阿达布思
计算机科学
文件夹
机器学习
人工智能
支持向量机
证券交易所
Boosting(机器学习)
投资组合优化
极限学习机
库存(枪支)
随机森林
梯度升压
计量经济学
风险价值
人工神经网络
金融经济学
经济
财务
风险管理
机械工程
工程类
作者
Jyotirmayee Behera,Ajit Kumar Pasayat,Harekrushna Behera,Prasanna Kumar
标识
DOI:10.1016/j.engappai.2023.105843
摘要
The future performance of stock markets is the most crucial factor in portfolio creation. As machine learning technique is advancing, new possibilities have opened up for incorporating prediction concepts into portfolio selection. A hybrid approach that constitutes machine learning algorithms for stock return prediction and a mean–VaR (value-at-risk) model for portfolio selection is illustrated in this paper as a unique portfolio construction technique. Machine learning regression models such as Random Forest, Extreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), Support Vector Machine Regression (SVR), k-Nearest Neighbors (KNN), and Artificial Neural Network (ANN) are adopted to forecast stock values for the next period. The stocks with greater prospective returns are chosen in the first stage of this affection. Further, the mean–VaR portfolio optimization model is employed for portfolio selection in the second stage. The monthly datasets of the Bombay Stock Exchange (BSE), India, Tokyo Stock Exchange, Japan, and Shanghai Stock Exchange, China, are used as the research sample, and the findings show that the mean–VaR model with AdaBoost prediction outperforms other models.
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