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

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Tytuł artykułu

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

Autorzy

Pîrvu Daniela , Bondoc Maria-Daniela , Apostol Luiza Mădălina

Treść / Zawartość

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DOI

10.2478/ftee-2024-0035

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Języki publikacji

Abstrakty

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.

Słowa kluczowe

emerging European countries listed companies performance indicators textile industry

Wydawca

Sieć Badawcza Łukasiewicz - Łódzki Instytut Technologiczny

Czasopismo

Fibres & Textiles in Eastern Europe

Rocznik

2024

Tom

Vol. 32, no. 5

Strony

39--48

Opis fizyczny

Bibliogr. 36 poz., rys., tab.

Twórcy

autor

Pîrvu Daniela

Daniela.Pirvu@Upb.Ro

National University of Science and Technology POLITEHNICA Bucharest, Pitești University Centre, Romania

autor

Bondoc Maria-Daniela

National University of Science and Technology POLITEHNICA Bucharest, Pitești University Centre, Romania

autor

Apostol Luiza Mădălina

National University of Science and Technology POLITEHNICA Bucharest, Pitești University Centre, Romania

Bibliografia

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Bibliografia

Identyfikator YADDA

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