Methodological Issues in Forecasting Corporate Failure: A Systematic Literature Review

Abstract

Research on predicting corporate failure has been reserved vital place over eighty years. Previous researchers adopted vivid methodological approaches in this area of study. Literature stems from the univariate model of Beaver (1966), and the Multivariate Discriminant Analysis model of Altman (1968) to models based on Logit, Probit, Artificial Neural Networks (ANN), Bayesian models, Fuzzy models, Genetic Algorithms (GA), Decision trees, Support vector machines, K-nearest neighbour, Hazard and Hybrid, model building has evolved during this period with the focus of enhancing prediction accuracy. Vast array of literature can be classified into three main methods, namely; statistical methods, intelligent techniques and theoretical approaches to forecast corporate failure. This research paper aims to contribute to the existing literature by analyzing methodological problems in the above three areas. A systematic review is performed by using 76 articles spanning a period from 1966 to 2018 appeared in scholarly reviewed journals. The results on the systematic literature review indicates that there has been significant prior work in the areas of forecasting corporate failure, but there lacks a sound theoretical view for a highly accurate, inclusive and widely used model. A best model should be evolved considering the relationship between the corporate failure and theoretical arguments through existing economic theory.