Peter Ciampi
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​​I'm a Private investor focused on closed-end fund events and statistical arbitrage of ETFs

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

1/7/2018

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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.
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    May23 Return .31%


    Out-of-Sample return 62%








    Updated at 12 and 4:15pm on trade days

    Author

    Peter Ciampi is the Managing Director of CEF Events LTD, a British Virgin Islands business company and the Managing Partner of Time-Zone Arbitrage,a Delaware LP. Both companies invest in special situations of closed-end funds and statistical arbitrage of international ETFs.

@ 2017 Peter Ciampi
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