  Government-run (Government-led) restoration has become a common and effective
approach to the mitigation of financial risks triggered by corporation credit
defaults. However, in practice, it is often challenging to come up with the
optimal plan of those restorations, due to the massive search space associated
with defaulted corporation networks (DCNs), as well as the dynamic and looped
interdependence among the recovery of those individual corporations. To address
such a challenge, this paper proposes an array of viable heuristics of the
decision-making that drives those restoration campaigns. To examine their
applicability and measure their performance, those heuristics have been applied
to two real-work DCNs that consists of 100 listed Chinese A-share companies,
whose restoration has been modelled based on the 2021 financial data, in the
wake of randomly generated default scenarios. The corresponding simulation
outcome of the case-study shows that the restoration of the DCNs would be
significantly influenced by the different heuristics adopted, and in
particular, the system-oriented heuristic is revealed to be significantly
outperforming those individual corporation-oriented ones. Therefore, such a
research has further highlighted that the interdependence-induced risk
propagation shall be accounted for by the decision-makers, whereby a prompt and
effective restoration campaign of DCNs could be shaped.
