金融时间序列预测:经济新闻的语义分析

K. Kononova, A. Dek
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引用次数: 0

摘要

本文提出了一种考虑新闻语义的金融时间序列预测方法。为了对财经新闻进行语义分析,在劳格兰麦克唐纳大词典的基础上形成了经济意义上的否定词和肯定词的抽样。抽样选取了金融市场新闻中出现频率较高的词语。对于单根单词,它只留下了公共部分,允许在一个请求中覆盖几个单词。采用神经网络进行建模和预测。为了实现经济新闻信息提取过程的自动化,在MATLAB Simulink编程环境下开发了一个基于生成的正负词采样的脚本。不同神经网络结构的实验研究表明,构建的模型具有较高的充分性,并证实了利用新闻源信息预测股票价格的可行性。
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Financial time series forecasting: semantic analysis of economic news
The paper proposes a method of financial time series forecasting taking into account the semantics of news. For the semantic analysis of financial news the sampling of negative and positive words in economic sense was formed based on Loughran McDonald Master Dictionary. The sampling included the words with high frequency of occurrence in the news of financial markets. For single-root words it has been left only common part that allows covering few words for one request. Neural networks were chosen for modeling and forecasting. To automate the process of extracting information from the economic news a script was developed in the MATLAB Simulink programming environment, which is based on the generated sampling of positive and negative words. Experimental studies with different architectures of neural networks showed a high adequacy of constructed models and confirmed the feasibility of using information from news feeds to predict the stock prices.
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