Statistical Industry Classification

TL;DR

This paper presents algorithms and source code for constructing multilevel statistical industry classifications, enhancing traditional classifications with data-driven clustering methods for improved quantitative trading strategies.

Contribution

It introduces complete algorithms and source code for statistical industry classification, including hybrid methods that improve existing fundamental classifications.

Findings

Backtests show clustering choices significantly impact results

Algorithms effectively create multilevel classifications

Hybrid classifications outperform traditional methods

Abstract

We give complete algorithms and source code for constructing (multilevel) statistical industry classifications, including methods for fixing the number of clusters at each level (and the number of levels). Under the hood there are clustering algorithms (e.g., k-means). However, what should we cluster? Correlations? Returns? The answer turns out to be neither and our backtests suggest that these details make a sizable difference. We also give an algorithm and source code for building "hybrid" industry classifications by improving off-the-shelf "fundamental" industry classifications by applying our statistical industry classification methods to them. The presentation is intended to be pedagogical and geared toward practical applications in quantitative trading.