[youngprogrammer]

I will agree that the methodology is not as rigorous as it could be but where can you prove it is "wrong"?

My blogpost shows that stock price predictions also show a terrible track record. They are wildly off and on average higher than actual results.

[cs702]

You are the one making a controversial (I'd say fantastical) claim, so you are the one who has to prove it's "right."

Here are some basic questions for you, related to the points made above:

* WHY did you remove outliers in the 10th and 90th percentiles? What happens if you don't remove them?

* WHY did you use a 10-day window centered on dates of recommendation? What happens if you use the price on the same day?

* Why did you choose those return horizons? What happens if you choose different ones?

* WHY did you pick out only the top 10 analysts? What happens if you don't?

* WHY did you not do statistical tests relating to removing outliers, significance, etc.?

* WHY did you choose those cutoffs for price, marketcap, and minimum analyst rating?

[youngprogrammer]

Outliers were removed to get a better measure of the "accuracy" of the price targets.

10 day windows were used to reduce the amount of volatility/noise in a time frame

Return horizons for 1 years was used because price targets are for one year.

Theres only 15 or so analysts I looked at.

I was doing this as an exploratory data analysis and didn't want to pull out my old stats textbook.

Cutoffs were chosen to reduce volatility of measurements since I was looking at percentages. A stock going from $1.5 to $2.0 is a 33% increase whereas the movement of $100 to $133 is significantly more impactful. Stock with lower market cap have more volatility. The minimum analyst rating was chosen to eliminate analysts with very small number of ratings as they would be unreliable.