Corporate failure

Companies rarely fail overnight. Sales slip, the overdraft creeps up, the best managers leave and the machines grow old. Someone who reads the signs early may act in time. Two schools disagree on what the signs are. One says failure comes from financial trouble, so ratios should warn us. The other says money problems are only symptoms of deeper weaknesses in management and strategy.

This topic covers the causes of corporate failure and two views on them, quantitative prediction with ratios, Beaver’s single-ratio study, the Z score approach and Altman’s model, the H score, qualitative prediction with Argenti’s A score, other indicators, how to use these models and how to avoid failure. The previous topic covered ratio analysis and strategy evaluation.

The next covers foreign exchange rates and exposure. Neither the May 2014 nor the November 2014 Pathfinder sets a stand-alone question on corporate failure.

Key terms in plain English

Syllabus check

This topic is in syllabus area B (business analysis). It covers:

• identifying the causes of corporate failure;
• explaining different approaches to predicting corporate failure.

CAMA 2020 and insolvency

The Companies and Allied Matters Act 2020 (CAMA 2020) is the company law of Nigeria.

Causes: two views

After a failure, it should be possible to analyse what went wrong. Prediction tries to find companies at risk before failure, so management can act. One view says failure is caused by financial problems, such as losses or inability to pay creditors, so ratios and quantitative analysis can predict it. The other says financial problems follow from non-financial causes, so qualitative measures and judgement are needed.

Key factors separating failing from surviving firms are generally accepted to be profitability, cash flow, return on equity and dividends, gearing or debt levels, and volatility of revenue.

Ratio analysis and Beaver

A low ROCE or losses show poor performance, high gearing suggests difficulty meeting interest, and weak current and quick ratios show a liquidity risk. In the 1960s Beaver compared failed and non-failed companies one ratio at a time and found that cash flow to total debt was the most significant. The weakness: one ratio alone is not enough.

It is hard to say when a ratio is dangerous, ratios change over time, and ratios may conflict (poor liquidity but a high ROCE).

Z score models

A Z score model is a formula of weighted key ratios producing a score. It is built by multiple discriminant analysis (MDA):

• (a) sample companies of one type and group them as failed or surviving
• (b) gather historical data and calculate ratios
• (c) use statistics to find the ratios that discriminate most
• (d) weight them by significance
• (e) set the Z score model

Models differ by industry, country and size. A score above a value is “safe”, below another value is high risk within a year or two, and between is a “grey area”.

Altman’s model

Altman was the first to use MDA, in the 1970s. The model, with X1 to X4 expressed as percentages:

Z = 0.012X1 + 0.014X2 + 0.033X3 + 0.006X4 + 0.999X5 (usually rounded to 1.0 for X5)

• X1 = working capital ÷ total assets
• X2 = retained earnings ÷ total assets
• X3 = earnings before interest and tax ÷ total assets
• X4 = market value of equity ÷ book value of total debt
• X5 = sales ÷ total assets

Altman’s cut-offs: 2.7 or higher not at risk; below 1.8 high risk; 1.8 to 2.7 grey area. Models are good for predicting failure up to about two years ahead but not longer. An airline-specific model (AIRSCORE, 1991) was based on Altman’s.

The H score

Devised by Company Watch, an H score from 0 to 100 shows the percentage of companies with characteristics more indicative of failure. An H score of 15 means only 15% of companies look more at risk; 80 means 80% do. 25 or less is high risk. It uses seven ratios across profit management, asset management and funding management.

Qualitative models: Argenti’s A score

Z scores focus on financial performance, but poor ratios may be a symptom, not the cause. Possible real causes are poor management (inexperienced or autocratic), inability to keep key staff, ownership concentrated in a few hands, poor management information, the loss of a big client, a large increase in interest rates and a big project that fails. Argenti scored qualitative factors, especially for small and medium companies, under three headings:

• Defects (total 45; satisfactory is 10 or less). Management: an autocratic or inexperienced chief executive (8), failure to separate chairman and chief executive (4), a passive board (2), unbalanced skills (4), a weak finance director (2), no management in depth (1), poor response to change (15). Accounting: no budgetary control (3), no cash flow planning (3), no costing system (3).
• Mistakes (satisfactory 15): overtrading (15), excessive borrowing and high gearing (15), a failed big project (15).
• Symptoms: worsening ratios in the two years before failure (4), creative accounting or window dressing (4), non-financial signs such as frozen salaries, postponed capital spending, falling market share and rising staff turnover (3), and terminal signs just before failure (1).

