Post 14. World-GDP weighted momentum works; US-weighted momentum doesn't ??

My model has powerful significance. Normal significance means a T-stat from a regression > 1.96 and less than 1 in 20 Monte Carlo tests beating a model. My model has a T-stat > 6 and only 1 in 1,000,000 Monte Carlo tests beat my model. Academics have noted, however, it's "out-of-sample" (OOS) performance that really matters. Given I published my model in July I now have six months of OOS data. The table to the right shows an OOS WinRatio of 70%. 

OK, so my model works, really works, and its story, momentum, has pedigree -it's  even accepted by academics who preach market efficiency. But I can't explain why only GDP-weighted momentum works. Why can't US-weighted momentum predict the US market? I've spent time with accepted statistical modeling and with some questionable data snooping techniques yet neither could find any relation between SPY AM moves and SPY PM moves.

Value	SPY  PM Win Ratio Model days	SPY PM Win Ratio All Days	Significance
US-weighted (Data-snooped model, hundreds of parameters)	57.3%	55.8%	None
World-GDP weighted	67.8%	55.8%	Only 1 out of 1,000,000 random trials better

The first row above shows the average data-snooped result. It has very little variance. All models produced about the same results as all days. It's as if an "invisible hand" makes the correlation zero. And yet the power of a GDP-weighted momentum model is undebatable. Thoughts from readers will be appreciated.