"Benvenuti in Italia" Profs. Altman and Beneish

What is the best way to resurrect a semi-dying financial blog? My answer is "by beginning a process of classification of Italian stocks based on two important accounting based detective models: Altman’s Z-Score and Beneish M-Score".

HOW WE WILL PROCEED

Every end of week:

-        we’ll calculate the scores for the two company whose stock have appreciated/depreciated more looking at a 12 months rolling window (performance in EUR);

-        we'll assign a precise title for each post: [date] + [ticker] + [name] + [1 yr. perf. {of the highest performing stock}] & [ticker] + [name] + [1 yr. perf. {of the lowest performing stock}] (for example see “191101_[EXSY] [Expert System S.p.A.] [+138%] and [ON] [Bio On Spa] [-80%]_Altman&Beneish”);

-        we'll take the numbers from the published annual reports, showing how we arrive at those numbers, providing all the dates of the financial statement of reference and the performance from the date of approval of the financial statements vs the FTSEMIB Index.

WHY

This exercise should be useful in many ways:

-        we will gradually build a library of facts: considering that we have only 317 names in Italy with full financials available and a at least 1 year of stock returns, if we can keep this promise of 2 stocks a week, at the end of year 1 we’ll already have 104 names and in only 3 year we’ll have built a full database of Italian stocks

-        thanks to this due-diligence exercise (checking out numbers directly from annual reports) our knowledge of the Italian market should accumulate gradually;

-        calculation of those scores can help to actively contribute detecting corporate malfunction and therefore avoiding permanent loss investing in the wrong stocks.

THE 1^st SCORE: “ALTMAN Z-SCORE”

The Z-Score is a model used to predict whether a company is in financial distress. First coined out in 1968 by Edward I. Altman, a professor at the Stern School of Business at New York University, it is a quantitative model used to distinguish between surviving and failing companies based on information gathered from published financial statements.

Z = (1.2X1) + (1.4X2) + (3.3X3) + (0.6X4) + (1.0X5)

where

X1 = Working Capital/Total Assets

X2 = Retained Earnings/Total Assets

X3 = EBIT/Total Assets

X4 = Equity at Market/Total Debt

X5 = Net Sales/Total Assets

Variable Name	Description	Rationale
X1	Working Capital/Total Assets	The working capital/total assets ratio, frequently found in studies of corporate problems, is a measure of the net liquid assets of the firm relative to the total capitalization. Working capital is defined as the difference between current assets and current liabilities. Liquidity and size characteristics are explicitly considered. Ordinarily, a firm experiencing consistent operating losses will have shrinking current assets in relation to total assets.
X2	Retained Earnings/Total Assets	Retained earnings is the account which reports the total amount of reinvested earnings and/or losses of a firm over its entire life. The age of a firm is implicitly considered in this ratio. For example, a relatively young firm will probably show a low RE/TA ratio because it has not had time to build up its cumulative profits. Therefore, it may be argued that the young firm is somewhat discriminated against in this analysis, and its chance of being classified as bankrupt is relatively higher than that of another older firm, ceteris paribus. But, this is precisely the situation in the real world. The incidence of failure is much higher in a firm’s earlier years. In addition, the RE/TA ratio measures the leverage of a firm. Those firms with high RE, relative to TA, have financed their assets through retention of profits and have not utilized as much debt.
X3	Earnings Before Interest and Taxes/Total Assets	This ratio is a measure of the true productivity of the firm’s assets, independent of any tax or leverage factors. Since a firm’s ultimate existence   is based on the earning power of its assets, this ratio appears to be particularly appropriate for studies dealing with corporate failure. Furthermore, insolvency in a bankrupt sense occurs when the total liabilities exceed a fair valuation of the firm’s assets with value determined by the earning power of the assets.
X5	Sales/Total Assets	The capital-turnover ratio is a standard financial ratio illustrating the sales generating ability of the firm’s assets. It is one measure of management’s capacity in dealing with competitive conditions.

Meaning of the cut-off points:

Distress Zones <--	Z < 1.81
Grey Zones <--	1.81 <Z < 2.67
Non-distress Zones <--	Z > 2.67

THE 2^nd SCORE: “BENEISH M-SCORE”

The M (“manipulation”)-Score is a so-called fraud prediction model: it uses eight financial ratios taken from the company accounts and apply statistical techniques in order to discriminate between companies that have ((high probability to have) manipulated their financial statements. Created by Professor Messod Beneish, it became famous after students at Cornell University used it to name Enron Company as earnings manipulators. In many ways it is similar to the Altman Z-Score, but it is focused on detecting earnings manipulation rather than bankruptcy. M-score model is a probability model, and as such cannot detect 100% manipulation. It is important to note that financial institutions were excluded from the sample in Beneish’s paper when calculating M-Score.

