Earnings Quality Rank℠ (EQR℠)

Increasingly, risk management and investment managers are becoming aware of earnings quality-related risks and are seeking solutions. The Earnings Quality Rank℠ (EQR℠) , developed by Sabrient and its subsidiary Gradient Analytics, serves as the latest quantitative tool in addressing this need.

EQR offer clients an objective and unbiased assessment of a company’s relative potential risk due to accrual accounting practices, as reflected in key relationships between information contained in the firm’s income statement, balance sheet, and cash flow statement

EQR covers approximately 3,000 US-listed stocks with a market capitalization greater than $150 million and a share price greater than $2. These constraints are applied to focus EQR on those companies that are of highest interest to clients and prospects.

Gradient Analytics was at the forefront of assessing the earnings quality of financial institutions and played a vital role in assisting clients with evaluating risks during the 2008-2009 financial crisis.

EQR represents the next step in quantitatively assessing earnings quality and accounting related risks. The variables used to construct EQR include evolved calculations of accruals and other unique earnings quality risk assessment factors developed by Gradient Analytics. Moreover, EQR features assessments of earnings quality of companies within the financial industry, which often is not offered by competing solutions.

EQR may be deployed in a variety of environments to detect potential accounting-related tail-risks.

EQR Methodology

To develop the EQR Model, we started by identifying the variables, found to be significant in academic research and in Gradient’s own analyst driven and empirical research. From these variables, Gradient’s research team selected the most salient accounting factors that are associated with a firm’s future returns.

The Gradient variables were then quantified and backtested by Sabrient’s research team, led by David Brown, founder, and Dan Tierney, senior analyst, to find those that have the highest correlation among financial statements and negative events. The final variables were sized and scaled in such a way as to avoid “false positives.” The most correlated ones became the model for the EQR rankings.