摘要：
This article explores the empirical application of variable selection and advanced algorithms on distress resolution modeling in 551 bankruptcy-filing firms. The results, derived from in-sample estimations and out-of-sample predictions, show that covariates bring inequivalenet impacts on distress resolution and quick restructuring. As accounting and filing information determine resolution outcomes, the potential for quick restructuring has leaked in equity market trading activities. The study validates the benefits of using advanced techniques in wurvival potential prediction, reinforcing the confidence in the reliability of the findings. The Post-r-Lasso method conveys superior predictive accuracy with high precision on false solvency; however, it yields high discriminative errors on false quick survival prediction. The support vector machine algorithm that best predicts the likelihood of quickly surviving the Chapter 11 bankruptcy protection towards safer targets, leading to conservative investment decisions.
