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Cloning mutual fund returns

Author

Listed:
  • Auer, Benjamin R.
  • Schuhmacher, Frank
  • Niemann, Sebastian

Abstract

Motivated by the increasing competition between traditional mutual funds and highly accessible exchange traded funds (ETFs), we analyze the potential of the latter to replicate the returns of the former. In a penalized big data regression setup, we find that clone portfolios perform remarkably well both in sample and out of sample. However, depending on the investment style of a targeted mutual fund, a rather large number of ETFs can be required for cloning.

Suggested Citation

  • Auer, Benjamin R. & Schuhmacher, Frank & Niemann, Sebastian, 2023. "Cloning mutual fund returns," The Quarterly Review of Economics and Finance, Elsevier, vol. 90(C), pages 31-37.
    • Handle: RePEc:eee:quaeco:v:90:y:2023:i:c:p:31-37
    DOI: 10.1016/j.qref.2023.04.006
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    1. Uniejewski, Bartosz & Marcjasz, Grzegorz & Weron, Rafał, 2019. "Understanding intraday electricity markets: Variable selection and very short-term price forecasting using LASSO," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1533-1547.
    2. Oguzhan Cepni & I. Ethem Guney & Norman R. Swanson, 2020. "Forecasting and nowcasting emerging market GDP growth rates: The role of latent global economic policy uncertainty and macroeconomic data surprise factors," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(1), pages 18-36, January.
    3. Laurens Swinkels & Pieter Van Der Sluis, 2006. "Return-based style analysis with time-varying exposures," The European Journal of Finance, Taylor & Francis Journals, vol. 12(6-7), pages 529-552.
    4. SE Satchell & S Hwang, 2001. "Tracking error: Ex ante versus ex post measures," Journal of Asset Management, Palgrave Macmillan, vol. 2(3), pages 241-246, December.
    5. Mateus, Irina B. & Mateus, Cesario & Todorovic, Natasa, 2019. "Review of new trends in the literature on factor models and mutual fund performance," International Review of Financial Analysis, Elsevier, vol. 63(C), pages 344-354.
    6. Joachim Freyberger & Andreas Neuhierl & Michael Weber & Andrew KarolyiEditor, 2020. "Dissecting Characteristics Nonparametrically," Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2326-2377.
    7. Chen, Hsiu-Lang & Gao, Sheldon & Hu, Xiaoqing, 2012. "Closing and cloning in open-end mutual funds," Journal of Banking & Finance, Elsevier, vol. 36(4), pages 1210-1223.
    8. Fernandez-Perez, Adrian & Fuertes, Ana-Maria & Miffre, Joëlle, 2019. "A comprehensive appraisal of style-integration methods," Journal of Banking & Finance, Elsevier, vol. 105(C), pages 134-150.
    9. Moraes, Fernando & Cavalcante-Filho, Elias & De-Losso, Rodrigo, 2021. "Unskilled fund managers: Replicating active fund performance with few ETFs," International Review of Financial Analysis, Elsevier, vol. 78(C).
    10. Cuthbertson, Keith & Nitzsche, Dirk & O'Sullivan, Niall, 2008. "UK mutual fund performance: Skill or luck?," Journal of Empirical Finance, Elsevier, vol. 15(4), pages 613-634, September.
    11. Bala Rajaratnam & Steven Roberts & Doug Sparks & Onkar Dalal, 2016. "Lasso regression: estimation and shrinkage via the limit of Gibbs sampling," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(1), pages 153-174, January.
    12. Ter Horst, J.R. & Nijman, T.E. & de Roon, F.A., 2004. "Evaluating style analysis," Other publications TiSEM 8a501733-7a06-4399-8a43-0, Tilburg University, School of Economics and Management.
    13. Eugene F. Fama & Kenneth R. French, 2010. "Luck versus Skill in the Cross‐Section of Mutual Fund Returns," Journal of Finance, American Finance Association, vol. 65(5), pages 1915-1947, October.
    14. Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
    15. Medeiros, Marcelo C. & Vasconcelos, Gabriel F.R., 2016. "Forecasting macroeconomic variables in data-rich environments," Economics Letters, Elsevier, vol. 138(C), pages 50-52.
    16. Francis X. Diebold, 2015. "Comparing Predictive Accuracy, Twenty Years Later: A Personal Perspective on the Use and Abuse of Diebold-Mariano Tests," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(1), pages 1-1, January.
    17. Wei Qian & Yuhong Yang, 2013. "Model selection via standard error adjusted adaptive lasso," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 65(2), pages 295-318, April.
    18. Jiang, He & Luo, Shihua & Dong, Yao, 2021. "Simultaneous feature selection and clustering based on square root optimization," European Journal of Operational Research, Elsevier, vol. 289(1), pages 214-231.
    19. Jun Duanmu & Yongjia Li & Alexey Malakhov, 2020. "Capturing hedge fund risk factor exposures: Hedge fund return replication with ETFs," The Financial Review, Eastern Finance Association, vol. 55(3), pages 405-431, August.
    20. Frank Schuhmacher & Hendrik Kohrs & Benjamin R. Auer, 2021. "Justifying Mean-Variance Portfolio Selection when Asset Returns Are Skewed," Management Science, INFORMS, vol. 67(12), pages 7812-7824, December.
    21. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    22. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
    23. Auer, Benjamin R. & Schuhmacher, Frank, 2013. "Performance hypothesis testing with the Sharpe ratio: The case of hedge funds," Finance Research Letters, Elsevier, vol. 10(4), pages 196-208.
    24. Dimitris Bertsimas & Leonid Kogan & Andrew W. Lo, 2001. "Hedging Derivative Securities and Incomplete Markets: An (epsilon)-Arbitrage Approach," Operations Research, INFORMS, vol. 49(3), pages 372-397, June.
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    More about this item

    Keywords

    Mutual funds; Exchange traded funds; Replication; Least absolute shrinkage and selection operator;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G23 - Financial Economics - - Financial Institutions and Services - - - Non-bank Financial Institutions; Financial Instruments; Institutional Investors
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

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