A publicly held company's probability of bankruptcy can be calculated using a method called the z-score, also known as the standard score. The formula, originated in the 1960s by New York University assistant professor Edward Altman, requires calculation of several financial statement ratios and the firm's equity value. These results are plugged into a simple formula that weighs the five financial ratios differently, producing a z-score that predicts the firm's likelihood of future bankruptcy.
Locate an income statement and balance sheet from a publicly held company you want to analyze. Make sure the statements represent the same time period. From the income statement, you will need the company's sales figure and earnings before income and taxes (EBIT). From the balance sheet, you will need to know current assets, total assets, current liabilities, total liabilities and retained earnings.
Find the current market value for the firm's equity. Using a site such as Yahoo! Finance, enter the company's ticker symbol. Look for "market capitalization" on the company's financial information page. This represents the market value of the company's equity, or the outstanding shares multiplied by the current share price.
Calculate the necessary ratios. Using R for ratio, R1 is working capital divided by total assets. Working capital is current assets minus current liabilities. R2 is retained earnings divided by total assets. R3 is EBIT divided by total assets. R4 is the market value of equity divided by total liabilities. R5 is sales divided by total assets.
Plug each ratio into the z-score formula as follows to calculate the company's z-score. The formula is: 1.2_R1 + 1.4_R2 + 3.3_R3 + .6_R4 + .999*R5.
Interpret the result. In general, the lower the result the higher risk the company runs of entering bankruptcy. Firms with a z-score above 3 are considered healthy, while those between 1.8 and 3 are considered in danger.
Warnings
The formula is a valuable predictive tool, and real-world experience has proven a 70 to 80 percent accuracy rate in the z-score's prediction of corporate bankruptcies within the two years before they filed for chapter 7 bankruptcy protection. However, the z-score should be used in conjunction with other evaluation methods to determine a company's future financial prospects.
References
- The CPA Journal Online; Z-Scores: A Guide to Failure Prediction; Gregory J. Eidleman; February 1995
- New York University; Predicting Financial Distress of Companies; Edward Altman; July 2009
- New York University. "Professor Edward Altman Launches Digital App for Renowned Z-Score, Altman Z-Score Plus." Accessed July 26, 2020.
- New York University. "Edward Altman." Accessed July 26, 2020.
- New York University. "Predicting Financial Distress of Companies: Revisiting the Z -Score and Zeta® Models," Page 18. Accessed July 26, 2020.
- New York University. "Predicting Financial Distress of Companies: Revisiting the Z -Score and Zeta® Models," Pages 10-13. Accessed July 26, 2020.
- New York University. "Predicting Financial Distress of Companies: Revisiting the Z -Score and Zeta® Models," Page 23. Accessed July 26, 2020.
Warnings
- The formula is a valuable predictive tool, and real-world experience has proven a 70 to 80 percent accuracy rate in the z-score's prediction of corporate bankruptcies within the two years before they filed for chapter 7 bankruptcy protection. However, the z-score should be used in conjunction with other evaluation methods to determine a company's future financial prospects.
Writer Bio
Cynthia Gaffney has spent over 20 years in finance with experience in valuation, corporate financial planning, mergers & acquisitions consulting and small business ownership. She has worked as a financial writer for online finance publications since 2011, including eHow Money, The Motley Fool, and Sapling.com. She has also edited for several online finance publications, including The Balance, Opposing Views:Money, Synonym:Money, and Zacks.com. A Southern California native, Cynthia received her Bachelor of Science degree in finance and business economics from USC.