Let's assume we have 6 companies with these returns:
10%, 8%, 7%, 7%, 1%, -5%
If I want to cut them by terciles, the grouping will be:
G1: 10%, 8% G2: 7%, 7% G3: -5%, 1%
If I want to cut them by the range of all the values into 3 groups, the grouping will be:
G1 (10% to 5%): 10%, 8%, 7%, 7% G2 (5% to 0%) : 1% G3 (0% to -5%): -5%
As you can see above, the second case splits the groups by the range of each group, where the range of each group is the same and is given by the number of breaks I want to do. To give another example: If the maximum return is 20% and the minimum is 0%, and I want four groups, the ranges would be (0-5%, 5-10%, 10-15%, 15-20%) no matter how many observations fit in each group.
My question is: How it's called the second process? In
r I use the function
cut(). Maybe it's a silly question, but I'm looking for the formal name of that process as I'm looking to understand what are the differences and possible implications when using one or another method to get conclusions from the data. For example, I don't have 6 companies, I actually have near 2k, and I'm trying to classify their returns by certain conditions given the distribution of their returns. However, I'm facing the problem above. So, to be specific:
What are the statistical biases when using percentiles/quantiles vs the "cut() method" to classify groups (or returns in this case)? Is it plain wrong classifying them by "the cut() method"?
R code used (the "cut method"):
cut(vector_of_returns, breaks=3, labels=c("Bad news", "Neutral","Good news")