[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126883-en":3,"doc-seo-126883-105":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},126883,1099523885336,"Violet","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","From Factor Models to Deep Learning - Machine Learning in Reshaping Empirical Asset Pricing","A comprehensive review examines how machine learning and AI reshape empirical asset pricing, moving beyond traditional factor models and their limits in capturing market nonlinearities. The survey outlines supervised, unsupervised, semi-supervised, and reinforcement learning frameworks, showing how advanced algorithms can be integrated with classical structures to improve return prediction and portfolio optimization. It also analyzes how models adapt to shifting market regimes via structural change modeling and heterogeneous data such as text and images, while addressing challenges like explainability demands and overfitting risk in complex methods.","From Factor Models to Deep Learning: Machine Learning in Reshaping Empirical Asset Pricing  \nJunyi Ye∗ , Bhaskar Goswami∗ , Jingyi Gu∗ , Ajim Uddin and Guiling Wang  \nNew Jersey Institute of Technology {jy394, bg362, jg95, ajim.uddin, [gwang](gwang}@njit.edu)[}](gwang}@njit.edu)[@njit.edu](gwang}@njit.edu)  \narXiv :2403 .06779v1 [ q-fin . ST] 11 Mar 2024  \nAbstract  \nThis paper comprehensively reviews the application of machine learning (ML) and AI in finance, specifically in the context of asset pricing. It starts by summarizing the traditional asset pricing models and examining their limitations in capturing the complexities of financial markets. It exploreshow 1) ML models, including supervised, unsupervised, semi-supervised, and reinforcement learning, provide versatile frameworks to address these complexities, and 2) the incorporation of advanced ML algorithms into traditional financial models enhances return prediction and portfolio optimization.  \nThese methods can adapt to changing market dynamics by modeling structural changes and incorporating heterogeneous data sources, such as text and images. In addition, this paper explores challenges in applying ML in asset pricing, addressing the growing demand for explainability in decisionmaking and mitigating overfitting in complex models. This paper aims to provide insights into novel methodologies showcasing the potential of ML toreshape the future of quantitative finance.  \n1 Introduction  \nThe finance sector, often recognized as the backbone of economic society, plays an indispensable role in facilitating smooth economic operations and growth. This sector faces unique challenges, stemming from the complexity of financial markets, regulatory constraints, the need for precise decision-making, big data, and the rapid pace of technological change. The integration of Machine Learning (ML) and Artificial Intelligence (AI) into this domain, particularly in asset pricing, is not merely an advancement but a necessity.  \nEmpirical asset pricing models, which aim to elucidate the complex relationships between financial assets and their expected returns, are crucial for investors, fund managers, and policymakers. Traditional models like the Capital Asset Pricing Model (CAPM) [Sharpe, 1964] and the Fama-French models [Fama and French, 2015] have been foundational yet  \n*These authors contributed equally to this work.  \noften struggle to capture the multifaceted and nonlinear dynamics of financial markets.  \nIn literature, the issues of less predictive accuracy, difficult variable selection, and less flexible functional forms of traditional models are well documented [Welch and Goyal, 2008; He and Krishnamurthy, 2013] . [Gu et al., [2020], propose addressing these issues by introducing ML in asset pricing. ML models offer better predictive power and the ability to model complex non-linear relationships with much more flexibility. They also enable the integration of non-traditional data sources, e.g., text, image, video, and audio data, enriching the decision-making processes. In addition, fine-tuning and parameter optimization strategies enable continuous learning based on new information, facilitating real-time decisionmaking. As a result, we see a surge in the application of ML in finance, especially for modeling complexities in asset pricing.  \nIn this paper, we offer a comprehensive review of empirical asset pricing using machine learning (ML) . We examine how ML-based approaches have transformed traditional models, providing a renewed perspective on challenges in asset pricing. Unlike some previous works that focused solely on either Finance [Giglio et al., 2022] or those detailing the taxonomy of financial-risk tasks linked to machine learning methods ([Mashrur et al., 2020]), our approach involves an examination of recent research contributions from both finance and computer science fields, offering a comprehensive view of the interdisciplinary advancements. Moreover, we conduc","cbCaijcWwh9Vzrec","https://ap.wps.com/l/cbCaijcWwh9Vzrec","pdf",893046,1,9,"English","en",105,"# Introduction\n## Risk Assessment and Price Prediction\n### Traditional Factor Models\n## Portfolio Optimization\n## Recent Advancements\n## Challenges and Future Trends","[{\"question\":\"How does machine learning improve empirical asset pricing compared with traditional models?\",\"answer\":\"It offers greater flexibility to model complex, nonlinear market relationships and can enhance predictive power for returns and portfolio decisions.\"},{\"question\":\"What types of machine learning approaches are discussed for asset pricing?\",\"answer\":\"The review covers supervised, unsupervised, semi-supervised, and reinforcement learning, along with ways to integrate advanced algorithms into traditional financial modeling.\"},{\"question\":\"What challenges arise when applying ML to asset pricing?\",\"answer\":\"Key issues include the growing need for explainability in decision-making and the risk of overfitting when using complex models.\"}]","From Factor Models to Deep Learning - Machine Learning in Reshaping Empirical Asset Pricing | PDF",1785935404,23,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"from-factor-models-to-deep-learning-machine-learning-in-reshaping-empirical-asset-pricing","",{"@graph":36,"@context":85},[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/from-factor-models-to-deep-learning-machine-learning-in-reshaping-empirical-asset-pricing/126883/",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","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How does machine learning improve empirical asset pricing compared with traditional models?","Question",{"text":75,"@type":76},"It offers greater flexibility to model complex, nonlinear market relationships and can enhance predictive power for returns and portfolio decisions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What types of machine learning approaches are discussed for asset pricing?",{"text":80,"@type":76},"The review covers supervised, unsupervised, semi-supervised, and reinforcement learning, along with ways to integrate advanced algorithms into traditional financial modeling.",{"name":82,"@type":73,"acceptedAnswer":83},"What challenges arise when applying ML to asset pricing?",{"text":84,"@type":76},"Key issues include the growing need for explainability in decision-making and the risk of overfitting when using complex models.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]