arXiv subjects

Jiu Zhang

Publications and source records attributed to Jiu Zhang.

4 recordsLinked to original sources

arXiv2026

Effects of analyst sentiment on volatility dynamics in financial market

Text emotions are extracted using natural language processing technique on a substantial corpus of analyst reports on the Chinese stock market. Subsequently, the text-based analyst sentiment indices are constructed. It is observed that both optimistic and pessimistic sentiments represent short-range memory. Optimistic and pessimistic sentiments are correlated with volatility positively and negatively, respectively. The analysis of transfer entropy reveals that past pessimistic sentiment affects future volatility. Further, we model the driving effect of analyst sentiment on volatility using a GARCH model. The results show that pessimistic sentiment is an explanatory factor for volatility, while optimistic sentiment is not.

physics.soc-ph↗

arXiv2026

Is a team only as strong as its weakest link? Quantifying the short-board effect with AI Agents

The short-board effect, analogous to Liebig's Law of the Minimum, postulates that the collective performance of a team is constrained by its weakest component. This principle has profound implications for the optimization of collaboration in a variety of contexts, including management, education, and organizational structures. Despite its theoretical significance, empirical validation remains elusive due to challenges of assessing individual capabilities, controlling real-world variables, and data biases towards successful outcomes, as well as high employee turnover.To address this absence of knowledge, we employ multi-agents driven by large language models to simulate a teamwork with standard operating procedure, revealing the relationship between individual capability and collective team performance.In homogeneous team configurations, three capability regimes are observed, particularly the Sisyphus predicament state at the critical capability threshold characterized by extensive ineffective efforts and pseudo-high efficiency. Furthermore, with a single weak link quantifying the short-board effect, we highlight different impacts across core and non-core members on the team performance.More importantly, when the team exhibits multiple weak links, a cumulative product effect emerges, demonstrating that team performance is shaped by the aggregated impact of all weaknesses rather than the weakest link solely.This suggests that mitigation strategies should extend beyond the remediation of individual weak links.These findings rigorously elaborate the short-board theory and provide actionable insights to optimize team management, organizational operations, and supply chain resilience.

physics.soc-ph↗

arXiv2025

Restoring Network Evolution from Static Structure

The dynamical evolution of complex networks underpins the structure-function relationships in natural and artificial systems. Yet, restoring a network's formation from a single static snapshot remains challenging. Here, we present a transferable machine learning framework that infers network evolutionary trajectories solely from present topology. By integrating graph neural networks with transformers, our approach unlocks a latent temporal dimension directly from the static topology. Evaluated across diverse domains, the framework achieves high transfer accuracy of up to 95.3%, demonstrating its robustness and transferability. Applied to the Drosophila brain connectome, it restores the formation times of over 2.6 million neural connections, revealing that early-forming links support essential behaviors such as mating and foraging, whereas later-forming connections underpin complex sensory and social functions. These results demonstrate that a substantial fraction of evolutionary information is encoded within static network architecture, offering a powerful, general tool for elucidating the hidden temporal dynamics of complex systems.

physics.soc-ph↗

arXiv2025

Critical Thresholds in Non-Pharmaceutical Interventions for Epidemic Control

Non-pharmaceutical interventions, such as contact tracing and social distancing, are critical for controlling epidemic outbreaks, yet their dynamic interactions remain underexplored. We introduce a probabilistic framework to analyze the synergy between contact tracing speed, quantified by the contact tracing period $\tau$, and the average number of close contacts, $\bar{k}_+$, reflecting social distancing measures. We identify critical thresholds ($R=1$) that separate pandemic and contained phases in the $\bar{k}_{+}-\tau$ plane, validated using high-resolution data from Shenzhen's 2022 Omicron outbreak (1,187 cases, 86,451 contacts). Our findings show that contact tracing alone can contain diseases with $R_0 < 2.12$ (95% CI 2.07-2.16), covering 43.33% of major infectious diseases, while combining with social distancing extends control to $R_0 < 7.82$ (95% CI 7.70-7.93), encompassing 86.67% of pathogens. These results, supported by empirical data, highlight the efficacy of rapid tracing and targeted social distancing as alternatives to mass PCR testing. Our framework offers actionable insights for optimizing NPI strategies, though challenges in scaling to regions with higher tracing miss rates or weaker infrastructure underscore the need for adaptive, data-driven policies.

physics.soc-ph↗