Key Takeaways

A traditional Z-score measures how much an individual observation differs from the average, providing context for data analysis.The Altman Z-score helps investors predict the likelihood of a company declaring bankruptcy.Investment decisions should be based on a variety of research techniques, not just Z-scores.

What Are Z-Scores?

Z-scores use standard deviation to show the difference between an observation and the mean of a dataset. For example, a Z-score of 2.0 means the data is two standard deviations away from the average. By using Z-scores, you can quickly evaluate the normalcy of an observation within a dataset.

How Z-Scores Work

Z-scores compare individual observations to the average and allow for standardized comparisons between different datasets. To calculate a Z-score, subtract the mean from the data point and divide by the standard deviation. Investors have adapted Z-scores to assess a company's financial health, with models like the Altman Z-score attempting to predict bankruptcy risk.

The Altman Z-Score

The Altman Z-score, developed by Edward Altman in the late 1960s, aims to quantify a company's financial health and creditworthiness. By combining data from financial statements, the Altman Z-score predicts the likelihood of bankruptcy. Different weights are assigned to various financial metrics, and the final score helps investors determine the company's risk level.

Implications for Individual Investors

Investing in a company that goes bankrupt can lead to substantial losses, making it crucial for investors to assess risk accurately. While Z-scores provide valuable information, they should be used alongside other research methods. Investors should conduct comprehensive financial statement analysis, industry research, and competitor analysis to make informed investment decisions.