Research on the Construction and Dynamic Optimization of a Quantitative Investment Model Based on LSTM-ElasticNet Hybrid Architecture

Yuanchen Wang

doi:10.47297/wspceWSP2516-251914.20250903

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Research on the Construction and Dynamic Optimization of a Quantitative Investment Model Based on LSTM-ElasticNet Hybrid Architecture

9卷第3期

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Research on the Construction and Dynamic Optimization of a Quantitative Investment Model Based on LSTM-ElasticNet Hybrid Architecture
Research on the Construction and Dynamic Optimization of a Quantitative Investment Model Based on LSTM-ElasticNet Hybrid Architecture
School of Business Administration, Jiangxi University of Finance and Economics, Nanchang, Jiangxi, 330032, China
2025年9卷第3期页码：72-76
纸质出版日期：2025-06-30
DOI：10.47297/wspceWSP2516-251914.20250903
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(2025). Research on the Construction and Dynamic Optimization of a Quantitative Investment Model Based on LSTM-ElasticNet Hybrid Architecture. 创意经济（英文版）, 9(3), 72-76.

Wang Yuanchen.Research on the Construction and Dynamic Optimization of a Quantitative Investment Model Based on LSTM-ElasticNet Hybrid Architecture[J].Creative Economy,2025,09(03):72-76.
(2025). Research on the Construction and Dynamic Optimization of a Quantitative Investment Model Based on LSTM-ElasticNet Hybrid Architecture. 创意经济（英文版）, 9(3), 72-76. DOI： 10.47297/wspceWSP2516-251914.20250903.

Wang Yuanchen.Research on the Construction and Dynamic Optimization of a Quantitative Investment Model Based on LSTM-ElasticNet Hybrid Architecture[J].Creative Economy,2025,09(03):72-76. DOI： 10.47297/wspceWSP2516-251914.20250903.

摘要

Abstract

With the explosive growth of financial market data and the significant improvement in computing power

the application of deep learning technology in the field of quantitative investment has gradually attracted attention. This paper aims to construct a deep learning-based quantitative investment model that achieves precise capture of stock market investment opportunities by integrating multiple financial factors and deep learning algorithms. The paper first introduces the basic concepts of quantitative investment and the current status of deep learning applications in the financial field. It then elaborates on the construction process of the deep learning-based quantitative investment model

including data preprocessing

feature engineering

model training

and optimization. Through empirical analysis

the effectiveness of the model in stock market investment is verified

and the performance differences of different deep learning algorithms are compared. The research results show that deep learning models can significantly enhance the return performance and risk control capabilities of quantitative investment strategies

providing new research directions and practical tools for the field of quantitative investment.

关键词

Keywords

references

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Xie Heliang . Research on the Improvement and Application of LSTM in Multi-Factor Quantitative Investment Models [D ] . Central University of Finance and Economics , 2019 .

Xie Heliang , Hu Di . Application of Multi-Factor Quantitative Models in Investment Portfolios—A Comparative Study Based on LASSO and Elastic Net [J ] . Statistics & Information Forum , 2017 , 32 ( 10 ): 36 - 42 .

Xu Jingzhao . Analysis of Quantitative Stock Selection Based on Multi-Factor Models [J ] . Exploration of Financial Theory , 2017 ( 3 ): 30 - 38 .

Li Xiang . Planning of a Multi-Factor Quantitative Stock Selection Scheme Based on the XGBoost Algorithm [D ] . Shanghai Normal University , 2017 .

Wang Zhaodong . Empirical Analysis of Multi-Factor Stock Selection Models in the Chinese Stock Market [D ] . Shandong University , 2014 .

Liu Yi . Empirical Study on Factor Stock Selection Models in the Chinese Market [D ] . Fudan University , 2012 .

Zhang Hu , Shen Hanlei , Liu Yecheng . Research on Multi-Factor Quantitative Stock Selection Based on Self-Attention Neural Networks [J ] . Journal of Applied Statistics and Management , 2020 , 39 ( 3 ): 556 - 570 .

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