Stock Turnover Prediction Using Search Engine Data

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.