{"id":718,"date":"2023-07-04T20:17:29","date_gmt":"2023-07-04T20:17:29","guid":{"rendered":"https:\/\/fieldsonginvestments.com\/?p=718"},"modified":"2025-09-07T15:41:57","modified_gmt":"2025-09-07T15:41:57","slug":"detecting-financial-fraud","status":"publish","type":"post","link":"https:\/\/fieldsonginvestments.com\/?p=718","title":{"rendered":"Detecting Financial Fraud: A Close Look at the Beneish M-Score"},"content":{"rendered":"
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Note: The M Score is used to identify companies that are engaged in manipulating their financials. I am using pseudonyms in this article and changing certain details to mask the identity of the companies in question.<\/span><\/p>\n

I. Getting Defrauded<\/h2>\n

Not too long ago, an investment research firm exposed a company I had invested in as a massive fraud. Most of this company\u2019s revenue apparently comes from some other companies that they had recently acquired, but it turned out to be practically impossible to verify that any of that revenue actually existed outside of the company\u2019s books.<\/p>\n

Based on their most recent financial statements, I gave this company a very high rating. I ranked it at 100 out of 100 for my investing group, the <\/span>Stock Evaluator<\/a>. And I put a lot of money behind it.<\/span><\/p>\n

This hurt. Not only did it hurt financially\u2014I lost over $80,000\u2014but it hurt my credibility as a quantitative investor. In the big picture, I could stomach the loss. But my investing philosophy is founded on the maxim that safer stocks are better investments than risky ones. And yet I put a large bet on a stock that this research firm would shortly and rightly label an easy-to-spot swindle with fictitious financials.<\/p>\n

One reason I was caught off-guard is that this company is listed on the NASDAQ as a US company. Listed stocks domiciled in the US are the safest stocks on earth simply because the SEC is better at going after fraudsters in the US stock market than any other government regulatory body.<\/p>\n

Stock market history, though, is full of frauds. It\u2019s in the very nature of the beast. We are taught that we should beware of companies engaging in manipulation of their financial statements. Yet every<\/em> company, by the very nature of financial reporting, has some leeway in constructing financial statements. There are hundreds of gray areas when reporting financials\u2014which is one reason I frequently say that there is absolutely nothing scientific about financial data. What am I doing when I rank stocks based on this discretionary data, then? Well, I\u2019m dealing in probabilities. If a stock\u2019s reported free cash flow is high, that doesn\u2019t mean that it\u2019s a winner: it means that it\u2019s more likely to be a winner than a stock whose reported free cash flow is extremely low.<\/p>\n

So what should have tipped me off that this company was a fraud? Is there something quant-based <\/em>that I should learn here? Is there a screening rule I can apply to my universes to prevent this from happening again?<\/p>\n

II. Messod D. Beneish and \u201cThe Detection of Earnings Manipulation\u201d<\/h2>\n

In 1999, a professor of accounting at Indiana University published \u201cThe Detection of Earnings Manipulation\u201d in Financial Analysts Journal<\/em>. (This was a thorough revision of a method he had first published in 1997.) Messod Beneish was not the first nor the last to attempt to use financial statement analysis to detect fraud. But his method has held up better than any other; in a 2020 follow-up paper, \u201cThe Cost of Fraud Prediction Errors<\/a>,\u201d he proved that his method had a lower ratio of false to true positives than any other method besides a machine-learning-based one introduced in 2020.<\/p>\n

Beneish arrived at his formula by examining 74 firms that manipulated earnings over a six-year period and comparing those to 2,332 similar firms that did not. He came up with eight factors whose higher values seemed to indicate a greater chance of manipulation, seven of which compare the latest fiscal year\u2019s figure to the previous one\u2019s. Out of those eight, only five were statistically significant (and one of those five, AQI, still had rather high p-values). Using probit regression, he came up with a formula that included all eight factors, but gave a negative coefficient to two of the statistically insignificant ones and a very low coefficient to the third.<\/p>\n

