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  • 标题:Short-term Stock Price Analysis Based on Order Book Information
  • 本地全文:下载
  • 作者:Kenichi Yoshida ; Akito Sakurai
  • 期刊名称:人工知能学会論文誌
  • 印刷版ISSN:1346-0714
  • 电子版ISSN:1346-8030
  • 出版年度:2015
  • 卷号:30
  • 期号:5
  • 页码:683-692
  • DOI:10.1527/tjsai.30_683
  • 出版社:The Japanese Society for Artificial Intelligence
  • 摘要: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.
  • 关键词:efficient market hypothesis ; high-frequency trading ; time series analysis ; stock market prediction ; technical analysis
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