Stock market anomaly detection: Case study of China’s securities market insider trading

Abstract

Insider trading has become a topic discussed globally. This trading is a criminal offence punishable by an attempt to gain profit using financial information that is not available to the public and can cause a significant market reaction. However, outlier detection studies using statistical approach on detecting insider trading practices are relatively scarce. Therefore, this study aims to identify outliers in the stock market in order to detect insider trading behaviour. This paper proposes an instrumental research regarding the using of sequential fences analysis in the identification of stock market anomaly values in China’s stock market. In order to attain the objective of this research, we exemplified the sequential fences analysis on data related to the China’s securities market insider trading. The results show the viability of sequential fences in detecting unusual behaviour in stock market data and showing abnormal activity in Chinese capital markets.