Predicting Bankruptcy of Companies in the Pharmacy and Technology Sectors Using Altman’s Z-score model

Numerous bankruptcy prediction models have been proposed and tested. Among them, the influential Altman Z-Score stands out as a powerful tool for assessing a business’s financial health and gauging its vulnerability to financial distress and bankruptcy. This model incorporates five key financial ratios: liquidity, profitability, efficiency, market value, and net worth. This study aims to comprehensively assess the bankruptcy risk of companies by employing Altman’s Z-Score model with versions of Z, Z’, and Z". These models categorize companies into three classifications: the ’Safe’ zone, the ’Distress’ zone, or the zone in between known as the ’Gray zone’. Additionally, this work investigates the accurate classification of both bankrupt and nonbankrupt firms into their respective categories. Hence, it was utilized to assess and contrast the classification precision of three different adaptations of the Altman Z-Score model. Concurrently, we employed a step wise T-test approach to systematically integrate multiple independent financial variables, encompassing key financial ratios such as liquidity, productivity, market value, profitability and net worth. The inclusion of each financial variable substantially enhanced the model’s discriminative capacity. This analysis delves into the financial histories of 60 companies spanning the Pharmacy and Technology sectors, encompassing the years from 2000 to 2018. Furthermore, this study utilizes K-means clustering with Z-score normalization as the primary variable to ensure impartial treatment and to mitigate scale-related biases. The K-Means algorithm, a significant clustering technique, adeptly organizes data, categorizing companies into three groups: distress zone, gray zone, and safe zone. These results offer an additional tool for predicting bankruptcies within the pharmacy, and technology sectors.