  In this paper, I discuss a method to tackle the issues arising from the small
data-sets available to data-scientists when building price predictive
algorithms that use monthly/quarterly macro-financial indicators. I approach
this by training separate classifiers on the equivalent dataset from a range of
countries. Using these classifiers, a three level meta learning algorithm (MLA)
is developed. I develop a transform, ASG, to create a country agnostic proxy
for the macro-financial indicators. Using these proposed methods, I investigate
the degree to which a predictive algorithm for the US 5Y bond price,
predominantly using macro-financial indicators, can outperform an identical
algorithm which only uses statistics deriving from previous price.
  This was an undergraduate project, subsequently the research was not
exhaustive.
