Stock market forecasting using recurrent neural network

By Qi-sheng Gao. 2016

36 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Jiang Bian
2. Ziniu Hu
3. Tie-Yan Liu
4. Weiqing Liu
5. Pan Wang
6. Xuanzhe Liu
7. Yanqiang Zhou
8. Weiwei Lin

Citations in the corpus

• Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction (2017, methodology): “To further model the long-term dependency in time series, recurrent neurual networks (RNN), especially Long Short-Term Memory (LSTM) network, have also been employed in financial predition [3, 7, 22].”
• An innovative neural network approach for stock market prediction (2018, methodology): “Gao used LSTM to forecast six kinds of different industries in the US stock market, using 359 stock features as input, with an average accuracy rate of 54.83% [27].”