Degree-Strength Correlation Reveals Anomalous Trading Behavior

• Published in: PloS one Vol. 7; no. 10; p. e45598
• Main Authors: Sun, Xiao-Qian, Shen, Hua-Wei, Cheng, Xue-Qi, Wang, Zhao-Yang
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 17.10.2012; Public Library of Science (PLoS)
• Subjects: Analysis; Behavior; Clustering; Commerce; Correlation; Correlation analysis; Electronic trading systems; Electronics; Ethical aspects; Humans; Investments; Markets; Mathematics; Network topologies; Networks; Neural networks; Price gouging; Securities markets; Securities trading; Social and Behavioral Sciences; Statistical analysis; Stock exchanges; Stock markets; Studies; United States; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0045598

Manipulation is an important issue for both developed and emerging stock markets. Many efforts have been made to detect manipulation in stock markets. However, it is still an open problem to identify the fraudulent traders, especially when they collude with each other. In this paper, we focus on the problem of identifying the anomalous traders using the transaction data of eight manipulated stocks and forty-four non-manipulated stocks during a one-year period. By analyzing the trading networks of stocks, we find that the trading networks of manipulated stocks exhibit significantly higher degree-strength correlation than the trading networks of non-manipulated stocks and the randomized trading networks. We further propose a method to detect anomalous traders of manipulated stocks based on statistical significance analysis of degree-strength correlation. Experimental results demonstrate that our method is effective at distinguishing the manipulated stocks from non-manipulated ones. Our method outperforms the traditional weight-threshold method at identifying the anomalous traders in manipulated stocks. More importantly, our method is difficult to be fooled by colluded traders.