How to detect earnings manipulation via Quant Investing
“After all, you only find out who is swimming naked when the tide goes out.” – Warren Buffett
There a basically two main reasons why companies swim naked and that may lead to you losing your total investment:
- Companies that are cooking the books -> financial statements manipulation and fraud
In most cases these risks are often found together, companies that cook their books a lot of the time also go bankrupt.
Management’s desire to put a positive spin on financial results has been around as long as corporations and investors themselves. And dishonest companies have long used tricks to prey on unsuspecting investors, and it is unlikely that they will ever stop doing it.
They are simply bad investments
It is an understatement to say that investing in companies subject to financial statement manipulation or fraud are not the best investments you can make.
But how can you detect these companies early so that you can remove them from your portfolio or investment watch-list?
Here are a few ideas.
There is a way to find financial shenanigans
The Beneish M-Score
Messod Beneish, an accounting professor at Indiana University’s Kelley School of Business, outlined a quantitative approach to detecting financial statement manipulation in his 1999 paper “The Detection of Earnings Manipulation.”
He based his model on forensic accounting principles, calling it the “probability of manipulation”, “PROBM” model or the Beneish M-Score.
This is how he built the model.
First Prof Beneish collected a sample of known earnings manipulating companies. Then he identified their main characteristics and used those characteristics to create a model for detecting manipulation.
The M-Score model includes variables that are designed to capture either the effects of manipulation or pre-conditions that may incentivise management to start manipulating results.
Predicts future manipulators
The Beneish M-Score can also predict future financial statement manipulators. In tests the M-Score identified approximately half of the companies involved in earnings manipulation before they were discovered.
The M-Score also correctly identified, ahead of time, 12 of the 17 highest-profile fraud cases in the period 1998 to 2002.
The M-Score can help your returns
In a back test the Beneish M-Score also consistently improved stock returns from 1993 to 2007.
During this 15-year period stocks that were identified as potential earnings manipulators by the M-Score returned 9.7% less than stocks that were not identified.
Interestingly, students from Cornell University using the Beneish M-Score correctly identified Enron as an earnings manipulator, while experienced financial analysts failed to do so.
Where to find it in the screener?
You can easily use the Beneish M-Score when looking for ideas in the screener.
Simply select M-Score (Beneish) as one of the output columns of your screen. You can then use the filter function (click the funnel icon) to screen out companies with a bad Beneish M-Score.
Source: Quant investing screener
These companies may go bankrupt
The Altman Z-Score formula for predicting bankruptcy was published in 1968 by Edward I. Altman, who was, at the time, an Assistant Professor of Finance at New York University.
The formula can be used to predict the probability that a firm will go into bankruptcy within the next two years.
The Z-score uses a few income statement and balance sheet ratios to measure the financial health of a company. You can find more information on how the Altman Z-Score is calculated in the Glossary under the heading Z-Score.
How the Z-Score is interpreted
The Z-Score values in the screener should be interpreted as follows:
Z-score of greater than 2.99 = Safe
Z-score between 1.8 and 2.99 = Middle or grey
Z-score smaller than 1.80 = Distress
The z-Score values in the screener are colour coded to help you to easily interpret its value.
The Altman Z-Score in the screener
You can easily use the Altman Z-Score in any of your favourite screens.
Simply select Altman Z-Score as one of the output columns of your screen. You can then use the filter function (click the funnel icon) to screen out companies with a bad Z-Score of less than 1.80.
You can see in the screenshot below that the z-Score values are colour coded so you can easily see what is good (green), middle is (black) and distress (Red).