If you want a real world example of this. There is a phenomenon in finance called Post Earnings Announcement Drift (http://en.wikipedia.org/wiki/Post-earnings-

If you want a real world example of this. There is a phenomenon in finance called Post Earnings Announcement Drift (http://en.wikipedia.org/wiki/Post-earnings-announcement_drift http://en.wikipedia.org/wiki/Post-earnings-announcement_drif...), meaning there is a tendency for companies to move too much or too little after announcing their earnings and their price eventually snaps back to a more suitable price. I spent the better part of 3 months trying all sorts of models to figure out how to trade this. The most profitable model I could come up with.... Take each analysts who follows the company, rank them by their previous historical success at predicting earnings and then weight each analysts' estimate by the analysts rank. This grade 8 level math beat out my "fancy" regression models. I was pretty humbled by that discovery:) Hopefully my blunder can save someone else here 3 months of their life:) The only caveat to this is, beware of crowds when guessing costs nothing. When you get free guesses then the results can become useless very quickly. The analysts rankings work as a financial analyst has exactly one currency, their reputation for predicting earnings. When this goes, so does their job:)