Multi-Industry Simplex 2.0 : Temporally-Evolving Probabilistic Industry Classification
Maksim Papenkov

TL;DR
This paper introduces MIS-2, an advanced probabilistic industry classification model that automatically infers industry structures over time, improving risk assessment for diversified conglomerates compared to traditional GICS.
Contribution
MIS-2 enhances previous models by using Bayesian Non-Parametrics, Markov Updating, and hierarchical industry adjustments for dynamic and correlated industry classification.
Findings
MIS-2 outperforms GICS in future correlation prediction.
Automatically infers the number of industries from data.
Provides a more robust industry classification for risk management.
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
