Complex Network Built From Stock Price Returns and Volumes to Predict Market Volatility and Volume

N-K-K. Nguyen, H-T. Dinh and Q. Nguyen

Complexity, 2026, vol. 2026, 1-12

Abstract: This study investigates if network features from stock return and trading volume correlations can improve one-month-ahead forecasts of Vietnam’s VNIndex volatility and volume (2018–2024). We construct dynamic financial networks using Threshold, Top-k, and minimum spanning tree (MST) filtering methods, calculating metrics like density, centrality, and clustering.Using these features in linear regression and random forest models, we find that threshold-based networks yield the strongest volatility predictions (R2 ≈ 0.56). Volume forecasts achieve very high accuracy (R2 ≈ 0.95), reflecting strong underlying correlations. Notably, surges in network density and centrality often precede periods of heightened market volatility.Our findings demonstrate that incorporating complex network measures derived from mixed return-volume correlations can meaningfully enhance market forecasts in an emerging market context.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:5670093

DOI: 10.1155/cplx/5670093

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