Global Stock Market Prediction Based on Stock Chart Images Using Deep Q-Network

TL;DR

This paper presents a deep reinforcement learning approach using Deep Q-Networks with CNNs to predict global stock market movements from chart images, trained on the US market and tested across 31 countries, demonstrating cross-market predictive patterns.

Contribution

Introduces a novel method combining Deep Q-Networks and CNNs for global stock prediction using chart images, showing effective transferability across diverse markets.

Findings

Model yields 0.1-1.0% return per transaction before costs across 31 countries.

Stock chart patterns can predict future prices globally.

Training in large markets can be effective for small market predictions.

Abstract

We applied Deep Q-Network with a Convolutional Neural Network function approximator, which takes stock chart images as input, for making global stock market predictions. Our model not only yields profit in the stock market of the country where it was trained but generally yields profit in global stock markets. We trained our model only in the US market and tested it in 31 different countries over 12 years. The portfolios constructed based on our model's output generally yield about 0.1 to 1.0 percent return per transaction prior to transaction costs in 31 countries. The results show that there are some patterns on stock chart image, that tend to predict the same future stock price movements across global stock markets. Moreover, the results show that future stock prices can be predicted even if the training and testing procedures are done in different countries. Training procedure could be done in relatively large and liquid markets (e.g., USA) and tested in small markets. This result demonstrates that artificial intelligence based stock price forecasting models can be used in relatively small markets (emerging countries) even though they do not have a sufficient amount of data for training.