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Back propagation mathematical model for stock price prediction

Yanran Ma

Yanran Ma

School of Economics and Finance, Xi’an International Studies UniversityXi’an, P.R. China
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Ma, Yanran
, 
Nan Chen

Nan Chen

School of Management, Northwestern Polytechnical UniversityXi’an, P.R. China
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Chen, Nan
 e 
Han Lv

Han Lv

School of Economics and Finance, Xi’an International Studies UniversityXi’an, P.R. China
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Lv, Han
  
30 dic 2021
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Pubblicato online: 30 dic 2021
Pagine: 165 - 174
Ricevuto: 17 giu 2021
Accettato: 24 set 2021
DOI: https://doi.org/10.2478/amns.2021.2.00144
Parole chiave
© 2021 Yanran Ma et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Fig. 1

Structure of a three-layer neural network
Structure of a three-layer neural network

Fig. 2

Forecast of Bank of China
Forecast of Bank of China

Fig. 3

Lag one error
Lag one error

Price range

Name Bank of China Vanke A Guizhou Maotai
Lowest price 2.00 5.65 81.13
Highest Price 5.01 40.04 788.42

100 times experiment

Statistics Mean Std.
Bank of China MSE 0.009 4.8×10−5
MAPE 0.019 0.0001
Vanke A MSE 2.976 0.0067
MAPE 0.049 0.0001
Guizhou Maotai MSE 395.1 1.3728
MAPE 0.026 7.7×10−5

Results of the five methods

Method BP RBF GRNN SVMR LS-SVMR
Bank of China MSE 0.009 0.014 0.02 0.012 0.018
MAPE 0.019 0.025 0.024 0.023 0.028
Vanke A MSE 2.976 4.686 6.036 3.422 5.472
MAPE 0.049 0.065 0.067 0.059 0.072
Guizhou Maotai MSE 395.1 740.1 1103.6 407.4 405.5
MAPE 0.026 0.036 0.048 0.029 0.027

Comparison of four kernels in SVMR

Kernel Linear Polynomial Sigmoid RBF
Bank of China MSE 0.01 0.01 0.011 0.012
MAPE 0.019 0.02 0.021 0.023
Vanke A MSE 2.993 3.292 3.515 3.422
MAPE 0.05 0.053 0.055 0.059
Guizhou Maotai MSE 395.6 403.5 405.7 407.4
MAPE 0.027 0.028 0.028 0.029
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Inglese
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Scienze biologiche, Scienze della vita, altro, Matematica, Matematica applicata, Matematica generale, Fisica, Fisica, altro
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