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 whose data was used for training our model but also generally yields profit in global stock markets. We trained our model only on US stock market data and tested it on the stock market data of 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 the stock markets of 31 countries. The results show that some patterns in stock chart images indicate the same stock price movements across global stock markets. Moreover, the results show that future stock prices can be predicted even if the model is trained and tested on data from different countries. The model can be trained on the data of relatively large and liquid markets (e.g., US) and tested on the data of small markets. The results demonstrate that artificial intelligence based stock price forecasting models can be used in relatively small markets (emerging countries) even though small markets do not have a sufficient amount of data for training. INDEX TERMS Artificial intelligence, finance, neural networks, stock markets.
Jinho Lee et al. REF used stock chart images as input to the model and used the Deep Q-Network and convolutional neural networks for global stock market forecasting.
Global Stock Market Prediction Based on Stock Chart Images Using Deep Q-Network
IEEE Access
IEEE Access
Computer Science Economics