论文标题
通过机器学习和应用程序分配资产分配给股票投资组合管理
Asset Allocation via Machine Learning and Applications to Equity Portfolio Management
论文作者
论文摘要
在本文中，我们记录了一种基于机器学习的新型自下而上的方法，以实现大量资产的静态和动态投资组合优化。该方法适用于一般受限的优化问题，并克服了当前优化方案中许多重大困难。以均值变化优化为例，我们不再需要计算协方差矩阵及其逆，因此该方法免受此数量上的估计误差的影响。此外，不需要明确的优化例程。研究了美国和中国股票市场的股票投资组合管理的申请，我们记录了对选定基准的大量超额回报。
In this paper, we document a novel machine learning based bottom-up approach for static and dynamic portfolio optimization on, potentially, a large number of assets. The methodology applies to general constrained optimization problems and overcomes many major difficulties arising in current optimization schemes. Taking mean-variance optimization as an example, we no longer need to compute the covariance matrix and its inverse, therefore the method is immune from the estimation error on this quantity. Moreover, no explicit calls of optimization routines are needed. Applications to equity portfolio management in U.S. and China equity markets are studied and we document significant excess returns to the selected benchmarks.
