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Applying Time Series Decomposition to Construct Index-Tracking Portfolio

Author

Listed:
  • Jun Nakayama

    (Hitotsubashi University Business School
    Nomura Asset Management Co., Ltd)

  • Daisuke Yokouchi

    (Hitotsubashi University Business School)

Abstract

This study proposes a new method for creating an index-tracking portfolio using time series decomposition. First, we construct index-tracking portfolios of stocks chosen because their price movements mimic that of the Dow-Jones Industrial Average. Our method utilizes similarities of constituent stocks to the benchmark that are assessed by distances of time series trends derived from decomposing original series. Although the portfolios chosen by our method reasonably tracked the performance of the benchmark, they did not surpass the clustering approach discussed in earlier studies. Therefore, we examined what causes tracking error and found that two causes for deficiencies in our similarity-based method, which are unintended irregular movements of holding stocks and highly correlated relationships within stocks in the portfolio. To overcome them and to improve tracking performance, we propose a similarity-balanced approach that is another index-tracking method with alternate use of similarity. Doing so improved the tracking performance by avoiding the problem of high correlation among the stocks chosen under the initial method.

Suggested Citation

  • Jun Nakayama & Daisuke Yokouchi, 2018. "Applying Time Series Decomposition to Construct Index-Tracking Portfolio," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 25(4), pages 341-352, December.
    • Handle: RePEc:kap:apfinm:v:25:y:2018:i:4:d:10.1007_s10690-018-9252-7
    DOI: 10.1007/s10690-018-9252-7
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    References listed on IDEAS

    as
    1. Diana Barro & Elio Canestrelli, 2009. "Tracking error: a multistage portfolio model," Annals of Operations Research, Springer, vol. 165(1), pages 47-66, January.
    2. 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.
    3. Nikolaos S. Thomaidis, 2013. "On the application of cointegration analysis in enhanced indexing," Applied Economics Letters, Taylor & Francis Journals, vol. 20(4), pages 391-396, March.
    4. 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.
    5. Dose, Christian & Cincotti, Silvano, 2005. "Clustering of financial time series with application to index and enhanced index tracking portfolio," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 355(1), pages 145-151.
    6. 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)

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    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.

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    More about this item

    Keywords

    Hierarchical clustering; Index-tracking; Locally weighted regression; Lowess; Portfolio management; Time series decomposition;
    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

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