基于混合DNN和TabNet模型的股票投资回报的预测模型
Tonghui Zhang1, Ming Da Huo2, Zhaozhao Ma3
1Brooks School of Public Policy, Cornell University, Ithaca, NY, United States of America.
PeerJ. Computer science
|December 16, 2024
概括
本研究介绍了一种混合机器学习模型用于股票市场预测,其性能优于传统方法. 这种新的方法结合了深度神经网络和TabNet,提高了财务预测的准确性和可解释性.
科学领域:
- 计算金融是指计算金融.
- 机器学习应用 机器学习应用
- 金融计量经济学 金融计量经济学
背景情况:
- 传统的股票市场预测依赖于具有有限准确性的主观方法.
- 机器学习为分析复杂的金融数据提供了先进的解决方案.
- 现有的算法如SVM,基于树的和神经网络在处理各种数据类型方面存在局限性.
研究的目的:
- 开发和评估混合机器学习模型,以提高股票市场预测.
- 解决传统分析和现有的ML算法的局限性.
- 结合深度神经网络和TabNet的优势,实现卓越的性能.
主要方法:
- 提出了一个混合模型,将深度神经网络 (DNN) 和TabNet集成在一起.
- DNN的构建涉及完全连接层的ID和数值特征的单独神经网络.
- 在TabNet的实现中,使用了注意力和特征转换器,然后将预测结果合并.
主要成果:
- 混合模型实现了最佳的皮尔森相关系数 (PCC) 和最低的根平均平方误差 (RMSE).
- 该模型在大型数据集上表现出卓越的性能,具有最小的功能工程.
- 该方法提供了强大的可解释性,量化特征贡献.
结论:
- 混合DNN-TabNet模型显著提高了股票回报预测的准确性.
- 该模型在大型数据集上的有效性和可解释性提供了理论和实际价值.
- 这种方法代表了利用机器学习进行财务预测的有希望的进步.
相关概念视频
01:03Prediction Intervals
2.2K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
2.2K
01:25Multiple Regression
2.9K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
2.9K
01:07Residual Plots
4.5K
A residual plot is a statistical representation of data used to analyze correlation and regression results. It helps verify the requirements for drawing specific conclusions about correlation and regression. To obtain the residual plot, first, the residual for each data value is calculated, which is simply the vertical distance between the observed and the predicted value obtained from the regression equation.
When the residual values are plotted against the variable x, it is called a residual...
When the residual values are plotted against the variable x, it is called a residual...
4.5K