Detecting earnings manipulation with Beneish M-score

Learn how to use the Beneish M-Score to detect earnings manipulation and avoid accounting red flags in your investments

What you'll learn
- Understand the structure, purpose, and components of the Beneish M-Score model
- Calculate and interpret a company's Beneish M-Score using real financial data
- Identify signals of earnings manipulation
- Analyze the 8 key financial ratios (DSRI, GMI, AQI, SGI, DEPI, SGAI, LVGI, TATA) behind the M-score
- Apply the Beneish M-Score to investment decisions, ccompany assessments, and portfolio management
- Distinguish between healthy, and potentially manipulating companies using clear M-Score thresholds
- Recognize the strengths and limitations of the Beneish M-Score
- Use supporting tools like the Beneish M-Score to verify the reliability of financial statements
- Integrate Beneish M-Score analysis into broader fundamental and financial analysis workflows

Requirements
- Understanding financial statements like balance sheet, cash flow statement & income statement
- Understanding the differences between cash & accrual accounting

Description
TheBeneish M-score courseprovides 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 flagsbeforethey 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 ofeight financial ratiosthat 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. Throughreal-world case studieslike 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.

Who this course is for:
- Aspiring and Professional Investors
- Financial Analysts and Credit Analysts
- Business Students and Finance Graduates
- Corporate Finance and Risk Management Professionals
- Value investors
- Financial Investors
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