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INDUSTRY AND SECTOR / IND AGGREGATE FACTORS / PRICE & VOLUME
Pr52W%ChgInd
Full Description

These factors return the representative value for the performance of the Industry of the stock. They are calculated by averaging the underlying stock factor returns with trimming of outliers using the Aggregate() function.

For a more traditional cap-weighted industry performance you can either:

  1. Create your own using #CapAvg in the Aggregate function
  2. Use a "Taxonomy Time Series"

NOTE: These are pre-built legacy Industry factors. See About section for important info.  

How to recreate Ind factors using Aggregate

1. Select the starting universe:

United States (Incl. Foreign Primary)
or
Europe (Primary)

2. ADR's 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.

For example to calculate Pr4W%ChgInd using Aggregate() use this:

Aggregate("Pr4W%Chg", #Industry, #Avg, 16.5, #Exclude, FALSE)

Alternate: Predefined Taxonomy Time Series

These time series are built compounding cap weighted daily returns of the constituents. They are accessible to technical functions by specified #Industry for the "series" parameter. For example to calculate the 4W% return of the industry use this:

Ret%Chg(20,#Industry)

There are also series for  #Sector, #SubSector and #SubIndustry. In addition there are distinct series depending on the starting universe region,  either: USA, Canada, North America, Europe, or North Atlantic.

NOTE: you can see these time series in the stock pages by selecting a "Taxonomy Benchmark"

 

About "Ind" Factors

These are defined in single region universes only: either Europe or North America. If you use the North Atlantic universe they generate an error. 

The values do not change between different universes of the same region. For example Easy to Trade USA, S&P500 or your own custom universe.

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