# Learned Sectors: A fundamentals-driven sector reclassification project

## TL;DR

This paper introduces a new, fundamentals-driven sector classification method that outperforms traditional heuristics like GICS in risk-adjusted returns by using a hierarchical clustering approach based on company fundamentals.

## Contribution

The paper presents a novel sector classification heuristic based on company fundamentals and a backtest-driven evaluation tool, outperforming traditional sectorization methods.

## Key findings

- Optimal learned sector universe identified with 17 sectors using complete linkage clustering.
- Learned sectors outperform GICS in risk-adjusted returns in backtests.
- Fundamentals-based classification provides better risk diversification.

## Abstract

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.