Stock fraud detection using peer group analysis |
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Authors: | Yoonseong Kim So Young Sohn |
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Affiliation: | 1. Sawyer Business School, Suffolk University, Boston, MA 02108, United States;2. Peter T. Paul College of Business and Economics, University of New Hampshire, Durham, NH 03824, United States;3. Sogang Business School, Sogang University, Seoul 121-742, South Korea;1. Institute for Financial Services Analytics, University of Delaware, Newark, DE 19711, USA;2. Rowe School of Business, Dalhousie University, Halifax, NS B3H 4R2, Canada |
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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. |
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