Abstract
Revealing the associations among stocks is fundamental for unveiling the underlying rules of global stock markets. In this work, we propose a combined entropy–complex network framework that detects fluctuation connections among stock prices. The results demonstrate that global stock markets show high uncertainty (high entropy). Based on our analysis of the system's symmetry and dominant correlation fluctuation patterns, we identified the least uncertain stock pair in the market: DEU and FRA. We also revealed the most reliable stocks as references for every stock. Quantifying the uncertainty found in global stock markets and identifying the driving fluctuation patterns can benefit investors by decreasing risk.
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