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Portfolio allocation with CEEMDAN denoising algorithm

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Abstract

Effective denoising strategies are increasingly important for portfolio investors. Considering that the common ensemble empirical mode decomposition (EEMD)-based stepwise denoising algorithms suffer from white noise interference and ignore the effect of low-frequency redundant noise components on the portfolio, a novel complete EEMD with adaptive noise (CEEMDAN) denoising algorithm is proposed to improve the portfolio performance. Specifically, we apply CEEMDAN to decompose noisy prices into a series of intrinsic mode functions (IMFs). Then, a series of tests based on the correlations between the original noisy prices and the decomposed IMFs are performed to identify which IMFs are noisy modes. If the tests accept the null hypothesis, the IMFs are considered as noisy components. Finally, we use the soft-threshold technique to process the noisy components and sum the non-noisy components to construct the denoised prices. The empirical results show that under the dynamic minimum-CVaR framework, the proposed CEEMDAN denoising algorithm is not affected by white noise and outperforms the EEMD denoising and stepwise denoising algorithms in improving out-of-sample portfolio returns. Overall, the proposed CEEMDAN denoising is the optimal denoising algorithm, which can help investors improve portfolio performance to the greatest extent.

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Data availability

The datasets used in this paper are available from the wind database (www.wind.com.cn).

Notes

  1. The test statistic is \(\displaystyle \rho _j \sqrt{\frac{T-2}{1-\rho _j^{2}}}\sim \chi (T\!-2)\), P-value is calculated by the formula \(p(z\ge \displaystyle \rho _j \sqrt{\frac{T-2}{1-\rho _j^{2}}})\), where z follows a \(\chi (T\!-2)\) distribution.

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Acknowledgements

Our research is supported by Humanities and Social Science Planning Fund Project of the Ministry of Education (16YJAZH078); Central University for Basic Research Business Expenses (CCNU19A06043, CCNU19TD006 and CCNU19TS062). Thanks to the support of Nanhu Scholars Program of XYNU.

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Authors and Affiliations

  1. School of Mathematics and Statistics, Xinyang Normal University, Xinyang, China

    Kuangxi Su

  2. School of Economics and Business Administration, Central China Normal University, Wuhan, China

    Chengli Zheng & Xing Yu

Authors
  1. Kuangxi Su
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  2. Chengli Zheng
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  3. Xing Yu
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Correspondence to Kuangxi Su.

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Appendices

Appendices

1.1 Appendix A: the IDs and names of the selected SSE 50 index’s constituents

See Tables 11, 12, 13, 14 and 15.

Table 11 The IDs and names of the selected SSE 50 index’s constituents
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1.2 Appendix B: Robustness check

Tables 12 and 13 report the performance metrics at the 99% and 90% confidence levels, respectively. Furthermore, Table 14 shows the portfolio results at 60% window width. Specifically, the first 60% of the full sample is used as in-sample estimates, while the remaining 20% is used as out-of-sample tests. Lastly, Table 15 presents the portfolio performance by optimizing the minimum-VaR objective. The overall conclusions are consistent with the previous. The proposed CEEMDAN denoising is the optimal denoising algorithm, which can help investors improve portfolio performance to the greatest extent.

Table 12 Portfolio performance at 99% confidence level
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Table 13 Portfolio performance at 90% confidence level
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Table 14 Portfolio performance with 60% window width
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Table 15 Portfolio performance under the min-VaR portfolio framework
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Su, K., Zheng, C. & Yu, X. Portfolio allocation with CEEMDAN denoising algorithm. Soft Comput 27, 15955–15970 (2023). https://doi.org/10.1007/s00500-023-08883-6

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