Characterizing Complexity and Self-Similarity Based on Fractal and Entropy Analyses for Stock Market Forecast Modelling
作者：
Highlights：
• Proposal of multifarious methodology with diverse stages for stock market forecast modelling.
• The significance of Hurst exponent and Entropy-based Indicators to attain optimal forecasting.
• Hurst exponent as a determining and prominent indicator for stock market indices.
• Guidance in volatile and uncertain financial markets characterized by unexpected developments.
• Characterizing complexity and self-similarity based on Fractal and Entropy analyses.
摘要
•Proposal of multifarious methodology with diverse stages for stock market forecast modelling.•The significance of Hurst exponent and Entropy-based Indicators to attain optimal forecasting.•Hurst exponent as a determining and prominent indicator for stock market indices.•Guidance in volatile and uncertain financial markets characterized by unexpected developments.•Characterizing complexity and self-similarity based on Fractal and Entropy analyses.
论文关键词：Hurst exponent,Shannon entropy,Rényi entropy,Artificial neural network,Regression algorithms,Fractal analysis,Complexity and Self-Similarity,Stock indices forecasting
论文评审过程：Received 20 June 2019, Revised 25 September 2019, Accepted 22 November 2019, Available online 22 November 2019, Version of Record 18 December 2019.
论文官网地址：https://doi.org/10.1016/j.eswa.2019.113098
