Using Online Data in Predicting Stock Price Movements

Research Anthology on Strategies for Using Social Media as a Service and Tool in Business Pub Date : 1900-01-01 DOI:10.4018/978-1-5225-5586-5.CH006

F. Dařena, Jonás Petrovský, J. Prichystal, J. Zizka

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Abstract

A lot of research has been focusing on incorporating online data into models of various phenomena. The chapter focuses on one specific problem coming from the domain of capital markets where the information contained in online environments is quite topical. The presented experiments were designed to reveal the association between online texts (from Yahoo! Finance, Facebook, and Twitter) and changes in stock prices of the corresponding companies. As the method for quantifying the association, machine learning-based classification was chosen. The experiments showed that the data preparation procedure had a substantial impact on the results. Thus, different stock price smoothing, the lags between the release of documents and related stock price changes, levels of a minimal stock price change, different weighting schemes for structured document representation, and classifiers were studied. The chapter also shows how to use currently available open source technologies to implement a system for accomplishing the task.

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使用在线数据预测股票价格走势

许多研究都集中在将在线数据整合到各种现象的模型中。这一章的重点是来自资本市场领域的一个具体问题，其中包含在网络环境中的信息是相当热门的。所展示的实验旨在揭示在线文本(来自雅虎!金融、Facebook和Twitter)以及相应公司的股价变化。作为量化关联的方法，我们选择了基于机器学习的分类方法。实验表明，数据准备过程对结果有很大的影响。因此，研究了不同的股价平滑、文档发布与相关股价变化之间的滞后、最小股价变化的水平、结构化文档表示的不同加权方案以及分类器。本章还展示了如何使用当前可用的开源技术来实现完成任务的系统。

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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Research Anthology on Strategies for Using Social Media as a Service and Tool in Business

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