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Inflation rate tracking portfolio optimization method: Evidence from Japan

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  • Nakagawa, Kei
  • Suimon, Yoshiyuki

Abstract

Index tracking portfolio is a portfolio that tracks economic indicators or stock indexes. In this study, we propose an inflation rate tracing portfolio method that considers the portfolio turnover and the target return in the past inflation phase. The method is formulated as a quadratic programming problem, and the optimization algorithm is derived. We verify the advantages of the method using Japanese inflation rate data.

Suggested Citation

  • Nakagawa, Kei & Suimon, Yoshiyuki, 2022. "Inflation rate tracking portfolio optimization method: Evidence from Japan," Finance Research Letters, Elsevier, vol. 49(C).
    • Handle: RePEc:eee:finlet:v:49:y:2022:i:c:s1544612322003531
    DOI: 10.1016/j.frl.2022.103130
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    References listed on IDEAS

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    1. Sant'Anna, Leonardo Riegel & de Oliveira, Alan Delgado & Filomena, Tiago Pascoal & Caldeira, João Frois, 2020. "Solving the index tracking problem based on a convex reformulation for cointegration," Finance Research Letters, Elsevier, vol. 37(C).
    2. repec:bla:jfinan:v:55:y:2000:i:4:p:1655-1703 is not listed on IDEAS
    3. 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.
    4. Sergei P. Sidorov & Alexey R. Faizliev & Andrew A. Khomchenko, 2017. "Algorithms for l 1 -norm minimisation of index tracking error and their performance," International Journal of Mathematics in Operational Research, Inderscience Enterprises Ltd, vol. 11(4), pages 497-519.
    5. Russ Wermers, 2000. "Mutual Fund Performance: An Empirical Decomposition into Stock‐Picking Talent, Style, Transactions Costs, and Expenses," Journal of Finance, American Finance Association, vol. 55(4), pages 1655-1695, August.
    6. S. Penev & P. V. Shevchenko & W. Wu, 2022. "Myopic robust index tracking with Bregman divergence," Quantitative Finance, Taylor & Francis Journals, vol. 22(2), pages 289-302, February.
    7. Saejoon Kim & Soong Kim, 2020. "Index tracking through deep latent representation learning," Quantitative Finance, Taylor & Francis Journals, vol. 20(4), pages 639-652, April.
    8. Boons, Martijn & Duarte, Fernando & de Roon, Frans & Szymanowska, Marta, 2020. "Time-varying inflation risk and stock returns," Journal of Financial Economics, Elsevier, vol. 136(2), pages 444-470.
    9. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    10. Rudolf, Markus & Wolter, Hans-Jurgen & Zimmermann, Heinz, 1999. "A linear model for tracking error minimization," Journal of Banking & Finance, Elsevier, vol. 23(1), pages 85-103, January.
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    1. Hammouda, Amira & Saeed, Asif & Vidal, Marta & Vidal-García, Javier, 2023. "On the short-term persistence of mutual fund performance in Europe," Research in International Business and Finance, Elsevier, vol. 65(C).

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