## About Ranking | more help |

A column's value is ranked according to *how far* it's value lies
from it's best. For any given parameter, say Price/Earnings in column C,
the companies are sorted so that column C appears in numerically
*ascending* order. The **rank** for any company
is then the number of the row where that company name
appears for this parameter. The same principle is used to determine the
column **rank** for Debt/Equity, and Price/Book.

Similarly the Return on Equity in Column D is sorted in *descending*
order, and each company's **rank** for this parameter is the rows number
where that company name appears. The same holds true for the other
parameters for which larger values are better namely:

- Revenue Growth per share Year over Year
- Earnings Growth per share Year over Year
- Net Profit Margin (NetPM)

For both types descending, and ascending parameters there is the problem of missing data. Any row,column for which the data is blank (displayed as N/A in the source of the data).

Two solutions are typically possible. First one could just ignore that
data. Clearly if one is trying to find correlations between 2 parameters
it makes little sense to compare NA to NA, or NA to some piece of data
that is available. However for purposes of Rank it seems more valid to
interpolate the missing data into the set of valid data. To do this NA's
are given their worst possible **rank**. In other words if there
were 200 companies in a sector, and 23 have a blank value for P/E then
these companies are all assigned a rank of max = 177 + ( 200-177 )/2 for
the Price/Earnings column **rank**.

Finally a composite *equal weighted* so called **H-Rank** for
all the parameters in a given row is created by summing all the individual
Column Ranks for that row. Each of the 4 Value and 2 Growth parameters
are not quite of equal **rank** because of the tie conditions created
by N/A values (as in the example above). However it is normalized by
dividing by the sample size ( max as defined above ) of
the collection *before* summation. So the summation is a number
between an idealized 0 (there are no perfect companies), and 6 (also
beyond believe that a company that bad is still in business). To further
normalize it is divided by 6, and multiplied by 100. That idealized
**rank** is now a value between 1, and 100. This is the so called H-Rank
an average of the individual parameter Ranks. A second Ranking method
called *C-Rank* treats each of those 6 normalized rank's [0-1]
as a vector along a 30 degree arc of circle. It is the area created by
these 6 vectors (forming a polygon) that is now the C-Rank making a
number between 0, and pi. That too is typically displayed as an integer
in the range [0,100].

The implicit assumption is that the 6 parameters which we are tracking for
any company are equally important. This is clearly not so. For example
financial companies (banks) often do not calculate Debt/Equity yet insurance
companies which are also in the financial *Sector* do. Does this mean
one can impose Debt/Equity statistics of insurance companies on banks ?.

Another problem occurs with certain descending parameters like

- Price to Cash Flow (PtoCF)
- Enterprise Value/EBIDTA

George Elgin | Questions or Comments |