Stock Price Analysis using Deep Learning and High-Frequency Order Information

Abstract

In this paper, we propose order-based approach to predict future movements of a stock price. Our models employ a convolutional neural network(CNN) over embedded orders that have quantitative and qualitative variables. For each dataset of stock codes, the models outperform traditional feature-based approaches. Furthermore, we show that training under less influence of noise can be performed by applying an averaging filter to embedded feature space. Analysis of the embedding layer reveals that the models put emphasis on the features of market orders that are correlated with price return.