The stock turnover values are sensitive to external factors, and remain great challenges in its prediction. The consideration is that search engine data can reflect market environment, policies and attentions on stocks. Therefore, a dual sides autoregression (DSAR) method is proposed to benefit from both observed turnover values and exogenous data. The proposed DSAR consists of linear representation stage and combination stage. In linear representation stage, the short-term patterns of turnover values and query data are represented, respectively. In combination stage, the outputs from previous stages are combined. Intensive experiments on two groups of data collections show the effectiveness of our proposed method.

中文翻译：

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使用搜索引擎数据预测股票成交量

股票换手率值对外部因素较为敏感，预测仍存在较大挑战。考虑是搜索引擎数据可以反映市场环境、政策和对股票的关注。因此，提出了一种双边自回归 (DSAR) 方法，以同时受益于观察到的营业额值和外生数据。提出的DSAR由线性表示阶段和组合阶段组成。在线性表示阶段，分别表示营业额值和查询数据的短期模式。在组合阶段，来自先前阶段的输出被组合。对两组数据收集的密集实验表明了我们提出的方法的有效性。