Stock Price Regression Based on Order Book Information

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).

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