If you are a Reputation Practitioner, M&A or Advisory, Supply Chain Director, Investor Relations, Insurer, Risk or Compliance Manager, Asset Owner or Asset Manager - take note. Following demand for greater transparency in emerging markets, on the 1st July 2014, new forensic Accounting and Governance Ratings (AGR) will be released across 10,000 listed companies. These ratings, by US intelligence provider GMI Ratings, highlight the fingerprints of fraud in a company and will cover companies listed in the emerging markets of LATAM, Eastern Europe, the Middle East and Africa and ASEAN.
Fraud events like Tyco, Sinoforest, and Enron have reduced our trust in a company's financial statements. With 10,000 new ratings highlighting the fingerprints for fraud across emerging markets companies, this should make for very interesting reading by the exchanges who list them and surveil them, asset owners/managers who actively invest in them, companies who partner with them, advisors who audit and guide them, customers who buy from them and the communities that support them.
This complements the 20,000 Corporate ratings that GMI Ratings already provide that reflect a broad spectrum of accounting irregularities and weaknesses in corporate governance statistically associated with an elevated risk of adverse events likely to cause precipitous contractions of equity value.
Dyck, Zingales and Morse (2013) found that the probability of a company engaging in a fraud in any given year is 14.5%. Their findings estimate that on average corporate fraud costs investors 22 percent of enterprise value in fraud committing firms and 3 percent of enterprise value across all firms. The social cost calculations they arrive at, along with that of prior researchers, also establishes that these frauds have substantial costs over and above hiding weaknesses in firm fundamentals.These findings have relevance both for investors in firms and for corporate, risk management and investment policies.
Forensic analysis measures a different risk than traditional fundamental analysis – the risk that
the reported financial numbers are in fact inaccurate and unreliable.The forensic measure of risk (as measured by the AGR model) precedes fundamental analysis – that is, the validity of the numbers must be confirmed first, before conclusions from them can be drawn.
According to 30+ forensic accounting and governance audit Professionals (2014),the way that people cook the books is similar all over the world. When implementing an early warning system for KYC/AML, risk management, black swan events and fraud screening, GMI's "inside out - outside in approach" - analyzing what a company publishes about itself (inside out) and examining it for the fingerprints of fraud using publicly available data (outside in) - can have an impact on predicting key risks - and opportunities.
How does it work?
The underlying components of the AGR ranking are informed by academic and professional knowledge about the nature of problems in corporate accounting and governance. Generally Accepted Accounting Principles (GAAP) leave many areas open to judgment and interpretation leaving room for financial manipulation. To identify high‐risk companies GMI Ratings examines both extensive accounting information and governance practices:
• Accounting Risks – a forensic assessment of financial statements is the foundation of AGR. This is different, and complementary to, fundamental risk assessment based on a company’s financial condition and general profitability.
• Governance Risks – quantifiable metrics measuring several key aspects of corporate governance and behavior broaden the scope of their analysis and further contribute to AGR effectiveness.
The predictive power of the AGR is predicated on access to an accurate and comprehensive database of corporate financial statements,corporate governance practices and corporate actions. This database is sourced from multiple respected data vendors and covers more than 24,000 companies; many North American companies going back as far as 1998 and Global companies going back to 2005.
In addition, it requires detailed historical data on negative corporate events in order to identify illegal or unethical patterns of behavior. Some of this information is gathered manually, while other elements are derived from external data feeds but manually annotated. The range of sources and derived data fields allow GMI Ratings to track nearly all aspects of corporate behavior and cumulatively provide a great deal of predictive power concerning future problems.
GMI Ratings’ objective is to use publicly available data to discriminate between fraudulent and non‐fraudulent companies.
Within each geographical region the AGR is reported in percentiles from 1 to 100, worst to best. They are classified as:
• Very Aggressive – highest risk companies, comprising 10% of the total universe
• Aggressive – high risk companies, comprising 25% of the total universe
• Average – moderate risk companies, comprising 50% of the total universe
• Conservative – low risk companies, comprising 15% of the total universe
AGR has been proven to assist in the early detection and mitigation of price erosion related to fraudulent and opaque financial reporting. The AGR model has also demonstrated significant strength in identifying sharp price declines.
