Accounting Quality + Risk Matrix Now Available from Audit Analytics
Audit Analytics is pleased to announce the release of the Accounting Quality + Risk Matrix (AQRM), now available on our company profile pages.
AQRM is an interactive tool designed to quickly identify and understand qualitative and contextual metrics of governance and reporting quality. Red flags and events highlighted in the risk matrix are used for screening, idea generation, portfolio monitoring, and risk management for every SEC registrant.
- Perform risk analysis using specialized, difiicult-to-find data.
- Gain insight into qualitative and contextual SEC disclosures.
- Easily identify indicators of potential earnings management and other accounting quality issues.
- Quickly understand potential governance and control risks.
- Drill down to assess detailed data and disclosure sources surrounding an issue.
- Track and benchmark many of the same metrics used by auditors and regulators.
Clients can easily access the AQRM Tool on the Company Profile Page by clicking on the AQRM Matrix button in the upper right hand corner of the company profile.
AQRM allows you to quickly review the history of a company's reportable events, including proprietary datasets such as Financial Restatements, Out of Period Adjustments, Changes in Accounting Estimates, and more. Access in-depth information and analysis for these events with a single click.
Example of AQRM results for a particular company
A financial restatement is perhaps the most immediate and significant red flag in regards to a company's accounting quality. In addition to calling into question the reliability of financial statements, a restatement can also give rise to significant suspicions about the company's management and overall health. The negatives associated with a restatement are numerous: it calls into question the accounting quality of the company; it raises doubts about the company's management and control structure; it exposes the company to shareholder litigation, regulatory action, and higher costs of capital. It should come as no surprise, then, that financial restatements have negative implications for stock performance.
Our restatement database is the leading source for academic research into this issue. AQRM provides the investor with access to the same data-rich, expert-level analysis of individual restatements.
Out of Period Adjustments
Out of Period Adjustments are accounting entries that affect the current period. That is, they are non-recurring adjustments. These adjustments correct immaterial errors from prior periods. Since the errors are immaterial, restatements and amended filings are not required. Instead, the change can be made in the current period on a prospective basis.
Changes in Accounting Estimates
A change in estimate is made at the discretion of management, and affects the operating results of the period in which the change occurs. This makes it an information-rich disclosure, specifically with respect to the quality of earnings. Estimates are often material to the financial statements, and are notoriously difficult to audit. Changes in accounting estimates could be a relatively easy source of earnings management. Unusual or opaque changes in accounting estimates should capture the attention of users of financial statements.
Deviation from Benford's Law
A little-known mathematical formula has proved to be an intriguing and useful tool for identifying accounting irregularities. Known as Benford's Law, it states that in many kinds of numerical data, such as the lengths of rivers or the populations of counties, the likelihood that any given number taken from the population begins with a given digit ("1", "2", etc.) occurs in a constant, predictable pattern. Numbers beginning with "1" occur about 30% of the time, with "2" about 18% of the time, all the way down to numbers beginning with "9", which occur at a rate of about 5%. Based on this law, a simple analysis of the first digit of each number in a data set has helped uncover fraud and other data problems in a number of instances, including accounting, scientific, and legal cases.
A number of academic studies have provided ample evidence that accounting numbers also follow the distribution predicted by Benford's Law — that is, unless the numbers are being manipulated or "fudged". As one might expect, manually adjusted or random numbers do not conform to Benford's, and this principle extends to accounting irregularities. Our calculations to measure the deviation from Benford's Law follow the methodology developed by Amiram, Bozanic, and Rouen (2014), with a few minor adjustments. This paper finds significant evidence of a correlation between abnormal Benford distributions of financial statements and the likelihood of restatements, fraud investigations, and other negative outcomes for a business.
Accounting Disclosure Complexity
This is a metric of a company's accounting disclosure complexity based on the nature and characteristics of the company's XBRL filing disclosures. The basic idea behind this flag is simple: the more complex a company's accounting, the more likely it is to have accounting-related distress, whether due to misstatement, fraud, or uncertainty. Further, since accounting necessarily reflects the underlying business, it stands to reason that a company with unusually complex accounting is either in an unusually complex (and therefore potentially risky) industry, or that the company is perhaps purposefully introducing opacity into its financial disclosures.
XBRL contains a large glossary of standardized tags, and most company filings use these standard tags. But sometimes a company may not think that a standard tag properly describes one of the company's particular financial numbers. In cases like this, the company may create what is called an "extension", which is an ad-hoc tag designed to describe the number in question more precisely. It turns out that a metric based on the ratio of unique "extensions" to standardized "tags" used by a company in a given filing is a very accurate measurement of the complexity of a company's accounting disclosure.
Our Accounting Disclosure Complexity measurement follows the methodology developed by Hoitash and Hoitash in "Measuring Accounting Complexity with XBRL"(2015), with a few adjustments. This recent paper finds that complexity measured in this way is correlated with many adverse outcomes, including financial restatements and internal control issues. Such firms also tend to have lower earnings quality (as measured by discretionary accruals) and higher audit fees, which can be associated with an auditor's perception of higher risk.
The Beneish M-Score is a mathematical formula that is designed to detect earnings manipulation. It was developed by Messod Beneish, a professor of accounting at Indiana University, in the late 1990s, and gained notoriety when a number of MBA students at Cornell used the metric to highlight Enron as a major risk - convincing one of the school's funds to close its position in the energy giant a year before its bankruptcy.