Detecting earnings manipulation with Beneish M-score

The Beneish M-score course provides a deep dive into one of the most powerful forensic tools for detecting earnings manipulation and assessing the quality of financial reporting. Developed by Professor Messod Beneish in the late 1990s, the M-score model has gained widespread recognition among forensic accountants, professional investors, and regulators for its ability to flag potential accounting red flags before they appear in headlines.

Participants will explore the theoretical foundation, empirical development, and statistical structure of the Beneish M-score, including its origins as a tool to distinguish manipulators from non-manipulators using publicly available financial data. The course focuses on the use of eight financial ratios that capture deviations in accruals, margins, asset quality, leverage, and sales growth—factors that often shift when management distorts earnings.

By unpacking metrics such as Days Sales in Receivables Index (DSRI), Gross Margin Index (GMI), and Total Accruals to Total Assets (TATA), students will gain a practical understanding of how each component contributes to the overall manipulation risk. Through real-world case studies like Enron and Big Tech companies, the course will demonstrate how the M-score can serve as an early warning system against aggressive accounting.

In addition, the course will cover a discussion of how M-score thresholds are interpreted and how to integrate the score into a broader investment research process.

By the end of this course, students will be able to:

• Calculate and interpret the Beneish M-score using raw financial statement data.

• Identify potential earnings manipulation

• Integrate forensic analysis techniques into fundamental research and portfolio decision-making.

This course is designed for investors, analysts, auditors, and financial professionals seeking to enhance their ability to detect accounting manipulation and protect capital from avoidable risks.