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Real Vs. Hypothetical

Before investing with some fund or using some advisory service that one way or another provides active portfolio management, we naturally want to first look at past performance. I'm going to discuss the different kinds of performance histories that can be made available, and argue that the kind that is generally considered to be the gold standard is not necessarily what it seems to be.

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So Retail Backtest's backtested program performance is labeled "hypothetical", by me, to match expectations in that regard. However, what Retail Backtest does is the same thing that you would be doing if you were to pick funds to invest in based on their past "real-money" performance. Yes, you would simply be competing with me, performing the same function.

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"Why Most Published Research Findings Are False"

Today's note is to point to an essay of that title by John P. A. Ioannidis, Professor of Medicine and of Health Research and Policy at Stanford University School of Medicine and Professor of Statistics at Stanford University School of Humanities and Sciences. By false findings he is referring to research that passed the usual test of statistical significance but was ultimately proven to be false, non-reproducible. The problem is that the number of false findings is much, much higher than would be predicted from the researchers' own assessments of statistical significance.

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I want to talk a little about how the circumstances are a bit different for findings by quantitative analysts who work for funds managing securities. Can you guess why? Come on... it's easy.

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Objectivity and the Trailing Performance Period

One of the most easily reached conclusions in all of logical thinking is that we should always do things the way that worked best in prior experience. Oh, that's not to say that we shouldn't or don't occasionally get inventive. But we surely believe that we should never do anything in a way that worked much worst in the past than something else that worked well.

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The mistake has to do with choices. I usually frame the issue by talking about "parameters". By that I mean numbers, numbers that specify your portfolio management scheme's way of learning from the past. The mistake is to simply find out what choice of parameters would have produced the very best results in the entire available history, to thus fix those parameters, and then to use them going forward.

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Use Relative Strength Sparingly?

There is a theory of investing out there that generally goes by one or the other of two names— "momentum" or "relative strength".

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But upon thinking relative strength through, we could worry a bit about the idea of focusing on owning only securities of particularly high momentum. What if the familiar commentaries about securities "getting ahead of themselves" or "overshooting" or "becoming overvalued" should fairly often prove to be applicable to our portfolio so that we would see our select few high-fliers reversing on us and returning to earth too often?

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Say What? You're Changing Your Hypothesis?

No rest for the wicked! Let's say that you're a doctor, a clinician, and you're reading a research article on a new treatment. How reliable is the conclusion in the article, which is that a certain treatment would work but only with female patients?

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Let's say you've put together a dandy system for portfolio management based on a promising new principle. You try it out on stocks of many kinds. It hardly works overall. Boo! What to do? I know... let's try it on just the financials. Hurray! It works!

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