Deep learning:an study on financial crisis forewarning in small and medium-sized listed enterprises  

作　　者：Shaonan Pang Lixia Du 

机构地区：[1]Department of Accounting,Hebei Vocational University of Technology and Engineering,Xingtai,People’s Republic of China [2]Department of Economic Management,Xinzhou Normal University,Xinzhou,People’s Republic of China

出　　处：《Journal of Control and Decision》2025年第1期159-166,共8页控制与决策学报(英文)

摘　　要：Early warning of financial crisis will greatly promote the stable development of small and medium-sized listed enterprises(listed SMEs).In this article,13 warning indicators were selected for financial crisis prediction from five aspects:profit level,debt service level,business level,cash level,and development level.Then,the parameters of the long short-term memory(LSTM)neural network model were optimised by the whale optimisation algorithm(WOA),resulting in the WOA-LSTM model.The WOA-LSTM model achieved an accuracy of 0.975 in predicting financial crises for listed SMEs.The performance of the WOA-LSTM model was significantly enhanced when using the filtered 13 indicators as inputs,compared to using the original 24 indicators.The findings prove the dependability of the WOA-LSTM model in warning financial crises of listed SMEs and the feasibility of its application in practice.

关 键 词：Deep learning small and medium-sized listed enterprise financial crisis whale optimisation algorithm 

分 类 号：F275[经济管理—企业管理]