Data Time Span’s Impact on Stock Price Prediction: An Empirical Study Across Multiple Markets Based on BP Neural Networks

Research Article
Open access

Data Time Span’s Impact on Stock Price Prediction: An Empirical Study Across Multiple Markets Based on BP Neural Networks

Hongfan Jin 1*
  • 1 School of Mathematics, China University of Mining and Technology, Xuzhou, China    
  • *corresponding author 2061266718@qq.com
AEMPS Vol.191
ISSN (Print): 2754-1177
ISSN (Online): 2754-1169
ISBN (Print): 978-1-80590-189-1
ISBN (Online): 978-1-80590-190-7

Abstract

Stock price prediction faces significant challenges due to the high complexity and non-linear characteristics of financial markets. Traditional models often struggle to effectively capture their dynamic patterns. This paper, based on the Backpropagation Neural Network (BPNN), constructs a multivariate time series forecasting model to explore the non-linear mapping relationship between historical trading data, such as opening price, closing price, lowest price, and highest price, and the next day’s closing price. To demonstrate the practical applicability of the model and the impact of data time span, two comparative experiments were designed, using two years and three years of historical data, respectively, to analyze stock price predictions for 13 major global securities markets. Empirical results show that BPNN exhibits strong forecasting ability in stable markets. Extending the time span can improve the prediction accuracy for some markets by covering a more complete market cycle. However, the effect is constrained by market volatility and external environmental factors. The research findings provide a theoretical basis for cross-market model adaptation and data governance strategies.

Keywords:

Stock Price Prediction, Backpropagation Neural Network (BPNN), Multivariate Time Series Analysis, Cross-Market Empirical Study, Time Span Effect

Jin,H. (2025). Data Time Span’s Impact on Stock Price Prediction: An Empirical Study Across Multiple Markets Based on BP Neural Networks. Advances in Economics, Management and Political Sciences,191,1-9.
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References

[1]. Cao, Q., Parry, M. E., & Leggio, K. B. (2011). The three-factor model and artificial neural networks: Predicting stock price movement in China. Annals of Operations Research, 185, 25–44.

[2]. Shi, H., & Liu, X. (2014). Application on stock price prediction of Elman neural networks based on principal component analysis method. In Proceedings of the 2014 11th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) (pp. 411–414).

[3]. Zahedi, J., & Rounaghi, M. M. (2015). Application of artificial neural network models and principal component analysis method in predicting stock prices on Tehran Stock Exchange. Physica A: Statistical Mechanics and Its Applications, 438, 178–187.

[4]. Gao, T., et al. (2016). Deep learning with stock indicators and two-dimensional principal component analysis for closing price prediction system. In Proceedings of the 7th IEEE International Conference on Software Engineering and Service Science (ICSESS) (pp. 166–169).

[5]. Yu, Z., et al. (2020). Stock price forecasting based on LLE-BP neural network model. Physica A: Statistical Mechanics and Its Applications, 553, 124197.

[6]. Dong, W., & Zhao, C. (2021). Stock price forecasting based on fractional grey model with convolution and BP neural network. In Proceedings of the 33rd Chinese Control and Decision Conference (CCDC) (pp. 1995–2000).

[7]. Bui, T. K., & Tran, T. H. (2022). Long short-term memory recurrent neural network for predicting the return of rate under the Fama-French 5 factor. Discrete Dynamics in Nature and Society, 2022, Article ID 3936122.

[8]. Bui, T. K., & Tran, T. H. (2022). Long short-term memory recurrent neural network for predicting the return of rate under the Fama-French 5 factor. Discrete Dynamics in Nature and Society, 2022, Article ID 3936122.

[9]. Wang, J., et al. (2023). A PCA-IGRU model for stock price prediction. Journal of Internet Technology.

[10]. Wang, J., & Chen, Z. (2024). Factor-GAN: Enhancing stock price prediction and factor investment with generative adversarial networks. PLOS ONE, 19(6), e0306094.

[11]. Sarıkoç, M., & Celik, M. (2024). PCA-ICA-LSTM: A hybrid deep learning model based on dimension reduction methods to predict S&P 500 index price. Computational Economics.

[12]. Rumelhart, D. E., et al. (1986). Learning representations by back-propagating errors. Nature, 323(6088), 533–536.

Cite this article

Jin,H. (2025). Data Time Span’s Impact on Stock Price Prediction: An Empirical Study Across Multiple Markets Based on BP Neural Networks. Advances in Economics, Management and Political Sciences,191,1-9.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of ICEMGD 2025 Symposium: The 4th International Conference on Applied Economics and Policy Studies

ISBN：978-1-80590-189-1(Print) / 978-1-80590-190-7(Online)
Editor：Florian Marcel Nuţă , Xuezheng Qin
Conference website: https://www.icemgd.org/
Conference date: 20 September 2025
Series: Advances in Economics, Management and Political Sciences
Volume number: Vol.191
ISSN：2754-1169(Print) / 2754-1177(Online)

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References

[1]. Cao, Q., Parry, M. E., & Leggio, K. B. (2011). The three-factor model and artificial neural networks: Predicting stock price movement in China. Annals of Operations Research, 185, 25–44.

[2]. Shi, H., & Liu, X. (2014). Application on stock price prediction of Elman neural networks based on principal component analysis method. In Proceedings of the 2014 11th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) (pp. 411–414).

[3]. Zahedi, J., & Rounaghi, M. M. (2015). Application of artificial neural network models and principal component analysis method in predicting stock prices on Tehran Stock Exchange. Physica A: Statistical Mechanics and Its Applications, 438, 178–187.

[4]. Gao, T., et al. (2016). Deep learning with stock indicators and two-dimensional principal component analysis for closing price prediction system. In Proceedings of the 7th IEEE International Conference on Software Engineering and Service Science (ICSESS) (pp. 166–169).

[5]. Yu, Z., et al. (2020). Stock price forecasting based on LLE-BP neural network model. Physica A: Statistical Mechanics and Its Applications, 553, 124197.

[6]. Dong, W., & Zhao, C. (2021). Stock price forecasting based on fractional grey model with convolution and BP neural network. In Proceedings of the 33rd Chinese Control and Decision Conference (CCDC) (pp. 1995–2000).

[7]. Bui, T. K., & Tran, T. H. (2022). Long short-term memory recurrent neural network for predicting the return of rate under the Fama-French 5 factor. Discrete Dynamics in Nature and Society, 2022, Article ID 3936122.

[8]. Bui, T. K., & Tran, T. H. (2022). Long short-term memory recurrent neural network for predicting the return of rate under the Fama-French 5 factor. Discrete Dynamics in Nature and Society, 2022, Article ID 3936122.

[9]. Wang, J., et al. (2023). A PCA-IGRU model for stock price prediction. Journal of Internet Technology.

[10]. Wang, J., & Chen, Z. (2024). Factor-GAN: Enhancing stock price prediction and factor investment with generative adversarial networks. PLOS ONE, 19(6), e0306094.

[11]. Sarıkoç, M., & Celik, M. (2024). PCA-ICA-LSTM: A hybrid deep learning model based on dimension reduction methods to predict S&P 500 index price. Computational Economics.

[12]. Rumelhart, D. E., et al. (1986). Learning representations by back-propagating errors. Nature, 323(6088), 533–536.

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