Key result
The proposed generalized cumulative residual entropy (GCRE) and weighted GCRE methods effectively distinguished US, European, and Chinese stock markets with different characteristics.
The study introduces GCRE and WGCRE as new measures to quantify uncertainty in nonlinear time series, demonstrating their utility in analyzing financial markets.
May support nonlinear time series differentiation in finance; leaves open validation against established benchmarks before wider use.
Cumulative residual entropy (CRE) has been suggested as a new measure to quantify uncertainty of nonlinear time series signals. Combined with permutation entropy and Rényi entropy, we introduce a generalized measure of CRE at multiple scales, namely generalized cumulative residual entropy (GCRE), and further propose a modification of GCRE procedure by the weighting scheme — weighted generalized cumulative residual entropy (WGCRE). The GCRE and WGCRE methods are performed on the synthetic series to study properties of parameters and verify the validity of measuring complexity of the series. After that, the GCRE and WGCRE methods are applied to the US, European and Chinese stock markets. Through data analysis and statistics comparison, the proposed methods can effectively distinguish stock markets with different characteristics.
No takes yet. Share an insight, caveat, or question.
Zhou et al. (2021) studied Stock market time series. Generalized cumulative residual entropy (GCRE) and weighted GCRE (WGCRE) was evaluated on Distinguishing stock markets with different characteristics. The proposed generalized cumulative residual entropy (GCRE) and weighted GCRE methods effectively distinguished US, European, and Chinese stock markets with different characteristics.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: