基于因子特征的高维稀疏投资组合优化
High-Dimensional Sparse Portfolio Optimization Based on Factor Characteristics
查看参考文献34篇
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文摘
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在高维情形下,为了实现对期望收益率的更准确估计,提高投资组合策略的稳定性及获得更好的样本外表现,文章利用流通市值和账面市值比的双因子排序组合信息,在回归形式的均值-方差策略目标函数中引入了Group-LASSO(GLASSO)正则项,构建了GLASSO-MV投资组合策略.相比包含权重${l_1}$-范数正则项的LASSO-MV策略,GLASSO-MV能够有效利用因子组合之间的定价差异信息,从而输出组间的稀疏权重,进而更有效地估计高维投资组合权重并取得更好的样本外表现.为了获得合适的正则项参数和权重稀疏度,文章在5折交叉验证寻优结果的基础上进行了稀疏度调整.为验证此策略,文章利用A股1995年至2019年共3695只股票的日际实证数据集,将GLASSO-MV与多种常见的投资组合策略进行了比较.结果显示相比LASSO-MV,MV,GMV,TZ(Tu-Zhou),BS(Bayes-Stein)等策略,GLASSO-MV实现了更好的样本外夏普率,更低的标准差风险和换手率. |
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其他语种文摘
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When facing high-dimensional situation,to better estimate expected return,increase the stability of portfolio strategy and obtain better out-of-sample performance,this paper uses information of double-sorted portfolio on circulating market size and book-to-market to introduce a Group-LASSO(GLASSO)regularization term into the regression-type mean-variance objective function,and constructs the GLASSO-MV portfolio strategy.Comparing to ${l_1}$-norm regularized LASSO-MV strategy,GLASSO-MV can effectively utilitize the pricing difference among factor portfolios,and output sparse between-group weights,attaining more effective high-dimensional weight estimation and better out-of-sample performance.To obtain suitable regularization term parameter and weight sparsity,this paper adopts 5-fold cross-validation and adjusts the sparsity based on the parameter result.In terms of empirical study,this paper uses daily data on Chinese A share market from 1995 to 2019 of 3695 stocks,and compares GLASSO-MV to multiple common portfolio strategies.The result shows that,compared to strategies including LASSO-MV,MV,GMV,TZ(Tu-Zhou),BS(Bayes-Stein),GLASSO-MV has better out-of-sample Sharpe ratio,lower standard deviation risk and turnover. |
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来源
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系统科学与数学
,2021,41(10):2716-2729 【核心库】
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关键词
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高维投资组合优化
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实证资产定价
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Group-LASSO
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因子特征
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地址
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1.
北京大学软件与微电子学院, 北京, 100871
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中山大学管理学院, 广州, 510275
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语种
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中文 |
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文献类型
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研究性论文 |
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ISSN
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1000-0577 |
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学科
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数学 |
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基金
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国家自然科学基金
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文献收藏号
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CSCD:7114052
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参考文献 共
34
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