{"title":"支持内幕交易检测的降维技术","authors":"Adele Ravagnani, Fabrizio Lillo, Paola Deriu, Piero Mazzarisi, Francesca Medda, Antonio Russo","doi":"arxiv-2403.00707","DOIUrl":null,"url":null,"abstract":"Identification of market abuse is an extremely complicated activity that\nrequires the analysis of large and complex datasets. We propose an unsupervised\nmachine learning method for contextual anomaly detection, which allows to\nsupport market surveillance aimed at identifying potential insider trading\nactivities. This method lies in the reconstruction-based paradigm and employs\nprincipal component analysis and autoencoders as dimensionality reduction\ntechniques. The only input of this method is the trading position of each\ninvestor active on the asset for which we have a price sensitive event (PSE).\nAfter determining reconstruction errors related to the trading profiles,\nseveral conditions are imposed in order to identify investors whose behavior\ncould be suspicious of insider trading related to the PSE. As a case study, we\napply our method to investor resolved data of Italian stocks around takeover\nbids.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"108 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Dimensionality reduction techniques to support insider trading detection\",\"authors\":\"Adele Ravagnani, Fabrizio Lillo, Paola Deriu, Piero Mazzarisi, Francesca Medda, Antonio Russo\",\"doi\":\"arxiv-2403.00707\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Identification of market abuse is an extremely complicated activity that\\nrequires the analysis of large and complex datasets. We propose an unsupervised\\nmachine learning method for contextual anomaly detection, which allows to\\nsupport market surveillance aimed at identifying potential insider trading\\nactivities. This method lies in the reconstruction-based paradigm and employs\\nprincipal component analysis and autoencoders as dimensionality reduction\\ntechniques. The only input of this method is the trading position of each\\ninvestor active on the asset for which we have a price sensitive event (PSE).\\nAfter determining reconstruction errors related to the trading profiles,\\nseveral conditions are imposed in order to identify investors whose behavior\\ncould be suspicious of insider trading related to the PSE. As a case study, we\\napply our method to investor resolved data of Italian stocks around takeover\\nbids.\",\"PeriodicalId\":501139,\"journal\":{\"name\":\"arXiv - QuantFin - Statistical Finance\",\"volume\":\"108 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - QuantFin - Statistical Finance\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2403.00707\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - QuantFin - Statistical Finance","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2403.00707","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
识别市场滥用是一项极其复杂的工作,需要对大量复杂的数据集进行分析。我们提出了一种用于上下文异常检测的无监督机器学习方法,可用于支持旨在识别潜在内部交易活动的市场监控。该方法属于基于重构的范例,采用了主成分分析和自动编码器作为降维技术。在确定了与交易概况相关的重构误差后,我们施加了几个条件,以识别其行为可能涉嫌与 PSE 相关的内幕交易的投资者。作为一项案例研究,我们将我们的方法应用于意大利股票收购要约前后的投资者解决数据。
Dimensionality reduction techniques to support insider trading detection
Identification of market abuse is an extremely complicated activity that
requires the analysis of large and complex datasets. We propose an unsupervised
machine learning method for contextual anomaly detection, which allows to
support market surveillance aimed at identifying potential insider trading
activities. This method lies in the reconstruction-based paradigm and employs
principal component analysis and autoencoders as dimensionality reduction
techniques. The only input of this method is the trading position of each
investor active on the asset for which we have a price sensitive event (PSE).
After determining reconstruction errors related to the trading profiles,
several conditions are imposed in order to identify investors whose behavior
could be suspicious of insider trading related to the PSE. As a case study, we
apply our method to investor resolved data of Italian stocks around takeover
bids.