Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/317473 
Year of Publication: 
2021
Citation: 
[Journal:] Business Economics and Management (JBEM) [ISSN:] 2029-4433 [Volume:] 22 [Issue:] 2 [Year:] 2021 [Pages:] 277-296
Publisher: 
Vilnius Gediminas Technical University, Vilnius
Abstract: 
Financial indicators are the most used variables in measuring the business performance of companies, signaling about the financial position, comprehensive income, and other significant reporting aspects. In a competitive environment, the performance measurement model allows performing comparative analysis in the same industry and between industries. This paper aims to design a composite financial index to determine the financial performance of listed companies, further used in predicting business performance through neural networks. Principal components analysis was used to build a composite financial index, employing four traditional accounting indicators and four value-based indicators for the period 2011-2018. Five experiments were conducted to predict business performance through the composite financial index. The results showed that observations from two years, of the first three experiments, indicate a better predictive behavior than the same experiments using observations from one year. Therefore, we concluded that observations from more than one year are necessary to predict the value of the financial performance index. Findings led us to the conclusion that recurrent neural networks model predicted better financial performance composite index when taken into consideration more real data for the financial performance index (2012-2018) instead of just for one year (2018).
Subjects: 
business performance
financial indicators
composite index
PCA
predictive behaviour
neural networks
JEL: 
M21
M41
L25
C45
G39
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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