Short-term stock price prediction by encoding high-frequency order information and deep learning

Abstract

Predicting the price movements of stocks based on deep learning and high frequency data has been studied intensively in recent years. Especialy, limit order book which describes supply-demand balance of the market is used as feature of a neural network, however, these methods do not utilize the properties of market orders. On the other hand, order encoding method of our prior work can take advantage of these properties. In this paper, we apply some types of convolutional neural network(CNN) architectures to order-based features to predict the direction of mid-price movements. The results show that smoothing filters which we propose to employ over embedding features of orders improve accuracy. Furthermore, inspection of embedding layer and investment simulation are conducted to demonstrate the practicality and effectiveness of our model.