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- W4381299111 abstract "Stock price prediction generates interesting outputs for investors. In recent years, stock technical indicators (STI) have played an important role in stock price prediction. However, the current performance on high-dimensional data remains problematic due to the error rate in the correlational analysis. In this paper, we proposed a new correlational strategy to tackle such problems with STI and deep neural networks. We designed this strategy based on the Pearson correlation coefficient with a close index. we took eight companies’s stock data for our analysis. The experimental results demonstrate that BiLSTM’s performance (0.892% of $$R^2$$ ) outperformed GRU and LSTM in various factors for stock price prediction." @default.
- W4381299111 created "2023-06-21" @default.
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- W4381299111 date "2023-01-01" @default.
- W4381299111 modified "2023-10-16" @default.
- W4381299111 title "A Correlational Strategy for the Prediction of High-Dimensional Stock Data by Neural Networks and Technical Indicators" @default.
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- W4381299111 doi "https://doi.org/10.1007/978-981-99-3300-6_29" @default.
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