Ensemble of supervised and unsupervised deep neural networks for stock price manipulation detection

Illegal practices that cause stock prices to vary from their fair values are known as stock price manipulations. Our prior study used unsupervised deep learning to detect these unlawful activities, in which the autoencoder model could catch five out of six cases in the Stock Exchange of Thailand (SET). Trading actions of good governance equities in a limit order book format were used to train the model, and abnormalities were viewed as manipulation. This unsupervised technique has the strength of discovering never-before-seen patterns, although it has a low recall rate. In this work, the findings were improved by incorporating knowledge about the popular pump-and-dump pattern into another supervised deep learning model, which has a strength in catching known patterns. To combine the advantages of both models, an ensemble of supervised and unsupervised deep neural networks was proposed. The main contribution of this paper is to demonstrate that the new ensemble model achieved a higher detection rate than the unsupervised model alone. The supervised learning model used was a long short-term memory (LSTM) network, the unsupervised learning model used was an LSTM-based autoencoder, and the stack classifier in the ensemble model was a support vector machine (SVM). The proposed method was evaluated on six real manipulation cases from the SET. The empirical results showed that our ensemble model could improve the detection strength, achieved high accuracy on pump-and-dump unseen data, and could identify six out of six real manipulation cases with low false alarm rates.