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基于神经网络LSTM的Markowitz扩展模型的投资组合优化

吴楠

（亚马逊公司，北京 100025）

栏目：信息化应用 · 第 2025 年第 5 期 · 页码：159-163 · 2096-4706（2025）05-0159-05 · 中图分类号：TP391 · 文献标识码：A · DOI：10.19850/j.cnki.2096-4706.2025.05.030

摘要

文章对长短期记忆网络（LSTM）模型在预测股票价格方面的应用进行了研究，并探讨了如何将LSTM模型的预测结果融入Markowitz传统投资组合优化模型中。报告了LSTM模型在投资组合管理中的新现状，特别是在预期收益波动率的预测方面。通过数据集调整和训练次数的优化实验，研究对模型预测精度的提升潜力进行了调查，并发现精确度可接近90%。最后，文章基于LSTM预测数据进行了最佳投资组合构建及其收益分析。

关键词

人工智能；机器学习；神经网络；股票价格预测；投资组合优化；Markowitz

参考文献

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作者简介

吴楠（1987—），女，汉族，陕西西安人，国家一级注册建造师，本科，研究方向：机器学习、大数据、金融。