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Table 4 Comparisons of the error terms based on our collected articles

From: Deep learning in finance and banking: A literature review and classification

Identified F&B domains Comparisons and ranking of commonly-used DL models
Exchange rate Prediction DBN > FNN
DBN + RBM > RW, ARMA, FNN
FNN + chaos theory + multi-objective evolutionary algorithms > FNN
CNN > MA
Prediction of stock market Generative algorithm + FNN > FNN, SVM
RNN + Stock2vec > LSTM, FNN
CNN > DT, SVM
CNN + special order feature set > LR > CNN
CNN > LSTM > RNN, ridge regression, Lasso, elastic net, random forecast, SVR, AdaBoost, gradient boosting, FNN, SVM, kNN, VAR, p-RBM.
LSTM + GARCH-type model > GARCH, dxponential weighted MA, LSTM
FNN + autoencoder + RBM > FNN, extreme learning machine, radial basis FNN
FNN + PCA > RNN, radial basis FNN
Stock trading FNN > buy-and-hold method
FNN > ARMA-GARCH
Fuzzy learning + RNN > CNN + RNN,
LSTM + wavelet transforms + LSTM + SAEs > LSTM, RNN
RL + Q-learning > buy-and-hold method
RL + SARSA > buy-and-hold method
GA + RNN+ RL > RL
RL + DNN + transfer learning > RL, DNN
Portfolio management, ListNet > RankNet
RNN + RL > RNN
Banking default risk and credit FNN + NLP > NLP
CNN + feature selection > LR
LSTM + Event2vec > random forest+ Event2vec
Macroeconomic prediction FNN > DT, LR
FNN > SVM, random forest, classification tree
MLP (FNN) is the most widely used model in price prediction on oil.