Paper Info
Title
Classifying and quantifying certain phenomena effect
Abstract
The goal of this paper is to create a hybrid system that will investigate possible stock market changes immediately after financial news article appear and how this information influences the stock market behavior in order to improve the profitability of a short or medium time period investment. We proposed a multi-agent system that uses text mining, information extraction, pattern recognition, sentiment analysis and a trust model. The system classifies and quantifies certain phenomena (financial news influence) in order to compute the effect of some properties and its size on the stock market and also checks if we can use turbulence to detect disasters. It also searches a correlation between the effect of news articles and the trader's behavior on the market. In order to validate our model a prototype was developed.
Year
DOI
Venue
2013
10.1109/SISY.2013.6662603
Intelligent Systems and Informatics
Keywords
Field
DocType
correlation methods,data mining,financial data processing,multi-agent systems,pattern recognition,profitability,stock markets,text analysis,trusted computing,disaster detection,financial news article,financial news influence,information extraction,medium time period investment,multiagent system,pattern recognition,phenomena effect classification,phenomena effect quantification,profitability,sentiment analysis,short time period investment,stock market behavior,text mining,trader behavior,trust model,Multi-Agent System,Sentiment Analysis,Stock Market Prediction,Text Mining,Trading Strategies,Trust
Data mining,Trusted Computing,Financial news,Sentiment analysis,Computer science,Multi-agent system,Information extraction,Profitability index,Hybrid system,Stock market
Conference
ISBN
Citations 
PageRank 
978-1-4799-0303-0
0
0.34
References 
Authors
11
2
Name
Order
Citations
PageRank
Monica Tirea1144.68
Viorel Negru231147.71