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Factor Models for Alpha Streams

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  • Zura Kakushadze

Abstract

We propose a framework for constructing factor models for alpha streams. Our motivation is threefold. 1) When the number of alphas is large, the sample covariance matrix is singular. 2) Its out-of-sample stability is challenging. 3) Optimization of investment allocation into alpha streams can be tractable for a factor model alpha covariance matrix. We discuss various risk factors for alphas such as: style risk factors; cluster risk factors based on alpha taxonomy; principal components; and also using the underlying tradables (stocks) as alpha risk factors, for which computing the factor loadings and factor covariance matrices does not involve any correlations with alphas, and their number is much larger than that of the relevant principal components. We draw insight from stock factor models, but also point out substantial differences.

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  • Zura Kakushadze, 2014. "Factor Models for Alpha Streams," Papers 1406.3396, arXiv.org, revised Oct 2014.
    • Handle: RePEc:arx:papers:1406.3396
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    Cited by:

    1. Zura Kakushadze & Willie Yu, 2017. "How to combine a billion alphas," Journal of Asset Management, Palgrave Macmillan, vol. 18(1), pages 64-80, January.
    2. Zura Kakushadze, 2015. "A Spectral Model of Turnover Reduction," Econometrics, MDPI, vol. 3(3), pages 1-13, July.
    3. Zura Kakushadze, 2015. "Combining Alphas via Bounded Regression," Risks, MDPI, vol. 3(4), pages 1-17, November.
    4. Zura Kakushadze & Willie Yu, 2017. "Decoding Stock Market with Quant Alphas," Papers 1708.02984, arXiv.org.
    5. Zura Kakushadze & Willie Yu, 2018. "Decoding stock market with quant alphas," Journal of Asset Management, Palgrave Macmillan, vol. 19(1), pages 38-48, January.
    6. Zura Kakushadze & Willie Yu, 2016. "How to Combine a Billion Alphas," Papers 1603.05937, arXiv.org, revised Jun 2016.

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