Z-Score Normalization

Z-score normalization is a statistical technique used to standardize data by converting values into deviations from their mean, measured in standard deviations. In quant investing, Z-scores allow different signals to be compared on a common scale. For example, profitability ratios and momentum returns have very different units. Z-scoring transforms both into standardized scores that can be combined meaningfully. The formula subtracts the mean and divides by the standard deviation. A Z-score of +2 indicates a value two standard deviations above average, while −1 reflects below-average strength. Z-scores are widely used in factor scoring, composite signal construction, and outlier detection. They help identify extreme observations and ensure no single metric dominates due to scale alone. Normalization also improves model stability by reducing sensitivity to changing distributions over time. However, Z-scores assume reasonably stable distributions. During extreme market events, standard deviations can spike, altering signal behavior. Robust implementations therefore monitor distribution shifts. Z-score normalization is a foundational building block in multi-factor models, enabling systematic ranking and fair signal aggregation.