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

<p>In order to establish a more accurate Stock Price Prediction Model, the Stock Price Prediction mathematical Model SPPM (Stock Price Prediction Model) based on BP neural network with high frequency data is proposed in this paper. The SPPM integrates several neural networks to predict the movement of stock prices over the next few days. The key problems in SPPM—such as data preprocessing, output fusion and the selection of nodes in the hidden layer of neural network—are discussed in detail. The experimental results show that the SPPM predicted the closing price of 2019-03-19 and 2019-03-20 as 207.16 and 207.22, respectively, which is very close to the actual observed value, and the back propagation mathematical model SPPM has a certain practical value. Our conclusion is that the back propagation model can predict the stock price with high accuracy.</p>
</abstract>

In order to establish a more accurate Stock Price Prediction Model, the Stock Price Prediction mathematical Model SPPM (Stock Price Prediction Model) based on BP neural network with high frequency data is proposed in this paper. The SPPM integrates several neural networks to predict the movement of stock prices over the next few days. The key problems in SPPM—such as data preprocessing, output fusion and the selection of nodes in the hidden layer of neural network—are discussed in detail. The experimental results show that the SPPM predicted the closing price of 2019-03-19 and 2019-03-20 as 207.16 and 207.22, respectively, which is very close to the actual observed value, and the back propagation mathematical model SPPM has a certain practical value. Our conclusion is that the back propagation model can predict the stock price with high accuracy.

Mathematical model of back propagation for stock price forecasting

Applied Mathematics and Nonlinear Sciences

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

{"article-title":"Mathematical model of back propagation for stock price forecasting"}

In order to establish a more accurate Stock Price Prediction Model, the Stock Price Prediction mathematical Model SPPM (Stock Price Prediction Model) based on...

The date of	The real value (yuan)	The real price	NN1(h=8)	NN2(h=6)	NN3(h=4)	NN4(h=2)	NN5(h=3)	Predictive value	Forecast as
2019-03-16	207.59
2019-03-19	207.64	rose	207.35	206.67	207.41	207.13	207.24	207.16	fall
2019-03-20	207.75	rose	207.15	206.85	207.56	207.71	206.84	207.22	rose

The date of	The real value (N/A)	The real price	NN1(h=8)	NN2(h=6)	NN3(h=4)	NN4(h=2)	NN5(h=3)	Predictive value (N/A)	Forecast as
2019-03-16	2404.74
2019-03-19	2410.18	rose	2355.0	2363.0	2345.6	2400.3	2409.3	2374.64	fall
2019-03-20	2376.84	fall	2352.7	2358.7	2346.0	2402.1	2409.7	2373.84	fall

Mathematical model of back propagation for stock price forecasting

Online veröffentlicht: 27. Dez. 2021

Seitenbereich: 523 - 532

Eingereicht: 16. Juni 2021

Akzeptiert: 24. Sept. 2021

DOI: https://doi.org/10.2478/amns.2021.1.00089

Schlüsselwörter
Neural network, Mathematical model of back propagation, Stock price forecast

© 2021 Feng Li et al., published by Sciendo.

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

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Forecast results of ‘Kweichow Moutai’ (parameter setting: k=5, N=10, M=2)

Forecast results of ‘Shanghai Composite Index’ (parameter setting: k=5, N=10, M=2)

Mathematical model of back propagation for stock price forecasting

Online veröffentlicht: 27. Dez. 2021

Seitenbereich: 523 - 532

Eingereicht: 16. Juni 2021

Akzeptiert: 24. Sept. 2021

DOI: https://doi.org/10.2478/amns.2021.1.00089

SchlüsselwörterNeural network, Mathematical model of back propagation, Stock price forecast

© 2021 Feng Li et al., published by Sciendo.

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

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Forecast results of ‘Kweichow Moutai’ (parameter setting: k=5, N=10, M=2)

Forecast results of ‘Shanghai Composite Index’ (parameter setting: k=5, N=10, M=2)

Schlüsselwörter
Neural network, Mathematical model of back propagation, Stock price forecast