| 摘要: |
| 针对时间序列分析方法和神经网络对于股价预测具有一定局限性的问题，将基于Attention机制的LSTM模型应用于股价预测；以2014-01-02—2020-09-22日的上证工业指数、上证环保指数等相关数据为样本，在LSTM模型中引入Attention机制，使模型聚焦于重要的股价特征信息，预测股票第二日的最高价；实证研究发现，相较于MLP，RNN和LSTM模型，基于Attention机制的LSTM模型的RMSE值比基准模型平均降低了3%-45%左右，4种模型对环保企业的预测精度均高于污染企业；随后将空气质量指数，温度和湿度纳入特征，提升反映污染企业和环保企业股价规律相关特征的数据质量，实证结果发现在加入新的特征以后，4种模型在预测上证工业指数和上证环保指数波动趋势时，RMSE值均下降了1%左右。 |
| 关键词: 空气质量 注意力机制 长短期记忆网络 股价预测 |
| DOI： |
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| Attention mechanism based LSTM Model for Stock Price Predicting |
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LIN Xin, ZHU Xiao-dong
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Business School, University of Shanghai for Science and Technology, Shanghai 200093, China
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| Abstract: |
| Time series analysis methods and neural networks have certain limitations for stock price prediction.Therefore, Attention mechanism was introduced into the LSTM model to predict the stock price. The related data of Shanghai Stock Industry Index and Shanghai Environmental Protection Index from January 2, 2014 to September 22, 2020 was collected as samples, and based on these samples, the LSTM combined with Attention mechanism was used to focus on important stock price characteristic information and predict the highest stock price of the second day. The empirical results found that compared with MLP, RNN and LSTM models, the RMSE value of the LSTM model combined with the Attention mechanism was lower than the benchmark model by about 3% 45% on average. The four models all had higher prediction accuracy for environmental protection companies than polluting companies. Subsequently, the air quality index, temperature and humidity were included into the characteristics to improve the quality of the data that reflects the characteristics of polluting companies and environmental protection companies. The empirical results found that after adding new features, the RMSE values of the four models all dropped by about 1% when predicting the fluctuating trend of the Shanghai Industrial Index and the Shanghai Environmental Protection Index. |
| Key words: air quality attention mechanism long short term memory stock price prediction |
