[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123266-en":3,"doc-seo-123266-105":30,"detail-sidebar-cat-0-en-105":95},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123266,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","Hedge Fund Performance - Classification with Machine Learning, and Managerial Implications","The paper addresses the lack of a universal hedge fund strategy classification scheme by using machine learning to evaluate whether reported fund strategies align with realized performance and risk. It investigates how mismatched classifications can distort investor expectations and interfere with portfolio decisions. Results indicate that most declared strategies show no alignment with fund performance, while classification still matters for abnormal returns and risk exposures, with the market factor remaining dominant across most clusters and strategies. The study highlights implications for asset and portfolio allocation and benchmark construction.","[eprints@whiterose.ac.uk](eprints@whiterose.ac.uk)[ ](eprints@whiterose.ac.uk)[https://eprints.whiterose.ac.uk](https://eprints.whiterose.ac.uk)  \nUniversities of Leeds, Sheffield and York  \nDeposited via The University of York.  \nWhite Rose Research Online URL for this paper:  \n[https://eprints.whiterose.ac.uk/id/eprint/227552/](https://eprints.whiterose.ac.uk/id/eprint/227552/)  \nVersion: Published Version  \nArticle:  \nPlatanakis, Emmanouil, Stafylas, Dimitrios, Sutcliffe, Charles et al. (2025) Hedge fund performance, classification with machine learning, and managerial implications. British Journal of Management. ISSN: 1467-8551  \n[https://doi.org/10.1111/1467-8551.70011](https://doi.org/10.1111/1467-8551.70011)  \nReuse  \nThis article is distributed under the terms of the Creative Commons Attribution (CC BY) licence. This licence allows you to distribute, remix, tweak, and build upon the work, even commercially, as long as you credit the authors for the original work. More information and the full terms of the licence here: [https://creativecommons.org/licenses/](https://creativecommons.org/licenses/)  \nTakedown  \nIf you consider content in White Rose Research Online to be in breach of UK law, please notify us by  \nemailing [eprints@whiterose.ac.uk](eprints@whiterose.ac.uk) including the URL of the record and the reason for the withdrawal request.  \nBritish Journal of Management, Vol. 0, 1–25 (2025) DOI: 10.1111/1467-8551.70011  \nHedge Fund Performance, Classification with Machine Learning, and Managerial Implications  \nEmmanouil Platanakis, 1 Dimitrios Stafylas,2 Charles Sutcliffe 3  \nand Wenke Zhang4  \n1 School of Management, University of Bath, Bath, BA2 7AY, UK, 2 School for Business and Society, University of York, York, YO10 5GD, UK, 3The ICMA Centre, Henley Business School, University of Reading, Reading, RG6 6DL, UK, and  \n4 College of Business, Arts and Social Sciences, Brunel University of London, Middlesex, Uxbridge, UB8 3PH, UK  \nCorresponding author: email: [c.m.s.sutcliffe@gmail.com](c.m.s.sutcliffe@gmail.com)  \nPrior academic research on hedge funds focuses predominantly on fund strategies in relation to market timing, stock picking and performance persistence, among others. However, the hedge fund industry lacks a universal classification scheme for strategies, leading to potentially biased fund classificationsand inaccurate expectations of hedge fund performance. This paper uses machine learning techniques to address this issue. First, it examines whether the reported fund strategies are consistent with their performance. Second, it examines the potential impact of hedge fund classification on managerial decision-making. Our results suggest that for most reported strategies there is no alignment with fund performance. Classification matters in terms of abnormal returns and risk exposures, although the market factor remains consistently the most important exposure for most clusters and strategies. An important policy implication of our study is that the classification of hedge funds affects asset and portfolio allocation decisions, and the construction of the benchmarks against which performance is  \njudged.  \nIntroduction  \nDuring the last decade, hedge funds (HFs) have received significant attention from both academic researchers and practitioners. As of the second quarter of 2024, the total assets under management for the HF industry were almost USD$5 . 