The eight factors are as follows:<\/p>\n

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  1. Days Sales in Receivables (DSRI).<\/strong> This compares the ratio of receivables to sales in the most recent fiscal year to the same ratio in the previous fiscal year. The ratio of receivables to sales is basically the percentage of sales for which cash has not yet been received. If that ratio is getting a lot bigger, it\u2019s a sign that the company may be overstating its sales.<\/li>\n
  2. Gross Margin (GMI).<\/strong> This compares the company\u2019s gross margin in the previous fiscal year to the company\u2019s present gross margin. If a company\u2019s gross margin is markedly deteriorating, that gives it a strong incentive to manipulate its financials.<\/li>\n
  3. Asset Quality (AQI)<\/strong>.<\/strong> This takes the company\u2019s non-current assets that are not in net plant, property, and equipment, divides that by its total assets, and compares that number to that of the previous fiscal year. If this number is going up, the company may be improperly deferring costs (or it may be engaging in substantial acquisitions; but Beneish notes that \u201csample manipulators undertake few acquisitions and those are primarily stock-for-stock exchanges accounted for using pooling of interests.\u201d) As he says, \u201cAn increase in asset realization risk indicates an increased propensity to capitalize and thus defer costs.\u201d<\/li>\n
  4. Sales Growth (SGI).<\/strong> This is a simple measure of sales growth from the most recent fiscal year to the previous one. High-growth firms are more likely to engage in financial statement fraud.<\/li>\n
  5. Depreciation (DEPI).<\/strong> Beneish measured depreciation by comparing it to the sum of depreciation and net plant. If this ratio is going down, that\u2019s a sign that the company has been revising upwards the estimates of the useful lives of assets, thus increasing the company\u2019s net income through financial manipulation.<\/li>\n
  6. Sales, General, and Administrative Expenses (SGAI).<\/strong> This is the ratio of SG&A to sales. In his original paper, Beneish is rather unclear about why he feels that if this ratio is increasing<\/em>, it\u2019s a sign of financial manipulation. The only justification he gives is that \u201canalysts would interpret a disproportionate increase in sales<\/em> as a negative signal about firms future prospects. I expect a positive relation between SGAI and the probability of manipulation.\u201d There\u2019s a contradiction between those two sentences. Later writers have suggested that the factor is justified by the idea that if administrative and marketing efficiency decreases, that could motivate managers to manipulate earnings; similarly, if managers are being paid a lot more, that\u2019s another good signal of financial misdeeds.<\/li>\n
  7. Leverage (LVGI).<\/strong> This compares the ratio of total debt to total assets between the most recent two fiscal years. If debt is rising, there\u2019s more incentive for earnings manipulation, Beneish reasoned.<\/li>\n
  8. Total Accruals to Total Assets (TATA).<\/strong> This ratio does not <\/em>compare the most recent two fiscal years, but simply takes accruals and divides by total assets. Beneish at first measured accruals using Richard Sloan\u2019s balance-sheet approach, but later<\/a> (in 2013) switched to a more widely accepted method based on the cash flow statement, which is simply net income less cash from operations.<\/li>\n<\/ol>\n

    The calculation of each of these factors is relatively easy. The first seven are simple quotients: if the compared figures are the same from one year to the other, the result is 1; for most of them you take the most recent fiscal year\u2019s number and divide it by the previous year\u2019s and for GMI and DEPI you do the reverse. Accruals are easily calculated as well. Each of the factors are then Winsorized by the 99th and 1st percentile. (For those unfamiliar with Winsorization, that means that any factor above the 99th percentile is given that value rather than its true value; it helps avoid outliers.) Lastly, if elements of AQI, DEPI, or SGAI are N\/A, the value is set to 1.<\/p>\n

    The final M-score (M stands for manipulation<\/em>) is then calculated using the following formula, based on an unweighted probit regression:<\/p>\n

    \u20134.84 + 0.92*DSR + 0.528*GMI + 0.404*AQI + 0.892*SGI + 0.115*DEPI \u2013 0.172*SGAI \u2013 0.372*LVGI + 4.679*TATA.<\/p>\n

    In his original paper, Beneish admitted that the three factors with low and negative coefficients (DEPI, SGAI, and LVGI) were not proven to be of value. They all have relatively high p-values. While he gave the above probit regression numbers, he did not propose that the M-score be calculated using them. Nonetheless, that is the way it has been calculated ever since, and Beneish has since touted this formula in both his 2013 and 2020 follow-up papers.<\/p>\n

    The threshold for whether a company is likely to be a manipulator is an M-score of \u20131.78 or higher, though he allowed plenty of leeway here. If a company\u2019s M-score is below \u20132 it is unlikely to be manipulating its financials.<\/p>\n

    There are a lot of online versions of the Beneish M-score, and many of them take slightly different approaches. Portfolio123<\/a> uses trailing-twelve-month or, for balance-sheet items, quarterly figures rather than annual ones, and does not Winsorize. YCharts<\/a> uses annual numbers and does not Winsorize; for AQI they deduct not only noncurrent assets and PPE, but also long-term investments, and for accruals they use operating income rather than net income. There\u2019s an excellent online calculator<\/a> from Indiana University\u2019s Kelley School of Business, where Beneish teaches, which breaks down any company\u2019s M-score by factor and gives probabilities and odds. It\u2019s faithful to the original calculation, using annual figures and Winsorizing. Other calculations can be found from GuruFocus, FinBox, MacroAxis, and so on. Seeking Alpha does not offer the M-score\u2014yet.<\/p>\n

    III. Digging into the M-score<\/h2>\n

    I\u2019m going to look a little more closely at how the M-score functions in practicality, examining each of its factors.<\/p>\n

    I recently looked at the M-scores and the eight factors for about forty companies that have received damning reports from an investment research firm that specializes in accounting irregularities, undisclosed related-party transactions, and illegal financial reporting practices. Not all of those companies engaged in financial manipulation, but a majority of them seem to have. The conclusions I arrive at below are informed by my examination of those reports.<\/p>\n

    As concrete examples, I\u2019m going to be looking at five companies. The first four manipulated their financial statements, while the last doesn\u2019t seem suspect in the least. I\u2019m identifying all of them besides Enron using pseudonyms and changing almost all of the details about them, including dates; but the financials I\u2019m giving are not <\/em>fictional. The companies are:<\/p>\n