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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Veröffentlicht in:	IEEE access 2020, Vol.8, p.135989-136003
Hauptverfasser:	Rizvi, Baqar, Belatreche, Ammar, Bouridane, Ahmed, Watson, Ian
Format:	Artikel
Sprache:	eng
Schlagworte:	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
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