[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119490-en":3,"doc-seo-119490-105":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},119490,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","Revealing the risk perception of investors using machine learning","Corporate disclosures communicate crucial information to financial market participants, and machine-learning methods are widely used to extract it. However, these approaches often miss idiosyncratic terminology and industry-specific vocabulary embedded in documents. The study applies an unsupervised Structural Topic Model to analyze risk factors in corporate disclosures (10-Ks) and links them to investors’ pricing behavior using a US REIT sample covering 2005 to 2019.","The European Journal of Finance  \nISSN: (Print) (Online) Journal [homepage: ](homepage: www.tandfonline.com/journals/rejf20)[www.tandfonline.com/journals/rejf20](homepage: www.tandfonline.com/journals/rejf20)  \nRevealing the risk perception of investors using machine learning  \nMarina Koelbl, Ralf Laschinger, Bertram I. Steininger & Wolfgang Schaefers  \nTo cite this article: Marina Koelbl, Ralf Laschinger, Bertram I. Steininger & Wolfgang Schaefers (15 Jul 2024): Revealing the risk perception of investors using machine learning, The European  \nJournal of Finance, DOI: 10. 1080/1351847X.2024.2364831  \nTo link to this article: [https://doi.org/10.1080/1351847X.2024.2364831](https://doi.org/10.1080/1351847X.2024.2364831)  \n© 2024 The Author(s) . Published by Informa UK Limited, trading as Taylor & Francis Group.  \n\n|  View supplementary material  |\n| --- |\n|  Published online: 15 Jul 2024. |\n|  Submit your article to this journal  |\n|  Article views: 230 |\n|  View related articles  |\n|  View Crossmark data |\n\nFull Terms & Conditions of access and use can be found at [https://www.tandfonline.com/action/journalInformation?journalCode=rejf20](https://www.tandfonline.com/action/journalInformation?journalCode=rejf20)  \nTHE EUROPEAN JOURNAL OF FINANCE  \n[https://doi.org/10.1080/1351847X.2024.2364831](https://doi.org/10.1080/1351847X.2024.2364831)  \nRevealing the risk perception of investors using machine learning  \nMarina Koelbla, Ralf Laschinger b, Bertram I. Steininger c and Wolfgang Schaefersa  \naIREBS International Real Estate Business School, University of Regensburg, Regensburg, Germany;b Department of Finance, University of Regensburg, Regensburg, Germany; cReal Estate Economics and Finance, KTH Royal Institute of Technology, Stockholm, Sweden  \nABSTRACT  \nCorporate disclosures convey crucial information to financial market participants. While machine learning algorithms are commonly used to extract this information, they often overlook the use of idiosyncratic terminology and industry-specific vocabulary within documents. This study uses an unsupervised machine learning algorithm, the Structural Topic Model, to overcome these issues. Our findings illustrate the link between machine-extracted risk factors discussed in corporate disclosures (10-Ks) and the corresponding pricing behavior by investors, focusing on a previously unexplored US REIT sample from2005to2019. Surprisingly, when disclosed, most risk factors counterintuitively lead to a decrease in return volatility. This resolution of uncertainties surrounding known risk factors or the provision of additional facts about these factors contributes valuable insights to the financial market.  \nARTICLE HISTORY  \nReceived 2 May 2023  \nAccepted 9 April 2024  \nKEYWORDS  \nRisk; textual analysis; machine learning; structural topic model; 10-K filing  \nJEL CLASSIFICATIONS  \nC45; C80; G14; G18; M41; R30  \n1. Introduction  \nIt is still a matter of academic debate, whether markets efficiently incorporate information into prices. In financial markets, pricing is a continuous process of investors’ reactions to new information (Fama 1970) characterized by its volatility around the expected value. A low volatility is a sign of consistent expectations across investors regarding values when new information emerges. Contrary, high volatility indicates dissent about how to value and incorporate new information. By revealing a piece of new information, a new pricing process begins after their release date resulting in three possible outcomes: no price reaction if the information is irrelevant or already known among the investors, increasing volatility if the investors are in disagreement with the pricing outcome of the information, or decreasing volatility if the investors coincide about the informational impact on the firm’s future prospect. From a theoretical perspective, new information can increase or decrease investors’ risk perception. In line with this ambiguity, empirical research identif","cbCaif4bRSswsxd5","https://ap.wps.com/l/cbCaif4bRSswsxd5","pdf",4006309,1,28,"English","en",105,"# Introduction\n## Market efficiency and volatility\n## Role of standardized disclosures and risk disclosure in 10-Ks","[{\"question\":\"What problem does the study address regarding machine learning and corporate disclosures?\",\"answer\":\"Machine learning models often overlook idiosyncratic terminology and industry-specific vocabulary in documents. The study targets this limitation when extracting risk-related information from disclosures.\"},{\"question\":\"How does the study extract risk factors from corporate disclosures?\",\"answer\":\"It uses an unsupervised machine learning algorithm, the Structural Topic Model, to identify and interpret risk factors discussed in 10-K filings.\"},{\"question\":\"What is the main finding about disclosed risk factors and investor outcomes?\",\"answer\":\"Most disclosed risk factors are associated with a decrease in investors’ return volatility, which contrasts with the intuitive expectation that risk-related disclosure would increase volatility.\"}]","Revealing the risk perception of investors using machine learning | PDF",1785724593,71,{"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},"revealing-the-risk-perception-of-investors-using-machine-learning","",{"@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/revealing-the-risk-perception-of-investors-using-machine-learning/119490/",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","2026-08-03",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},"What problem does the study address regarding machine learning and corporate disclosures?","Question",{"text":75,"@type":76},"Machine learning models often overlook idiosyncratic terminology and industry-specific vocabulary in documents. The study targets this limitation when extracting risk-related information from disclosures.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the study extract risk factors from corporate disclosures?",{"text":80,"@type":76},"It uses an unsupervised machine learning algorithm, the Structural Topic Model, to identify and interpret risk factors discussed in 10-K filings.",{"name":82,"@type":73,"acceptedAnswer":83},"What is the main finding about disclosed risk factors and investor outcomes?",{"text":84,"@type":76},"Most disclosed risk factors are associated with a decrease in investors’ return volatility, which contrasts with the intuitive expectation that risk-related disclosure would increase volatility.","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},[93,97,101,105,110,115,120,123,128,131,135],{"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":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]