Text Mining News System - Quantifying Certain Phenomena Effect on the Stock Market Behavior

2015 17th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) Pub Date : 2015-09-21 DOI:10.1109/SYNASC.2015.65

M. Tirea, V. Negru

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引用次数: 2

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.

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文本挖掘新闻系统-量化某些现象对股票市场行为的影响

股票市场预测受许多内部和外部因素的影响。其中一个因素是与每个上市公司相关的新闻文章和财务报告。本文描述了一个系统，该系统能够从这类文本文件中提取相关信息，将它们与股票价格运动联系起来，并确定新发布的新闻是否能够影响市场行为，以及影响市场行为的比例。采用预定义本体对新闻文章进行分类，采用自动本体提取对概念和超概念进行分类，试图对文本新闻进行语义挖掘。该系统基于多代理体系结构，将调查、提取文本数据消息并将其与价格演变相关联，以便更好地确定买入/卖出时刻、趋势方向并优化投资组合。为了验证我们的模型，开发了一个原型，并应用于布加勒斯特证券交易所市场的上市公司。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

2015 17th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)

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