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首页> 外文学位 >Intelligent day trading agent: A natural language processing approach to financial information analysis.
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Intelligent day trading agent: A natural language processing approach to financial information analysis.

机译：智能日间交易代理：一种用于财务信息分析的自然语言处理方法。

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摘要

Traders with immediate access to real-time news services (such as Dow Jones News Wire, Bloomberg News Service) constantly monitor and track financial news that is expected to have a significant impact on stock prices. A precondition for successful news-based trading is fast and accurate analysis of the news content. However, manually identifying relevant newswire articles and performing human analysis on the selected news items within a reasonable timeframe represents a daunting task due to the fact that traders face large amounts of financial news releases and reports throughout the trading hours. This research seeks to develop a prototype trading system that automates the procedure of news tracking and analysis. The design of our system adopts an integrated approach using a variety of natural language processing (NLP) techniques to extract the relevant information from merger and acquisition news announcements and generate trading signals based on a set of predefined rules. First, a learning algorithm is employed to identify and classify merger-and-acquisition-related newswire articles from Dow Jones News Wire database. Then the selected news texts are run through an Information Extraction system that performs in sequence the individual tasks of preprocessing, name entity recognition and semantic analysis. Finally, by simulating human-like analysis of all the collected informational elements, the system produces a trading signal by following the simple rule of "buying the target firm and shorting the acquiring firm".; The core of our system is built with hand-crafted rules that are obtained through an iterative training process. Following the Knowledge Engineering approach enables us to achieve a high level of system performance, which is critical to the practical application of automated trading systems. Our system reports a precision rate of 98.3% and high scores in other performance measurements as well. Further empirical evidence is obtained through an event study to lend support to the hypothesis that our prototype system is capable of capturing a small portion of the post-announcement stock price movement despite the fact that the bulk of the price reaction is completed within the first few trades.
机译：立即可使用实时新闻服务（例如道琼斯新闻通讯社，彭博新闻社）的交易员不断监视和跟踪财务新闻，这些新闻预计将对股价产生重大影响。快速，准确地分析新闻内容是成功进行基于新闻的交易的前提。但是，由于交易者在整个交易时间内都面临大量的金融新闻发布和报告，因此在合理的时间内手动识别相关新闻通讯社的文章并在选定的时间范围内对选定的新闻项目进行人为分析是一项艰巨的任务。本研究旨在开发一种使新闻跟踪和分析过程自动化的原型交易系统。我们系统的设计采用一种集成方法，该方法使用多种自然语言处理（NLP）技术从合并和收购新闻公告中提取相关信息，并根据一组预定义规则生成交易信号。首先，采用一种学习算法从道琼斯通讯社数据库中识别并分类与并购相关的新闻专线文章。然后，选定的新闻文本将通过信息提取系统运行，该系统依次执行预处理，名称实体识别和语义分析的各个任务。最后，通过模拟所有收集到的信息元素的类人分析，系统遵循简单的“买入目标公司并卖空收购公司”的规则来产生交易信号。我们系统的核心是通过手工制定的规则构建的，这些规则是通过反复的培训过程获得的。遵循知识工程的方法使我们能够实现高水平的系统性能，这对于自动交易系统的实际应用至关重要。我们的系统报告的准确率达到98.3％，在其他性能测量中也获得高分。通过事件研究获得了更多的经验证据，以支持以下假设：我们的原型系统能够捕获公告后股价波动的一小部分，尽管价格反应的大部分是在前几个阶段完成的。交易。

著录项

  • 作者

    Jiang, Wei.;

  • 作者单位

    Rutgers The State University of New Jersey - Newark.;

  • 授予单位 Rutgers The State University of New Jersey - Newark.;
  • 学科 Business Administration Accounting.; Language Linguistics.; Information Science.
  • 学位 Ph.D.
  • 年度 2005
  • 页码 137 p.
  • 总页数 137
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 财务管理、经济核算;语言学;信息与知识传播;
  • 关键词

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