Stock fraud detection using peer group analysis

Authors:
Yoonseong Kim;So Young Sohn
Affiliations:
Department of Information & Industrial Engineering, Yonsei University, 134 Shinchon-dong, Seoul 120-749, Republic of Korea;Department of Information & Industrial Engineering, Yonsei University, 134 Shinchon-dong, Seoul 120-749, Republic of Korea
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

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Abstract

This study proposes a method to detect suspicious patterns of stock price manipulation using an unsupervised data mining technique: peer group analysis. This technique detects abnormal behavior of a target by comparing it with its peer group and measuring the deviation of its behavior from that of its peers. Moreover, this study proposes a method to improve the general peer group analysis by incorporating the weight of peer group members into summarizing their behavior, along with the consideration of parameter updates over time. Using real time series data of Korean stock market, this study shows the advantage of the proposed peer group analysis in detecting abnormal stock price change. In addition, we perform sensitivity analysis to examine the effect of the parameters used in the proposed method.