关键词: 股票价格预测, 随机森林（RF）, 自注意力机制（SA）, 门控循环单元（GRU）

Abstract: Aiming at the complex characteristics of stocks such as multi-factor, high randomness and non-stationaryness, the use of gated recurrent unit（GRU） network to directly predict the stock price is less effective. Based on fusing the self-attention mechanism（SA） and GRU to construct the SA-GRU model, the dimensionality reduction processing technique random forest（RF） algorithm is introduced to filter other factors for stock closing prices, and the reduced-dimensional stock factor data is used as the input to the SA-GRU model. Based on the two-layer GRU network, the dependencies between the stock factors are extracted, and then the SA is used to strengthen the attention to the important factors and the internal connections of the factors, and the stock factor data after the attention weight is added, and the stock price prediction values are output through the fully connected layer, so as to construct the RF-SA-GRU hybrid model. Eighteen stocks involving 18 basic industries are selected for stock price prediction, and the experiments show that the RF-SA-GRU model achieves the best prediction results on all 18 stocks, and the prediction accuracy and stability are better than other models. In addition, three indexes are selected for closing point prediction, and the experiment shows that the RF-SA-GRU model still has better prediction performance in terms of stock index forecasting.

Key words: stock price prediction, random forest（RF）, self-attention mechanism（SA）, gated recurrent unit（GRU）