DP-LSTM: Differential Privacy-inspired LSTM for Stock Prediction Using Financial News
Table 1: Predicted Mean MPA results.
['Method', 'Mean MPA']
The result means the DP framework can make the prediction result more accuracy and robustness. Note that the results are obtained by running many trials, since we train stocks separately and predict each price individually due to the different patterns and scales of stock prices. This in total adds up to 451 runs. The performance of our DP-LSTM is always better than the LSTM with news. Based on the sentiment-ARMA model and adding noise for training, the proposed DP-LSTM is more robust. The investment risk based on this prediction results is reduced.