Multifractal Characteristics and Information Flow Analysis of Stock Markets Based on Multifractal Detrended Cross-Correlation Analysis and Transfer Entropy DOI Creative Commons
Wenjuan Zhou, Jingjing Huang,

Maofa Wang

и другие.

Fractal and Fractional, Год журнала: 2024, Номер 9(1), С. 14 - 14

Опубликована: Дек. 30, 2024

Understanding cross-correlation and information flow between stocks is crucial for stock market analysis. However, traditional methods often struggle to capture financial markets’ complex multifaceted dynamics. This paper presents a robust combination of techniques, integrating three advanced methods: Multifractal Detrended Cross-Correlation Analysis (MFDCCA), transfer entropy (TE), networks. To address inherent non-stationarity noise in data, we employ Ensemble Empirical Mode Decomposition (EEMD) preprocessing, which helps reduce handle non-stationary effects. The application effectiveness this techniques are demonstrated through examples, uncovering significant multifractal properties long-range cross correlations among the studied. also captures magnitude direction stocks. holistic analysis provides valuable insights investors policymakers, enhancing their understanding behavior supporting better-informed portfolio decisions risk management strategies.

Язык: Английский

Stock price prediction for new energy vehicle companies based on multi-source data and hybrid attention structure DOI
Xueyong Liu, Yanhui Wu, Min Luo

и другие.

Expert Systems with Applications, Год журнала: 2024, Номер 255, С. 124787 - 124787

Опубликована: Июль 14, 2024

Язык: Английский

Процитировано

2
NeuroFuzzyMan: A hybrid neuro-fuzzy BiLSTM stacked ensemble model for financial forecasting and analysis: Dataset case studies on JPMorgan, AMZN and TSLA DOI
Ashkan Safari, Sehraneh Ghaemi

Expert Systems with Applications, Год журнала: 2024, Номер 266, С. 126037 - 126037

Опубликована: Дек. 11, 2024

Язык: Английский

Процитировано

1
Multifractal Characteristics and Information Flow Analysis of Stock Markets Based on Multifractal Detrended Cross-Correlation Analysis and Transfer Entropy DOI Creative Commons
Wenjuan Zhou, Jingjing Huang,

Maofa Wang

и другие.

Fractal and Fractional, Год журнала: 2024, Номер 9(1), С. 14 - 14

Опубликована: Дек. 30, 2024

Understanding cross-correlation and information flow between stocks is crucial for stock market analysis. However, traditional methods often struggle to capture financial markets’ complex multifaceted dynamics. This paper presents a robust combination of techniques, integrating three advanced methods: Multifractal Detrended Cross-Correlation Analysis (MFDCCA), transfer entropy (TE), networks. To address inherent non-stationarity noise in data, we employ Ensemble Empirical Mode Decomposition (EEMD) preprocessing, which helps reduce handle non-stationary effects. The application effectiveness this techniques are demonstrated through examples, uncovering significant multifractal properties long-range cross correlations among the studied. also captures magnitude direction stocks. holistic analysis provides valuable insights investors policymakers, enhancing their understanding behavior supporting better-informed portfolio decisions risk management strategies.

Язык: Английский

Процитировано

1