To a retail investor like myself who rely mainly on a company's annual report for assessing whether it is investable, ensuring that the numbers in the report are true reflections of the company is extremely important.Other than believing in the regulatory bodies of the financial world, it is theoretically possible to identify dishonesty by analysing the numbers within the annual reports itself.
By cross referencing various financial ratios, and running these ratios through models designed to detect fraud, we would be able to screen for suspicious companies to be eliminated from our "Universe of Stocks" for analysis.
IMPLICATIONS
Earnings manipulation can mislead investors into investing in companies that are not as good as presented, or it could delay investors from exiting a declining company by smoothing earnings. The scary truth is that such unethical acts could actually be done legally by exploiting accounting loopholes.
Not only does fraudulent financial reporting causes huge financial losses to investors, it disrupts productive capital allocation, diminishes investor confidence in capital market, and increases the need for costly regulations to be imposed.
One of the most common earning manipulation is the use of Accruals to boost revenue. In an oversimplified example: a company may book an extraordinary order before the closing of its books with a customer who is in cahoot with its schemes. This booked order would boosts revenue and reported earnings.
But after the books are closed, the customer could either cancel the order or fail to pay up, causing the company to write-off the receivables. The same trick on a bigger magnitude could be pulled in the coming financial reporting period so that revenue is smoothed. Investors who are not trained to detect such dishonesty would pay a high price for the illusive earnings.
Eventually, the antics can never make up for the truly poor performance of the company and stock prices will suffer due to earnings decline or revelation of fraud.
EARNINGS MANIPULATION DETECTING MODELS
Through my readings, I have managed to identify 3 models that are designed to detect earning manipulation. They are Richard Sloan's Scaled Total Accruals (STA), Hirshleifer et al's Scaled Net Operating Assets (SNOA), and Benish's M-Score (PROBM Model).
STA identifies the current flow of accruals. The greater the scaled total accrual, the more likely earnings are being manipulated.
SNOA captures the growth of accruals over time, the higher the net operating assets, the lower the stock returns.
Both STA and SNOA are actually accrual based earning manipulation detectors. They utilize relatively simple formula that scales Accrual against total assets as a gauge for earnings manipulation.
However, the predictive ability of both STA and SNOA have diminished over the years, probably due its wide adoption and simplicity that resulted in the ease of companies side-stepping it. Hence, I shall not go in-depth for these 2 models.
WHAT IS BENISH M-SCORE?
Benish M-Score is a mathematical model that aims to determine if a company has manipulated its earnings in its annual report. The model uses financial ratios calculated using the company's financial statements to describe the degree to which the earnings have been manipulated. The M-Score is published by Professor Messod Beneish from Indiana University in the paper: The Predictable Cost of Earnings Manipulation.
The paper stipulated that firms with a high probability of overstated earnings have lower future earnings, less persistent income increasing accruals, and lower future returns. In order to identify such companies, the M-Score uses 8 variables and a mathematical formula to derive a score.
The 8 variables are as follows:
Financial Statement distortion
| DSRI = days' sales in receivables index (ratio of days' sales in receivables in year t to year t − 1) |
| AQI = asset quality index (ratio of noncurrent assets other than plant, property and equipment to total assets) |
| DEPI = depreciation index (ratio of the rate of depreciation in year t – 1 to the corresponding rate in year t) |
| TATA = total accruals to total assets (change in working capital accounts other than cash less depreciation) |
Predisposition to engage in earnings manipulation GMI = gross margin index ( ratio of gross margin in year t − 1 to gross margin in year t) |
| SGI = sales growth index (ratio of sales in year t to sales in year t − 1) |
| SGAI = sales, general and administrative expenses index (ratio of SGA expenses in year t relative to year t − 1) |
| LVGI = leverage index (ratio of total debt to total assets in year t relative to year t − 1) |
After obtaining the value of the 8 variables, they are substituted into the following formula:
| PROBM =
−4.84 + 0.92 × DSRI + 0.528 × GMI + 0.4.404 × AQI + 0.892 × SGI + 0.115 ×
DEPI −0.172 × SGAI + 4.679 × TATA − 0.327 × LVGI As a general guide, a PROBM of more than -1.78 indicates possibility of earnings manipulation. From the book Quantitative Value which I have introduced in this post, I found a breakdown of the range for individual variables to distinguish between manipulators and non-manipulators:
Although the model is not 100% accurate, and do mistakenly flags innocent companies as manipulators, it still has a 64% rate of identifying fraudulent companies. This rate is pretty decent and I would not take my chances, even if it means removing some companies that might actually be good. Better safe than sorry right? For those who are not willing to calculate the M-Score, you can actually subscribe to Gurufocus' screener where it does the heavy lifting for you. CONCLUSION Earnings Manipulation will affects my portfolio's overall performance and may result in irrecoverable losses. Besides relying on regulators to ensure that companies are honest in their reporting, I can use financial models developed by really smart people to screen away suspicious companies. By doing so, I would have a wider margin of safety and at the same time save my resources from being wasted on unethical companies. If a company scores more than -1.78 for its Benish M-Score, those companies would be eliminated from my "Universe of Stocks" that I analyse for my portfolio construction. Thank you for reading. If you have any comments or feed backs, please post them in the comment section below or drop me an email at ohhanwee@gmail.com.
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