Transcript Quantitative Stock Selection: Dynamic Factor Weights Campbell R. Harvey Duke University National Bureau of Economic Research.

Quantitative Stock Selection:
Dynamic Factor Weights
Campbell R. Harvey
Duke University
National Bureau of Economic Research
Dynamic Factor Weights
1. We have identified five factors based on
univariate quintile sorts: PB(1), PB(5),
Mom(1), RR(1), ROE(5)
2. Fixed weight optimization involves
pushing these five portfolio returns through
an optimizer at some set level of volatility.
The weights would serve as scores for
individual securities.
Dynamic Factor Weights
3. Suppose the optimized weights are:
1.0
-2.0
.5
1.1
-1.6
=PB(1)
=PB(5)
=Mom(1)
=RR(1)
=ROE(5)
[Note the weights are set to sum to zero]
We would use these weights as scores and come
up with a final scoring screen.
Dynamic Factor Weights
4. Suppose we think that RR only works when the
term structure slope is greater than 1%. We
create a 6th factor which is the interaction
between RR and a dummy variable (Dum=1 if
TS>100bp; 0 otherwise). The Dum is lagged one
period.
5. We reoptimize with six portfolios (five ‘basis’ and
one ‘dynamic trading strategy or DTS’)
6. We call this a DTS because is the return of an
active strategy of investing in the RR portfolio
only when TS>100bp.
Dynamic Factor Weights
8. The new weights are (all weights likely to
change):
1.5 =PB(1)
-2.0 =PB(5)
0.8 =Mom(1)
-0.2 =RR(1)
-1.2 =ROE(5)
1.1 = RR(1)xDum
Dynamic Factor Weights
9. These new weights are dynamic. When
lagged term structure is greater than
100bp, the RR factor gets a 0.9 weight (0.2+1.1). When term structure is flat or
negative, the weight is -0.2.
10. We don’t need to use discrete variables
like dummies. We could use continuous
variables.
Dynamic Factor Weights
11. We could have many different interaction
variables. The variables could be different for
each factor. It is even possible to interact two
factors (however, be careful, to preserve the
implementation, you must interact with the
lagged value of the factor).
12. As the number of factors and interactions
increase, we might need to Monte Carlo to
achieve a stable frontier.