Fundamental analysis powered by Semantic Web

Author

Li, Xian ; Bao, Jie ; Hendler, James A.

Author_Institution

Tetherless World Constellation, Rensselaer Polytech. Inst. Troy, Troy, NY, USA

fYear

2011

fDate

11-15 April 2011

Firstpage

1

Lastpage

8

Abstract

Conducting fundamental analysis within subsets of comparable firms has been demonstrated to provide more reliable inferences and increase the prediction quality in equity research. However, incorporating and representing both firm-specific information and common economic determinants has been widely recognized as the key challenge. This paper investigates how to leverage Semantic Web technologies to assist fundamental analysis by generating flexible and meaningful selections of comparable firms at low costs. We approach the problem by proposing Linked Open Financial Data as the data organization model and ontology modeling for knowledge representation. Results are verified in terms of efficiency with examples of quick mashups, and feasibility by adapting to existing valuation models.

Keywords

financial data processing; inference mechanisms; knowledge representation; ontologies (artificial intelligence); semantic Web; common economic determinant; data organization model; fundamental analysis; knowledge representation; linked open financial data; ontology modeling; prediction quality; reliable inference; semantic Web; Analytical models; Business; Data models; Joining processes; Resource description framework; Semantics; Semantic Web; financial data; financial statement analysis; fundamental analysis; investment; linked data;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence for Financial Engineering and Economics (CIFEr), 2011 IEEE Symposium on

Conference_Location

Paris

ISSN

pending

Print_ISBN

978-1-4244-9933-5

Type

conf

DOI

10.1109/CIFER.2011.5953565

Filename

5953565