论文标题
高维投资组合选择具有基数约束
High Dimensional Portfolio Selection with Cardinality Constraints
论文作者
论文摘要
不断扩大的资产为投资者提供了更多机会，但对现代投资组合管理（PM）构成了新的挑战。作为PM的中央板，通过预期效用最大化（EUM）选择投资组合在超高维情况下面对不可控制的估计和优化错误。高维总理的过去策略主要涉及大型公司并选择许多股票，这使得PM不切实际。我们提出了一种基于样本平均近似的投资组合策略，以解决以上的基础性限制。我们的策略绕过了平均值和协方差的估计，即在高维情况下的中国墙。 S＆P 500和Russell 2000的经验结果表明，适当数量的精心选择的资产可提高样本外均值差异效率。在Russell 2000上，我们最好的投资组合利润与同样加权的投资组合一样多，但分别减少了最大资产和平均资产数量10％和90％。还展示了合并因子信号以增强样本外部性能的灵活性和稳定性。我们的策略平衡了收益，风险和具有基数限制的资产数量之间的权衡。因此，我们提供了一种理论上合理的和计算高效的策略，以使PM在不断发展的全球金融市场中实用。
The expanding number of assets offers more opportunities for investors but poses new challenges for modern portfolio management (PM). As a central plank of PM, portfolio selection by expected utility maximization (EUM) faces uncontrollable estimation and optimization errors in ultrahigh-dimensional scenarios. Past strategies for high-dimensional PM mainly concern only large-cap companies and select many stocks, making PM impractical. We propose a sample-average approximation-based portfolio strategy to tackle the difficulties above with cardinality constraints. Our strategy bypasses the estimation of mean and covariance, the Chinese walls in high-dimensional scenarios. Empirical results on S&P 500 and Russell 2000 show that an appropriate number of carefully chosen assets leads to better out-of-sample mean-variance efficiency. On Russell 2000, our best portfolio profits as much as the equally-weighted portfolio but reduces the maximum drawdown and the average number of assets by 10% and 90%, respectively. The flexibility and the stability of incorporating factor signals for augmenting out-of-sample performances are also demonstrated. Our strategy balances the trade-off among the return, the risk, and the number of assets with cardinality constraints. Therefore, we provide a theoretically sound and computationally efficient strategy to make PM practical in the growing global financial market.
