Multiclass Discriminant Analysis using Ensemble Technique: Case Illustration from the Banking Industry. (March 2022)

• Record Type: Journal Article
• Authors: Viswanathan, P. K.; Srivathsan, Sandeep; Winston, Wayne L.
• Is Part Of: Journal of emerging market finance. Volume 21:Number 1(2022)
• Page Start: 92
• Page End: 115
• Publication Date: 2022-03
• Subjects: LDA -- separation -- cutoff score -- confusion matrix -- ensemble technique -- banking industry -- bankruptcy prediction; Banks and banking -- Developing countries -- Periodicals; Financial institutions -- Developing countries -- Periodicals; Securities -- Developing countries -- Periodicals; 332.09172405
• DOI: 10.1177/09726527211070947
• Languages: English
• ISSNs: 0972-6527

Abstract

Linear discriminant analysis (LDA) has found extensive application in predicting bankruptcy. In this article, we elucidate a novel modelling approach for LDA that can also aid in gaining useful insights regarding the relative importance and ranking of factors in the banking industry. The model steers away from the traditional computation of the variance/covariance matrix and employs an ensemble technique to assign records to classes. The efficacy of our model is tested using two datasets. Specifically, a large dataset from the banking industry was partitioned into the testing and training datasets, and an accuracy of 87.9% was achieved JEL Codes: C38, G33