Market Model Thingy

[CallMeIshmael]

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When you have a lot of data and you try to do things with it you will find lots of relationships, most of which have no predictive ability. It's called data mining. I think a lot of the time you just have to ask yourself if it intuitively makes sense, ie you can say a low price/book strategy makes sense to outperform or buying tax loss stocks at the end of the year will outperform in the next year. But when you have 41 variables all mashed up and you think you have something meaningful then that wouldn't make sense to me.

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Obviously predictive ability is the key here, I certainly agree to that. Regressing data means nothing if what is about to happen isnt related to what has already happened.


But, the model produced gusses for the top 100 movers for each day over 15 years that were 19.96 and 26.30 (for each 2000 day period) standard deviations above what you'd expect if the model had no predictive ability (technically speaking its not binomial so this isnt 100% correct, but its close, and doing it right wont change the conclusion)


Also, at least to someone without much knowledge of the market, a model like this does make intuitive sense. How a stock is doing today relative to its past recent prices, its max/min over the previous 2 months, and how much volume its been trading for over the past few days, seem like they should have a small correlation to how it will to today.


(just to note: 41 = 10 variables * 4 transformations (x,x^2,ln(x),log10(x)) + 1 constant)