使用机器学习结合数据包裹分析和自动功能工程来预测股票回报:越南股票市场的案例研究
Hoang Thanh Nhon1, Nga Do-Thi2, Thao Nguyen-Trang3,4
1Faculty of Commerce, Van Lang University, Ho Chi Minh City, Vietnam.
PloS one
|September 25, 2025
概括
本研究引入了商业效率评分,以预测股票回报,提高机器学习模型的准确性. 将这些分数与自动化特征工程相结合,显著改善了越南股票市场的预测.
科学领域:
- 金融市场 金融市场
- 机器学习 机器学习
- 计量经济学 计量经济学
背景情况:
- 预测股票回报对于投资者来说至关重要.
- 现有的方法往往忽略了业务效率指标.
- 越南股票市场对回报预测提出了独特的挑战.
研究的目的:
- 评估商业效率评分在预测股票回报中的有效性.
- 为了比较各种机器学习模型的性能,用于股票回报预测.
- 调查自动功能工程对预测准确性的影响.
主要方法:
- 数据包围分析 (DEA) 用于计算业务效率得分.
- 收集26家房地产公司 (2019-2024) 的财务数据.
- 机器学习模型的应用和比较 (例如,深度神经网络,梯度增强树) 与技术,基本和效率指标.
主要成果:
- 商业效率得分显著提高了股票回报预测的准确性.
- 深度神经网络模型显示了效率得分的减少错误指标 (RMSE,MAE,MAPE).
- 使用效率得分和自动化功能工程的梯度增强树实现了最佳性能 (MAE: 0.122,MAPE: 103.19).
结论:
- 商业效率得分是股票回报的有价值预测指标.
- 自动化功能工程进一步提高预测能力,当与效率指标相结合时.
- 这些发现为改善越南等新兴市场的投资策略提供了一种新的方法.
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