Stock Price Regression Based on Order Book Information

Kenichi Yoshida, Akito Sakurai

研究成果: Conference contribution

抄録

Efficient market hypothesis, which entails the unpredictability of future stock prices, is widely accepted in financial market studies. In [1], we showed that a rule obtained by a simple analysis classifies short-term stock price changes with an 82.9% accuracy. In the analysis, the order book of high-frequency trading was the subject. The volume of high-frequency trading is increasing dramatically in these days, which is mainly responsible for short-term stock price changes, therefore, our study suggests the necessity of analyzing short-term market fluctuations caused by the high-frequency trading, an aspect that has not been well studied in conventional financial theories. In this paper, we extend our study to predict stock price by changing research framework from classification problem to regression problem. As expected based on [1], the regression model based on the proposed method can achieve very accurate results (e.g., correlation coefficient 0.48).

本文言語English
ホスト出版物のタイトルProceedings - 2016 IEEE 40th Annual Computer Software and Applications Conference Workshops, COMPSAC 2016
編集者Ling Liu, Dejan Milojicic, Zhiyong Zhang, Zhiyong Zhang, Sheikh Iqbal Ahamed, Hiroyuki Sato, Stevlio Cimato, William Claycomb, Sorel Reisman, Motonori Nakamura, Chung Horng Lung, Mihhail Matskin
出版社IEEE Computer Society
ページ89-94
ページ数6
ISBN(電子版)9781467388450
DOI
出版ステータスPublished - 2016 8月 24
イベント2016 IEEE 40th Annual Computer Software and Applications Conference, COMPSAC 2016 - Atlanta, United States
継続期間: 2016 6月 102016 6月 14

出版物シリーズ

名前Proceedings - International Computer Software and Applications Conference
2
ISSN(印刷版)0730-3157

Other

Other2016 IEEE 40th Annual Computer Software and Applications Conference, COMPSAC 2016
国/地域United States
CityAtlanta
Period16/6/1016/6/14

ASJC Scopus subject areas

  • ソフトウェア
  • コンピュータ サイエンスの応用

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