[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118780-en":3,"doc-seo-118780-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},118780,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","How can machine learning advance quantitative asset management?","Machine learning offers promising tools for quantitative asset management by identifying and leveraging nonlinearities and interaction effects that simpler modeling may overlook. The paper reviews existing ML literature through a practitioner’s lens, highlighting both methodological promises and pitfalls that can materially shape predictive outcomes. It evaluates the common claim of spectacular performance improvements, concluding that advantages may appear smaller in practice while still meaningfully assisting investors who follow a sound research protocol to manage intrinsic ML risks.","How can machine learning advance quantitative asset management?*  \nDavid Blitz  \nRobeco, [d.c.blitz@robeco.com](d.c.blitz@robeco.com)  \nTobias Hoogteijling  \nRobeco, [t.hoogteijling@robeco.com](t.hoogteijling@robeco.com)  \nHarald Lohre  \nRobeco, Lancaster University Management School, [h.lohre@robeco.com](h.lohre@robeco.com)  \nPhilip Messow  \nRobeco, [p.messow@robeco.com](p.messow@robeco.com)  \nJanuary 9, 2023  \nJournal of Portfolio Management, forthcoming.  \nAbstract  \nThe emerging literature suggests that machine learning (ML) is beneficial in many asset pricing applications because of its ability to detect and exploit nonlinearities and interaction effects that tend to go unnoticed with simpler modelling approaches. In this paper, we discuss the promisesand pitfalls of applying machine learning to asset management, by reviewing the existing ML literature from the perspective of a prudent practitioner. The focus is on the methodological design choices that can critically affect predictive outcomes and on an evaluation of the frequent claim that ML gives spectacular performance improvements. In light of the practical considerations, the apparent advantage of ML is reduced, but still likely to make a difference for investors who adhere to a sound research protocol to navigate the intrinsic pitfalls of ML.  \nKeywords: machine learning; asset management; portfolio management; factor investing  \nJEL Classification: G10, G12, G17  \n*  \nThe authors thank Guido Baltussen, Joris Blonk, Matthias Hanauer, Robbert-Jan ´t-Hoen, Fabio Martinetti, Frederik Muskens, and participants at the Robeco research seminars for valuable comments and suggestions. The views expressed in this article are not necessarily shared by Robeco or its subsidiaries.  \n1 Introduction  \nMachine learning (ML) has made inroads into many research areas to augment or improve upon human expertise. Activities that involve a lot of data and numbers are particularly amenable to benefit from the application of ML methods. Although finance and investment is ultimately asocial science that revolves around human behavior, the use of quantitative methods and analysis has become firmly established. With the large amounts of data and numbers available, it goes without saying that many believe that ML has the potential to revolutionize the investment industry.  \nWhile ML theory has been around for many years, recent advances in (cloud) computing power have made it operationally feasible to assess how ML can contribute to investment management. The increasing popularity is reflected in the number of research papers published in recent years investigating the use of \"artificial intelligence\" and \"machine learning\" in quantitative asset management. Whereas just five years ago only a handful of studies on this topic were available, there is now a considerable body of literature that provides many interesting insights and keeps growing by the day.  \nBefore the surge in computing power and available data, one would instead have resorted to an economic model to describe optimal decisions of rational individuals, rather than just letting the data speak in an ML model. In economics, theory prescribes the model, and data determines the associated estimates of model parameters. Such econometric modelling is looking to establish a relationship among features and target variables, but also whether these make sense; in other words, one is generally looking for causality and not just correlation. Machine Learning can be atan advantage over classical econometric approaches whenever the focus is on forecasting, especially in the absence of a theoretical model. It is well-equipped to deal with large and complex data sets, and particularly suitable for capturing nonlinearities and interaction effects.  \nIn this article we describe the specific challenges and opportunities of ML methods in quantitative asset management and the road ahead from an institutional asset manager perspective. In the follow","cbCaibRlzLbRpfMU","https://ap.wps.com/l/cbCaibRlzLbRpfMU","pdf",478379,1,21,"English","en",105,"# Introduction\n## Machine learning versus classical econometrics\n### Benefits of Machine Learning\n### Pitfalls and practical design choices\n## Generating alpha and alternative applications","[{\"question\":\"Why is machine learning considered useful for asset pricing and asset management?\",\"answer\":\"Machine learning can detect and exploit nonlinearities and interaction effects that simpler models may miss, improving the ability to learn from complex data patterns.\"},{\"question\":\"What practical pitfalls does the paper emphasize when applying machine learning to asset management?\",\"answer\":\"Methodological design choices and the evaluation of performance claims can strongly affect predictive outcomes; intrinsic risks require careful navigation using a sound research protocol.\"},{\"question\":\"How does machine learning differ from classical econometrics according to the paper?\",\"answer\":\"The paper defines ML as learning from data to make out-of-sample predictions, while classical econometric models are typically fit on the full dataset with a more explicit parametric structure.\"}]","How can machine learning advance quantitative asset management? 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