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Related Concept Videos

Standard Deviation01:10

Standard Deviation

The most commonly used measure of variation is the standard deviation. It is a numerical value measuring how far data values are from their mean. The standard deviation value is small when the data are concentrated close to the mean, exhibiting slight variation or spread. The standard deviation value is never negative, it is either positive or zero. The standard deviation is larger when the data values are more spread out from the mean, which means the data values are exhibiting more...
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Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
Regression Toward the Mean01:52

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Functional Near-Infrared Spectroscopy Hyperscanning Study in Psychological Counseling
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Degree-strength correlation reveals anomalous trading behavior.

Xiao-Qian Sun1, Hua-Wei Shen, Xue-Qi Cheng

  • 1Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.

Plos One
|October 20, 2012
PubMed
Summary

This study introduces a new method to detect fraudulent traders in stock markets by analyzing trading networks. The approach effectively identifies manipulated stocks and is robust against colluding traders.

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Area of Science:

  • Financial markets
  • Network analysis
  • Data science

Background:

  • Stock market manipulation is a persistent challenge in both developed and emerging economies.
  • Identifying colluding fraudulent traders remains a significant open problem in market surveillance.

Purpose of the Study:

  • To develop a novel method for identifying anomalous traders in stock markets.
  • To analyze trading networks for distinct characteristics of manipulated stocks.

Main Methods:

  • Utilized transaction data from manipulated and non-manipulated stocks over one year.
  • Analyzed trading networks, focusing on degree-strength correlation.
  • Proposed a detection method based on statistical significance analysis of degree-strength correlation.

Main Results:

  • Trading networks of manipulated stocks show significantly higher degree-strength correlation compared to non-manipulated and randomized networks.
  • The proposed method effectively distinguishes manipulated from non-manipulated stocks.
  • Outperformed traditional weight-threshold methods in identifying anomalous traders.

Conclusions:

  • The developed method accurately detects anomalous traders in manipulated stock markets.
  • The approach is resilient to collusion among fraudulent traders.
  • Offers a more effective tool for market surveillance and fraud detection.