analyzing the performance of dea models for bankruptcy prediction in the energy sector: with emphasis on dynamic dea approach

Predicting bankruptcy risk stands as one of the most critical challenges in corpo-rate financial decision-making. investors continually seek ways to foresee a firm's bankruptcy in order to mitigate the risk of losing their assets. consequently, they actively explore avenues for predicting bankruptcy risk. in this study, we endeav-or to predict the standings of companies operating within the oil and gas industry based on their financial health, using the 2020 s&p global rankings, up to three years before 2020. to achieve this, we employ three data envelopment analysis models (ccr, bcc, and ddea) in conjunction with the traditional altman model for forecasting. our findings underscore the effectiveness of dynamic data envelopment analysis as a potent tool for predicting bankruptcy risk.