{"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/online-portfolio-selection-a-survey","title":"Online Portfolio Selection: A Survey","arxiv_id":"1212.2129","date":"2012-12-10","proceeding":null,"authors":["Bin Li","Steven C. H. Hoi"],"abstract":"Online portfolio selection is a fundamental problem in computational finance,\nwhich has been extensively studied across several research communities,\nincluding finance, statistics, artificial intelligence, machine learning, and\ndata mining, etc. This article aims to provide a comprehensive survey and a\nstructural understanding of published online portfolio selection techniques.\nFrom an online machine learning perspective, we first formulate online\nportfolio selection as a sequential decision problem, and then survey a variety\nof state-of-the-art approaches, which are grouped into several major\ncategories, including benchmarks, \"Follow-the-Winner\" approaches,\n\"Follow-the-Loser\" approaches, \"Pattern-Matching\" based approaches, and\n\"Meta-Learning Algorithms\". In addition to the problem formulation and related\nalgorithms, we also discuss the relationship of these algorithms with the\nCapital Growth theory in order to better understand the similarities and\ndifferences of their underlying trading ideas. This article aims to provide a\ntimely and comprehensive survey for both machine learning and data mining\nresearchers in academia and quantitative portfolio managers in the financial\nindustry to help them understand the state-of-the-art and facilitate their\nresearch and practical applications. We also discuss some open issues and\nevaluate some emerging new trends for future research directions.","url_abs":"http://arxiv.org/abs/1212.2129v2","url_pdf":"http://arxiv.org/pdf/1212.2129v2.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":"online-portfolio-selection-a-survey","repo_url":"https://github.com/Marigold/universal-portfolios","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"online-portfolio-selection-a-survey","repo_url":"https://github.com/chrischia06/AlgoTradingSimulatedPaths","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"GPL-3.0"}}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"meta-learning","task_name":"Meta-Learning"},{"task_slug":"survey","task_name":"Survey"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1212.2129","atlas_url":"https://app.syntology.ai/?focus=1212.2129","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}