Can the Content of Public News Be Used to Forecast Abnormal Stock Market Behaviour?

Seventh IEEE International Conference on Data Mining (ICDM 2007) Pub Date : 2007-10-28 DOI:10.1109/ICDM.2007.74

Calum S. Robertson, S. Geva, R. Wolff

引用次数: 14

Abstract

A popular theory of markets is that they are efficient: all available information is deemed to provide an accurate valuation of an asset at any time. In this paper, we consider how the content of market- related news articles contributes to such information. Specifically, we mine news articles for terms of interest, and quantify this degree of interest. We then incorporate this measure into traditional models for market index volatility with a view to forecasting whether the incidence of interesting news is correlated with a shock in the index, and thus if the information can be captured to value the underlying asset. We illustrate the methodology on stock market indices for the USA, the UK, and Australia.

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公共新闻内容能否用于预测股市异常行为?

一个流行的市场理论是，市场是有效的:所有可用的信息都被认为在任何时候都能提供对资产的准确估值。在本文中，我们考虑与市场相关的新闻文章的内容如何有助于这些信息。具体来说，我们挖掘新闻文章的兴趣条款，并量化这种兴趣程度。然后，我们将这一措施纳入市场指数波动的传统模型，以预测有趣新闻的发生率是否与指数中的冲击相关，从而是否可以捕获信息以对标的资产进行估值。我们以美国、英国和澳大利亚的股票市场指数为例说明了这种方法。

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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Seventh IEEE International Conference on Data Mining (ICDM 2007)

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