Detection of Stock Price Manipulation Using Kernel Based Principal Component Analysis and Multivariate Density Estimation

Abstract

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 Fmeasure values and a significant reduction in false alarm rate (FAR) has been achieved.

Stock Price Manipulation Detection Using Deep Unsupervised Learning: The Case of Thailand

“…Even though manipulations were effectively detected using their method, many normal cases were pinpointed incorrectly because the precision of the algorithm was low. There are three papers from the same group of researchers who implemented kernel density estimation (KDE) for stock manipulation detection [48], [49], [50]. They used an opensource LOBSTER database and injected manipulative patterns (e.g., such as sawtooth and spike pattern) into the normal trading data.…”

“…Rizvi et al [52] implemented AE using the information captured by the affinity matrix to detect stock price manipulation. They used the same dataset and injected the same manipulative patterns as in [50]. The input features were extracted using a discrete wavelet transform and were then used to compute affinity and grouped using the proposed clustering techniques.…”

Ensemble Approach Using k-Partitioned Isolation Forests for the Detection of Stock Market Manipulation

“…By employing labeled data, these models are trained to recognize and respond to the specific patterns they have been exposed to during the training process. This focused learning approach, however, introduces a significant limitation: the model's difficulty in identifying novel manipulation patterns-those which it has not been previously taught [8]. This inherent challenge emphasizes the need for models that can adapt to and detect emerging patterns of manipulation, thus extending beyond the confines of their initial training set.…”

“…Finally, they used clustering based on kernel density estimation (MKDE) to detect manipulated operations, where nonclustered data were treated as manipulated. Rizvi et al [8] used kernel PCA to obtain vectors of characteristics delivered to MKDE to detect manipulations. To this end, they used a dataset with information on 13 stocks from NASDAQ and the London Stock Exchange (LSE), with the information of manipulations generated in synthetic form.…”

LEAN-DMKDE: Quantum Latent Density Estimation for Anomaly Detection

“…Anomaly detection is a critical task in several applications, such as fraud detection [19], video surveillance [25], industrial defects [4], and medical image analysis [24], among others. In addition, it can be used as a preprocessing step in a machine learning system.…”