Financial Calculations & Valuation Advanced

Beneish M-Score Forensic Earnings Manipulation Detector in Python

Compute Professor Messod Beneish's 8-variable M-Score in Python to detect aggressive revenue recognition and balance sheet earnings manipulation.

#numpy

Problem Statement

Companies inflating profits via fake receivables or capitalized expenses often look profitable on the surface. An M-Score greater than -1.78 flags high probability of earnings manipulation.

Formula / Core Logic

M = -4.84 + 0.92*DSRI + 0.528*GMI + 0.404*AQI + 0.892*SGI + 0.115*DEPI - 0.172*SGAI + 4.679*TATA - 0.327*LVGI M > -1.78 indicates potential manipulation.

Installation

pip install numpy

beneish-m-score-earnings-manipulation-detector-python.py

def calculate_beneish_mscore(dsri, gmi, aqi, sgi, depi, sgai, lvgi, tata):
    m_score = (
        -4.84
        + 0.920 * dsri
        + 0.528 * gmi
        + 0.404 * aqi
        + 0.892 * sgi
        + 0.115 * depi
        - 0.172 * sgai
        + 4.679 * tata
        - 0.327 * lvgi
    )
    flag = 'HIGH_MANIPULATION_RISK' if m_score > -1.78 else 'CLEAN_ACCOUNTING'
    return {'Beneish_M_Score': round(m_score, 3), 'Forensic_Status': flag}

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Frequently Asked Questions

Q: What does a spike in DSRI (Days Sales in Receivables Index) mean?

A DSRI >> 1.0 indicates receivables are growing much faster than sales, a classic sign of channel stuffing or aggressive revenue booking.