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Quantitative Stock Selection:
Practical Insights
Campbell R. Harvey
Duke University
National Bureau of Economic Research
Practical Insights
1. Factors are often viewed in terms of a
continuous distribution. We standardize
the factor by subtracting the crosssectional mean and dividing by the
cross-sectional standard deviation.
2. A simple equally weighted scoring
system would add up these standardized
values. Sorting by the sum is the scoring
screen.
Practical Insights
3. The ability of factors to separate good and bad
performance is probably sector specific. It is
important to run sector based univariate
screens.
4. In implementing a scoring screen with sector
differences, it is probably not practical to have
different factor scores for each sector. Best to
flag the sectors where the factor works well (or
works very poorly). The scoring screen will
take that information into account.
Practical Insights
5. It is important to evaluate not just the
historical consistency of your screens but
to understand the fundamental economic
determinants of historical performance.
This involves looking at the month by
month and correlating with the macro
financial environment.
Practical Insights
6. Attribution analysis is also important. This
involves a regression of portfolio returns on a
set of known styles, i.e. value, growth, etc. You
can determine how much of fund performance
came from each source. The regression
usually uses 12 months of data and about 4 or
five style factors. Note: if you know the scoring
weights then you already know your portfolio’s
tilt. However, if we are evaluating someone,
the attribution analysis is crucial.
Practical Insights
7. There is another optimization after the scoring
screen optimization (which gives optimal
scores for each factor).
8. Given each stock’s score, an optimization is run
which maximizes the total score (for longs) but
includes a number of practical constraints
(such as the must but stocks, caps and floors
on sector weights, caps and floors on style
exposure).