  A geometric analysis of the time series of returns has been performed in the
past and it implied that the most of the systematic information of the market
is contained in a space of small dimension. Here we have explored subspaces of
this space to find out the relative performance of portfolios formed from the
companies that have the largest projections in each one of the subspaces. It
was found that the best performance portfolios are associated to some of the
small eigenvalue subspaces and not to the dominant directions in the distances
matrix. This occurs in such a systematic fashion over an extended period
(1990-2008) that it may not be a statistical accident.
