The material contained herein is for informational purposes only, does not constitute tax advice, and should not be used as a substitute for consultation with professional accounting, tax, legal or other professional advisers.
Financial statement analysis has always been regarded as a critical component for a variety of financial activities such as fund raising, transaction & valuation, performance reporting and litigation. Traditionally it required a lot of patience, with stakeholders having to carefully read hundreds of pages of annual reports for each company under consideration.
The emergence of databases such as the S&P Capital IQ and Market Intelligence platforms has greatly eased the needs of financial data users having to spend their own time and energy to collect a large number of data points that they need to inform the decision-making processes. The rapid development in the data technology field has not only fueled deal flows, but also increased the dependence of financial users on the quality of the data provided.
Since data is considered to be the new oil in today’s financial world, a question that we get from our clients is how do you look out for potential financial or accounting anomalies?
Fraudulent reporting is nothing new. In the early 2000s, the Enron scandal caused by accounting and corporate fraud resulted in the company’s shareholders losing $74 billion in the four years leading up to its bankruptcy, and its employees also losing billions of dollars in pension benefits. In responses to such corporate fraud, the U.S. Congress passed Sarbanes-Oxley Act on July 30, 2002 to help protect investors from fraudulent financial reporting by corporations.
However, detecting fraud in accounting data remains one of the long-standing challenges for auditors, fraud investigators, regulators and investors. Most fraud-detecting techniques used today are derived from known fraud schemes, and there is no panacea that can completely prevent accounting fraud. Luckin Coffee and Wirecard being two examples facing new accounting fraud allegations in recent years.
Is there any method or any way to help stakeholders discover accounting abnormalities and basic warning signs in the financial statements which may point to hidden risks?
A systematic way to analyse potential financial fraud is to use the Beneish model, a model created by Professor M. Daniel Beneish of the Kelley School of Business at Indiana University in the 1999 paper “The detection of earning manipulation”. The Beneish model uses financial ratios and eight variables to create a score (M-score), which is then used to determine whether a company has manipulated its earnings, and is often used as a tool to detect financial fraud. Due to the collection of data points, the calculation of Beneish model used to require a lot of manual work, but now it can be done through the S&P Capital IQ Excel plug-In which comes with a number of customizable templates (including the Beneish model), or through various other delivery mechanisms (such as application programming interface (API), XpressfeedTM and third party data warehouses, such as Snowflake), which can shorten the process to a few seconds.
Famously, a group of Cornell University business students used the Beneish model to predict that Enron was manipulating their earnings.4 In hindsight, the Beneish model also seems to have some predictive power for the recent Kraft Heinz case, which announced a $15 billion impairment charge on 21 Feb of 2019. According to Duff & Phelps, this is the largest write-down in the U.S. consumer staples industry in at least a decade . The company’s share price plummeted by a third immediately following the announcement.
As shown in the figure below, in the three years of 2013, 2015 and 2017, Kraft Heinz’s M-score calculated with the data obtained from S&P Capital IQ repeatedly exceeded the manipulator score of -2.22, indicating that the company had a high risk of being a manipulator. Tight cash flow and ever-increasing debt burden both led to a soaring score. For comparison, the M-score of Kraft Heinz’s rival General Mills was below the baseline throughout the years.
Figure 1: Kraft Heinz’s M-score through 2010-2019
A quick overview of the calculated M-score of S&P 500 constituent companies can be visualized in the dashboard below. Interact with our dashboard by applying the filters to explore sectorial impact across different fiscal years.
Source: S&P Global Market Intelligence data as of 8 Oct. 2020. Companies are for illustrative purposes only
 Segal, T. (2020, September 22). Enron Scandal: The Fall of a Wall Street Darling. Retrieved October 13, 2020, from https://www.investopedia.com/updates/enron-scandal-summary/
 Kenton, W. (2020, August 29). Sarbanes-Oxley (SOX) Act of 2002 Definition. Retrieved October 13, 2020, from http://www.investopedia.com/terms/s/sarbanesoxleyact.asp
 Kenton, W. (2020, August 29). What Is the Beneish Model? Retrieved October 13, 2020, from https://www.investopedia.com/terms/b/beneishmodel.asp
 Shumsky, T. (2019, February 22). Kraft Heinz's Goodwill Charge Tops Consumer-Staples Record. Retrieved October 13, 2020, from http://www.wsj.com/articles/kraft-heinzs-goodwill-charge-tops-consumer-staples-record-11550874959
 Kraft Heinz: Playing ketchup. (2019, February 22). Retrieved October 13, 2020, from http://www.ft.com/content/85fdff6c-3649-11e9-bd3a-8b2a211d90d5
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