The overall pass mark is an A score of 25 or less (a defects score of 10 plus a mistakes score of 15); companies scoring higher should worry about their future.

Other indicators and avoiding failure

Failure indicators can be company-specific (poor management, reliance on one customer or supplier, a qualified audit report), general (the industry) or environmental (the economy, credit availability). Common reasons: poor business planning, poor financial planning (cash flow, too little start-up capital), poor marketing and poor management. Signs include:

• declining sales and market share, worse in a declining market;
• low or falling profit;
• worsening cash flow, an overdraft near its limit and loans falling due;
• a lack of innovation;
• old non-current assets not replaced;
• weak internal controls;
• loss of key employees, often the first to see the problem.

Ross and Kami’s “Ten Commandments”:

• have a strategy and controls
• involve the board
• avoid one-man rule
• have management in depth
• stay informed of change
• the customer is king
• do not misuse computers
• do not manipulate accounts
• organise to meet employees’ needs

Using the models

A model is useful only before failure, to judge how serious the risk is (the score) and what the causes are. Z scores identify symptoms but not causes. A scores rely on subjective judgement. A company may not know it is at risk and so not commission an analysis.

The rules in more depth

Why several ratios beat one

One ratio can mislead or conflict with others. A weighted set uses more information.

Why models are limited to about two years

Ratios and conditions change, so predictive power fades.

Why models differ by industry and country

Capital structures and norms differ, so the best ratios and weights differ too.

Why the cause matters

Fixing a symptom (cash) without the cause (a weak board or a failed strategy) will not save the firm.

Effect on analysis

Combine both approaches: score the ratios and ask what the real causes are.

Worked examples

1. Z = 0.012×10 + 0.014×20 + 0.033×12 + 0.006×150 + 0.999×1.5.
2. Z = 0.12 + 0.28 + 0.396 + 0.90 + 1.4985 = 3.19.
3. This is above 2.7, so the company is not at risk.

1. Z = 0.024 + 0 + 0.099 + 0.24 + 0.999.
2. Z = 1.36.
3. This is below 1.8, so the risk of failure is high.

1. Defects = 8 + 4 + 3 + 3.
2. Defects = 18.
3. This exceeds the satisfactory level of 10, so the firm should worry.

1. Ratio = 45 ÷ 300.
2. Ratio = 15%.
3. Compare it with failed and surviving firms of the same type before judging.

How to score full marks

• Contrast the financial and non-financial views.
• Calculate the score with the formula given and state the zone.
• Say what the score cannot tell you (the cause).
• Add qualitative signs from the case.
• Recommend actions that treat causes.

Common mistakes in the exam

• Entering X1 to X4 as decimals when percentages were intended.
• Using Altman’s cut-offs for a different model.
• Saying a Z score explains why a firm fails.
• Treating a grey-area score as safe.
• Ignoring qualitative factors.
• Using a model for forecasts beyond two years.

Quick revision

• Two views: financial causes or non-financial causes.
• Beaver: cash flow to total debt; one ratio is not enough.
• Z score by MDA; Altman: above 2.7 safe, below 1.8 high risk.
• H score: 25 or less is high risk.
• A score: defects, mistakes, symptoms.
• Other signs: sales, profit, cash, innovation, old assets, controls, staff.
• CAMA 2020: ss. 305, 308, 377(1), 378, 385.

Practice questions

These are original questions written in the style of the Institute of Chartered Accountants of Nigeria (ICAN) exam. Try each one before you open the answer. Full model answers follow the written questions.

Recall check: multiple choice

Recent ICAN papers are written, although older papers such as May 2014 included multiple-choice questions. These questions are for quick recall. The written questions that follow are closer to the exam.

Written questions

Model answers

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