Development and test of an artificial-immune- abnormal-trading-detection system for financial markets

Authors:
Vincent C. S. Lee;Xingjian Yang
Affiliations:
School of Business Systems, Faculty of Information Technology, Monash University, Victoria, Australia;School of Business Systems, Faculty of Information Technology, Monash University, Victoria, Australia
Venue:
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
Year:
2005

Citing 1
Cited 1

Artficial Immune Systems and Their Applications

Artficial Immune Systems and Their Applications

Outlier Mining on Multiple Time Series Data in Stock Market

PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we implement a pilot study on the detection of abnormal financial asset trading activities using an artificial immune system. We develop a prototype artificial immune abnormal-trading-detecting system (AIAS)to scan the proxy data from the stock market and detect the abnormal trading such as insider trading and market manipulation, etc. among them. The rapid and real time detection capability of abnormal trading activities has been tested under simulated stock market as well as using real intraday price data of selected Australian stocks. Finally, three parameters used in the AIAS are tested so that the performance and robustness of the system are enhanced.