论文标题
机器学习投资组合分配
Machine Learning Portfolio Allocation
论文作者
论文摘要
当使用机器学习来进行市场指数和无风险资产之间的投资组合分配时，我们发现经济和统计学上具有显着的收益。两种随机森林模型实施了时间变化的预期收益和波动率的最佳投资组合规则。一种模型用于预测具有支出收益率的超额回报的符号概率。第二个用于构建优化的波动率估计。机器学习的奖励风险时机对实用程序的买入和持有效果，风险调整后的收益和最大缩水量提供了重大改进。本文提出了一个新的理论基础和统一的机器学习框架，用于返回和波动性。
We find economically and statistically significant gains when using machine learning for portfolio allocation between the market index and risk-free asset. Optimal portfolio rules for time-varying expected returns and volatility are implemented with two Random Forest models. One model is employed in forecasting the sign probabilities of the excess return with payout yields. The second is used to construct an optimized volatility estimate. Reward-risk timing with machine learning provides substantial improvements over the buy-and-hold in utility, risk-adjusted returns, and maximum drawdowns. This paper presents a new theoretical basis and unifying framework for machine learning applied to both return- and volatility-timing.
