These factors return the representative value for the Industry of the stock. They are calculated by averaging the underlying stock factor with trimming of outliers. For example for the factor "Pr2SalesTTMInd", or the Price-to-Sales for the Industry, the underlying stock factor used is "Pr2SalesTTM" (strip off the "Ind" to get the stock factor).
NOTE: These are pre-built legacy Industry factors. See About section for important info.
How to recreate Ind factors using the Aggregate() function
1. The starting primary universe is selected. (See the table below.)
2. ADR's and OTC stocks in USA are excluded
3. The underlying stock factor is calculated
4. The values are sorted
5. Outliers are eliminated: 16.5% of values from the top and bottom
6. The remaining values are averaged.
You can calculate the Ind factors using the Aggregate() function. For example to calculate Pr2SalesTTMInd using Aggregate() use this:
Aggregate("Pr2SalesTTM", #Industry, #Avg, 16.5, #Exclude, FALSE)To exclude zeros for factors like Yield use this
Aggregate("Yield", #Industry, #Avg, 16.5, #Exclude, FALSE, TRUE)About "Ind" Factors
The Region you select determines which universe is used to compute Ind factors (industry averages). Each Region has its own Primary universe.
| Region | Primary Universe used for Ind factors |
|---|---|
| United States, Canada, North America | United States (Primary), excluding ADRs and OTC |
| North Atlantic | North Atlantic (Primary) |
| Europe | Europe (Primary) |
| Global | Global (Primary) |
| APAC & Emerging | APAC & Emerging (Primary) |
Industry averages are always calculated from the region’s Primary universe, not from the specific universe selected in your screen, ranking system, or simulation. This means the averages are the same no matter which universe you use within a region. For example, Easy to Trade US and the S&P 500 both produce identical averages.
United States: averages are calculated from the Primary universe excluding ADRs and OTC stocks.
For other groupings like Sector you can use the Aggregate function (but values will change between different universes).
To read more about why we choose average vs cap-average see the paper Peer Comparison