Isn’t the market allocation contribution (Global - Reading 42) the same as the pure sector allocation (Domestic - Reading 41)? Why are the formulas different? Global multiples by the sector benchmark only and domestic multiples by the differential of the sector benchmark and the Global Benchmark.
And for security selection they are different also. On a global attribution (Reading 42), we use the weight in the portfolio while on a domestic level (Reading 41) we use the weight in the sector benchmark.
I went through this same beating last year, and I can tell you that I eventually stopped trying to figure out why there was a difference and just memorize it. It can be difficult not confusing the two, so I suggest you make some mnemonics for it. This whole section = tired head for me. Someone did have what appeared to be an explanation for the difference last year, but it was complex and looked like more trouble than it was worth to understand, it had to do with the consolidation of all the formulas coming out the same, if I remember correctly.
Reading 41 and 42 use different attribution models. Reading 41 uses Brinson-Fachler (which is generally the industry standard for equity attribution), and Reading 42 uses Brinson-Hood-Beebower. These two different models have a different assumption with respect to the allocation effect. The curriculum doesn’t think to mention this at all so it’s confusing if you’re not familiar with the material. However, the selection and interaction effects also differ. In Reading 41, the interaction term is separate, but a lot of people don’t really want to see this term so it will get collapsed into the selection term, which is why selection is different between the two readings. Using either BF or BHB you can decide if you want to show it or not (and adjust the formulas accordingly). They don’t explain this either…go figure. Does that help?
exactly as shown mathematically above, however I didn’t know at all about the model names - BF or BHB, so thanks darkstar for your enlightening! it is always good to learn something!