Detection of Stock Price Manipulation Using Kernel Based Principal Component Analysis and Multivariate Density Estimation
Stock price manipulation uses illegitimate means to artificially influence market prices of several stocks. It causes massive losses and undermines investors' confidence and the integrity of the stock market. Several existing research works focused on detecting a specific manipulation scheme us...
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description | Stock price manipulation uses illegitimate means to artificially influence market prices of several stocks. It causes massive losses and undermines investors' confidence and the integrity of the stock market. Several existing research works focused on detecting a specific manipulation scheme using supervised learning but lacks the adaptive capability to capture different manipulative strategies. This begets the assumption of model parameter values specific to the underlying manipulation scheme. In addition, supervised learning requires the use of labelled data which is difficult to acquire due to confidentiality and the proprietary nature of trading data. The proposed research establishes a detection model based on unsupervised learning using Kernel Principal Component Analysis (KPCA) and applied increased variance of selected latent features in higher dimensions. A proposed Multidimensional Kernel Density Estimation (MKDE) clustering is then applied upon the selected components to identify abnormal patterns of manipulation in data. This research has an advantage over the existing methods in overcoming the ambiguity of assuming values of several parameters, reducing the high dimensions obtained from conventional KPCA and thereby reducing computational complexity. The robustness of the detection model has also been evaluated when two or more manipulative activities occur within a short duration of each other and by varying the window length of the dataset fed to the model. Validation on multiple datasets and a comprehensive assessment of the model performance has been conducted without providing any prior information about the location of the manipulation. The results show a significant performance enhancement in terms of the F-measure values and a significant reduction in false alarm rate (FAR) has been achieved. |
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It causes massive losses and undermines investors' confidence and the integrity of the stock market. Several existing research works focused on detecting a specific manipulation scheme using supervised learning but lacks the adaptive capability to capture different manipulative strategies. This begets the assumption of model parameter values specific to the underlying manipulation scheme. In addition, supervised learning requires the use of labelled data which is difficult to acquire due to confidentiality and the proprietary nature of trading data. The proposed research establishes a detection model based on unsupervised learning using Kernel Principal Component Analysis (KPCA) and applied increased variance of selected latent features in higher dimensions. A proposed Multidimensional Kernel Density Estimation (MKDE) clustering is then applied upon the selected components to identify abnormal patterns of manipulation in data. This research has an advantage over the existing methods in overcoming the ambiguity of assuming values of several parameters, reducing the high dimensions obtained from conventional KPCA and thereby reducing computational complexity. The robustness of the detection model has also been evaluated when two or more manipulative activities occur within a short duration of each other and by varying the window length of the dataset fed to the model. Validation on multiple datasets and a comprehensive assessment of the model performance has been conducted without providing any prior information about the location of the manipulation. The results show a significant performance enhancement in terms of the F-measure values and a significant reduction in false alarm rate (FAR) has been achieved.</description><identifier>ISSN: 2169-3536</identifier><identifier>EISSN: 2169-3536</identifier><identifier>DOI: 10.1109/ACCESS.2020.3011590</identifier><identifier>CODEN: IAECCG</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>anomaly detection ; Clustering ; Computational modeling ; Confidence ; Data models ; Datasets ; Density ; False alarms ; Feature extraction ; Kernel ; kernel principal component analyses ; Kernels ; Manipulators ; Market abuse ; Mathematical models ; multi-dimensional kernel density estimate clustering ; Parameters ; Performance enhancement ; Pricing ; Principal component analysis ; Principal components analysis ; Robustness (mathematics) ; Stock markets ; stock price manipulation ; Supervised learning</subject><ispartof>IEEE access, 2020, Vol.8, p.135989-136003</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2020</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c408t-6a5482b32e791ba9703d50b0881a171549a2d0db69b930d8e33e48fd5db3348a3</citedby><cites>FETCH-LOGICAL-c408t-6a5482b32e791ba9703d50b0881a171549a2d0db69b930d8e33e48fd5db3348a3</cites><orcidid>0000-0002-1474-2772 ; 0000-0002-6913-976X ; 0000-0003-1927-9366</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9146609$$EHTML$$P50$$Gieee$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,860,2096,4010,27610,27900,27901,27902,54908</link.rule.ids></links><search><creatorcontrib>Rizvi, Baqar</creatorcontrib><creatorcontrib>Belatreche, Ammar</creatorcontrib><creatorcontrib>Bouridane, Ahmed</creatorcontrib><creatorcontrib>Watson, Ian</creatorcontrib><title>Detection of Stock Price Manipulation Using Kernel Based Principal Component Analysis and Multivariate Density Estimation</title><title>IEEE access</title><addtitle>Access</addtitle><description>Stock price manipulation uses