{"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/neural-networks-for-stock-price-prediction","title":"Neural networks for stock price prediction","arxiv_id":"1805.11317","date":"2018-05-29","proceeding":null,"authors":["Yue-Gang Song","Yu-Long Zhou","Ren-Jie Han"],"abstract":"Due to the extremely volatile nature of financial markets, it is commonly\naccepted that stock price prediction is a task full of challenge. However in\norder to make profits or understand the essence of equity market, numerous\nmarket participants or researchers try to forecast stock price using various\nstatistical, econometric or even neural network models. In this work, we survey\nand compare the predictive power of five neural network models, namely, back\npropagation (BP) neural network, radial basis function (RBF) neural network,\ngeneral regression neural network (GRNN), support vector machine regression\n(SVMR), least squares support vector machine regresssion (LS-SVMR). We apply\nthe five models to make price prediction of three individual stocks, namely,\nBank of China, Vanke A and Kweichou Moutai. Adopting mean square error and\naverage absolute percentage error as criteria, we find BP neural network\nconsistently and robustly outperforms the other four models.","url_abs":"http://arxiv.org/abs/1805.11317v1","url_pdf":"http://arxiv.org/pdf/1805.11317v1.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":"neural-networks-for-stock-price-prediction","repo_url":"https://github.com/aflorial/DeepDayTrade","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"neural-networks-for-stock-price-prediction","repo_url":"https://github.com/atharvacc/SigmaNewsProject","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"neural-networks-for-stock-price-prediction","repo_url":"https://github.com/xrndai/DeepDayTrade","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"stock-price-prediction","task_name":"Stock Price Prediction"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}