Research on Stock Price Prediction Using the BPNN Neural Network Model

Yifeng Yin

doi:10.54254/2753-8818/2025.GL26775

Theoretical and Natural ScienceOpen access

Research on Stock Price Prediction Using the BPNN Neural Network Model

Research Article

Open Access

Research on Stock Price Prediction Using the BPNN Neural Network Model

Yifeng Yin ^1*

¹ Donghua University

^*Corresponding author: 15140313998@163.com

Published on 9 September 2025

TNS Vol.136

ISSN (Print): 2753-8826

ISSN (Online): 2753-8818

ISBN (Print): 978-1-80590-375-8

ISBN (Online): 978-1-80590-376-5

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Abstract

Against the backdrop of an increasingly large stock market and a growing number of investors, many individuals have faced bankruptcy due to blindly following trends in stock investment. This situation has created a demand for methods to predict stock prices in advance. To address this issue, researchers have previously proposed various approaches, including traditional analytical methods based on historical data, statistical analysis methods, machine learning and deep learning methods, etc. The research focus of this paper is to investigate the specific conditions under which the Backpropagation Neural Network model delivers superior performance in stock price prediction. This study utilizes 44 years of historical stock price data from Apple Inc. (AAPL), encompassing key features such as Date, Opening Price, Highest Price of the Day, Lowest Price of the Day, Closing Price, Adjusted Closing Price, and Trading Volume. Specifically, the study focuses on selecting the Opening Price, Highest Price of the Day, Lowest Price of the Day, Closing Price, and Trading Volume as input features. The research ultimately achieved relatively accurate results. These findings are then leveraged to extend insights into effective configuration strategies for a wider range of other prediction methods.

Keywords:

BPNN neural network, stock price prediction, feature selection, time-span selection, prediction performance evaluation

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