[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124097-en":3,"doc-seo-124097-105":30,"detail-sidebar-cat-0-en-105":83},{"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},124097,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Machine Learning-Based Art Price Forecasting - Generalization for Selected Belgian Painters","Machine learning is applied to predict painting prices for 11 selected Belgian artists using data from Christie’s covering 1994–2023. The study evaluates how representation and observed market outcomes relate, comparing model performance against human expert judgment. Results show limited accuracy overall, with Random Forest outperforming Gradient Boosting. Generalization is constrained by a small dataset, so recommendations focus on expanding data volume and adding variables derived from online information, including bidder counts, to improve appraisal efficiency.","A Work Project, presented as part ofthe requirements for the Award of a Master’s degree in Finance from the Nova School of Business and Economics.  \nMachine Learning-Based Art Price Forecasting: Generalization for Selected Belgian Painters  \nMiserez Jean-Philippe  \nWork Project carried out under the supervision of:  \nProfessor Miguel Lebre de Freitas  \nAbstract:  \nThis study uses Machine Learning (ML) to predict prices for paintings by 11 selected Belgian artists based on data from Christie’s spanning the period from 1994 to 2023. Examining the nuanced relationship between representation and reality, the research reveals ML's limited accuracy compared to human experts. While the Random Forest model outperforms Gradient Boosting, constraints such as a small dataset impact generalization. Recommendations include expanding the dataset and integrating features related to online information and the number of bidders. Despite current disparities, ML shows promise as a complementary tool to enhance efficiency in the appraisal landscape of paintings.  \nKeywords: Finance, Forecasting, Machine Learning, Art, Painting, Belgium, Regression, Random Forest, Gradient Boosting, Ensemble Methods, Web Scraping  \nAcknowledgment:  \nThis work used infrastructure and resources funded by Fundação para a Ciência e a Tecnologia (UID/ECO/00124/2013, UID/ECO/00124/2019 and Social Sciences DataLab, Project 22209), POR Lisboa (LISBOA-01-0145-FEDER-007722 and Social Sciences DataLab, Project 22209) and POR Norte (Social Sciences DataLab, Project 22209) .  \nTable of Contents  \n1 Introduction ......................................................................................................................................................... 1  \n2 Literature review ................................................................................................................................................. 2  \nA. Art market industry ................................................................................................................................... 2  \nPrice dynamics ............................................................................................................................................... 4  \nAuction houses ............................................................................................................................................... 5  \nArt auctions .................................................................................................................................................... 5  \nBelgian painting market ................................................................................................................................ 5  \nB. Problem formulation ................................................................................................................................. 7  \nC. Motivation / Objective .............................................................................................................................. 7  \nD. Scope.......................................................................................................................................................... 8  \n3 Methodology ....................................................................................................................................................... 9  \nA. History of pricing methods ........................................................................................................................ 9  \nB. Machine Learning explained ..................................................................................................................... 9  \nC. Data.......................................................................................................................................................... 10  \nData Collection........................................................................................................................................","cbCaitBbJvmQwZ1c","https://ap.wps.com/l/cbCaitBbJvmQwZ1c","pdf",751100,1,35,"English","en",105,"# Introduction\n## Literature review\n## Methodology\n## Analysis\n## Conclusion","[{\"question\":\"What main factors restrict the models’ ability to generalize?\",\"answer\":\"A small dataset reduces generalization, and the study recommends expanding the dataset and adding features such as online information and bidder counts.\"}]","Machine Learning-Based Art Price Forecasting - Generalization for Selected Belgian Painters | PDF",1785820305,88,{"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":78,"head_meta":80,"extra_data":82,"updated_unix":28},"machine-learning-based-art-price-forecasting-generalization-for-selected-belgian-painters","",{"@graph":36,"@context":77},[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/machine-learning-based-art-price-forecasting-generalization-for-selected-belgian-painters/124097/",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],{"name":72,"@type":73,"acceptedAnswer":74},"What main factors restrict the models’ ability to generalize?","Question",{"text":75,"@type":76},"A small dataset reduces generalization, and the study recommends expanding the dataset and adding features such as online information and bidder counts.","Answer","https://schema.org",{"og:url":52,"og:type":79,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":81,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":84},[85,89,93,97,102,107,112,115,120,123,127],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":86,"show_sort_weight":87,"slug":88},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":90,"show_sort_weight":91,"slug":92},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Exam",70,"exam",{"id":98,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},5,"Comic",60,"comic",{"id":103,"doc_module":4,"doc_module_name":46,"category_name":104,"show_sort_weight":105,"slug":106},6,"Technology",50,"technology",{"id":108,"doc_module":4,"doc_module_name":46,"category_name":109,"show_sort_weight":110,"slug":111},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":113,"slug":114},30,"research-report",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":124,"slug":126},10,"Lifestyle","lifestyle",{"id":128,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":98,"slug":130},19,"General","general"]