IDEAS home Printed from https://ideas.repec.org/a/kap/compec/v63y2024i1d10.1007_s10614-022-10345-4.html
   My bibliography  Save this article

Portfolio Selection Based on EMD Denoising with Correlation Coefficient Test Criterion

Author

Listed:
  • Kuangxi Su

    (Xinyang Normal University)

  • Yinhong Yao

    (Capital University of Economics and Business)

  • Chengli Zheng

    (Central China Normal University)

  • Wenzhao Xie

    (Changjiang Securities Company Limited)

Abstract

Noise is an important factor affecting portfolio performance, how to construct an effective denoising strategy is becoming increasingly important for investors. In this study, we theoretically explain the impact of noise on portfolio and argue the necessity of denoising. Next, the empirical mode decomposition (EMD) denoising strategy based on the correlation coefficient test criterion is proposed to improve portfolio performance. In detail, EMD is used to decompose the noisy price, then, a series of correlation coefficient tests are performed to determine which intrinsic mode functions (IMFs) are noise. In the empirical analysis, we apply the proposed method to denoise the SSE 50 index’s constituents, and further test the out-of-sample performance under the mean–variance framework. The empirical results show that the proposed denoising method outperforms four common EMD, Ensemble EMD (EEMD) and wavelet denoising methods in return-risk ratio. The proposed method is the optimal denoising strategy, which can help investors improve portfolio performance to the greatest extent.

Suggested Citation

  • Kuangxi Su & Yinhong Yao & Chengli Zheng & Wenzhao Xie, 2024. "Portfolio Selection Based on EMD Denoising with Correlation Coefficient Test Criterion," Computational Economics, Springer;Society for Computational Economics, vol. 63(1), pages 391-421, January.
    • Handle: RePEc:kap:compec:v:63:y:2024:i:1:d:10.1007_s10614-022-10345-4
    DOI: 10.1007/s10614-022-10345-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10614-022-10345-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10614-022-10345-4?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. Victor DeMiguel & Lorenzo Garlappi & Raman Uppal, 2009. "Optimal Versus Naive Diversification: How Inefficient is the 1-N Portfolio Strategy?," The Review of Financial Studies, Society for Financial Studies, vol. 22(5), pages 1915-1953, May.
    2. Felix Scheller & Benjamin R. Auer, 2018. "How does the choice of Value-at-Risk estimator influence asset allocation decisions?," Quantitative Finance, Taylor & Francis Journals, vol. 18(12), pages 2005-2022, December.
    3. Zhu, Bangzhu & Han, Dong & Wang, Ping & Wu, Zhanchi & Zhang, Tao & Wei, Yi-Ming, 2017. "Forecasting carbon price using empirical mode decomposition and evolutionary least squares support vector regression," Applied Energy, Elsevier, vol. 191(C), pages 521-530.
    4. Zhu, Pengfei & Tang, Yong & Wei, Yu & Dai, Yimin & Lu, Tuantuan, 2021. "Relationships and portfolios between oil and Chinese stock sectors: A study based on wavelet denoising-higher moments perspective," Energy, Elsevier, vol. 217(C).
    5. Olivier Dessaint & Thierry Foucault & Laurent Frésard & Adrien Matray, 2019. "Noisy Stock Prices and Corporate Investment," The Review of Financial Studies, Society for Financial Studies, vol. 32(7), pages 2625-2672.
    6. Moura, Guilherme V. & Santos, André A.P. & Ruiz, Esther, 2020. "Comparing high-dimensional conditional covariance matrices: Implications for portfolio selection," Journal of Banking & Finance, Elsevier, vol. 118(C).
    7. Linlin Ma & Yuehua Tang & Juan‐Pedro Gómez, 2019. "Portfolio Manager Compensation in the U.S. Mutual Fund Industry," Journal of Finance, American Finance Association, vol. 74(2), pages 587-638, April.
    8. Hamdi, Besma & Aloui, Mouna & Alqahtani, Faisal & Tiwari, Aviral, 2019. "Relationship between the oil price volatility and sectoral stock markets in oil-exporting economies: Evidence from wavelet nonlinear denoised based quantile and Granger-causality analysis," Energy Economics, Elsevier, vol. 80(C), pages 536-552.
    9. Joel Peress & Daniel Schmidt, 2020. "Glued to the TV: Distracted Noise Traders and Stock Market Liquidity," Journal of Finance, American Finance Association, vol. 75(2), pages 1083-1133, April.
    10. An, Ning & Zhao, Weigang & Wang, Jianzhou & Shang, Duo & Zhao, Erdong, 2013. "Using multi-output feedforward neural network with empirical mode decomposition based signal filtering for electricity demand forecasting," Energy, Elsevier, vol. 49(C), pages 279-288.
    11. Terrance Odean, 1999. "Do Investors Trade Too Much?," American Economic Review, American Economic Association, vol. 89(5), pages 1279-1298, December.
    12. Mengmeng Ao & Li Yingying & Xinghua Zheng, 2019. "Approaching Mean-Variance Efficiency for Large Portfolios," The Review of Financial Studies, Society for Financial Studies, vol. 32(7), pages 2890-2919.
    13. Kondor, Imre & Pafka, Szilard & Nagy, Gabor, 2007. "Noise sensitivity of portfolio selection under various risk measures," Journal of Banking & Finance, Elsevier, vol. 31(5), pages 1545-1573, May.
    Full references (including those not matched with items on IDEAS)

