BANKRUPTCY PREDICTION USING MACHINE LEARNING – A META-ANALYSIS
BANKRUPTCY PREDICTION USING MACHINE LEARNING – A META-ANALYSIS

Summary/Abstract: This study is based on a meta-analysis of 64 studies in bankruptcy prediction using machine learning. The data on these studies was collected on six levels: algorithms, data balance, variable categories, variables types, industry, and region. The aim of this paper is to analyse the determinants of accuracy in bankruptcy prediction models. To achieve this aim, five Linear Mixed Effects models were developed. The results obtained show that while some factors are significant determinants for the accuracy of machine learning models in bankruptcy prediction (algorithm, data balance, industry, region), some factors as data type (continuous or continuous and categorical) and data category (financial or financial and non-financial) do not have an impact on accuracy prediction.

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