  Market sectors play a key role in the efficient flow of capital through the
modern Global economy. We analyze existing sectorization heuristics, and
observe that the most popular - the GICS (which informs the S&P 500), and the
NAICS (published by the U.S. Government) - are not entirely quantitatively
driven, but rather appear to be highly subjective and rooted in dogma. Building
on inferences from analysis of the capital structure irrelevance principle and
the Modigliani-Miller theoretic universe conditions, we postulate that
corporation fundamentals - particularly those components specific to the
Modigliani-Miller universe conditions - would be optimal descriptors of the
true economic domain of operation of a company. We generate a set of potential
candidate learned sector universes by varying the linkage method of a
hierarchical clustering algorithm, and the number of resulting sectors derived
from the model (ranging from 5 to 19), resulting in a total of 60 candidate
learned sector universes. We then introduce reIndexer, a backtest-driven sector
universe evaluation research tool, to rank the candidate sector universes
produced by our learned sector classification heuristic. This rank was utilized
to identify the risk-adjusted return optimal learned sector universe as being
the universe generated under CLINK (i.e. complete linkage), with 17 sectors.
The optimal learned sector universe was tested against the benchmark GICS
classification universe with reIndexer, outperforming on both absolute
portfolio value, and risk-adjusted return over the backtest period. We conclude
that our fundamentals-driven Learned Sector classification heuristic provides a
superior risk-diversification profile than the status quo classification
heuristic.
