The Importance of Financial Position
The analysis of whether a company is financially strong or weak is one of the most important aspects when performing a financial analysis of a business as a potential investment candidate.
The financial integrity-approach, as discussed by Marty Whitman in the book The Aggressive Conservative Investor (2006, p. 19), consists of four building blocks, the first one about a company’s financial position of which Whitman wrote:
”The company ought to have a strong financial position that is measured not so much by the presence of assets as by the absence of significant encumbrances, whether a part of a balance sheet, disclosed in financial statement footnotes, or an element that is not disclosed at all in any part of financial statements.”
The subject of a company’s financial strength and weakness was also discussed by Benjamin Graham in connection to balance-sheet analysis in Security Analysis (6th edition, p. 519), where he wrote (emphasis added):
“OUR DISCUSSION IN THE preceding chapters has related chiefly to situations in which the balance-sheet exhibit apparently justified a higher price than prevailed in the market. But the more usual purpose of balance-sheet analysis is to detect the opposite state of affairs, viz., the presence of financial weaknesses that may detract from the investment or speculative merits of an issue. Careful buyers of securities scrutinize the balance sheet to see if the cash is adequate, if the current assets bear a suitable ratio to the current liabilities, and if there is any indebtedness of near maturity that may threaten to develop into a refinancing problem.”
For now, let us leave Whitman and Graham and focus on the main topic of this post, that is the Altman Z-score, and how it could serve as potential tool in a business analysis when measuring the financial strength and bankruptcy risk of a specific company.
The rest of this is all about the Altman Z-score, starting with an excerpt from the originally published article, and continues with an article from Business Insider. In the end of this post there are a few links to some further reading.
Altman Z-Score and the Prediction of Corporate Bankruptcy
The article Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy written by Edward I. Altman was originally published in The Journal of Finance back in 1968. In this article Altman laid out the Z-score formula for predicting bankruptcy. At the time of publication Altman was an Assistant Professor of Finance at New York University.
The formula may be used to predict the probability that a firm will go into bankruptcy within two years. Z-scores are used to predict corporate defaults. The Z-score uses multiple corporate income and balance sheet values to measure the financial health of a company.
In the paper Altman introduced the reader to the subject and wrote:
ACADEMICIANS SEEM to be moving toward the elimination of ratio analysis as an analytical technique in assessing the performance of the business enterprise. Theorists downgrade arbitrary rules of thumb, such as company ratio comparisons, widely used by practitioners. Since attacks on the relevance of ratio analysis emanate from many esteemed members of the scholarly world, does this mean that ratio analysis is limited to the world of “nuts and bolts”? Or, has the significance of such an approach been unattractively garbed and therefore unfairly handicapped? Can we bridge the gap, rather than sever the link, between traditional ratio “analysis” and the more rigorous statistical techniques which have become popular among academicians in recent years?
The purpose of this paper is to attempt an assessment of this issue-the quality of ratio analysis as an analytical technique. The prediction of corporate bankruptcy is used as an illustrative case. Specifically, a set of financial and economic ratios will be investigated in a bankruptcy prediction context wherein a multiple discriminant statistical methodology is employed. The data used in the study are limited to manufacturing corporations.
A brief review of the development of traditional ratio analysis as a technique for investigating corporate performance is presented in section 1. In section II the shortcomings of this approach are discussed and multiple discriminant analysis is introduced with the emphasis centering on its compatibility with ratio analysis in a bankruptcy prediction context. The discriminant model is developed in section III, where an initial sample of sixty-six firms is utilized to establish a function which best discriminates between companies in two mutually exclusive groups: bankrupt and non-bankrupt firms. Section IV reviews empirical results obtained from the initial sample and several secondary samples, the latter being selected to examine the reliability of the discriminant model as a predictive technique. In section V the model’s adaptability to practical decision-making situations and its potential benefits in a variety of situations are suggested. The final section summarizes the findings and conclusions of the study, and assesses the role and significance of traditional ratio analysis within a modern analytical context.
In 2000 Altman published a follow-up paper, entitled: Predicting the Financial Distress of Companies: Revisiting the Z-Score. For some discussion and background to the Altman Z-Score, see article below Business Insider.
The Altman Z-score is a combination of five weighted business ratios that is used to estimate the likelihood of financial distress. If the credit crunch itself wasn’t lesson enough, respected fund manager Anthony Bolton has emphasised the importance of understanding credit risk when investing in equities: “When I analysed the stocks that have lost me the most money, about two-thirds of the time it was due to weak balance sheets. You have to have your eyes open to the fact that if you are buying a company with a weak balance sheet and something changes, then that’s when you are going to be most exposed as a shareholder.”
Background to the Z-Score
The Z-Score was developed in 1968 by Edward I. Altman, an Assistant Professor of Finance at New York University, as a quantitative balance-sheet method of determining a company’s financial health. A Z-score can be calculated for all non-financial companies and the lower the score, the greater the risk of the company falling into financial distress.
