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Portfolio Selection Based on EMD Denoising with Correlation Coefficient Test Criterion.

Kuangxi Su1, Yinhong Yao2, Chengli Zheng3

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

Computational Economics
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Summary

This study introduces a novel denoising strategy using empirical mode decomposition (EMD) to enhance investment portfolio performance. The proposed method effectively removes noise, leading to improved risk-adjusted returns for investors.

Keywords:
Correlation coefficient testEmpirical mode decompositionFinancial data denoisingPortfolio selection

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Area of Science:

  • Quantitative Finance
  • Financial Signal Processing
  • Investment Management

Background:

  • Financial market data often contains noise, which can negatively impact portfolio performance.
  • Developing effective noise reduction strategies is crucial for investors seeking to optimize returns.

Purpose of the Study:

  • To theoretically explain the impact of noise on portfolios and establish the necessity of denoising.
  • To propose and validate a novel empirical mode decomposition (EMD) based denoising strategy for improving portfolio performance.

Main Methods:

  • Theoretical explanation of noise impact on portfolios.
  • Development of an EMD denoising strategy utilizing a correlation coefficient test criterion.
  • Decomposition of noisy price data into intrinsic mode functions (IMFs).
  • Identification of noise-containing IMFs via correlation coefficient tests.
  • Empirical analysis on SSE 50 index constituents with out-of-sample testing.

Main Results:

  • The proposed EMD denoising method significantly improves the return-risk ratio compared to conventional methods.
  • Empirical results demonstrate superior performance over EMD, Ensemble EMD (EEMD), and wavelet denoising techniques.
  • The strategy effectively identifies and removes noise from financial data.

Conclusions:

  • The developed denoising strategy is optimal for enhancing portfolio performance.
  • Investors can leverage this method to maximize their investment outcomes by mitigating noise.
  • The study confirms the practical utility of advanced signal processing techniques in finance.