Predicting Financial Distress Using a MIDAS Hazard Model: Evidence from Listed Companies in China

ABSTRACTThis study aims to predict financial distress in an emerging country using data on ST listed companies in China from 2001 to 2021. A new Aalen hazard model with mixed data sampling (MIDAS) is adopted to investigate the impact of monthly macroeconomic variables and quarterly financial variables on financial distress. The empirical results show that the current ratio, operating profit ratio, current capital ratio, retention ratio, profit ratio and income ratio of listed companies have a significant impact on the time-varying intensity of financial distress. The consumer price index has a negative relation with the intensity of financial distress, while the production price index and credit spreads have a positive influence. Finally, the results of the robustness tests are consistent with those with different lag orders.KEYWORDS: Financial distressAalen modelmixed data samplingspecial treatmentJEL: C52G32G33 AcknowledgmentsThe authors would like to thank the editor and the reviewers for their valuable comments and suggestions which are very helpful to improve our article.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThe work is supported by a National Natural Science Foundation of China Grant [No.71961004, 72061007, 71461005], Social Science Foundation of Jiangsu Province [No. 22GLB009], the Guangxi Science and Technology base and Talent Project [No. AD22080047], the National Social Science Key Fund of China [No. 17AJL012], the Science Foundation of Suzhou University of Science and Technology [No. 332111807, 332111801], the Interdisciplinary Scientific Research Foundation of Applied Economics of GuangXi University [No. 2023JJJXA08], the Guangxi Vocational Education Teaching Reform Project [No. GXGZJG2020A055].