M = -4.84 +0.92*DSRI +0.528*GMI +0.404*AQI +0.892*SGI +0.115*DEPI -0.172*SGAI +4.679*TATA -0.327*LEVI

where

Variable Name	Description	Rationale
DSRI = Days Sales in Receivables Index	(Receiv. t/Sales t) / (Receiv. t-1/Sales t-1)	When DSRI > 1 examine the situation. This variable gauge whether receivables and revenues are in or out-of-balance in two consecutive years. A large increase in days sales in receivables could be the result of a change in credit policy to spur sales in the face of increased competition, but disproportionate increases in receivables relative to sales may also be suggestive of revenue inflation.
GMI = Gross Margin Index	(Gross Margin t-1) / (Gross Margin t)	When GMI > 1 it indicates that gross margins have deteriorated (declining operational efficiency). Deteriorating margins predispose firms to manipulate earnings.
AQI = Asset Quality Index	[(TA t -CA t -PPE t)/TA t] / [(TA t-1 -Cur.A. t-1 -PPE t-1)/TA t-1] where PPE is property plant and equipment net, CA are Current Assets and TA are Total Assets	All non-current assets other than PPE as a percent of total assets in t divided by the same ratio in t-1. When AQI > 1 it indicates that the firm has potentially increased its involvement in cost deferral or excessive expenditure capitalization and deferred costs.
SGI = Sales Growth Index	(Sales t) / (Sales t-1)	Managing the perception of continuing growth and capital needs predispose growth firms to manipulate sales and earnings. Growth does not imply manipulation, but growth firms are viewed by professionals as more likely to commit financial statement fraud because their financial position and capital needs put pressure on managers to achieve earnings targets.
DEPI = Depreciation Index	Depr.R. t-1 / Depr.Rate t where Depr.R.=depreciation rate, equals Depreciation / (Depreciation+PPE])	When > 1 it indicates that the rate at which assets are depreciated has slowed down, raising the possibility that the firm has revised upwards the estimates of assets useful lives. Captures declining depreciation rates as a form of earnings manipulation.
SGAI = Sales General and Administrative Expenses Index	(SGA t / Sales t) / (SGA t-1/Sales t-1)	Decreasing administrative and marketing efficiency (larger fixed SGA expenses) predisposes firms to manipulate earnings
TATA = Total Accruals to Total Assets	(Income Before Extraordinary Items t- Cash from Operations t)/ Total Assets t	When TATA > 1 indicates that a growing percentage of the entity’s working capital is comprised of non cash items. Examine the situation. Capture where accounting profits are not supported by cash profits. (…) expect higher positive accruals (less cash) to be associated with a higher likelihood of earnings manipulation.
LVGI = Leverage Index	Leverage t / Leverage t-1 where Leverage is calculated as debt to assets	Increasing leverage tightens debt constraints and predisposes firms to manipulate earnings

LEGENDA:

“Receiv./Sales” = Accounts Receivable to Sales Ratio --> is a business liquidity ratio that measures how much of the company’s sales occur on credit.

When a company has a larger percentage of its sales happening on a credit basis, it may run into short-term liquidity problems. Such a scenario may happen if a company is running low on cash due to a lack of cash sales in the business cycle. It refers to sales that have occurred on credit, meaning that the company has not yet collected the cash proceeds from these sales. Found in the “current assets” section of the balance sheet. Sales refers to all sales that the company has realized over the given accounting period, including sales on credit and cash sales. Found on the income statement.

“Gross Margin” = gross profit margin ratio --> is a profitability ratio. It shows how much profit a company makes after paying off its Cost of Goods Sold (COGS). Gross Margin Ratio = (Revenue – COGS) / Revenue

Meaning of the cut-off points:

Financial statements may have been manipulated <--	M > -2.22
Financial statements not manipulated <--	M < -2.22

CONCLUSIONS

I like using these kind of accounting-based fundamental analysis methodologies (another great example is [Piotroski, 2000]) because they force me to purely follow the numbers, step by step, limiting the interference that market driven price actions (stimulating "behavioral economics instincts") could have on our rational decision making process.

I hope this exercise, surely useful for me, could be useful for other people too.

Sources:

-         Altman, E.I. (2000). Predicting Financial Distress Of Companies-Revisiting The Z-Score And Zeta Models.

-         Beneish, M.D. (1999). The Detection of Earnings Manipulation.

-         Beneish M.D., Lee C.M.C., Nichols D.C. (2012). Fraud Detection and Expected Returns.

-         MacCarthy, J. (2017). Using Altman Z-score and Beneish M-score Models to Detect Financial Fraud and Corporate Failure: A Case Study of Enron Corporation. International Journal of Finance and Accounting.

-         https://corporatefinanceinstitute.com/