This is the most important strategy :
Please use on 1000 DJIA drop days...
Please use on 1000 DJIA drop days...
Quote from mizhael:
I tried past returns, and using historical means and covs to approximate expected means and covs...
Never worked... too sensitive to noise, even in backtest, you've got good results from using the mean-variance stuff? And you did mean-variance portfolio optimization?
Quote from sjfan:
Glad to see there are people on ET who have actually done some work... yes - mv optimization is very sensitive to inputs. This is a result of an implied 100% confidence in those expected return and var/covar numbers. In reality, we are never 100% certain of our forecasts (be it based on historical time series analysis or some other methods). There are some good refinements to mean-variance analysis that introduces forecast risk into those expected returns and var/covars. They tend to produce more 'sane' asset allocations. Black-litterman is one well known formulation - but I personally don't like it because it requires yet another set of inputs that are difficult to calibrate.

Quote from LVMises:
Have you optimized your system to include the Austrian theory yet?![]()
Quote from sjfan:
Can't - the Austrian School doesn't contain much of testable hypothesis that can be used as constraints or objectives. The Austrian School is a collection of (interesting) qualitative statements that aren't sufficient to form a scientific (that is, falsifiable) theory.