The Altman Z-score course offers a comprehensive exploration of one of the most influential and enduring models for predicting corporate bankruptcy and assessing financial strength. Developed by Professor Edward Altman in 1968, the Z-score model remains a cornerstone tool for investors, analysts, and financial professionals worldwide.

Participants will delve into the historical background, rationale, and construction of the Altman Z-score, gaining insight into how and why it continues to dominate failure prediction models even after more than 45 years. The course emphasizes the selection of critical financial ratios, the application of Multiple Discriminant Analysis (MDA), and the establishment of objective statistical weights to predict financial distress with high accuracy.

Through an in-depth breakdown of the five key financial ratios—working capital to total assets, retained earnings to total assets, EBIT to total assets, market value of equity to total liabilities, and sales to total assets—students will learn how each factor contributes to a company’s financial stability. Practical examples and real-world case studies will be used to illustrate interpretation, providing clear guidelines for classifying companies into distress, grey, and safe zones based on Z-score thresholds.

Additionally, the course discusses:

• The relationship between financial statement trustworthiness and supporting tools like the Beneish M-score.

• The evolution of Altman's work into the Z'-Score and Z''-Score for broader international applicability.

• The limitations of applying these models, particularly for financial companies.

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

• Accurately calculate and interpret Altman Z-scores.

• Use Z-scores to support investment decisions and risk assessments.

• Understand the practical applications and limitations of bankruptcy prediction models in various industries and geographies.

This course is essential for anyone engaged in financial analysis, investment management, or corporate finance who seeks to enhance their toolkit with a proven, statistically sound methodology.