1 trillion (BarclayHedge, 2024) . Each HF declares its investment strategy, which is both advertised to potential investors and used by databases when reporting HF performance.1 Investors seek to achieve a diversified portfolio when making asset allocation deci-  \nWe are grateful to the editor, an associate editor and three anonymous reviewers of this journal, as well as conference participants atthe 2023 Annual Meeting of the European Financial Management Association (EFMA) 2023 and the BAFA Annual Conference 2023 for their useful comments and suggestions","cbCaijzXnosu9Y0J","https://ap.wps.com/l/cbCaijzXnosu9Y0J","pdf",544627,1,25,"English","en",105,"# Introduction\n## Motivation and background\n## Research approach and contribution\n## Main findings and implications","[{\"question\":\"Why is hedge fund strategy classification considered problematic in the study?\",\"answer\":\"The paper notes that hedge funds lack a universal strategy classification scheme, and databases may apply different classifications. This can bias or fragment hedge fund data and create inaccurate expectations about performance.\"},{\"question\":\"How does the paper use machine learning?\",\"answer\":\"It applies machine learning techniques to test whether reported fund strategies are consistent with realized fund performance. It also assesses how hedge fund classification influences managerial decision-making.\"},{\"question\":\"What do the results suggest about reported strategies and performance alignment?\",\"answer\":\"For most reported strategies, the results suggest there is no alignment with fund performance. Classification nonetheless affects abnormal returns and risk exposures, with the market factor consistently being the most important exposure for most clusters and strategies.\"},{\"question\":\"What policy or managerial implications does the study highlight?\",\"answer\":\"It emphasizes that hedge fund classification affects asset and portfolio allocation decisions and the construction of benchmarks used to judge performance.\"}]","Hedge Fund Performance - Classification with Machine Learning, and Managerial Implications | PDF",1785815582,63,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":90,"head_meta":92,"extra_data":94,"updated_unix":28},"hedge-fund-performance-classification-with-machine-learning-and-managerial-implications","",{"@graph":36,"@context":89},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/hedge-fund-performance-classification-with-machine-learning-and-managerial-implications/123266/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81,85],{"name":72,"@type":73,"acceptedAnswer":74},"Why is hedge fund strategy classification considered problematic in the study?","Question",{"text":75,"@type":76},"The paper notes that hedge funds lack a universal strategy classification scheme, and databases may apply different classifications. This can bias or fragment hedge fund data and create inaccurate expectations about performance.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the paper use machine learning?",{"text":80,"@type":76},"It applies machine learning techniques to test whether reported fund strategies are consistent with realized fund performance. It also assesses how hedge fund classification influences managerial decision-making.",{"name":82,"@type":73,"acceptedAnswer":83},"What do the results suggest about reported strategies and performance alignment?",{"text":84,"@type":76},"For most reported strategies, the results suggest there is no alignment with fund performance. Classification nonetheless affects abnormal returns and risk exposures, with the market factor consistently being the most important exposure for most clusters and strategies.",{"name":86,"@type":73,"acceptedAnswer":87},"What policy or managerial implications does the study highlight?",{"text":88,"@type":76},"It emphasizes that hedge fund classification affects asset and portfolio allocation decisions and the construction of benchmarks used to judge performance.","https://schema.org",{"og:url":52,"og:type":91,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":93,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":96},[97,101,105,109,114,119,124,127,132,135,139],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},"Exam",70,"exam",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},5,"Comic",60,"comic",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},6,"Technology",50,"technology",{"id":120,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":122,"slug":123},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":125,"slug":126},30,"research-report",{"id":128,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":130,"slug":131},9,"Religion & Spirituality",20,"religion-spirituality",{"id":130,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":130,"slug":134},"World Cup","world-cup",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":136,"slug":138},10,"Lifestyle","lifestyle",{"id":140,"doc_module":4,"doc_module_name":46,"category_name":141,"show_sort_weight":110,"slug":142},19,"General","general"]