Table 2.
DNN in economics
| Article | Aim of study | Specific approach | Benchmark methods for comparison | Superiority of the proposed method |
|---|---|---|---|---|
| (Zhong & Enke, 2019) | Predict the daily return direction of the SPDR S&P 500 ETF | DNN, ANN | Single DNN or single ANN | Significantly higher classification accuracy |
| (Chatzis et al., 2018) | Forecast stock market crisis events | MXNET DNN | LOGIT, CART, RF, SVM, NN, XGBoost | Higher discriminatory power and superior predictive accuracy |
| (He et al., 2019) | Forecast financial time series | DNN, LSTM | Five strategies | Outperforms others in MAPE, RMSE, R2 |
| (Kremsner et al., 2020) | Compute risk measure | DNN | Classical methods in references | Can solve problems with high dimension |
| (Alaminos et al., 2019) | Predict currency crisis event | DNN, DNDT | LOGIT, MLP, SVM, AdaBoost | Higher levels of accuracy |
| (Galeshchuk & Mukherjee, 2017) | Predict exchange rate | DNN, LSTM | Shallow neural network | Significantly higher predictive accuracy |
| (Lukman et al., 2020) | Predict the amount of salvage and waste materials | DNN | The component based neural network model | Higher and more steady prediction accuracy |
| (Bazan-Krzywoszanska & Bereta, 2018) | Forecast real estate value | DNN | Linear regression | Perform better in test data according to prediction criteria MAE, MRE |
| (Ding et al., 2019) | Estimate socioeconomic status | S2S models containing DNN and LSTM | Random Guess, STL, GBDT | Outperform other models in precision, recall, and F1-score |
| (Yuan & Lee, 2020) | Forecast intelligent sales volume | DNN, grey analysis, LSSVR | GA-ANN, GA-LSSVR, and PSO- LSSVR | Superior performance in Google Index |
| (Feng et al., 2018) | Recognize pattern and make classification | DNN | Traditional auction | Better performance when the number of SUs exceeds a certain value |
| (Frey et al., 2019) | Predict for investment decisions | DNN, Gradient Boosting, RF | GLM | Higher prediction accuracy |
| (Tan et al., 2020) | Estimate poverty | Deep ResNet, FPN | Linear regression model with night-time light data, linear regression model with both night-time light data and spectral index data | Outperform other models with the Pearson correlation coefficient |
Note: ANN (Artificial Neural Network), LOGIT (Logistic Regression), MXNET (Deep Learning Techniques), CART (Classification and Regression Trees), RF (Random Forest), SVM (Support Vector Machine), NN (Neural Network), XGBoost (Extreme Gradient Boosting), DNDT (Deep Neural Decision Tree), MLP (Multilayer Perceptron), LSTM (Long Short-Term Memory), RMSE (Root Mean Square Error), STL (Standard Template Library), GBDT (Gradient Boosting Decision Tree), LSSVR (Least-Square Support Vector Regression), GA (Genetic Algorithm), PSO (Particle Swarm Optimization), GLM (Generalized Linear Models), FPN (Feature Pyramid Network), ResNet (Residual neural Network)