  Accurate industry classification is critical for many areas of portfolio
management, yet the traditional single-industry framework of the Global
Industry Classification Standard (GICS) struggles to comprehensively represent
risk for highly diversified multi-sector conglomerates like Amazon. Previously,
we introduced the Multi-Industry Simplex (MIS), a probabilistic extension of
GICS that utilizes topic modeling, a natural language processing approach.
Although our initial version, MIS-1, was able to improve upon GICS by providing
multi-industry representations, it relied on an overly simple architecture that
required prior knowledge about the number of industries and relied on the
unrealistic assumption that industries are uncorrelated and independent over
time. We improve upon this model with MIS-2, which addresses three key
limitations of MIS-1 : we utilize Bayesian Non-Parametrics to automatically
infer the number of industries from data, we employ Markov Updating to account
for industries that change over time, and we adjust for correlated and
hierarchical industries allowing for both broad and niche industries (similar
to GICS). Further, we provide an out-of-sample test directly comparing MIS-2
and GICS on the basis of future correlation prediction, where we find evidence
that MIS-2 provides a measurable improvement over GICS. MIS-2 provides
portfolio managers with a more robust tool for industry classification,
empowering them to more effectively identify and manage risk, particularly
around multi-sector conglomerates in a rapidly evolving market in which new
industries periodically emerge.
