A New Multi-Dimensional Framework for Start-Ups Lifespan Assessment Using Bayesian Networks

As historical data are typically unavailable for a start-up, risk assessment is always complex and challenging. Traditional methods incapable of capturing all facets this complexity; therefore, more sophisticated tools necessary. Using an expert-elicited Bayesian networks (BNs) methodology, paper aims to provide method combining diverse sources information, such as data, expert knowledge, the unique characteristics each estimate default rate at various stages life cycle. The proposed not only reduces cognitive error opinion new start-up but also considers learning feature BNs effect lifespan when updating estimations. In addition, model impact investors’ appetite. Furthermore, can rank most effective factors stages. receiver operating characteristic (ROC) curve was utilized assess model’s explanatory power. Moreover, three distinct case studies were used demonstrate capabilities.