Understanding Credit Risk for Chinese Companies

The Ability of Altman’s Z”-score Model to Detect the Economic Distress of Kazakh Banks

“…This highlights the importance of advancing our understanding of country-specific classification performance of Altman's models. Although there is growing number of studies using Altman's models to assess firms' financial health and predict financial distress in emerging markets (e.g., Zhang et al, 2010;Al Zaabi, 2011;Chouhan et al, 2014;Altman et al 2021;Wu et al, 2022), there are only limited number of studies examining these models' classification performance in emerging markets (e.g., Zhang et al, 2010). For example, Hájek et al (2017; calculate the Z-and Z'-scores for two small samples of confectionary companies operating in Kazakhstan and then compare the scores to the boundary values for the "safe", "grey", and "distress" zones to assess the financial strength of these companies.…”

Understanding Corporate Bond Defaults in Korea Using Machine Learning Models*

“…Among the few studies in this domain, Altman (2005) and Bandyopadhyay (2006) adapt the Z-score model for Mexican and Indian firms, respectively. Altman et al (2021) utilize the LASSO algorithm to construct a probability of default measure using Chinese corporate bond default data. More recently, Asis et al (2021) developed a financial distress model specific to emerging markets using the Credit Research Initiative (CRI) database.…”

“…Altman et al . (2021) utilize the LASSO algorithm to construct a probability of default measure using Chinese corporate bond default data. More recently, Asis et al .…”