Paper Info
Title
Text Mining News System - Quantifying Certain Phenomena Effect on the Stock Market Behavior.
Abstract
Stock market prediction is influenced by manyinternal and external factors. One of these factors are the newsarticles and financial reports related to each listed company. This paper describes a system that is able to extract relevantinformation from this type of textual documents, correlate themwith the stock price movement and determine whether ornot a new released news can and in which proportion willinfluence the market behavior. Predefined ontologies are used forclassifying the news articles and automated ontology extractionfor classifying concepts and super - concepts, on an attempt tomake a semantic mining of the text news. The system is basedon a Multi-Agent Architecture that will investigate, extract andcorrelate the textual data message with the price evolution inorder to better determine buy/sell moments, the trend directionand optimize an investment portfolio. In order to validate ourmodel a prototype was developed and applied to the BucharestStock Exchange Market listed companies.
Year
DOI
Venue
2015
10.1109/SYNASC.2015.65
SYNASC
Keywords
Field
DocType
Knowledge Extraction, Stock Market Prediction, Ontology, Multi-Agent System
Data science,Ontology (information science),Data mining,Ontology,Architecture,Computer science,Theoretical computer science,Multi-agent system,Knowledge extraction,Stock market,Stock market prediction,Market research
Conference
ISSN
Citations 
PageRank 
2470-8801
0
0.34
References 
Authors
15
2
Name
Order
Citations
PageRank
Monica Tirea1144.68
Viorel Negru231147.71