Abstract

This paper demonstrates that portfolio optimization techniques represented by Markowitz mean-variance and Hierarchical Risk Parity (HRP) optimizers increase the risk-adjusted return of portfolios built with stocks preselected with a machine learning tool. We apply the random forest method to predict the cross-section of expected excess returns and choose n stocks with the highest monthly predictions. We compare three different techniques—mean-variance, HRP, and 1/N— for portfolio weight creation using returns of stocks from the S&P500 and STOXX600 for robustness. The out-of-sample results show that both mean-variance and HRP optimizers outperform the 1/N rule. This conclusion is in the opposition to a common criticism of optimizers’ efficiency and presents a new light on their potential practical usage.

摘要

本文证明了以 Markowitz 均值方差和分层风险平价 (HRP) 优化器为代表的投资组合优化技术增加了使用机器学习工具预先选择的股票构建的投资组合的风险调整回报。我们应用随机森林方法来预测预期超额收益的横截面，并选择n 个月预测最高的股票。我们使用 S&P500 和 STOXX600 的股票回报来比较三种不同的技术——均值方差、HRP 和 1/N——来创建投资组合权重，以获得稳健性。样本外结果表明均值方差和 HRP 优化器都优于 1/N 规则。这一结论与对优化器效率的普遍批评相反，并为它们的潜在实际用途提供了新的视角。