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Statistical Industry Classification

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  • Zura Kakushadze
  • Willie Yu

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.

Suggested Citation

  • Zura Kakushadze & Willie Yu, 2016. "Statistical Industry Classification," Papers 1607.04883, arXiv.org, revised Dec 2018.
    • Handle: RePEc:arx:papers:1607.04883
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    References listed on IDEAS

    as
    1. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    2. Zura Kakushadze & Willie Yu, 2016. "How to Combine a Billion Alphas," Papers 1603.05937, arXiv.org, revised Jun 2016.
    3. Zura Kakushadze & Willie Yu, 2016. "Statistical Risk Models," Papers 1602.08070, arXiv.org, revised Jan 2017.
    4. Connor, Gregory & Korajczyk, Robert A, 1993. "A Test for the Number of Factors in an Approximate Factor Model," Journal of Finance, American Finance Association, vol. 48(4), pages 1263-1291, September.
    5. Sugar, Catherine A. & James, Gareth M., 2003. "Finding the Number of Clusters in a Dataset: An Information-Theoretic Approach," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 750-763, January.
    6. Zura Kakushadze & Willie Yu, 2016. "Multifactor Risk Models and Heterotic CAPM," Papers 1602.04902, arXiv.org, revised Mar 2016.
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