An Effective Herd Behavior Detection Approach based on Density Clustering
Herd behavior is a phenomenon that often appears in the stock market.It is caused by the irrational imitation of investors and is expressed as major investors make similar investing decisions in a short time period.Detecting herd behavior is necessary for both regulatory authorities and investors to reduce investment risk.Previously, many researchers focused on herd behavior detection and proposed numbers of detection methods.Although most of these methods only can judge whether herd behavior exists in the stock market, they cannot detect which stocks are in the same herd and which stock is the leader of the herd.In this paper, we proposed an efficient herd behavior detection approach based on density clustering.It firstly uses stock return rates in a period to calculate similarity of stock-pairs at that time.Then, it finds the leader of herds by finding local density peaks.After that, it clusters stocks following their probable leaders and discovers herds in stock markets.Finally, it determines whether existing herd behavior and the degree of herd behavior according to the size of herds, i.e.the number of stock in herd.Experimental results on the Chinese stock market demonstrate the effectiveness and accuracy of this approach.
Herd Behavior Detection Density Clustering Behavior Computing
Qian Li Chunsheng Tao Xv Lan Chengzhang Zhu
School of Economics,Minzu University of China,Beijing 100083,China College of Computer,National University of Defense Technology,Changsha 410073,China
国内会议
金华
英文
1-9
2015-10-30（万方平台首次上网日期，不代表论文的发表时间）