No clever modeling is going to generate 3000% quarterly returns in mostly efficient markets without taking significant risk. I do this kind of stuff for a living. Either the model is excessively curve-fit or they didn't account for real-world concerns like slippage, latency, market impact, the bid-offer spread, and transaction costs. A typical user of their signal services won't be able to reproduce those results.

Statistical techniques have some interesting uses in quantitative finance, but it's important to find an underlying economic reason to explain why your model works. Stat arb <http://en.wikipedia.org/wiki/Statistical_arbitrage>; is a common strategy, but there are sensible reasons why it (sometimes) works. If two securities are in the same industry, they'll tend to move in tandem since they're affected by similar factors. Thorp's articles (linked in the Wikipedia page) are worth a read if it's something that interests you.

Also, the difficult thing about modeling financial markets vs. other phenomena is that there's a huge incentive for market participants to avoid leaking information. Most inefficiencies are arbitraged away quickly or players who "show their hands" wise up; the market is a harsh mistress. Maybe years ago a big execution in a particular stock was indicative of its direction, but buy-side traders are smarter now and use algorithmic strategies that split orders temporally and physically across exchanges or trade in dark pools to avoid information leakage. There's not nearly as much incentive for you to hide your Google searches or ad clicks, so there's more opportunity for useful (and profitable) modeling using these techniques.