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.
姚宏亮，洪竞帆，王浩. 深度计算的同辈群体股市态势预测算法*[J]. 模式识别与人工智能, 2016, 29(1): 54-62.
YAO Hongliang, HONG Jingfan, WANG Hao. Peer Group Stock Market Trend Prediction Algorithm Based on Deep Computing. , 2016, 29(1): 54-62.
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