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Equity style allocation: A nonparametric approach

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Abstract

In this article we provide a framework to assist with style allocation in Asian equity funds. We implement a nonparametric methodology to capture short-term stable time-varying relationships of otherwise long-term unstable relationships between numerous macroeconomic variables and style returns. We find that a nonparametric forecasting methodology produces positive performance after allowing for transaction costs, while the equivalent parametric forecasts are negative. The model can be implemented through tilting a funds style exposure to enhance performance. Even in the context of a long-only fund, the style exposures of the proposed model can be implemented as long–short exposures relative to a benchmark. Because the model is presented as a self financing market-neutral model, its implementation can be leveraged directly in a market-neutral fund or indirectly as (leveraged) style exposures in a long-only fund.

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Notes

  1. Although we accept an alternative ‘bottom-up’ approach of using style indices characteristics (for example, the momentum or valuation of a style index) can be used to forecast style returns as perfectly valid, we consider it as beyond the scope of this article.

  2. iShares exist in MSCI US Size, Value, Momentum and Quality factors.

  3. Asness and Frazzini (2013) show that the classical FF3 HML factor is best thought of as an 80/20 combination of value and momentum.

  4. Bernanke and Boivin (2001) reconfirm the findings in Stock and Watson papers that large data sets can be utilized to improve forecast accuracy.

  5. Early papers describing nonparametric regression include Rosenblatt (1956), Parzen (1962), Watson (1964), Nadaraya (1964) and Sheather and Jones (1991).

  6. The Bloomberg codes for the variables are available upon request.

  7. This technique is also known as the latent root criterion, or the eigenvalue-one criterion.

  8. Turlach (1993) provides a summary of some of the possible choices.

  9. A description of some of these methods and their implementation in statistical packages is provided in Sheather (2004).

  10. Bloomberg categories {1, 16, 18, 2, 5, 19} and {6, 20, 3}.

  11. See Table 2 for the definition of nrd.

  12. The regression results are available upon request.

  13. Sometimes referred to as the mean square prediction error.

  14. A linear regression on a constant is equivalent to a 1-sample t-test.

  15. The results of these regressions are available upon request.

  16. The results from these regressions are available upon request.

  17. Available upon request.

  18. Tests related to OOS predictive models are described more fully in Clark and West (2007) and Clark and McCracken (2012).

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Correspondence to Mohan Subbiah.

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Subbiah, M., Fabozzi, F. Equity style allocation: A nonparametric approach. J Asset Manag 17, 141–164 (2016). https://doi.org/10.1057/jam.2016.1

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