IDEAS home Printed from https://ideas.repec.org/a/taf/quantf/v15y2015i2p359-370.html
   My bibliography  Save this article

Selection of balanced portfolios to track the main properties of a large market

Author

Listed:
  • Donatien Tafin Djoko
  • Yves Till�

Abstract

Index-based investment products are becoming increasingly popular among passive managers. So far, empirical studies have focused on complex heuristic-related optimization techniques. In this article, we adopt a different perspective and apply a survey sampling framework in the context of stock market tracking. We describe a novel and automatic method that enables us to construct a small portfolio to track the Total Market Capitalization (TMC). The constructed portfolio is randomly selected using a new method of balanced sampling. Empirical studies are performed on constituents of the S&P500. Our findings suggest that balanced sampling portfolios can efficiently track a market.

Suggested Citation

  • Donatien Tafin Djoko & Yves Till�, 2015. "Selection of balanced portfolios to track the main properties of a large market," Quantitative Finance, Taylor & Francis Journals, vol. 15(2), pages 359-370, February.
    • Handle: RePEc:taf:quantf:v:15:y:2015:i:2:p:359-370
    DOI: 10.1080/14697688.2013.859389
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/14697688.2013.859389
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/14697688.2013.859389?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Corielli, Francesco & Marcellino, Massimiliano, 2006. "Factor based index tracking," Journal of Banking & Finance, Elsevier, vol. 30(8), pages 2215-2233, August.
    2. Dietmar Maringer & Olufemi Oyewumi, 2007. "Index tracking with constrained portfolios," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 15(1‐2), pages 57-71, January.
    3. Laurent Barras & Olivier Scaillet & Russ Wermers, 2010. "False Discoveries in Mutual Fund Performance: Measuring Luck in Estimated Alphas," Journal of Finance, American Finance Association, vol. 65(1), pages 179-216, February.
    4. Victor DeMiguel & Lorenzo Garlappi & Francisco J. Nogales & Raman Uppal, 2009. "A Generalized Approach to Portfolio Optimization: Improving Performance by Constraining Portfolio Norms," Management Science, INFORMS, vol. 55(5), pages 798-812, May.
    5. Sergio Focardi & Frank Fabozzi, 2004. "A methodology for index tracking based on time-series clustering," Quantitative Finance, Taylor & Francis Journals, vol. 4(4), pages 417-425.
    6. Bajgrowicz, Pierre & Scaillet, Olivier, 2012. "Technical trading revisited: False discoveries, persistence tests, and transaction costs," Journal of Financial Economics, Elsevier, vol. 106(3), pages 473-491.
    7. Kirby, Chris & Ostdiek, Barbara, 2012. "It’s All in the Timing: Simple Active Portfolio Strategies that Outperform Naïve Diversification," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 47(2), pages 437-467, April.
    8. Tu, Jun & Zhou, Guofu, 2011. "Markowitz meets Talmud: A combination of sophisticated and naive diversification strategies," Journal of Financial Economics, Elsevier, vol. 99(1), pages 204-215, January.
    9. Rüdiger Krause & Gerhard Tutz, 2006. "Genetic algorithms for the selection of smoothing parameters in additive models," Computational Statistics, Springer, vol. 21(1), pages 9-31, March.
    10. Malkiel, Burton G, 1995. "Returns from Investing in Equity Mutual Funds 1971 to 1991," Journal of Finance, American Finance Association, vol. 50(2), pages 549-572, June.
    11. Desislava Nedyalkova & Yves Tillé, 2008. "Optimal sampling and estimation strategies under the linear model," Biometrika, Biometrika Trust, vol. 95(3), pages 521-537.
    12. Jean-Claude Deville & Yves Tille, 2004. "Efficient balanced sampling: The cube method," Biometrika, Biometrika Trust, vol. 91(4), pages 893-912, December.
    13. Canakgoz, N.A. & Beasley, J.E., 2009. "Mixed-integer programming approaches for index tracking and enhanced indexation," European Journal of Operational Research, Elsevier, vol. 196(1), pages 384-399, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Julio Cezar Soares Silva & Adiel Teixeira de Almeida Filho, 2023. "A systematic literature review on solution approaches for the index tracking problem in the last decade," Papers 2306.01660, arXiv.org, revised Jun 2023.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hsu, Po-Hsuan & Han, Qiheng & Wu, Wensheng & Cao, Zhiguang, 2018. "Asset allocation strategies, data snooping, and the 1 / N rule," Journal of Banking & Finance, Elsevier, vol. 97(C), pages 257-269.
