Abstract Aiming at the deficiency of peer group (PG) algorithm, a peer group generation algorithm based on depth computing is proposed. Firstly, the band similarity between target stock and candidate stock is calculated. Then, grounded on depth calculation of intimacy, correlation and liveness, peer group of the target stock is generated, and it is proved that the quality of peer group generated by depth calculation is superior to that of PG algorithm. Since PG algorithm is unable to predict, autoregressive stock market trend prediction algorithm based on peer group (DPG-AR) is proposed by combining peer group algorithm and autoregression model. Peer group is generated by deep computing. Thus, the weights of peer group members are updated. The target stock trend is prodicted by autoregression model. The effectiveness of DPG-AR is verified in the experiment on Shanghai composite index and the corresponding stock.
About author:: (YAO Hongliang， born in 1972， Ph.D.， associate professor. His research interests include machine learning and data mining.)(HONG Jingfan(Corresponding author)， born in 1993， master student. His research interests include artificial intelligence and data mining.)(WANG Hao， born in 1962， Ph.D.， professor. His research interests include artificial intelligence and data mining.)
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2016, Vol. 29 
