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Stock Market Prediction Using Keywords from Expert Articles

  • Ko Ichinose
  • Kazutaka Shimada
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 700)

Abstract

The market analysis is one of the important tasks for text mining. In this situation, Web news has an important role to predict stock prices. In this paper, we propose a method to predict the Nikkei Stock Average, which is one of the most important stock market indexes. We extract viewpoints from experts’ articles for analyzing Web news. The extracted words are index words in the vector space of a machine learning technique. We also incorporate word embedding and bootstrap approaches into our method. It predicts “UP” or “DOWN” of the next day by using the articles of a day. We also evaluate our method with not only one-day prediction but also simulated trading. The experimental result shows that index words based on expert articles were effective for both one-day prediction and simulated trading.

Keywords

Stock market prediction Experts’ articles Bootstrap Feature extraction 

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Department of Artificial IntelligenceKyushu Institute of TechnologyFukuokaJapan

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