Sector Normalization
Comparing each stock to its sector peers rather than to the entire market — accounting for the fact that 'normal' looks different in each industry.
Definition
Sector normalization is the practice of computing reference statistics (mean, standard deviation) within each sector, rather than across the whole market. A 30% gross margin is mediocre in software and excellent in retail; a P/E of 40 is high for a utility and unremarkable for a fast-growing tech company.
Cross-sectional scoring without sector normalization tends to produce predictable artifacts: technology stocks dominate growth and profitability rankings, energy and financials dominate value rankings, utilities dominate risk rankings. The output ends up being more a sector classification than a stock pick.
Sector-normalized scoring puts each stock head-to-head with peers facing roughly similar economic dynamics. The trade-off is that the best stock in a weak sector can score the same as the best stock in a strong sector — sector tilts have to be made elsewhere.
How QScoring uses it
QScoring z-scores each metric against the distribution of that metric across the stock's sector. If the sector has fewer than 15 covered names, the system falls back to the full universe of US large-caps. The reference universe is currently US-listed stocks above $15B market cap. See the combining section and the limitations for known coverage gaps.