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Style-neutral funds of funds: Diversification or deadweight?

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Abstract

This article aims to determine whether style-neutral portfolios built out of value and growth equity/mutual funds deliver benefits in terms of returns and diversification or whether they result in costly benchmark tracking products. We analyze style-neutral portfolios by building synthetic funds of funds (FoFs) out of both value- and growth-oriented equity funds and contrast their properties with the applicable benchmark and with style FoFs. Although a beneficial effect with respect to diversification and a resulting reduction in return dispersion can be seen, the simulated FoFs do not deliver a general risk-adjusted outperformance against the benchmark or the better performing style of a period. The variety of results indicates that FoFs may indeed benefit from investing in a style-neutral portfolio of growth and value funds, but only given that FoF managers are able to select the well-performing funds of the respective styles. In addition, we find that being able to shift between styles over time may lead to better results than locking FoFs into being style neutral.

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Notes

  1. Another popular extension is provided by the four-factor model of Carhart (1997), who augmented the analysis with a momentum factor. See Haugen and Baker (1996) for a discussion of 50 possible influencing factors.

  2. See Brown et al (2004) for a discussion of fees on fees in FoFs.

  3. Connelly acknowledges that this measure is obtained from a presentation by William Jacques at a conference on active versus passive investment management sponsored by the Institute for International Research.

  4. See Chan et al (2005) for an examination of managers’ foreign and domestic biases.

  5. According to information from Morningstar, three value and 13 growth funds were obsolete from the data set chosen. However, the focus of this study is the effect of style-neutrality, such that the survivorship influence is not crucial.

  6. While some funds report prices at the end of the day, others report prices for the day before. The latter method, called forward-pricing, aims at preventing speculative trading against the fund.

  7. Other possibilities include setting the upper and lower percentage to equal values in order to obtain a symmetric reward-to-risk measure rather than one that controls for large underperformances that serve as risk measures in the denominator.

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Acknowledgements

The authors thank Frank J. Fabozzi for helpful comments. We bear responsibility for any remaining errors. The views expressed herein are those of the authors, and do not necessarily represent those of Credit Suisse.

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Authors and Affiliations

  1. KIT Karlsruhe Institute of Technology Chair of Statistics, Econometrics & Mathematical Finance, Karlsruhe, 76128, Germany

    Svetlozar T Rachev

Authors
  1. Michael Stein
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  2. Svetlozar T Rachev
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Correspondence to Svetlozar T Rachev.

Appendix

Appendix

Figure A1, Figure A2, Figure A3 and Figure A4, Table A1, Table A2 and Table A3

Table A1 Average statistics for FoFs versus the S&P 500 over all 209 time periods of the average of the respective statistic for 10 000 simulated portfolios for value, growth and neutral FoFs
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Table A2 Average statistics for FoFs versus the S&P 500 over all 209 time periods of the minimum of the respective statistic for 10 000 simulated portfolios for value, growth and neutral FoFs
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Table A3 Average statistics for FoFs versus the S&P 500 over all 209 time periods of the maximum of the respective statistic for 10 000 simulated portfolios for value, growth and neutral FoFs
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Figure A1
figure 6

(a) Difference in lowest annualized geometric mean return for style-neutral FoFs against the benchmark. (b) Difference in lowest annualized geometric mean return for value sub FoFs against the benchmark. (c) Difference in lowest annualized geometric mean return for growth sub FoFs against the benchmark. (d) Difference in highest annualized geometric mean return for style-neutral FoFs against the benchmark. (e) Difference in highest annualized geometric mean return for value sub FoFs against the benchmark. (f) Difference in highest annualized geometric mean return for growth sub FoFs against the benchmark.

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Figure A2
figure 7

(a) Difference in lowest annualized standard deviation for style-neutral FoFs against the benchmark. (b) Difference in lowest annualized standard deviation for value sub FoFs against the benchmark. (c) Difference in lowest annualized standard deviation for growth sub FoFs against the benchmark. (d) Difference in highest annualized standard deviation for style-neutral FoFs against the benchmark. (e) Difference in highest annualized standard deviation for value sub FoFs against the benchmark. (f) Difference in highest annualized standard deviation for growth sub FoFs against the benchmark.

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Figure A3
figure 8

(a) Difference in lowest skewness for style-neutral FoFs against the benchmark. (b) Difference in lowest skewness for value sub FoFs against the benchmark. (c) Difference in lowest skewness for growth sub FoFs against the benchmark. (d) Difference in highest skewness for style-neutral FoFs against the benchmark. (e) Difference in highest skewness for value sub FoFs against the benchmark. (f) Difference in highest skewness for growth sub FoFs against the benchmark.

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Figure A4
figure 9

(a) Difference in lowest kurtosis for style-neutral FoFs against the benchmark. (b) Difference in lowest kurtosis for value sub FoFs against the benchmark. (c) Difference in lowest kurtosis for growth sub FoFs against the benchmark. (d) Difference in highest kurtosis for style-neutral FoFs against the benchmark funds. (e) Difference in highest kurtosis for value sub FoFs against the benchmark. (f) Difference in highest kurtosis for growth sub FoFs against the benchmark.

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Stein, M., Rachev, S. Style-neutral funds of funds: Diversification or deadweight?. J Asset Manag 11, 417–434 (2011). https://doi.org/10.1057/jam.2010.5

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