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Table 4 Comparison with other studies

From: Predicting LQ45 financial sector indices using RNN-LSTM

Authors (Ref)

Case study

Method

MAPE

Patel et al. [25]

CNX Nifty and S&P Bombay Stock Exchange (BSE) Sensex

ANN

SVR-ANN

SVR

SVR-SVR

RF

SVR-RF

2.96 (average)

2.62 (average)

2.69 (average)

2.63 (average)

3.02 (average)

2.70 (average)

Hiransha et al. [26]

MARUTI, HCL, AXIS BANK, BANK OF AMERICA, CHESAPEAK ENERGY

RNN

LSTM

CNN

MLP

8.00 (average)

7.29 (average)

6.84 (average)

6.89 (average)

Sun et al. [27]

S&P 500, SHCI

ARIMA

MLPNN

SVR

ELM

LSTM

5.07 (average)

2.89 (average)

2.08 (average)

1.50 (average)

0.99 (average)

Jin et al. [9]

AAPL (Apple stock)

LSTM

LS_RF

S_LSTM

S_AM_LSTM

S_EMDAM_LSTM

4.58

3.15

2.23

1.82

1.65

Nabipour et al. [28]

Diversified financials from the Tehran Securities Exchange Technology Management Co (TSETMC)

Decision tree

Bagging

Random forest

Adaboost

Gradient boosting

XGBoost

ANN

RNN

LSTM

2.07

1.91

1.91

1.59

1.70

1.72

3.86

1.85

0.60

Our approach

LQ45 financial indices from IDX

3 layers LSTM

0.26 (average)