Table 2.
RNN applications in financial research (1)
| Models | Targets | Markets | Data | Results | Innovation | References |
|---|---|---|---|---|---|---|
| RNN, DBN |
Stock price trend prediction | Stock | 834,882 Nikkei Financial News (1999–2008) | The proposed DBN combination has the lowest error rate. | RNN is combined with restricted Boltzmann machine to predict the stock market trend. | Yoshihara et al.72 |
| LSTM (RNN) |
Stock index forecast | Stock | Google Domestic Trends Data (October 19, 2004 July 24, 2015) | The MAPE was 24.2%. | The impact of public sentiment and macroeconomic factors on the volatility of the S&P 500 index. | Xiong et al.73 |
| RNN | Stock price trend and stock index forecast | Stock | The S&P 500 index and the stock prices of 20 companies are included. | It improves the predictability of traditional financial applications. | A hierarchical decision model for the classification of financial forecasts is proposed. | Heaton et al.74 |
| RNN, DNN |
Stock price prediction | Stock | Multimedia data of Google’s stock price in the NASDAQ | The correlation coefficient between actual and predicted income for DNN is 17.1% higher than for RBFNN and 43.4% higher than for RNN. | Compared with traditional neural networks, (2D) ZPCA + has improved accuracy on Google datasets | Singh and Srivastava75 |
| RNN | Establish a real-time financial trading system based on deep learning |
Stock and futures | The first index-based IF stock futures contract in China and the first silver (AG) and sugar (SU) futures contract in the commodity market (2014.1–2015.9) | The model has good application effect and robustness in both stock market and commodity futures market | A model consisting of deep learning and reinforcement learning is proposed | Deng et al.76 |
| RNN | The performance of the model is examined in interperiod time series data | Stock | Istanbul Stock Exchange fixed time interval data | The model has good performance and has been successful in data trading. | RNN is improved to make it more suitable for time series data and detect excessive movement in noisy time series data streams | Karaoglu et al.79 |
| RNN | Stock price prediction | Stock | Daily stock prices for all Do230 stocks between 1997 and 2007 | In most cases, the results of the correct buy-and-hold strategy are achievable. | A stock price prediction and trading system based on neural network technical analysis index is proposed. | Sezer et a l.80 |
| LSTM (RNN), WT, SAEs |
Forecasts of stock prices and stock indices | Stock | CSI 300 index, NIFTY 50 Index, Hang Seng Index, Nikkei 225 index, S&P 500 index and Dow Jones Index | This model outperforms other similar models in terms of forecasting accuracy and profitability. | A deep learning framework combining wavelet transform, stacked autoencoder and LSTM is proposed to predict stock prices | Bao et al.81 |