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

ArXiv ID: 2407.16437 “View on arXiv”

Authors: Unknown

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 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.

Keywords: Industry classification, Bayesian Non-Parametrics, Topic modeling, Multi-Industry Simplex (MIS), Risk management, Equities

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 6.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced mathematical techniques like Bayesian non-parametrics and Markov updating, requiring dense probabilistic modeling and LaTeX notation. It also includes out-of-sample testing and specific data pre-processing details for backtest-readiness, though it mentions future work for richer backtests.

  flowchart TD
    A["Research Goal:<br>Improve industry classification for multi-sector firms<br>like Amazon, moving beyond static GICS"] --> B["Data Inputs:<br>Company fundamental data & textual disclosures"]
    B --> C{"Key Methodology - MIS-2"}
    C --> D["Bayesian Non-Parametrics<br>Infers # of industries automatically"]
    C --> E["Markov Updating<br>Models temporal evolution of industries"]
    C --> F["Correlation Adjustment<br>Hierarchical structure (broad & niche)"]
    D & E & F --> G["Computational Process:<br>Probabilistic Multi-Industry Assignment"]
    G --> H["Key Findings/Outcomes"]
    H --> I["Out-of-sample test:<br>MIS-2 outperforms GICS in<br>future correlation prediction"]
    H --> J["Result:<br>Robust tool for risk management<br>in evolving markets"]