Z-scores in Investing and Finance: Are You Using Them Right?
When risk managers or quant professionals need to compare different metric types or normalize data, z-scores are their go-to measure. In theory, Z-scores are powerful and easy to understand. In practical use, however, they have two strong limitations that can affect their interpretation:
- A high sensitivity to the distribution of underlying data
- Their interpretations relative to each other
When using a z-score for finance or investing, these limitations have real-world consequences.
Deep Dive: What Is a Z-score?
A z-score is defined as ‘a numerical measurement that describes a value’s relationship to the mean of a group of values’. Essentially, it is a measure of how far a data point deviates from the mean. Z-scores are often used to normalize raw data because they show the standard deviation below or above the average value.
To calculate a z-score, you need three numbers: the mean (the average raw value), the value of your data point (the raw value), the standard deviation of all data points within the dataset (standard deviation…
