Papers › Long Term Stock Prediction based on Financial Statements
Long Term Stock Prediction based on Financial Statements
Shujia Liu
This paper proposes a model with LSTM and fully connected layers to predict long term stock trendings based on financial statements. Two data augmentation techniques are applied on structured data: 1) adding random noise to data fields; 2) erasing partial information from training examples. The performance of the proposed approach is demonstrated on real-world data of about 7,000 stocks listed on Nasdaq.
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