McGraw-Hill

232 Managing Portfolios
portfolios. The benchmark may be a broad market index such as the
Wilshire 5000, a large-capitalization index such as the S&P 500, a
small-capitalization index such as the Russell 2000, or a growth or
value style index. Whatever the specific benchmark chosen, optim
zation aims to provide a portfolio that has a of level systematic risk
similar to the benchmark's risk and to ensure that the portfolio incurs
no more incremental, or residual, risk than is warranted by the
portfolio's expected excess return.
We find that this task is enhanced by the use of an optimizer
that is customized to include exactly the same dimensions found
relevant by the stock selection model. A commercially available
optimizer applied in a one-size-fits-all manner is likely to result in
mismatches between model insights and portfolio exposures, hen
may detract from portfolio return and/or add to portfolio risk. R
reduction using a commercial optimizer, for example, will reduce
the portfolio's exposures only along the dimensions the optimizer
recognizes, which are unlikely to be fully congruent with dimensions
of the selection model. As a result, the portfolio is likely to
wind up less exposed to those variables common to both the model
and the optimizer and more exposed to those variables recognized
by the model, but not the optimizer.
Imagine a manager who seeks low-P/E stocks that analysts are
recommending for purchase, but who uses a commercial optimize
that incorporates a P/E factor but not analyst recommendations.
The resulting portfolio will likely have a lower exposure P/E to low
than the model would deem optimal and a higher exposure to analyst
buy recommendations. Optimization using all relevant variables
ensures a portfolio whose risk and return opportunities are
balanced in accordance with the selection model's insights. Furthermore,
the use of more numerous variables allows portfolio to risk be
more finely tuned.
Portfolio implementation involves trading. As we noted in
Part 1, estimates of the expected returns to insights from the stock
selection model must be combined with estimates of trading costs in
order to arrive at realistic returns net of trading costs. The use of
electronic trading venues can help to reduce trading costs and
thereby enhance portfolio returns.
Electronic trading generally involves lower commissions and
less market impact. And, of course, an automated trading system