    2. Sant’Anna, Leonardo R. & Filomena, Tiago P. & Caldeira, João F., 2017. "Index tracking and enhanced indexing using cointegration and correlation with endogenous portfolio selection," The Quarterly Review of Economics and Finance, Elsevier, vol. 65(C), pages 146-157.
    3. Yu Zheng & Timothy M. Hospedales & Yongxin Yang, 2018. "Diversity and Sparsity: A New Perspective on Index Tracking," Papers 1809.01989, arXiv.org, revised Feb 2020.
    4. Yu Zheng & Bowei Chen & Timothy M. Hospedales & Yongxin Yang, 2019. "Index Tracking with Cardinality Constraints: A Stochastic Neural Networks Approach," Papers 1911.05052, arXiv.org, revised Nov 2019.
    5. Michael Curran & Patrick O'Sullivan & Ryan Zalla, 2020. "Can Volatility Solve the Naive Portfolio Puzzle?," Papers 2005.03204, arXiv.org, revised Feb 2022.
    6. Chinco, Alex & Neuhierl, Andreas & Weber, Michael, 2021. "Estimating the anomaly base rate," Journal of Financial Economics, Elsevier, vol. 140(1), pages 101-126.
    7. Lassance, Nathan & Vanderveken, Rodolphe & Vrins, Frédéric, 2022. "On the optimal combination of naive and mean-variance portfolio strategies," LIDAM Discussion Papers LFIN 2022006, Université catholique de Louvain, Louvain Finance (LFIN).
    8. Bredin, Don & Cuthbertson, Keith & Nitzsche, Dirk & Thomas, Dylan C., 2014. "Performance and performance persistence of UK closed-end equity funds," International Review of Financial Analysis, Elsevier, vol. 34(C), pages 189-199.
    9. Bj�rn Fastrich & Sandra Paterlini & Peter Winker, 2014. "Cardinality versus q -norm constraints for index tracking," Quantitative Finance, Taylor & Francis Journals, vol. 14(11), pages 2019-2032, November.
    10. Andrea Rigamonti, 2024. "Can machine learning make technical analysis work?," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 38(3), pages 399-412, September.
    11. Chavez-Bedoya, Luis & Rosales, Francisco, 2021. "Reduction of estimation risk in optimal portfolio choice using redundant constraints," International Review of Financial Analysis, Elsevier, vol. 78(C).
    12. Johannes Bock, 2018. "An updated review of (sub-)optimal diversification models," Papers 1811.08255, arXiv.org.
    13. Hwang, Inchang & Xu, Simon & In, Francis, 2018. "Naive versus optimal diversification: Tail risk and performance," European Journal of Operational Research, Elsevier, vol. 265(1), pages 372-388.
    14. Yang, Junmin & Cao, Zhiguang & Han, Qiheng & Wang, Qiyu, 2019. "Tactical asset allocation on technical trading rules and data snooping," Pacific-Basin Finance Journal, Elsevier, vol. 57(C).
    15. Kircher, Felix & Rösch, Daniel, 2021. "A shrinkage approach for Sharpe ratio optimal portfolios with estimation risks," Journal of Banking & Finance, Elsevier, vol. 133(C).
    16. Hsu, Po-Hsuan & Taylor, Mark P. & Wang, Zigan & Li, Yan, 2024. "The out-of-sample performance of carry trades," Journal of International Money and Finance, Elsevier, vol. 143(C).
    17. Cesarone, Francesco & Lampariello, Lorenzo & Sagratella, Simone, 2019. "A risk-gain dominance maximization approach to enhanced index tracking," Finance Research Letters, Elsevier, vol. 29(C), pages 231-238.
    18. Reza Bradrania & Davood Pirayesh Neghab & Mojtaba Shafizadeh, 2022. "State-dependent stock selection in index tracking: a machine learning approach," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 36(1), pages 1-28, March.
    19. Füss, Roland & Miebs, Felix & Trübenbach, Fabian, 2014. "A jackknife-type estimator for portfolio revision," Journal of Banking & Finance, Elsevier, vol. 43(C), pages 14-28.
    20. Platanakis, Emmanouil & Sutcliffe, Charles & Ye, Xiaoxia, 2021. "Horses for courses: Mean-variance for asset allocation and 1/N for stock selection," European Journal of Operational Research, Elsevier, vol. 288(1), pages 302-317.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:quantf:v:15:y:2015:i:2:p:359-370. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/RQUF20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.