Short-term stock price analysis based on order book information

Kenichi Yoshida, Akito Sakurai

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Efficient market hypothesis is widely accepted in financial market studies and entails the unpredictability of future stock prices. In this study, we show that a simple analysis can classify short-term stock price changes with an 82.9% accuracy. Our analysis uses the order book information of high-frequency trading. The volume of high-frequency trading, which is responsible for short-term stock price changes, is increasing dramatically; therefore, our study suggests the importance of analyzing short-term market fluctuations, an aspect that is not well studied in conventional market theories. The experimental results also suggest the importance of the new data representation and analysis methods we propose, neither of which have been thoroughly investigated in conventional financial studies.

Original languageEnglish
Pages (from-to)683-692
Number of pages10
JournalTransactions of the Japanese Society for Artificial Intelligence
Volume30
Issue number5
DOIs
Publication statusPublished - 2015 Aug 27

Keywords

  • Efficient market hypothesis
  • High-frequency trading
  • Stock market prediction
  • Technical analysis
  • Time series analysis

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software

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