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Multiclass Discriminant Analysis using Ensemble Technique: Case Illustration from the Banking Industry

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  • P. K. Viswanathan
  • Sandeep Srivathsan
  • Wayne L. Winston

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

Suggested Citation

  • P. K. Viswanathan & Sandeep Srivathsan & Wayne L. Winston, 2022. "Multiclass Discriminant Analysis using Ensemble Technique: Case Illustration from the Banking Industry," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 21(1), pages 92-115, March.
    • Handle: RePEc:sae:emffin:v:21:y:2022:i:1:p:92-115
    DOI: 10.1177/09726527211070947
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    References listed on IDEAS

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    More about this item

    Keywords

    LDA; separation; cutoff score; confusion matrix; ensemble technique; banking industry; bankruptcy prediction;
    All these keywords.

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation

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