<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...

Back propagation mathematical model for stock price prediction

Data publikacji: 30 gru 2021

Zakres stron: 165 - 174

Otrzymano: 17 cze 2021

Przyjęty: 24 wrz 2021

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

Słowa kluczowe
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.

Back propagation mathematical model for stock price prediction

Data publikacji: 30 gru 2021

Zakres stron: 165 - 174

Otrzymano: 17 cze 2021

Przyjęty: 24 wrz 2021

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

Słowa kluczoweback 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.

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