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Subject:  Re: Optimizing Blends with Sharpe/(GSD^x) Date:  11/17/2007  9:20 PM
Author:  JeffLandon Number:  204008 of 258146

1) Whether CAGR optimizes for smooth returns.

It does in this sense: When you write an optimizing backtester that looks for highest arithmetic mean, the results are much less even than when you write a backtester that optimizes for highest geomean. Bad years beat down CAGR unmercifully, at least compared to how arithmetic average is affected. (There is one interesting case where arithmetic is what you want--a young person who so far has little saved, but who is investing a fair amount monthly, can take advantage of volatility and is well served by optimizing for arithmetic average.)

2) Concerning the knowledge that GSD predicts GSD, but CAGR does not predict CAGR.

True. I never disputed that. But that's not my point at all. I agree that GSD is a better predictor of GSD than CAGR is of CAGR, but that doesn't mean something else doesn't predict CAGR.

Ever time you find a measuring stick, you should find out what it's good at measuring. Perhaps for some x, CAGR/(GSD^x) does turn out to be a good predictor of CAGR. By default, you're focusing on how it measures Sharpe.

Perhaps if you go back through all the screens pre- and post-discovery, you'll see that CAGR/(GSD*GSD) does predict CAGR. You'll never find what does because you've given up looking.

(And if you're convinced that you can't predict returns but only volatility, why are you even looking at Sharpe? You should only be interested in GSD if that's all you have