基于复杂多维场景生成的M-CVaR-高阶矩投资组合研究
Sophisticated Multi-dimensional Scenario Generation and Its Application to the M-CVaR-Higher Order Moment Portfolio Selection
王帅 1王建州1
作者信息
摘要
构建合理的投资组合能够实现资金的有效配置,对于提高收益和降低风险至关重要.随着机器学习方法的蓬勃发展,深度学习和智能优化算法等新兴技术与投资组合的融合正在不断改变传统的投资方式.本文首先基于一种生成式深度学习方法——最小二乘生成对抗网络,模拟证券未来收益的复杂多维场景,从而有效把握资产未来收益的不确定性信息.其次,拓展不确定性场景下投资组合的CVaR及收益率高阶矩的估计方式,以解决复杂多维场景计算下的投资损失控制问题.构建多目标投资组合优化问题,弥补仅考虑预期收益和CVaR而忽略高阶矩风险的不足.最后,引入Tent混沌映射和混合惩罚函数以改进NSGA-Ⅲ,从而求解该高维、高阶矩、非线性问题.实证研究以市场规模大且流动性好的上证50指数成份股构建投资组合,结果表明所构建的新模型在样本外累积收益和夏普比率等评价指标上表现更好.本文提出的投资组合模型有助于提高样本外投资绩效,同时也丰富了目前投资组合方法论研究.
Abstract
Constructing a reasonable portfolio can achieve an effective allocation of funds,which is crucial to improving returns and reducing risks.With the vigorous development of machine learning methods,the combination of new technologies such as deep learning and intelligent algorithms with portfolios is continuously changing the traditional way of portfolio selection.Firstly,this paper simulates a large number of future scenarios of securities return based on a generative deep learning method—least square generative adversarial networks,thereby effectively grasping the uncertainty information of future asset returns.Secondly,the estimation methods of CVaR and higher order moment of the portfolio under uncertainty scenarios are expanded to address the problem of investment loss control under sophisticated multi-dimensional scenario calculations.The multi-objective portfolio optimization problem is constructed,which remedies the shortcoming of only considering the expected returns and CVaR but ignoring the high-order moment risk.Finally,by introducing the Tent chaotic map and the mixed penalty function to improve the NSGA-Ⅲ,this high-dimensional,higher-order moment,and nonlinear problem is solved.The empirical study constructs the investment portfolio with the constituent stocks of SSE 50 Index with large market scale and good liquidity.The results show that the new model constructed in this paper performs better on the cumulative return,Sharpe ratio and other metrics.The proposed model can improve the performance of the portfolio,and enrich the current research on portfolio methodology.
关键词
投资组合/条件风险价值/最小二乘生成对抗网络/机器学习/多目标优化Key words
Portfolio Selection/CVaR/Least Squares Generative Adversarial Networks/Machine Learning/Multi-objective Optimization引用本文复制引用
出版年
2024
NETL
NSTL
万方数据