Analysis of Stock Market Manipulation using Generative Adversarial Nets and Denoising Auto-Encode Models

Subject Areas : Financial Mathematics

Hamed  Hamedinia ¹ (Ph.D. student in university of Tehran Chief investment Officer in Maskan Investment Bank)
Reza  Raei ² (Prof. finance University of Tehran)
Saeed  Bajalan ³ (Finance university of Tehran)
Saeed  Rouhani ⁴ (IT management University of Tehran)

Received: 2021-05-12 Accepted : 2021-08-04 Published : 2022-01-01

Keywords: Generative Adversarial Net (GAN), Stock Manipulation Detection, Anomaly detection, deep learning,

Abstract :

Market manipulation remains the biggest concern of investors in today’s securities market. The development of technologies and complex trading algorithms seems to facilitate stock market manipulation and make it inevitable for regulators to use Deep Learning models to prevent manipulation. In this research, a Denoising GAN-based model has been designed. The proposed model (GAN-DAE4) consists of a three-layer encoder along with a 2-dimension encoder as the discriminator and a three-layer decoder as the generator. First, using statistical methods such as sequence, skewness, and kurtosis tests and some unsupervised learning methods such as Contextual Anomaly Detection (CAD) and some visual and graphical methods, the manipulated stocks have been detected in the Tehran Stock Exchange from 2015 to 2020; then GAN-DAE4 and some supervised deep learning models have been applied to the prepared data set. The results show that GAN-DAE4 outperformed other deep learning models (with F2-measure 73.71%) such as Decision Tree (C4.5), Random Forest, Neural Network, and Logistic Regression.

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