[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127234-en":3,"doc-seo-127234-105":31,"detail-sidebar-cat-0-en-105":92},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127234,2336475104042,"Skyler","https://ap-avatar.wpscdn.com/avatar/22000c4c32af1715be0?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786537525561427321",8,"Research & Report","A machine learning approach to support decision in insider trading detection","Identifying market abuse activity from investor trading data is difficult due to large data volumes and weak signals relative to noise. Two complementary unsupervised machine learning methods are proposed to support market surveillance for potential insider trading. One method uses clustering to detect discontinuities around price sensitive events by comparing an investor’s behavior to their own history and peers. The second finds coherent small investor groups around such events, suggesting coordinated insider rings.","Mazzarisi et al. EPJ Data Science (2024) 13:66  \n[https://doi.org/10.1140/epjds/s13688-024-00500-2](https://doi.org/10.1140/epjds/s13688-024-00500-2)  \nRESEARCH Open Access  \nA machine learning approach to support decision in insider trading detection  \nPiero Mazzarisi 1,2*†, Adele Ravagnani2†, Paola Deriu3 , Fabrizio Lillo2,4 , Francesca Medda5,3 and Antonio Russo3  \n* Correspondence:  \n[piero. mazzarisi@unisi.it](piero. mazzarisi@unisi.it)  \n1 Dipartimento di Economia Politicae Statistica, Università di Siena, Siena, Italy  \n2 Scuola Normale Superiore, Pisa, Italy  \nFull list of author information is available at the end of the article † Equal contributors  \nAbstract  \nIdentifying market abuse activity from data on investors’ trading activity is very challenging both for the data volume and for the low signal to noise ratio. Here we propose two complementary unsupervised machine learning methods to support market surveillance aimed at identifying potential insider trading activities. The firstone uses clustering to identify, in the vicinity of a price sensitive event such as a takeover bid, discontinuities in the trading activity of an investor with respect to her own past trading history and on the present trading activity of her peers. The second unsupervised approach aims at identifying (small) groups of investors that act coherently around price sensitive events, pointing to potential insider rings, i. e. a group of synchronised traders displaying strong directional trading in rewarding position in a period before the price sensitive event. As a case study, we apply our methods to investor resolved data of Italian stocks around takeover bids.  \nKeywords: Machine learning; Insider trading; Market abuse; Unsupervised learning; Statistically validated networks  \n1 Introduction  \nIn financial markets, market abuse refers to an intentional conduct that violates market integrity and natural demand-supply dynamics through misuse of privileged information, unlawful disclosure of inside information, unfair trading practices, price manipulation, creation of unfair market conditions, and deception of market players, to name but a few examples.  \nIn the literature, the area of market abuse covers a number of different conducts that nonetheless could be grouped into two main categories: 1) insider dealing: the act of utilizing inside information in order to make, change, or cancel deals, orto encourage a thirdparty to deal using this knowledge and unlawful disclosure of inside information, by releasing information without correct permissions; 2) market manipulation, subdivided in trade base manipulation, action trade manipulation, or information based manipulation: in other terms an umbrella for a series of actions which distort market performance.  \nIn this paper the focus is on insider trading, which is maybe the simplest market abuse conduct to conceive, but also one of the most widespread and difficult to enforce, since  \n© The Author(s) 2024 . Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://creativecommons.org/licenses/b","cbCaiaJJ9DrgEKO8","https://ap.wps.com/l/cbCaiaJJ9DrgEKO8","pdf",6320997,2,1,44,"English","en",105,"# Introduction\n## Market abuse and insider trading context\n# Proposed methods\n## Clustering around price sensitive events\n## Coherent investor groups for insider rings\n# Case study and application","[{\"question\":\"Why is insider trading detection challenging with trading data?\",\"answer\":\"Detection is difficult because investor-trading datasets are large while the abusive signal is weak compared with noise levels.\"},{\"question\":\"What does the first unsupervised method do?\",\"answer\":\"It uses clustering to identify discontinuities in an investor’s trading activity near a price sensitive event, relative to both the investor’s past behavior and peers’ current activity.\"},{\"question\":\"How does the second method support the identification of insider rings?\",\"answer\":\"It identifies small groups of investors that act coherently around price sensitive events, indicating synchronized directional trading in the period before the event.\"}]","A machine learning approach to support decision in insider trading detection | 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is insider trading detection challenging with trading data?","Question",{"text":76,"@type":77},"Detection is difficult because investor-trading datasets are large while the abusive signal is weak compared with noise levels.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What does the first unsupervised method do?",{"text":81,"@type":77},"It uses clustering to identify discontinuities in an investor’s trading activity near a price sensitive event, relative to both the investor’s past behavior and peers’ current activity.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the second method support the identification of insider rings?",{"text":85,"@type":77},"It identifies small groups of investors that act coherently around price sensitive events, indicating synchronized directional trading in the period before the 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