基于Web大数据挖掘的证券价格波动实时影响研究

随着Web大数据的发展,互联网中海量、快捷的信息为证券市场变化预测提供了丰富的数据支撑,如何利用大数据分析技术进行实时可靠的证券市场价格变化预测成为重要的科学问题.从证券市场价格变化的核心价值问题研究出发,分析了股票价值所反映的基本面要求,建立了影响股票价值内涵和价格表现的10项准确可度量的特征因素:经济周期、财政政策、利率变动、汇率变动、物价变动、通货膨胀、政治政策、行业变化、经营状况、上下游影响等.在此基础上,构造互联网中信息内容与各个特征因素的提取方法、变化关系和影响模型,提出了针对大盘、行业、个股的互联网信息指标来反映Web数据对其的支撑程度,最终实现了基于Web大数据的综合特征因素度量来预测证券市场的方法.实验表明,该方法具有良好的可行性,将带来明显的学术和商业价值.

Research on Stock Price Real-time Fluctuation Influence Based on Web Big Data Mining

With the growing of the Internet,the network of large data has become an important distribution center for the financial sector.These data give valuable opportunities and severe challenges to stock analysis and prediction.The development of Web2.0 allows investors to actively participate in all aspects of the creation of network of information,communication and accessing.This paper disclosed information on the Internet and generated complete and effective fundamental of information to get 10 actors that may impact the stock market,which are economic and cycle,fiscal policy,changes in interest rates and exchange rates,price changes,inflation,political policies,changes in the industry,operating conditions and the downstream effects.We proposed an algorithm for the stock market prediction based on these 10 factors.Experiments show that the method has good feasibility,and can bring significant academic and commercial values.