An Artificial Immune Model for Abnormal Fluctuation of Stock Price

The abnormal fluctuation of stock price is a harmful factor for the stock market, and how to correctly identify the abnormal fluctuation seems to be important. For the prevailing uncertain conditions in the stock market, static method is limited during detecting anomaly. In this paper, artificial immune principle is used to distinguish the "self" and "non-self" of stock price fluctuation through immune method, such as Negative Selection Algorithm, and a capable artificial immune model SPAF-M is built to intelligently sense, detect and defense the abnormal fluctuation of stock price. In order to improve risk management of the stock market, a novel risk evaluation function is proposed to judge an unknown object anomaly or not. It will be a challenge to apply immunity idea to stock market in our future work.