Statistical Industry Classification

Journal of Risk & Control 3(1) (2016) 17-65, Invited Editorial

44 Pages Posted: 3 Jul 2016 Last revised: 31 Dec 2018

See all articles by Zura Kakushadze

Zura Kakushadze

Quantigic Solutions LLC; Free University of Tbilisi

Willie Yu

Duke-NUS Medical School - Centre for Computational Biology

Date Written: June 29, 2016


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.

Keywords: industry classification, clustering, cluster numbers, machine learning, statistical risk models, industry risk factors, optimization, regression, mean-reversion, correlation matrix, factor loadings, principal components, hierarchical agglomerative clustering, k-means, statistical methods, multilevel

JEL Classification: G00

Suggested Citation

Kakushadze, Zura and Yu, Willie, Statistical Industry Classification (June 29, 2016). Journal of Risk & Control 3(1) (2016) 17-65, Invited Editorial, Available at SSRN: or

Zura Kakushadze (Contact Author)

Quantigic Solutions LLC ( email )

680 E Main St #543
Stamford, CT 06901
United States
6462210440 (Phone)
6467923264 (Fax)


Free University of Tbilisi ( email )

Business School and School of Physics
240, David Agmashenebeli Alley
Tbilisi, 0159

Willie Yu

Duke-NUS Medical School - Centre for Computational Biology ( email )

8 College Road
Singapore, 169857

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