Study on the risk-informed heuristic of decision-making on the restoration of defaulted corporation networks

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.