文本挖掘新闻系统-量化某些现象对股票市场行为的影响

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

摘要

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

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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来源期刊

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

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