Explanation:
Quadratic programming takes into account the variances and pairwise correlations (covariances) of all stocks in the portfolio, utilizing many more risk parameters than simpler techniques. However, estimating a large covariance matrix introduces significant estimation error (or "noise"). Option B is incorrect because screening techniques can certainly apply additional risk controls, such as weighting stocks by market capitalization. Option C is incorrect because stratification maintains risk control by matching the portfolio's weights in specific categories (like sectors or sizes) to the benchmark, not by arbitrarily overweighting lower-risk categories. Option D is incorrect because linear programming incorporates multiple dimensions of risk (such as factors, size, industry) as linear constraints, whereas it is quadratic programming that relies heavily on pairwise correlations.
A
The quadratic programming technique takes into account additional risk parameters compared to other major portfolio construction techniques but also requires more inputs, which leads to more noise.
B
The screening technique ranks stocks by risk-adjusted alpha but it does not apply any additional risk control measures such as weighting the selected stocks by their relative capitalization.
C
The stratification technique splits the list of stocks into categories and maintains risk control by overweighing the categories with lower risks and underweighting the categories with higher risks.
D
The linear programming technique focuses on the pair-wise correlations of stocks rather than characterizing each stock along multiple dimensions of risk such as size, industry, volatility, or beta.
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