Financial Statement Analysis Blog #9 – earnings quality per Beineish Score
“Fraud in business is no different from infidelity in marriage or plagiarism in scholarly work. Even people committed to high moral standards succumb.”
When looking at direct investment opportunities, investors should look for companies with high earnings quality – as evidenced by earnings that are sustainable, represent returns equal to or in excess of the companies’ costs of capital, and driven by recurring operational activities. High earnings quality also implies that the financial reporting quality is high. This principle also applies to revenue/profit-driven acquisitions.
Two commonly used tools that financiers usually used in assessing earnings quality are Beneish Model and Piotroski Score. We will cover Beneish Model in this blog post and Piotroski score in the next blog post. Beneish Model is intended to identify quantitative indicators of earnings manipulation and to assess the likelihood of misreporting (Beneish 1999; Beneish, Lee, and Nichols 2013). The model calculates the probability of manipulation (M-score) using a number of weighted variables as follow:
M-score = -4.84 + 0.920 DSR + 0.528 GMI + 0.404 AQI + 0.892 SGI + 0.115 DEPI - 0.172 SGAI + 4.670 Accruals – 0.327 LEVI
With ‘?’ explaining the reason for the variable inclusion, each of the variable is explained below:
M-score = score indicating earnings manipulation probability
DSR = Days Sales Receivables index
= this period (receivables / sales) / last period (receivables / sales)
? changes in receivables-sales relations indicating improper revenue recognition
GMI = Gross Margin Index
= last period gross margin / this period gross margin
? deterioration in margins could predispose companies to manipulate earnings
AQI = Asset Quality Index
= this period [1-(PPE+CA)/TA] / last period [1-(PPE+CA)/TA]
Where PPE = property, plant, equipment; CA = current assets; TA = total assets
? change in the percentage of assets other than PPE and CA could signal excessive expenditure capitalization
SGI = Sales Growth index
= this period sales / last period sales
? producing the illusion of continuing growth and capital needs could predispose companies to manipulate sales and earnings
DEPI = Depreciation Index
= last period depreciation rate / this period depreciation rate
Where depreciation rate = Depreciation / (Depreciation + PPE)
? declining depreciation rates could indicate understated depreciation as a means of manipulating earnings
SGAI = Sales, General, and Administration expenses Index
= this period (SGA / Sales) / last period (SGA / Sales)
? An increase in fixed SGA expenses suggests decreasing administrative and marketing efficiency that could predispose companies to manipulate earnings
Accruals = (Income before extraordinary items – Cash from operations) / Total assets
? higher accruals can indicate earnings manipulation
LEVI = Leverage Index
= this period Leverage / last period Leverage
Where Leverage = ratio of debt to assets
? Increasing leverage could predispose companies to manipulate earnings
Interpreting the M-score requires understanding a bit of statistics. To simplify the explanation, we can use -1.78 as a cutoff. An M-score of -1.78 means that there is a 2.9% probability of earnings manipulation. Higher M-scores (i.e. less negative numbers) indicate an even higher probability of earnings manipulation.
With our example company Caterpillar (CAT), the Beneish model calculation is calculated as follow:
The M-score is calculated as:
The Beneish score is -2.8, corresponding to a probability of earnings manipulation of 0.25%. Since the score is less than -1.78 (the cut off for Beneish model), I don't have any concerns over CAT's earnings quality.
The Excel file that calculates the above figures and drives the conclusion is embedded.