{"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/deep-learning-for-predicting-asset-returns","title":"Deep Learning for Predicting Asset Returns","arxiv_id":"1804.09314","date":"2018-04-25","proceeding":null,"authors":["Guanhao Feng","Jingyu He","Nicholas G. Polson"],"abstract":"Deep learning searches for nonlinear factors for predicting asset returns.\nPredictability is achieved via multiple layers of composite factors as opposed\nto additive ones. Viewed in this way, asset pricing studies can be revisited\nusing multi-layer deep learners, such as rectified linear units (ReLU) or\nlong-short-term-memory (LSTM) for time-series effects. State-of-the-art\nalgorithms including stochastic gradient descent (SGD), TensorFlow and dropout\ndesign provide imple- mentation and efficient factor exploration. To illustrate\nour methodology, we revisit the equity market risk premium dataset of Welch and\nGoyal (2008). We find the existence of nonlinear factors which explain\npredictability of returns, in particular at the extremes of the characteristic\nspace. Finally, we conclude with directions for future research.","url_abs":"http://arxiv.org/abs/1804.09314v2","url_pdf":"http://arxiv.org/pdf/1804.09314v2.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":"deep-learning-for-predicting-asset-returns","repo_url":"https://github.com/jsun1/CS230Project","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}