure because they are less able to make interest payments if trading conditions deteriorate.
Both Freeze and Thor publish their financial results, so the data required to calculate financial gearing will be readily available.
The financial gearing ratio is defined as (preference share capital + long-term debt)/total equity. Freeze’s financial gearing ratio is 0·11 (355/3,135), whilst Thor’s is 0·13 (382/ 2,943). Thor is marginally more highly geared and therefore more likely to suffer corporate failure.
Conclusion
Both Thor’s operational and financial gearing are higher than those of Freeze. In this respect, Thor is more likely to suffer corporate failure than Freeze.
It is unclear, however, whether the colleague’s view that operational and financial gearing are the two most important indicators in predicting corporate failure in this industry is correct, as both Freeze and Thor have not yet failed. There may be many other important indicators of corporate failure, including non-financial ones.