    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. Zheng, Chengli & Su, Kuangxi & Yao, Yinhong, 2021. "Hedging futures performance with denoising and noise-assisted strategies," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    2. Dindo, Pietro & Massari, Filippo, 2020. "The wisdom of the crowd in dynamic economies," Theoretical Economics, Econometric Society, vol. 15(4), November.
    3. Jacobs, Heiko & Müller, Sebastian & Weber, Martin, 2014. "How should individual investors diversify? An empirical evaluation of alternative asset allocation policies," Journal of Financial Markets, Elsevier, vol. 19(C), pages 62-85.
    4. Olivier Ledoit & Michael Wolf, 2022. "Markowitz portfolios under transaction costs," ECON - Working Papers 420, Department of Economics - University of Zurich, revised Sep 2024.
    5. Wang, Albert Y. & Young, Michael, 2023. "Mood, attention, and household trading: Evidence from terrorist attacks," Journal of Financial Markets, Elsevier, vol. 66(C).
    6. Peress, Joel & Schmidt, Daniel, 2021. "Noise traders incarnate: Describing a realistic noise trading process," Journal of Financial Markets, Elsevier, vol. 54(C).
    7. Tae-Hwy Lee & Ekaterina Seregina, 2024. "Optimal Portfolio Using Factor Graphical Lasso," Journal of Financial Econometrics, Oxford University Press, vol. 22(3), pages 670-695.
    8. Hiraki, Kazuhiro & Sun, Chuanping, 2022. "A toolkit for exploiting contemporaneous stock correlations," Journal of Empirical Finance, Elsevier, vol. 65(C), pages 99-124.
    9. Khaki, Audil & Prasad, Mason & Al-Mohamad, Somar & Bakry, Walid & Vo, Xuan Vinh, 2023. "Re-evaluating portfolio diversification and design using cryptocurrencies: Are decentralized cryptocurrencies enough?," Research in International Business and Finance, Elsevier, vol. 64(C).
    10. Istvan Varga-Haszonits & Fabio Caccioli & Imre Kondor, 2016. "Replica approach to mean-variance portfolio optimization," Papers 1606.08679, arXiv.org.
    11. Guillaume Coqueret, 2016. "Empirical properties of a heterogeneous agent model in large dimensions," Post-Print hal-02088097, HAL.
    12. Al-Fayoumi, Nedal & Bouri, Elie & Abuzayed, Bana, 2023. "Decomposed oil price shocks and GCC stock market sector returns and volatility," Energy Economics, Elsevier, vol. 126(C).
    13. Imre Kondor & G'abor Papp & Fabio Caccioli, 2016. "Analytic solution to variance optimization with no short-selling," Papers 1612.07067, arXiv.org, revised Jan 2017.
    14. Wolfgang Bessler & Georgi Taushanov & Dominik Wolff, 2021. "Factor investing and asset allocation strategies: a comparison of factor versus sector optimization," Journal of Asset Management, Palgrave Macmillan, vol. 22(6), pages 488-506, October.
    15. Francesco Cesarone & Andrea Scozzari & Fabio Tardella, 2020. "An optimization–diversification approach to portfolio selection," Journal of Global Optimization, Springer, vol. 76(2), pages 245-265, February.
    16. Guillaume Coqueret, 2017. "Empirical properties of a heterogeneous agent model in large dimensions," Post-Print hal-02000726, HAL.
    17. Thomas Conlon & John Cotter & Iason Kynigakis, 2021. "Machine Learning and Factor-Based Portfolio Optimization," Papers 2107.13866, arXiv.org.
    18. Fabio Caccioli & Imre Kondor & G'abor Papp, 2015. "Portfolio Optimization under Expected Shortfall: Contour Maps of Estimation Error," Papers 1510.04943, arXiv.org.
    19. Wei-Xing Zhou & Guo-Hua Mu & Si-Wei Chen & Didier Sornette, "undated". "Strategies used as Spectroscopy of Financial Markets Reveal New Stylized Facts," Working Papers ETH-RC-11-005, ETH Zurich, Chair of Systems Design.
    20. Arnold, Marc & Pelster, Matthias & Subrahmanyam, Marti G., 2022. "Attention triggers and investors’ risk-taking," Journal of Financial Economics, Elsevier, vol. 143(2), pages 846-875.

    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:kap:compec:v:63:y:2024:i:1:d:10.1007_s10614-022-10345-4. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.