Forecasting the Profitability of the Textile Sector in Emerging European Countries Using Artificial Neural Networks

Pîrvu, Daniela; Bondoc, Maria-Daniela; Apostol, Luiza Mădălina

doi:10.2478/ftee-2024-0035

Abstract

This study analyzes a set of key performance indicators for listed companies in the textile industry in emerging European countries: EBITDA margin, operating margin, pretax ROA, pretax ROE. Several statistical-econometric methods (dynamics analysis, structural analysis and regression) were used to provide an overview of the evolution of the public companies studied for the period 2012–2022, as well as a number of forecasts for the period 2023–2025. GMDH Shell software was used for public companies' pretax ROA forecast analysis in the textile industry in emerging European countries. The factor regression models that were constructed are valid for eight of the nine countries studied.

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Forecasting the Profitability of the Textile Sector in Emerging European Countries Using Artificial Neural Networks

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