After exploring the relationship between trading volume and price performance, this article further analyzes their systemic correlation from a statistical perspective. By using “trading volume growth rate / market capitalization” as a relative capital activity indicator and calculating its correlation coefficient with price fluctuations, we identify token types that are more easily driven by capital flows. In the chart, the size of the circles represents the strength of relative capital activity; larger circles indicate more significant trading volume expansion under unit market cap, and higher sensitivity of price to capital inflows.

From the chart, it can be seen that most tokens have correlation coefficients concentrated in the 0.65–0.85 range, indicating that in the current market, price fluctuations are still largely driven by trading activity, but there is no widespread strong synchronization, reflecting some stratification in capital behavior.

In the high correlation range, tokens such as STCUSD, WFLR, WGLUE, XCN, etc., have correlation coefficients close to or above 0.85–0.90, indicating that their price movements are highly consistent with changes in trading volume. These assets often feature strong trading attributes, high liquidity dependence, or narrative-driven characteristics, making their prices more susceptible to rapid upward or downward movements during volume surges, typical high Beta, sentiment-sensitive assets.

Tokens with correlations in the 0.75–0.85 range show “volume-driven but with controllable amplitude” characteristics, with prices influenced by capital inflows and outflows while maintaining some fundamental or functional support; whereas low-correlation assets like WAL, BARD, CTC, QTUM are less sensitive to changes in trading volume, driven more by medium- to long-term demand and ecosystem development, exhibiting relative defensiveness. Overall, the distribution of correlation in this period reveals a clear structural stratification: high-correlation assets are more driven by trading and sentiment, medium-correlation assets balance capital and logic, and low-correlation assets are more independent, reflecting that the market has entered a stage of fine-tuned asset attribute pricing.

The correlation between trading volume activity and price fluctuations shows that trading and sentiment tokens have significantly higher correlation, while infrastructure and mature ecosystem tokens are relatively less sensitive to changes in trading volume, indicating a clear market structure stratification.