The original research was based on data from publicly held manufacturers (66 firms, half of which had filed for bankruptcy). Altman calculated 22 common financial ratios for all of them and then used multiple discriminant analysis to choose a small number of those ratios that could best distinguish between a bankrupt firm and a healthy one. To test the model, Altman then calculated the Z Scores for new groups of bankrupt and nonbankrupt but sick firms (i.e. with reported deficits) in order to discover how well the Z Score model could distinguish between sick firms and the terminally ill.
The results indicated that, if the Altman Z-Score is close to or below 3, it is wise to do some serious due diligence before considering investing. The Z-score results usually have the following quot;Zonesquot; of interpretation:
1. Z Score above 2.99 -“Safe” Zones. The company is considered ‘Safe’ based on the financial figures only.
2. 1.8 lt; Z lt; 2.99 -“Grey” Zones. There is a good chance of the company going bankrupt within the next 2 years of operations.
3. Z below 1.80 -“Distress” Zones. The score indicates a high probability of distress within this time period.
The Z-score has subsequently been re-estimated based on other datasets for private manufacturing companies, as well as non-manufacturing / service companies.
Does the Altman Z-Score Work?
In its initial test, the Altman Z-Score was found to be 72% accurate in predicting bankruptcy two years prior to the event. In subsequent tests over 31 years up until 1999, the model was found to be 80-90% accurate in predicting bankruptcy one year prior to the event.
In 2009, Morgan Stanley strategy analyst, Graham Secker, used the Z-score to rank a basket of European companies. He found that the companies with weaker balance sheets underperformed the market more than two thirds of the time. Morgan Stanley also found that a company with an Altman Z-score of less than 1 tended to underperform the wider market by more than 4%.
Calculation / Definition
For public companies, the z-score is calculated as follows: 1.2*T1 + 1.4*T2 + 3.3*T3 + 0.6*T4 + 1.0*T5.
1. T1 = Working Capital / Total Assets. This measures liquid assets as firm in trouble will usually experience shrinking liquidity.
2. T2 = Retained Earnings / Total Assets. This indicates the cumulative profitability of the firm, as shrinking profitability is a warning sign.
3. T3 = Earnings Before Interest and Taxes / Total Assets. This ratio shows how productive a company in generating earnings, relative to its size.
4. T4 = Market Value of Equity / Book Value of Total Liabilities. This offers a quick test of how far the company’s assets can decline before the firm becomes technically insolvent (i.e. its liabilities exceed its assets).
5. T5 = Sales/ Total Assets. Asset turnover is a measure of how effectively the firm uses its assets to generate sales.
The usefulness of the original Z score measure was limited by two of the first ratio is T4, the Market Value of Equity divided by Total Liabilities. Obviously, if a firm is not publicly traded, its equity has no market value. To deal with this, there is a revised Z score for private companies:
– Z1 = .717*T1 + .847*T2 + 3.107*T3 + .42*T4A + .998*T5 (in this case, T4 = Book Value of Equity / Total Liabilities).
The other ratio is Asset Turnover. This ratio varies significantly by industry but, because of the original sample, the Z Score expects a value that is common to manufacturing. To deal with this, there is a more general revised Z-score for non-manufacturing businesses:
– Z2 = 6.56*T1 + 3.26*T2 + 6.72*T3 + 1.05*T4A
NB: Both these revised measures have slightly different Zones of Interpretation.
Watch Out for
The Z Score is not intended to predict when a firm will actually file for legal bankruptcy. It is instead a measure of how closely a firm resembles other firms that have filed for bankruptcy, i.e. it tries to assess the likelihood of economic bankruptcy. The model has also drawn several statistical objections over the years. The model uses unadjusted accounting data; it uses data from relatively small firms; and it uses data that is around 60 years old. Nevertheless, despite these flaws, the original Z Score model is stil the most widely used measure of corporate financial distress.
From the Source
Altman’s original paper is reasonably heavy going and you might in any case be better off reading his follow-up paper published in 2000, entitled: Predicting the Financial Distress of Companies: Revisiting the Z-Score.
Other Resources on Altman Z-Score
FT article: New Study rewrites the A to Z of value investing
Wikipedia on Altman Z-Score
Moneyweek on Altman: How Z scores can help you beat the slump
Europesharelab: How the Z-Score can help your investment returns
Further Reading: A Few Links
- Criticism of Z Score
- Morningstar Methodology Paper, Mar. 26, 2008: Stock Grade Methodology
for Financial Health
- Morningstar, June 2012: Corporate Credit Rating Methodology
- Geoff Gannon Investor Questions Podcast #13: How Do You Find A Stock’s Z-Score?
- Geoff Gannon Investor Questions Podcast #10: How Do You Avoid Falling Into A Value Trap?
- Gannon and Hoang on Investing: My Investing Checklist
Disclosure: I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it. I have no business relationship with any company or individual mentioned in this article. I have no positions in any stocks mentioned.