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Investing/Strategies / Mechanical Investing
|Subject: Re: Optimizing Blends with Sharpe/(GSD^x)||Date: 11/17/2007 11:04 PM|
|Author: TGMark||Number: 204013 of 251794|
I'm in the middle of Taleb's book Fooled by Randomness. Unfortunately, that means that not only does this thread seem like noise, so does this board and my whole investing foundation. Eh, philosophy will do that to you, I'll recover.
Earlier in the book, I felt OK. Everything is relative, and I think that this board is not fooled by randomness as much as the general public, or perhaps even many investment professionals. Over time, that should be an advantage.
However, he discusses backtesting, and not favorably. He mentions Tradestation and legions of data hounds searching for the optimum strategy. However, his treatment is a little shallow, from what I've read so far. Sure, tuning a backtest for a particular parameter value is a suspicious activity, obviously; whereas the simplicity of using a few fundamental parameters and a sort that takes advantage of past human behavior would seem qualitatively different. I also think the quality of our backtesters and datasets are better than what he's familiarized himself with. Of course, I could easily be wrong.
I agree it would be silly to aim for high arithmetic mean return.
Some time back I made these control charts of MI screens. They were made using, by definition, arithmetic averages. However, it is a difficult way of thinking, and doesn't translate into the normal metrics without assumptions about serial autocorrelation, or whatever.
But CAGR magnifies the noise, the random variation, especially for high volatility screens. You happen to get 5 months of great returns in a row, and your CAGR looks fantastic, and you attribute greatness to the screen. So I wonder if simpler statistics might have some value after all. Something like arithmetic average cycle return/GSD^x ;) ?
(Don't take anything I say about this book too seriously; I usually have to read things a couple times for them to make sense, if they ever do).
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