DeepAI AI Chat
Log In Sign Up

Improving CNN-base Stock Trading By Considering Data Heterogeneity and Burst

by   Keer Yang, et al.

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.


page 5

page 6

page 8


A Deep Learning Based Illegal Insider-Trading Detection and Prediction Technique in Stock Market

The stock market is a nonlinear, nonstationary, dynamic, and complex sys...

Financial Trading Model with Stock Bar Chart Image Time Series with Deep Convolutional Neural Networks

Even though computational intelligence techniques have been extensively ...

Can We Learn to Beat the Best Stock

A novel algorithm for actively trading stocks is presented. While tradit...

Sentiment and Knowledge Based Algorithmic Trading with Deep Reinforcement Learning

Algorithmic trading, due to its inherent nature, is a difficult problem ...

Deep Portfolio Optimization via Distributional Prediction of Residual Factors

Recent developments in deep learning techniques have motivated intensive...

An intelligent financial portfolio trading strategy using deep Q-learning

A goal of financial portfolio trading is maximizing the trader's utility...

Stochastic Portfolio Theory: A Machine Learning Perspective

In this paper we propose a novel application of Gaussian processes (GPs)...