Improving CNN-Based Stock Trading by Considering Data Heterogeneity and Burst


Keer Yang1, Guanqun Zhang2, Chuan Bi3, Qiang Guan4, Hailu Xu5, Shuai Xu1, 1Case Western Reserve University, USA, 2Nankai University, USA, 3National Institute of Health, USA, 4Kent State University, USA, 5California State University Long Beach, USA


In recent years, there have been quite a few attempts to apply intelligent techniques to financial trading, i.e., constructing automatic and intelligent trading framework based on historical stock price. Due to the unpredictable, uncertainty and volatile nature of financial market, researchers have also resorted to deep learning to construct the intelligent trading framework. In this paper, we propose to use CNN as the core functionality of such framework, because it is able to learn the spatial dependency (i.e., between rows and columns) of the input data. However, different with existing deep learning-based trading frameworks, we develop novel normalization process to prepare the stock data. In particular, we first empirically observe that the stock data is intrinsically heterogeneous and bursty, and then validate the heterogeneity and burst nature of stock data from a statistical perspective. Next, we design the data normalization method in a way such that the data heterogeneity is preserved and bursty events are suppressed. We verify out developed CNN-based trading framework plus our new normalization method on 29 stocks. Experiment results show that our approach can outperform other comparing approaches.


data normalization, intelligent stock trading, CNN