<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Due to the extremely volatile nature of financial markets, it is commonly accepted that stock price prediction is a task filled with challenges. However, in order to make profits or understand the essence of equity market, numerous market participants or researchers try to forecast stock prices using various statistical, econometric or even neural network models. In this work, we survey and compare the predictive power of five neural network models, namely, back propagation (BP) neural network, radial basis function neural network, general regression neural network, support vector machine regression (SVMR) and least squares support vector machine regression. We apply the five models to make price predictions for three individual stocks, namely, Bank of China, Vanke A and Guizhou Maotai. Adopting mean square error and average absolute percentage error as criteria, we find that BP neural network consistently and robustly outperforms the other four models. Then some theoretical and practical implications have been discussed.</p>
</abstract>

Due to the extremely volatile nature of financial markets, it is commonly accepted that stock price prediction is a task filled with challenges. However, in order to make profits or understand the essence of equity market, numerous market participants or researchers try to forecast stock prices using various statistical, econometric or even neural network models. In this work, we survey and compare the predictive power of five neural network models, namely, back propagation (BP) neural network, radial basis function neural network, general regression neural network, support vector machine regression (SVMR) and least squares support vector machine regression. We apply the five models to make price predictions for three individual stocks, namely, Bank of China, Vanke A and Guizhou Maotai. Adopting mean square error and average absolute percentage error as criteria, we find that BP neural network consistently and robustly outperforms the other four models. Then some theoretical and practical implications have been discussed.

Back propagation mathematical model for stock price prediction

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Back propagation mathematical model for stock price prediction"}

Due to the extremely volatile nature of financial markets, it is commonly accepted that stock price prediction is a task filled with challenges. However, in...

Name	Bank of China	Vanke A	Guizhou Maotai

Lowest price	2.00	5.65	81.13
Highest Price	5.01	40.04	788.42

Statistics		Mean	Std.

Bank of China	MSE	0.009	4.8×10⁻⁵
Bank of China	MAPE	0.019	0.0001

Vanke A	MSE	2.976	0.0067
Vanke A	MAPE	0.049	0.0001

Guizhou Maotai	MSE	395.1	1.3728
Guizhou Maotai	MAPE	0.026	7.7×10⁻⁵

Method		BP	RBF	GRNN	SVMR	LS-SVMR

Bank of China	MSE	0.009	0.014	0.02	0.012	0.018
Bank of China	MAPE	0.019	0.025	0.024	0.023	0.028

Vanke A	MSE	2.976	4.686	6.036	3.422	5.472
Vanke A	MAPE	0.049	0.065	0.067	0.059	0.072

Guizhou Maotai	MSE	395.1	740.1	1103.6	407.4	405.5
Guizhou Maotai	MAPE	0.026	0.036	0.048	0.029	0.027

Kernel		Linear	Polynomial	Sigmoid	RBF

Bank of China	MSE	0.01	0.01	0.011	0.012
Bank of China	MAPE	0.019	0.02	0.021	0.023

Vanke A	MSE	2.993	3.292	3.515	3.422
Vanke A	MAPE	0.05	0.053	0.055	0.059

Guizhou Maotai	MSE	395.6	403.5	405.7	407.4
Guizhou Maotai	MAPE	0.027	0.028	0.028	0.029

Back propagation mathematical model for stock price prediction

Published Online: Dec 30, 2021

Page range: 165 - 174

Received: Jun 17, 2021

Accepted: Sep 24, 2021

DOI: https://doi.org/10.2478/amns.2021.2.00144

Keywords
back propagation, stock price prediction, radial basis function, support vector machine regression, least squares support vector machine regression

© 2021 Yanran Ma et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Fig. 1

Fig. 2

Fig. 3

Price range

100 times experiment

Results of the five methods

Comparison of four kernels in SVMR

Back propagation mathematical model for stock price prediction

Published Online: Dec 30, 2021

Page range: 165 - 174

Received: Jun 17, 2021

Accepted: Sep 24, 2021

DOI: https://doi.org/10.2478/amns.2021.2.00144

Keywordsback propagation, stock price prediction, radial basis function, support vector machine regression, least squares support vector machine regression

© 2021 Yanran Ma et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Fig. 1

Fig. 2

Fig. 3

Price range

100 times experiment

Results of the five methods

Comparison of four kernels in SVMR

Keywords
back propagation, stock price prediction, radial basis function, support vector machine regression, least squares support vector machine regression