Stock fraud detection using peer group analysis

Yoonseong Kim, So Young Sohn

Research output: Contribution to journalArticlepeer-review

38 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)8986-8992
Number of pages7
JournalExpert Systems with Applications
Issue number10
Publication statusPublished - 2012 Aug

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence


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