{"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/can-we-learn-to-beat-the-best-stock","title":"Can We Learn to Beat the Best Stock","arxiv_id":null,"date":"2004-05-01","proceeding":"Advances in Neural Information Processing Systems 16 2004 5","authors":["Allan Borodin","Ran El-Yaniv","Vincent Gogan"],"abstract":"A novel algorithm for actively trading stocks is presented.  While traditional universal algorithms (and technical trading heuristics) attempt topredict winners or trends, our approach relies on predictable statistical relations between all pairs of stocks in the market. Our empirical results on historical markets provide strong evidence that this type of technical trading can “beat the market” and moreover, can beat the best stock in the market. In doing so we utilize a new idea for smoothing critical parameters in the context of expert learning.","url_abs":"https://arxiv.org/pdf/1107.0036.pdf","url_pdf":"https://arxiv.org/pdf/1107.0036.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":"can-we-learn-to-beat-the-best-stock","repo_url":"https://github.com/Marigold/universal-portfolios","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}