Composite Factors

Beneish M-Score

The M-Score was created in June 1999 by Messod D. Beneish, professor at the Indiana University. It provides a quick and easy way to detect companies that are likely to have manipulated their reported earnings. In his most recent paper, he demonstrates that his model correctly identified, in advance of public disclosure, a large majority (71%) of the most famous accounting fraud cases that surfaced subsequent to the model's estimation period. The model attained widespread recognition after a group of MBA students posted the earliest warning about Enron's accounting manipulation using the Beneish model a full year before the first analist reports.

While very few companies get indicted for accounting fraud, the M-Score helps predict a firms future prospects. A typical earnings manipulator as defined by Beneish is a firm that (1) is growing extremely fast (extremely high year-over-year sales), (2) is experiencing deteriorating fundamentals (as evidenced by a decline in asset quality, eroding profit margins, and increasing leverage) and (3) is adopting aggressive accounting practices (receivables growing much faster than sales; large income-inflating accruals; decreasing depreciation expense). These companies are particulary risky to invest in as they're very likely to be overpriced (because of their high recent growth trajectory) and they exhibit a number of problematic characteristics (either lower earnings quality or more challenging economic conditions). These companies are more likely to disappoint investors in the future.

"To the extent that the pricing implications of these accounting-based indicators are not fully transparent to investors, firms that “look like” past earnings manipulators will also earn lower future returns."

Beneish initially described his M-Score as a detector companies that manipulate earnings. (click here for more info). In his more recent work, he reveals that the M-Score is also an excellent predictor of future stock returns. He summarized his main findings as follows:

  1. The firms with a higher probability of manipulation (M-Score) earn lower returns in every decile portfolio sorted by Size, Book-to-Market, Momentum, Accruals, and Short-Interest.
  2. The predictive power of M-Score is related to its ability to forecast the persistence of current-year accruals. High M-Score firms have income-increasing accruals that are much more likely to disappear next year and income-decreasing accruals that are more likely to persist.
  3. The predictive power of the M-Score is most pronounced for low-accrual (ostensibly high quality-earnings) companies.
  4. The variables that relate to the predisposition to commit fraud (higher sales growth, change in assets quality and increase in leverage) , rather than the variables associated with the level of aggressive accounting, are the primary drivers of the incremental power of the model.
  5. Abnormal returns are witnessed in the three-day windows centered on the next 4 earnings announcements.

How is it calculated?

The M-Score is based on 8 variables, of which some are designed to capture the effects of manipulation while others show preconditions that may prompt firms to engage in such activity. While Beneish takes data from the fiscal years, we use the last trailing twelve month (TTM) numbers available as current year, year t. For year t-1 we take the TTM results for the 12-month period before year t.

  1. Days Sales in Receivables Index (DSRI): The ratio in days sales in receivables during the last year (t) compared to the year before (t-1). A disproportionate increase in receivables relative to sales may be suggestive of revenue inflation.
  2. Gross Margin Index (GMI): GM in year t-1 / year t. If A value greater than 1 indicates that margins have deteriorated and this signals a negative signal about firms' prospects.
  3. Asset Quality Index (AQI): Asset Quality in year t / year t-1. Asset Quality is the ratio of non-current assets other than plan, property and equipment as a proportion of total assets. An AQI greater than 1 indicates that a firm has potentially increased its involvement in cost deferral.
  4. Sales Growth Index (SGI): Sales in year t / year t-1. Growth does not imply manipulation, but growth firms are more likely to commit fraud because their financial position and capital needs put pressure on managers to achieve earnings targets. In addition, controls and reporting tend to lag behind operations in periods of high growth. Any perception of decelerating growth can have a significant impact on the value of the stock and be very costly to management. A value greater than 1 increases the probability of earnings manipulation.
  5. Depreciation Index (DEPI): The rate of depreciation in year t-1 / year t. The rate of depreciation is equal to depreciation / (depreciation + net property, plant & equipment). If this value is greater than 1 this means that the rate at which assets are depreciated has slowed down. Either management revised upwards the estimates of assets usefull lives or adopted a new method that is income increasing.
  6. Sales General and Administrative Expenses Index (SGAI): The ratio of SGA to sales in year t / year t-1. Analists would interpret a disproportionate increase in sales as a negative signal about firms future prospects. Beneish expects a positive relation between SGAI and the probability of manipulation.
  7. Leverage Index (LVGI): The ratio of total debt to total assets in year t relative to year t-1. A value greater than 1 indicates an increase in leverage.
  8. Total Accruals to Total Assets (TATA): Total accruals is calculated as the change in working capital accounts other than cash less depreciation. This ratio proxies the extent to which cash underlies reported earnings. Higher positive accruals (less cash) indicates a higher likelihood of earnings manipulation.

The calculation is as follows: M-Score = -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 * lvgi

A score greater than -1.78 indicates a strong likelyhood of earnings manipulation.

M-score as secondary ratio

Our members typically use the Beneish score to filter the results of other screens. They typically use it when scanning new markets for value stocks, since they're not that familiar with the companies. Since a share of the companies discovered of manipulating earnings will eventually see their stocks plummet in value, it provides an extra security to filter these potential manipulators out of the screener.

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