人工知能学会論文誌
Online ISSN : 1346-8030
Print ISSN : 1346-0714
ISSN-L : 1346-0714
原著論文
Short-term Stock Price Analysis Based on Order Book Information
Kenichi YoshidaAkito Sakurai
著者情報
ジャーナル フリー

2015 年 30 巻 5 号 p. 683-692

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抄録

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.

著者関連情報
© The Japanese Society for Artificial Intelligence 2015
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