Browsing by Author "Samaraweera, A.S.A."

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Methodological Issues in Forecasting Corporate Failure: A Review
(International Conference on Business and Information (ICBI – 2019), [Doctoral Colloquium], Faculty of Commerce and Management Studies, University of Kelaniya, Sri Lanka, 2019) Samaraweera, A.S.A.; Thilakerathne, P.M.C.
Forecasting corporate failure has been a hot topic during for more than eighty years. 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, Knearest neighbor, Hazard and Hybrid, model building has evolved during this period with the focus of enhancing prediction accuracy. The literature can be classified into three main methods, namely; statistical methods, intelligent techniques and theoretical approaches to forecast corporate failure. The paper aims to contribute to the existing literature by analyzing methodological problems in the above three areas. A systematic review is performed on 76 articles spanning a period from 1966 to 2018 in scholarly reviewed journals. The results on the SLR 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, simple and widely used model.
Methodological Issues in Forecasting Corporate Failure: A Systematic Literature Review
(19th Conference on Postgraduate Research, International Postgraduate Research Conference 2018, Faculty of Graduate Studies,University of Kelaniya, Sri Lanka., 2018) Samaraweera, A.S.A.; Thilakerathne, P.M.C.
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.