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Machine Learning Risk Models

Author

Listed:
  • Zura Kakushadze
  • Willie Yu

Abstract

We give an explicit algorithm and source code for constructing risk models based on machine learning techniques. The resultant covariance matrices are not factor models. Based on empirical backtests, we compare the performance of these machine learning risk models to other constructions, including statistical risk models, risk models based on fundamental industry classifications, and also those utilizing multilevel clustering based industry classifications.

Suggested Citation

  • Zura Kakushadze & Willie Yu, 2019. "Machine Learning Risk Models," Papers 1903.06334, arXiv.org, revised Apr 2019.
    • Handle: RePEc:arx:papers:1903.06334
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    File URL: http://arxiv.org/pdf/1903.06334
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    References listed on IDEAS

    as
    1. Zura Kakushadze & Willie Yu, 2016. "Statistical Industry Classification," Journal of Risk & Control, Risk Market Journals, vol. 3(1), pages 17-65.
    2. Zura Kakushadze, 2016. "Shrinkage=factor model," Journal of Asset Management, Palgrave Macmillan, vol. 17(2), pages 69-72, March.
    3. Zura Kakushadze & Willie Yu, 2016. "Statistical Risk Models," Papers 1602.08070, arXiv.org, revised Jan 2017.
    4. Zura Kakushadze & Willie Yu, 2016. "Multifactor Risk Models and Heterotic CAPM," Papers 1602.04902, arXiv.org, revised Mar 2016.
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    Citations

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    Cited by:

    1. Zura Kakushadze & Willie Yu, 2022. "ETF Risk Models," Bulletin of Applied Economics, Risk Market Journals, vol. 9(1), pages 1-17.
    2. Emmanouil S. Rigas & Tatiana Pourliaka & Maria Papoutsoglou & Hariklia Proios, 2023. "Towards a topic modeling approach to semi-automatically detect self-reported stroke symptoms (FAST symptoms) and their correlation with aphasia types," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(2), pages 1321-1336, April.
    3. Zura Kakushadze, 2020. "Quant Bust 2020," Papers 2006.05632, arXiv.org.
    4. Zura Kakushadze & Willie Yu, 2021. "ETF Risk Models," Papers 2110.07138, arXiv.org.

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