{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/global-stock-market-prediction-based-on-stock","title":"Global Stock Market Prediction Based on Stock Chart Images Using Deep Q-Network","arxiv_id":"1902.10948","date":"2019-02-28","proceeding":null,"authors":["Jinho Lee","Raehyun Kim","Yookyung Koh","Jaewoo Kang"],"abstract":"We applied Deep Q-Network with a Convolutional Neural Network function\napproximator, which takes stock chart images as input, for making global stock\nmarket predictions. Our model not only yields profit in the stock market of the\ncountry where it was trained but generally yields profit in global stock\nmarkets. We trained our model only in the US market and tested it in 31\ndifferent countries over 12 years. The portfolios constructed based on our\nmodel's output generally yield about 0.1 to 1.0 percent return per transaction\nprior to transaction costs in 31 countries. The results show that there are\nsome patterns on stock chart image, that tend to predict the same future stock\nprice movements across global stock markets. Moreover, the results show that\nfuture stock prices can be predicted even if the training and testing\nprocedures are done in different countries. Training procedure could be done in\nrelatively large and liquid markets (e.g., USA) and tested in small markets.\nThis result demonstrates that artificial intelligence based stock price\nforecasting models can be used in relatively small markets (emerging countries)\neven though they do not have a sufficient amount of data for training.","url_abs":"http://arxiv.org/abs/1902.10948v1","url_pdf":"http://arxiv.org/pdf/1902.10948v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"global-stock-market-prediction-based-on-stock","repo_url":"https://github.com/armelf/Financial-Algorithms","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"stock-market-prediction","task_name":"Stock Market Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}