机器学习模型用于股票市场预测的性能比较与新的投资策略
Azaz Hassan Khan1, Abdullah Shah1, Abbas Ali1
1Department of Electrical Engineering and Computer Science, Jalozai Campus, University of Engineering and Technology, Peshawar, Pakistan.
PloS one
|September 21, 2023
概括
本研究引入了一项新策略,用于增强用于股票市场预测的机器学习 (ML). 拟议的方法显著提高预测准确性,优化回报,同时最大限度地降低财务风险.
科学领域:
- * 计算金融学
- * 金融机器学习
背景情况:
- * 股票市场预测本质上是复杂的,有效市场假设表明完美的预测是不可能的.
- *机器学习 (ML) 为股票市场预测准确性提供了潜在的改进.
- * 传统的ML验证方法可能无法完全捕捉现实金融场景中的业绩.
研究的目的:
- * 提出一种新的策略,以提高金融市场中ML模型的预测效率.
- * 通过传统和拟议的方法来评估九个ML模型的性能.
- * 评估ML模型的风险回报概况,而不仅仅是简单的准确度指标.
主要方法:
- *通过使用历史1天的股票市场数据,训练和验证了9个ML模型.
- *开发了一种新的培训方法,并应用于这些模型.
- * 用金融市场模拟来评估风险,最大提款和回报.
主要成果:
- *在传统方法下,物流回归产生了最高的准确性 (85.51%).
- * 拟议的策略导致Random Forest获得最高准确率 (91.27%),超过了其他模型,如XG Boost,ADA Boost和ANN.
- *模拟结果表明,拟议的策略提高了股票市场的回报率,并降低了相关风险.
结论:
- *新的策略显著提高了ML模型对股票市场方向的预测准确度.
- *仅在分类报告上评估ML模型是不够的;风险回报模拟是至关重要的.
- * 拟议的方法为基于ML的股票市场预测提供了更强大的方法,平衡业绩和风险.
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