multi-factor

You build a multi-factor portfolio by standardizing financial factors like momentum, value, and quality using Z-scores, then ranking stocks cross-sectionally to select top performers for equal or custom-weighted allocation.

Z-score normalization in multi-factor analysis standardizes diverse financial factor values into a uniform scale, ensuring statistical comparability across different metrics before combining them into a composite stock ranking score.

Yes, you need pandas and numpy installed, as these libraries handle the core data manipulation and statistical calculations required to compute standardized multi-factor scores for quantitative stock ranking.

Yes, you can apply custom weights during portfolio construction, allowing flexible allocation across top-ranked stocks based on standardized multi-factor scores instead of being restricted to default equal weighting.

For cross-sectional stock ranking, you should use financial factors such as momentum, value, and quality, which the system standardizes and combines to generate comparable composite scores across multiple instruments.