In an independent academic study - Detecting and Predicting Accounting Irregularities: A Comparison of Commercial and Academic Risk Measures - authors Price, Sharp and Wood (2011) found that AGR outperformed six academic risk metrics in detecting accounting irregularities. The study compared the ability of these measures to predict SEC accounting and auditing enforcement releases (AAERs), restatements related to accounting irregularities, and shareholder litigation for accounting improprieties.
The authors compared the various risk metrics on 36 specific criteria, and they found that AGR outperformed its academic counterparts in 28 of the comparisons. When the criteria focused on detecting accounting irregularities, AGR performed better in 18 out of 18 comparisons. When the criteria focused on predicting irregularities one period before the irregularities occur, AGR performed better than the academic measures in 10 out of the 18 tests.
AGR and Class Action Litigation
On January 17, 2014, a Litigation Risk Model study by GMI was released for full-year 2013. The study found that 51% of companies facing Federal class action lawsuits were ranked in the lowest quintile of the risk ratings distribution a year before the lawsuit was filed, while fewer than 3% were ranked in the highest quintile. These results confirm that the Litigation Risk Model is a valuable tool for insurers and investors hoping to anticipate and avoid companies prone to class actions, which frequently lead to reputational damage, stock price declines and settlement costs.
In a study examining the periods from 2002-2012 it also reports the incidence of monthly price drops of 30% to 60% over the last several years in North America and Western Europe. Drops of this magnitude happen rarely but are roughly twice as common in the worst rated AGR companies, than in the best rated group.
GMI Ratings collects a vast amount of data from multiple high quality external data feeds and a dedicated internal collection process. The multiple data sources are integrated and processed to ensure data quality. They currently compute hundreds of derived data items that represent a computational step between source data (inputs from data vendors) and computed metrics. Many items are numerical conversions of qualitative source data that are required when computing the frequency of events such as the number of mergers and acquisitions, late filings, or officer turnover. Another area of derived data is Oversight Events used in AGR calculation and validation that are required in order to identify illegal or unethical patterns of behavior.
Building on these data items, GMI computes hundreds of metrics from financial statement accounts and footnoted items. These include more traditional fundamental ratios used to evaluate corporate strength and profitability, as well as forensic metrics that contribute to these Accounting and Governance Risk rankings. Metric operations are flagged in the AGR Model if they are outliers and are predictive of fraud, based on correlation to past fraud cases. The statistical model creates weights for the metric operations, the higher the correlation to fraud, the higher the weight and the greater the impact on the model.
The GMI Ratings Metrics Engine generates the measures which are ultimately the building blocks in identifying potentially fraudulent behavior.
The AGR is based on identifying the measures most highly associated with SEC fraud actions. Approximately 60 metrics are currently included in AGR having been found to be most effective in identifying the fingerprints of fraud. A quantitative model – when rigorously validated – has advantages in being comprehensive and objective.
With 10,000 new ratings across emerging markets companies, this should make for very interesting reading by exchanges who list them, asset owners/managers who invest in them, companies who partner with them, customers who buy from them and the communities that support them.
When implementing an early warning system for risk management, black swan events and fraud screening, an "inside out - outside in approach" using the AGR to analyse what a company says about it's own economic performance and examining it for the fingerprints of fraud using publicly available data - can have an impact on predicting the risk of fraud in listed companies - and opportunities.
Dyck, Morse, Zingales (2013) How pervasive is corporate fraud: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2222608
For more information I have included the GMI Ratings video below:
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About RL EXPERT Leesa Soulodre:
Managing Partner and Founder of RL Expert Group. A Member of the Global Advisory Council of NY Investment Advisory Firm, Cornerstone Capital; an Innovation Advisor to the University of Illinois Urbana Champaign Advanced Digital Science Centre, Singapore and Board Advisor to Belgian PR Software firm, Prezly and US Sports Analytics firm, Autoscout.
An Adjunct in Corporate Communications at Singapore Management University, lecturing part time on "Risk Issues and Crisis Management "and "Content Strategy" at the Lee Kong Chian School of Business. Prior to moving to Asia, spent 7 years part time in European Academia, lecturing on the Luxury MBA programs in Marketing, Communications and Reputation Management at two french business schools, Ecole Superieur de Gestion and Mod'Art International.
Connect: Leesa Soulodre, Managing Partner, RL Expert Group - firstname.lastname@example.org
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