[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123712-en":3,"doc-seo-123712-105":30,"detail-sidebar-cat-0-en-105":91},{"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},123712,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Machine learning, anomalies, and the expected market return: Evidence from China","Investigates whether machine learning techniques that forecast overall market returns using cross-sectional stock return anomalies can generate useful predictions in the China equity market. Using an out-of-sample evaluation, the study finds that a combined approach based on ordinary least squares and an elastic net model successfully forecasts China’s market return, delivering meaningful out-of-sample R2. Other four ML methods do not achieve comparable forecasting performance, highlighting both promise and the need to address possible model mining.","University of Birmingham  \nMachine learning, anomalies, and the expected market return: Evidence from China  \nDu, Qingjie; Wang, Yang; Wei, Chishen; Wei, K.C. John  \nDOI:  \n10.1016/j.pacfin.2023.102168  \nLicense:  \nCreative Commons: Attribution-NonCommercial-NoDerivs (CC BY-NC-ND)  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nCitation for published version (Harvard):  \nDu, Q, Wang, Y, Wei, C & Wei, KCJ 2023, 'Machine learning, anomalies, and the expected market return: Evidence from China', Pacific-Basin Finance Journal, vol. 82, 102168.  \n[https://doi.org/10.1016/j.pacfin.2023.102168](https://doi.org/10.1016/j.pacfin.2023.102168)  \nLink to publication on Research at Birmingham portal  \nGeneral rights  \nUnless a licence is specified above, all rights (including copyright and moral rights) in this document are retained by the authors and/or the copyright holders. The express permission of the copyright holder must be obtained for any use of this material other than for purposes permitted by law.  \n•Users may freely distribute the URL that is used to identify this publication.  \n•Users may download and/or print one copy of the publication from the University of Birmingham research portal for the purpose of private study or non-commercial research.  \n•User may use extracts from the document in line with the concept of ‘fair dealing’ under the Copyright, Designs and Patents Act 1988 (?)  \n•Users may not further distribute the material nor use it for the purposes of commercial gain.  \nWhere a licence is displayed above, please note the terms and conditions of the licence govern your use of this document.  \nWhen citing, please reference the published version.  \nTake down policy  \nWhile the University of Birmingham exercises care and attention in making items available there are rare occasions when an item has been uploaded in error or has been deemed to be commercially or otherwise sensitive.  \nIf you believe that this is the case for this document, [please contact UBIRA@lists.bham.ac.uk](please contact UBIRA@lists.bham.ac.uk) providing details and we will remove access to the work immediately and investigate.  \nDownload date: 03. Aug. 2026  \nPacific-Basin Finance Journal 82 (2023) 102168  \nContents lists available at ScienceDirect  \nPacific-Basin Finance Journal  \n[journal homepage: www.elsevier.com/locate/pacfin](journal homepage: www.elsevier.com/locate/pacfin)  \n| Machine learning, anomalies, and the expected market return: Evidence from China☆\u003Cbr>Qingjie Dua, Yang Wang b, Chishen Wei c, K.C. John Wei c, *\u003Cbr>a University of Birmingham, United Kingdom b University of Sydney, Australia\u003Cbr>c The Hong Kong Polytechnic University, Hong Kong, China |  |  |  |\n| --- | --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| JEL classification: G11\u003Cbr>G14\u003Cbr>G15\u003Cbr>Keywords:\u003Cbr>Machine learning Chinese stock market Anomalies\u003Cbr>Return predictability |  | We investigate whether machine learning (ML) techniques that forecast overall [U.S. market](U.S. market)[ ](U.S. market)[returns using cross-sectional stock return anomalies in Dong et al.](returns using cross-sectional stock return anomalies in Dong et al.) (2022) are useful for the China equity market. We successfully forecast out-of-sample R2 of the market return in China using a combined version of ordinary least squares and an elastic net model. However, the other four ML methods cannot forecast the market return. Overall, our exercise highlights the potential of ML techniques, but also calls for future research to rule out the possibility of model mining. |  |\n\n1. Introduction  \nMachine learning (ML) holds great promise for furthering our understanding of fundamental questions in asset pricing. ML encompasses the use of high-dimensional models for statistical prediction, model selection, and algorithms that search for optimal specifications. Gu et al. (2020) show that ML methods improve the measurement of risk premium in U.S. equities relati","cbCaigmYfebCWorq","https://ap.wps.com/l/cbCaigmYfebCWorq","pdf",521345,1,9,"English","en",105,"# Introduction\n## Research aims\n## Why the China stock market is used\n# Data and methods\n## Out-of-sample forecasting setup\n## Forecasting models\n# Results\n## Forecasting performance comparison\n# Discussion\n## Implications for machine learning in asset pricing\n## Model mining concern","[{\"question\":\"What is the main research question about machine learning in this study?\",\"answer\":\"The study tests whether machine learning methods using cross-sectional stock return anomalies can forecast market returns in the China equity market.\"},{\"question\":\"Which model successfully forecasts China’s market return, and how is it evaluated?\",\"answer\":\"A combined ordinary least squares and elastic net model successfully forecasts out-of-sample market return performance in China, evaluated using out-of-sample R2.\"},{\"question\":\"Do all machine learning methods perform equally well for China?\",\"answer\":\"No. The study reports that four other ML methods cannot forecast the market return effectively, unlike the combined OLS-elastic net approach.\"}]","Machine learning, anomalies, and the expected market return: Evidence from China | 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