英文标题：
《Financial Time Series Forecasting: Semantic Analysis Of Economic News》
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作者：
Kateryna Kononova, Anton Dek
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最新提交年份：
2017
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英文摘要：
  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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中文摘要：
本文提出了一种考虑新闻语义的金融时间序列预测方法。对于财经新闻的语义分析，以《洛兰-麦克唐纳主词典》为基础，形成了经济意义上的否定词和肯定词的样本。抽样调查包括金融市场新闻中出现频率较高的词语。对于单根单词，只剩下公共部分，允许为一个请求覆盖几个单词。选择神经网络进行建模和预测。为了自动化从经济新闻中提取信息的过程，在MATLAB Simulink编程环境中开发了一个脚本，该脚本基于正负词的生成采样。对不同结构的神经网络进行的实验研究表明，所构建的模型具有很高的充分性，并证实了利用新闻提要中的信息预测股价的可行性。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：General Finance        一般财务
分类描述：Development of general quantitative methodologies with applications in finance
通用定量方法的发展及其在金融中的应用
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一级分类：Quantitative Finance        数量金融学
二级分类：Computational Finance        计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
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