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Comment by PaulHoule | Hacker News Reader

Lots of people use the Dow precisely because it is a bad indicator. If they don't like the conclusion they get with something better, like the W5000 or the S&P500, they can try it again with the Dow for a "second opinion."

The whole methodology is a joke anyway. If you evaluate a huge number of search terms, some of them are going do better than others. So terms that might mean something ("debt") get mixed up with terms like ("color") that got lucky.

What you have to do is come up with one strategy that trains a classifier on the combined words, tunes it up with logistic regression and derives optimal trading actions from that. And you've got to factor in what you're paying to the broker and the market makers.

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Relevant technical terms: 'look-elsewhere effect' and 'false alarm rate'

http://en.wikipedia.org/wiki/Look-elsewhere_effect http://en.wikipedia.org/wiki/False_positive_rate

There are more similar considerations. Statisticians have been very busy for the last century or so.

As my statistics professor liked to say, "Numbers are meek creatures. If you torture them long enough, they will say whatever you want to hear."

This is the key to the accounting profession

The DJIA is a "bad" index but it is highly correlated with the S&P 500. I'm including a link to a study looking at ~70 years of data which shows a 0.95 correlation coefficient between DJIA and SP500.

http://www-stat.wharton.upenn.edu/~steele/Courses/434/434Con...

So, while the methodology might be spurious (random search terms may give false positives) i don't think the use of the Dow vs SP500 is a key criticism.

Sometimes known as data dredging. https://en.wikipedia.org/wiki/Data_dredging

This has led to some interesting neurological findings: Neural correlates of interspecies perspective taking in the post-mortem Atlantic Salmon

http://prefrontal.org/files/posters/Bennett-Salmon-2009.jpg via http://www.metafilter.com/85322/The-Wisdom-of-Salmon

An extreme example, illustrating a common mistake in the field. https://xkcd.com/882/ has the gist of it.

The "Salmon study" was an illustration of the necessity of correcting for multiple comparisons in voxel-wise testing. The problem it's addressing isn't itself data dredging, based on the definition linked above on Wikipedia.

Uncorrected multiple comparisons are, of course, a big portion of the statistical dubiousness inherent it "data dredging"

Logistic regression and other parametric, non-regularized linear learners tend to do poorly with NLP forecasting -type modeling. (They usually overfit.)

You're right.

That's why you build another threshold-type learner and then apply logistic regression to convert the score produced by learner A into a probability score.

Then you can tune at the exact point of the precision-recall curve that maximizes business value.

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