MCN 投资组合:一个高效的投资组合预测和选择模型,使用混合元启发优化算法混合元启发优化算法的多序列级联网络
Meeta Sharma1, Pankaj Kumar Sharma1, Hemant Kumar Vijayvergia1
1Government Mahila Engineering College Ajmer, Ajmer, Rajasthan, India.
概括
本研究引入了投资组合预测和优化的新框架. 开发的多序列级联网络 (MCNet) 提高了预测准确性,从而导致更好的投资选择.
科学领域:
- 金融建模和计算智能.
- 数据科学和机器学习在金融中的应用.
背景情况:
- 投资组合管理需要准确的金融投资预测,而这些预测往往因现有技术而复杂化.
- 有效的错误分析和绩效测量对于验证投资组合预测模型至关重要.
研究的目的:
- 开发一个新的框架,用于投资组合预测和优化.
- 提高财务预测的准确性,并确定最佳投资组合.
- 解决当前投资组合预测方法中的复杂性和问题.
主要方法:
- 采用了多序列级联网络 (MCNet),集成自动编码器,1D卷积神经网络 (1DCNN) 和循环神经网络 (RNN) 来进行益处预测.
- 为了培训和验证,收集了一组公司投资组合的数据集.
- 整合人工子和蜂鸟算法 (IARHA) 用于根据预测利选择最佳投资组合.
主要成果:
- 在预测公司利益方面,MCNet模型表现出强的表现.
- 该框架实现了较低的错误率,根平均平方误差 (RMSE) 为0.89%,平均绝对误差 (MAE) 为0.56%.
- 开发的模型在实验分析中显示了丰富的性能.
结论:
- 拟议的投资组合预测框架显著提高了预测准确性.
- 集成MCNet和IARHA有效地帮助选择最佳投资组合.
- 该研究强调了先进的机器学习技术在改善财务决策方面的潜力.
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