Stock Price Manipulation Detection based on Autoencoder Learning of Stock Trades Affinity

Stock price manipulation, a major problem in capital markets surveillance, uses illegitimate means to influence the price of traded stocks in order to reap illicit profit. Most of the existing attempts to detect such manipulations have either relied upon annotated trading data, using supervised methods, or have been restricted to detecting a specific manipulation scheme. There have been a few unsupervised algorithms focusing on general detection yet none of them explored the innate affinity among the stock trades, be it normal or manipulative. This paper proposes a fully unsupervised model based on the idea of learning the relationship among stock prices in the form of an affinity matrix. The proposed affinity matrix based features are used to train an under-fitting autoencoder in order to learn an efficient representation of the normal stock prices. A kernel density estimate of the normal trading data is used as the reconstruction error of the autoencoder. During the detection phase, the normal dataset has been injected with synthetic manipulative trades. A kernel density estimation based clustering technique is then used to detect manipulative trades based on their autoencoder representation. The proposed approach is validated on benchmark stock price data from the LOBSTER project and the obtained results show dramatic improvements in the detection performance over existing price manipulation detection techniques.