Detecting Earnings Manipulation with the Beneish M-Score: Python Implementation

In 1998, students at Cornell University flagged Enron as a likely earnings manipulator using a statistical model. Wall Street analysts were still recommending "buy." The model? The Beneish M-Score. Here's how to implement it in Python. What is the Beneish M-Score? Developed by Professor Messod D. Beneish at Indiana University in 1999, the M-Score is a mathematical model that uses 8 financial ratios to detect whether a company has manipulated its reported earnings. The key threshold: -1.78 M-Score > -1.78: Likely manipulator M-Score < -1.78: Unlikely manipulator The 8 Variables Each variable captures a different dimension of potential manipulation: Variable Name What It Detects DSRI Days Sales in Receivables Index Revenue inflation through receivables GMI Gross Margin Index Deteriorating margins (incentive to manipulate) AQI Asset Quality Index Capitalization of expenses SGI Sales Growth Index High growth (more pressure to manipulate) DEPI Depreciation Index Slower depreciation to boost