Supervision of Financial Market Sentiment and Quantitative Investment Analysis Based on BP Neural Networks

DOI: https://doi.org/10.62517/jse.202411402

Author(s)

Fengyi Guan1, Chuxin Deng1, Tianci Chen2, Dongwu Wu1

Affiliation(s)

1School of Economics and Management, Wuyi University, Jiangmen, Guangdong, China 2College of Liberal Arts, Wuyi University, Jiangmen, Guangdong, China

Abstract

In the complex and volatile financial market, sentiment significantly impacts investment decisions. Despite advancements using BP neural networks for sentiment supervision and quantitative investment analysis, challenges like high complexity and overfitting persist. This study employs structural equation modeling to analyze factors influencing the "digital economy" sector, utilizes Prophet time series for volume prediction, and constructs a BP neural network for index forecasting. The model achieves an 85.79% and 92.74% prediction accuracy for peak and trough prices, respectively, and demonstrates robust risk management with a 13.26% total investment return and a 2.79% information ratio. Stability tests indicate reliable model predictions, offering valuable insights for stock fund selection.

Keywords

Structural Equation Modeling; BP Neural Network; Prophet Time Series; Risk Management Model; Quantitative Analysis

References

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