DocumentCode :
175619
Title :
Comparing corporate financial performance and qualitative information from annual reports using self-organizing maps
Author :
Hajek, Petr ; Olej, Vladimir
Author_Institution :
Inst. of Syst. Eng. & Inf., Univ. of Pardubice, Pardubice, Czech Republic
fYear :
2014
fDate :
19-21 Aug. 2014
Firstpage :
93
Lastpage :
98
Abstract :
This paper develops a methodology to extract concepts containing qualitative information from corporate annual reports. The concepts are extracted from the corpus of U.S. corporate annual reports using WordNet ontology and singular value decomposition, and further visualized using self-organizing maps. The methodology makes it possible to easily compare the concepts with future financial performance. The results suggest that annual reports differ in terms of the concepts emphasized reflecting future financial performance.
Keywords :
financial data processing; ontologies (artificial intelligence); self-organising feature maps; U.S. corporate annual reports; WordNet ontology; annual reports; comparing corporate financial performance; qualitative information; self-organizing maps; singular value decomposition; Companies; Data mining; Eigenvalues and eigenfunctions; Neurons; Profitability; annual report; financial performance; self-organizing map; singular value decomposition;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Natural Computation (ICNC), 2014 10th International Conference on
Conference_Location :
Xiamen
Print_ISBN :
978-1-4799-5150-5
Type :
conf
DOI :
10.1109/ICNC.2014.6975816
Filename :
6975816
Link To Document :
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