GM(1, 1) in bankruptcy forecasting

Abstract

Purpose -The present paper tries to give a new vision on the firm's future evolution forecasting. By taking into account some of the current values of its symptoms and applying one of the most used models in the grey systems theory, namely the GM(1,1), the predictions related to its future symptoms' values can be determined. Having these projected values and the grey economic-financial matrix, K, the future diseases that can hit a company can be depicted along with their causes. The paper aims to discuss these issues. Design/methodology/approach -Forecasting the future development of a firm is always an important issue in firm's survival in nowadays economy. Most of all, it is extremely important to be aware all the time about the inner and outer factors than can make a difference between a successful and a bankrupt firm. For this, here the authors have used three GM(1,1) models for forecasting the future symptoms (expressed through financial indicators) and performance indicator of a firm. Each time, based on the determined accuracy rate, a specific GM model has been chosen for every indicator's forecasting. Findings -Considering some previous researches and findings in bankruptcy modelling and diagnosis, this paper enlarges their applicability by adding the possibility to make future predictions on the indicators' evolution and to observe and to better manage their causes. As it was expected, the GM(1,1) models used for the forecasting of the various time series variables taken into account were different from one case to another, choosing the best-specific model for each variable case conducted to more accurate data-fit, with direct results in the causes hierarchy. Practical implications -By knowing the main causes that determine a certain state in firms' development and understanding them, the manager can action upon them in a manner that can make the difference between a bankrupt and a real successful firm. Originality/value -The paper succeeds in enlarging the view regarding bankruptcy forecasting by adding a dynamic view over the considered variables. If, in most of the cases when facing with financial forecasting, a single model is used for predictions, here the best GM model has been chosen for each variable based on the obtained accuracy rate. The results are concluding.

Adjusting the errors of the GM(1, 2) grey model in the financial data series using an adaptive fuzzy controller

“…The minimum and maximum limits of the output variables can be adjusted according to user requirements and the operation accuracy. The linguistic variables and the input and output variables can be changed (Delcea et al , 2013c). The reconstituted financial data sets for financial planning in the public sector need an adjustor determined by two input variables to improve the quality of planning estimates. The errors adjuster has low values, as shown in the simulation, when errors variation and the distance between two values is low or very low.…”

“…The minimum and maximum limits of the output variables can be adjusted according to user requirements and the operation accuracy. The linguistic variables and the input and output variables can be changed (Delcea et al , 2013c).…”

Two-stage multi-level equipment grey state prediction model and application

“…Professor Sifeng Liu and colleagues have carried out a detailed study on the application scope of the four basic forms of the classical GM(1,1) prediction model, which provided good guidance and reference for us in the actual model selection (Liu et al , 2015). This methodology has been effectively used in various fields (Delcea et al , 2013; Ofosu-Adarkwa et al , 2020). The grey prediction model is also widely used in equipment trouble prediction.…”