illegitimate means to artificially influence market prices of several stocks. It causes massive losses and undermines investors' confidence and the integrity of the stock market. Several existing research works focused on detecting a specific manipulation scheme using supervised learning but lacks the adaptive capability to capture different manipulative strategies. This begets the assumption of model parameter values specific to the underlying manipulation scheme. In addition, supervised learning requires the use of labelled data which is difficult to acquire due to confidentiality and the proprietary nature of trading data. The proposed research establishes a detection model based on unsupervised learning using Kernel Principal Component Analysis (KPCA) and applied increased variance of selected latent features in higher dimensions. A proposed Multidimensional Kernel Density Estimation (MKDE) clustering is then applied upon the selected components to identify abnormal patterns of manipulation in data. This research has an advantage over the existing methods in overcoming the ambiguity of assuming values of several parameters, reducing the high dimensions obtained from conventional KPCA and thereby reducing computational complexity. The robustness of the detection model has also been evaluated when two or more manipulative activities occur within a short duration of each other and by varying the window length of the dataset fed to the model. Validation on multiple datasets and a comprehensive assessment of the model performance has been conducted without providing any prior information about the location of the manipulation. The results show a significant performance enhancement in terms of the F-measure values and a significant reduction in false alarm rate (FAR) has been achieved.</description><subject>anomaly detection</subject><subject>Clustering</subject><subject>Computational modeling</subject><subject>Confidence</subject><subject>Data models</subject><subject>Datasets</subject><subject>Density</subject><subject>False alarms</subject><subject>Feature extraction</subject><subject>Kernel</subject><subject>kernel principal component analyses</subject><subject>Kernels</subject><subject>Manipulators</subject><subject>Market abuse</subject><subject>Mathematical models</subject><subject>multi-dimensional kernel density estimate clustering</subject><subject>Parameters</subject><subject>Performance enhancement</subject><subject>Pricing</subject><subject>Principal component analysis</subject><subject>Principal components analysis</subject><subject>Robustness (mathematics)</subject><subject>Stock markets</subject><subject>stock price manipulation</subject><subject>Supervised learning</subject><issn>2169-3536</issn><issn>2169-3536</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>DOA</sourceid><recordid>eNpNkU9r3DAQxU1poSHNJ8hF0PNu9c-ydNw62zY0IYVtzmJsjYO2juRK2sJ--3rXIUSXETPv_YbhVdU1o2vGqPmyadvtbrfmlNO1oIzVhr6rLjhTZiVqod6_-X-srnLe0_npuVU3F9XxBgv2xcdA4kB2JfZ_yK_keyT3EPx0GOE8e8w-PJGfmAKO5CtkdCdV6P0EI2nj8xQDhkI2AcZj9plAcOT-MBb_D5KHguQGQ_blSLa5-Ocz81P1YYAx49VLvawev21_tz9Wdw_fb9vN3aqXVJeVglpq3gmOjWEdmIYKV9OOas2ANayWBrijrlOmM4I6jUKg1IOrXSeE1CAuq9uF6yLs7ZTm9eloI3h7bsT0ZCEV349oNWDDFadCKS4HPYBCI7nqKVUza2hm1ueFNaX494C52H08pPnobLmspZJcNmxWiUXVp5hzwuF1K6P2FJldIrOnyOxLZLPrenF5RHx1GCaVokb8B54Rkdw</recordid><startdate>2020</startdate><enddate>2020</enddate><creator>Rizvi, Baqar</creator><creator>Belatreche, Ammar</creator><creator>Bouridane, Ahmed</creator><creator>Watson, Ian</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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It causes massive losses and undermines investors' confidence and the integrity of the stock market. Several existing research works focused on detecting a specific manipulation scheme using supervised learning but lacks the adaptive capability to capture different manipulative strategies. This begets the assumption of model parameter values specific to the underlying manipulation scheme. In addition, supervised learning requires the use of labelled data which is difficult to acquire due to confidentiality and the proprietary nature of trading data. The proposed research establishes a detection model based on unsupervised learning using Kernel Principal Component Analysis (KPCA) and applied increased variance of selected latent features in higher dimensions. A proposed Multidimensional Kernel Density Estimation (MKDE) clustering is then applied upon the selected components to identify abnormal patterns of manipulation in data. This research has an advantage over the existing methods in overcoming the ambiguity of assuming values of several parameters, reducing the high dimensions obtained from conventional KPCA and thereby reducing computational complexity. The robustness of the detection model has also been evaluated when two or more manipulative activities occur within a short duration of each other and by varying the window length of the dataset fed to the model. Validation on multiple datasets and a comprehensive assessment of the model performance has been conducted without providing any prior information about the location of the manipulation. 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subjects | anomaly detection Clustering Computational modeling Confidence Data models Datasets Density False alarms Feature extraction Kernel kernel principal component analyses Kernels Manipulators Market abuse Mathematical models multi-dimensional kernel density estimate clustering Parameters Performance enhancement Pricing Principal component analysis Principal components analysis Robustness (mathematics) Stock markets stock price manipulation Supervised learning |
title | Detection of Stock Price Manipulation Using Kernel Based Principal Component Analysis and Multivariate Density Estimation |
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