McGraw-Hill

Managing Portfolios 231
universe model predicts GM will outperform Ford, while the val
model shows the opposite? Should the investor start the day with
multiple estimates of a single stock's alpha? A broad, unified approach
avoids this investment conundrum.
It is also poised to take advantage of more information than a
narrower view of the market might provide. The effects of inflation
on value stocks, for example, may have repercussions for growth
stocks; after all, the two segments represent opposite ends of the
same P/E continuum. But the interrelationships between individual
stocks and between stock subsets become clear only when
viewed from the perspective of the whole.
A wide-angle approach that considers all the stocks in the universe-value
and growth stocks, large- and small-cap stocks-benefits
from all the information to be gleaned from a wide and diverse
range of stock price behavior. It is not only poised to take advantage
of more profit opportunities than a more narrowly focused approach
affords, but its results, based as they are on a more heterogeneous
set of variables, are likely to be more robust.
A broad, unified approach, combined with the power of a se-
curity selection system based on an appropriate multivariate anal
sis of a large number of variables, allows for numerous insight
profit opportunities and improves the goodness of those insights.
More insights, and better insights, translate into superior performance.
The translation process, from insights into performance, in
volves portfolio construction. As we noted in the Introduction to
Part 1, combining securities in portfolios that preserve excess returns
without adding undue risk constitutes the second basic of ta
investment management.
With respect to portfolio construction, quantitative management
has a major advantage over traditional qualitative investm
processes: The numerical estimates for expected retums and risks
that emerge from a quantitative stock selection system are eminently
suitable for portfolio construction via portfolio optimiza
Optimization employs quantitative methods to combine securities
into portfolios that offer the maximum expected returns for given
levels of risk, while eliminating unintended risk exposures.
Most often in quantitative management, portfolio construction
is designed to deliver performance relative to a chosen benchmark,
although, as we will see later, this goal may be relaxed for certain