Predicting CSI 300 Index and NASDAQ Index by Simple RNN and LSTM

Stocks are an important part of the financial market, and their prices can reflect the economic level of a country. It is significant to predict the trends of stocks. With high noise, non-linearity and other complex features, stock systems are hard to be predicted accurately by traditional statistics models and deep learning methods are suitable to be used for stock predictions. In this study, CSI 300 index and NASDAQ index are selected as research targets in this study. Considering one model cannot fit all of the stocks, Simple Recurrent Neural Networks (RNNs) model and its variant model, Long-Short Term Memory (LSTM) model are chosen as two main methods for forecasting these data, and their prediction results will be compared to determine which model fits better. For assessment indicators, graphs and root mean square error (RMSE) can evaluate the accuracy both visually and numerically of prediction results. Experimental results show that simple RNN and LTSM predicts better for CSI 300 index than NASDAQ index. Both simple RNN and LSTM cannot perform well in the test set of NASDAQ index. High discreteness and sudden changes of NASDAQ index may be potential reasons.