ISSN: 2056-3736 (Online Version) | 2056-3728 (Print Version)
Zura Kakushadze and Willie Yu
Correspondence: Zura Kakushadze , zura@quantigic.com
Quantigic Solutions LLC, USA
pdf (389.1 Kb) | doi:
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.
Ιndustry 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.
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