ArticleEntropy (Basel, Switzerland)2021
Multifractal Behaviors of Stock Indices and Their Ability to Improve Forecasting in a Volatility Clustering Period.
Article in Entropy (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Change-point detection and early warning systems.Scientific reports · 2026Article
- A survey of deep learning applications in cryptocurrency.iScience · 2024Review
- Skewed multifractal scaling of stock markets during the COVID-19 pandemic.Chaos, solitons, and fractals · 2023Article
- The Complexity Behavior of Big and Small Trading Orders in the Chinese Stock Market.Entropy (Basel, Switzerland) · 2023Article
- An Improved Temporal Fusion Transformers Model for Predicting Supply Air Temperature in High-Speed Railway Carriages.Entropy (Basel, Switzerland) · 2022Article
- Asymmetric Fractal Characteristics and Market Efficiency Analysis of Style Stock Indices.Entropy (Basel, Switzerland) · 2022Article
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2 authors.
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Abstract
The financial market is a complex system, which has become more complicated due to the sudden impact of the COVID-19 pandemic in 2020. As a result there may be much higher degree of uncertainty and volatility clustering in stock markets. How does this "black swan" event affect the fractal behaviors of the stock market? How to improve the forecasting accuracy after that? Here we study the multifractal behaviors of 5-min time series of CSI300 and S&P500, which represents the two stock markets of China and United States. Using the Overlapped Sliding Window-based Multifractal Detrended Fluctuation Analysis (OSW-MF-DFA) method, we found that the two markets always have multifractal characteristics, and the degree of fractal intensified during the first panic period of pandemic. Based on the long and short-term memory which are described by fractal test results, we use the Gated Recurrent Unit (GRU) neural network model to forecast these indices. We found that during the large volatility clustering period, the prediction accuracy of the time series can be significantly improved by adding the time-varying Hurst index to the GRU neural network.
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