{"id":12691,"date":"2025-03-12T10:53:38","date_gmt":"2025-03-12T09:53:38","guid":{"rendered":"https:\/\/www.uni.lu\/research-en\/?post_type=research-projects&p=12691"},"modified":"2025-03-25T14:46:28","modified_gmt":"2025-03-25T13:46:28","slug":"datart-data-analytics-for-art-valuation","status":"publish","type":"research-projects","link":"https:\/\/www.uni.lu\/research-en\/research-projects\/datart-data-analytics-for-art-valuation\/","title":{"rendered":"DATART: DATA analytics for ART-valuation"},"content":{"rendered":"\n
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\nDATART: DATA analytics for ART-valuation<\/strong><\/h1>\n<\/div>\n<\/header>\n
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    \n\n<\/ul>\n<\/div>\n<\/div>\n <\/div>\n<\/section>\n\n
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    \nThe project at a glance<\/h2>\n\n\n
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      Start date:
      01 December 2021<\/strong><\/p>\n<\/div>\n<\/li>\n

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      Duration in months:
      48<\/strong>
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      Funding:
      IAS Luxembourg<\/strong><\/p>\n<\/div>\n<\/li>\n

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      Principal Investigator(s):
      Alessandro TUGNETTI<\/strong><\/p>\n<\/div>\n<\/li>\n <\/ul>\n<\/div>\n\n\n\n

      \nAbout<\/h2>\n\n\n\n

      The importance that collectibles play in wealth management is becoming increasingly crucial, and this necessitates the development of models which can efficiently predict their price fluctuations. In this research, we will exploit the ways in which technology is changing the art market by applying supervised machine learning methods to artistic products. The objective will be to develop a pricing method that is on one hand more accurate, and on the other that maintains the level of interpretability of the models currently used in the industry. In particular, art pricing is typically concerned with estimating Ordinary Least Squares (OLS) models, leveraging on their interpretability potential. However, it is known how these algorithms have small predictive capacity when compared with more flexible models in sectors where the uncertainty and human bias of valuation experts become stronger. Once we take advantage of the strength and abundance of data existing on the \u201cphysical\u201d art framework, we will extend the reasoning to the Non Fungible Token (NFT) market. The goal here would be to understand the “”value-creating factors”” of NFTs, to compare them with those of classical works of art and discover points of synergy and detachment between these two worlds.<\/p>\n\n\n

      <\/div>\n\n\n

      \nOrganisation and Partners<\/h2>\n\n\n\n

      Faculty of Law, Economics and Finance (FDEF)
      Institute for Advanced Studies (IAS)<\/strong><\/p>\n\n\n

      \n \n

      \nProject team<\/h2>\n
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      • \n
        \n
        \n
        \n \n\"Prof\n <\/figure>\n <\/div>\n
        \n

        Prof Christos KOULOVATIANOS<\/h3>\n

        Head of DF, Full professor<\/p>\n

        \n \n \n Learn more<\/span>\n <\/span>\n <\/use><\/svg> <\/a>\n<\/div>\n <\/div>\n <\/div>\n<\/div>\n<\/div><\/li>
      • \n
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        \n \n\"Prof\n <\/figure>\n <\/div>\n
        \n

        Prof Gilbert FRIDGEN<\/h3>\n

        Full professor in Digital Financial Services \/ Paypal-FNR PEARL Chair<\/p>\n

        \n \n \n Learn more<\/span>\n <\/span>\n <\/use><\/svg> <\/a>\n<\/div>\n <\/div>\n <\/div>\n<\/div>\n<\/div><\/li><\/ul>\n\n<\/div>\n\n<\/div><\/section>\n","protected":false},"excerpt":{"rendered":"

        The importance that collectibles play in wealth management is becoming increasingly crucial, and this necessitates the development of models which can efficiently predict their price fluctuations. In this research, we will exploit the ways in which technology is changing the art market by applying supervised machine learning methods to artistic products. The objective will be to develop a pricing method that is on one hand more accurate, and on the other that maintains the level of interpretability of the models currently used in the industry. In particular, art pricing is typically concerned with estimating Ordinary Least Squares (OLS) models, leveraging on their interpretability potential. However, it is known how these algorithms have small predictive capacity when compared with more flexible models in sectors where the uncertainty and human bias of valuation experts become stronger. Once we take advantage of the strength and abundance of data existing on the \u201cphysical\u201d art framework, we will extend the reasoning to the Non Fungible Token (NFT) market. The goal here would be to understand the “”value-creating factors”” of NFTs, to compare them with those of classical works of art and discover points of synergy and detachment between these two worlds.<\/p>\n","protected":false},"author":203,"featured_media":12786,"parent":0,"menu_order":0,"template":"","meta":{"featured_image_focal_point":[],"show_featured_caption":false,"ulux_newsletter_groups":"","uluxPostTitle":"DATART","uluxPrePostTitle":"Research project","_trash_the_other_posts":false,"_price":"","_stock":"","_tribe_ticket_header":"","_tribe_default_ticket_provider":"","_tribe_ticket_capacity":"0","_ticket_start_date":"","_ticket_end_date":"","_tribe_ticket_show_description":"","_tribe_ticket_show_not_going":false,"_tribe_ticket_use_global_stock":"","_tribe_ticket_global_stock_level":"","_global_stock_mode":"","_global_stock_cap":"","_tribe_rsvp_for_event":"","_tribe_ticket_going_count":"","_tribe_ticket_not_going_count":"","_tribe_tickets_list":"[]","_tribe_ticket_has_attendee_info_fields":false,"rpp_rp_identifier":"13131","rpp_taxonomies_in_sync":true},"research-project-status":[],"research-project-type":[],"field-of-interest":[],"organisation":[],"authorship":[203,329],"acf":[],"yoast_head":"\nDATART: DATA analytics for ART-valuation I Uni.lu<\/title>\n<meta name=\"description\" content=\"The importance that collectibles play in wealth management is becoming increasingly crucial, and this necessitates the development of models which can efficiently predict their price fluctuations. 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