Via google PDF viewer
https://docs.google.com/gview?url=http://nerdsonwallstreet.t...
Terrible generalization of polynomials is useful for demonstrating overfitting (I've done it myself in tutorials). However, responsible tutorials should mention that the other obvious lesson is that the polynomials (1, x, x², x³, etc) are a terrible set of basis functions for regression. Don't just watch for overfitting, but use a sensible regression model! For complicated fits some methods to consider are: local regression, splines, various artificial neural nets, or Gaussian processes.
What's with the [scribd] tag when direct linking to a .pdf file? It's becoming common, but I can't understand it.
"If the NFL wins, the market goes up, otherwise, it takes a dive. What’s happened over the last thirty years? Well, most of the time, the NFL wins the Superbowl"
Standards of editing have really gone down over the years. The "NFL" always wins the Superbowl...
TLDR: Correlation != causation; if you have high dimensional data, you can always find a correlation, but it's probably meaningless; polynomial wiggle is a bitch, so don't fit high dimensional polynomials to your data.
Related question: if I despite the warnings fancy my chances at this sort of thing, what sort of historical data can I get? Is free [machine-readable] stock market data easy to come by, or impossible?
Does interpolation ever work in forecasting?
My gut instinct would be that markets and human systems are chaotic in nature. Even in the most chaotic systems, if you look at a suitably small sample, you can see some correlations and patterns between different factors which really don't exist. These are mirage correlations.
Take the lorenz attractor as an example. At some points, it will cycle on the same "wing" of the butterfly many times. But betting that it will do it again is a really lousy bet.
Polynomial approximation and curve fitting in general works when we're trying to explicate relationships between variables in a problem space in which we understand causal linkages very well (and they're constant) - it can be really useful in engineering.