Global Business Review Pub Date : 2021-07-12 , DOI: 10.1177/09721509211026785 Nandita Mishraz 1 , Shruti Ashok 2 , Deepak Tandon 3
Financial distress is a socially and economically significant issue that affects almost every firm across the world. Predicting financial distress in the banking industry can substantially aid in the reduction of losses and can help avoid misallocation of banks’ financial resources. Models for financial distress prediction of banks are being increasingly employed as important tools to identify early warning signals for the whole banking system. This study attempts to forecast the financial distress of commercial banks by developing a bankruptcy prediction model for banks. The sample size for the study is 75 Indian banks. Logistic, linear discriminant analysis (LDA) and artificial neural network (ANN) models have been applied on the last 5 years’ (2015–2019) data of these banks. Data analysis results reveal the logistic and LDA models exhibiting similar prediction accuracy. The results of the ANN prediction model exhibit better prediction accuracy. It is expected that the results of this study will be useful for managers, depositors, regulatory bodies and shareholders to better manage their interests in the banking sector of the country.
中文翻译:
预测印度银行业的财务困境:逻辑回归、LDA 和 ANN 模型之间的比较研究
财务困境是一个具有社会和经济意义的重大问题,几乎影响到世界各地的每一家公司。预测银行业的财务困境可以极大地帮助减少损失,并有助于避免银行财务资源的错配。银行财务困境预测模型越来越多地被用作识别整个银行系统早期预警信号的重要工具。本研究试图通过开发银行破产预测模型来预测商业银行的财务困境。该研究的样本量为 75 家印度银行。Logistic、线性判别分析 (LDA) 和人工神经网络 (ANN) 模型已应用于这些银行过去 5 年(2015-2019 年)的数据。数据分析结果表明,logistic 和 LDA 模型表现出相似的预测精度。ANN 预测模型的结果显示出更好的预测精度。预计这项研究的结果将有助于管理人员、存款人、监管机构和股东更好地管理他们在该国银行业的利益。