Stock Price Manipulation Detection using Variational Autoencoder and Recurrence Plots

Stock price manipulation refers to deceptive traders' practices which aim to influence the normal market behaviour in order to make illicit profit at the expense of other genuine market participants. Such manipulation undermines investors' confidence in the financial market and damages its efficiency and integrity. There remains a growing need for developing robust anomaly detection methods capable of reliably identifying increasingly sophisticated manipulation attempts. Inspired by the recent success of deep learning in computer vision, we propose a novel approach for stock market manipulation detection that leverages the combined power of Recurrence Plots (RP) and beta Variational Autoencoders (beta-VAEs). The proposed approach first splits stock price time series data into overlapping temporal windows, each of which is transformed into a colour image using Recurrence Plots (RP). Then, a beta-VAE network composed of two Convolutional Neural Networks (CNNs) is trained on these images derived from normal market activity. The resulting model effectively learns the inherent characteristics of normal (i.e legitimate) trading behaviour. Finally, the mean square error between the original images and the reconstructed ones is used as the manipulation (i.e anomaly) detection score measure for flagging significant deviations indicative of potential manipulation. The efficacy of the proposed approach is rigorously validated on 1-level tick data obtained from the LOBSTER project. Evaluation results demonstrate superior manipulation detection performance which is evidenced by a highly promising area under the ROC curve (AUC). The robustness of this performance can be attributed to the beta-VAE's ability to extract pertinent features of normal market behavior from the proposed RP-generated 2D representations of stock price data. This novel framework paves the way for the development of more effective market abuse detection systems which contribute to a safer, fairer and more transparent financial ecosystem for both investors and regulators alike.