Pacific-Basin Finance Journal, 51, October 2018, Pages 1–12

利用机器学习的增强Black–Litterman框架探索低风险异象：来自韩国的证据

作者👷🏽：Sujin Pyo (Seoul National University, South Korea), Jaewook Lee (Seoul National University, South Korea)

摘要

许多研究已经表明全球金融市场正在经历着低风险异象。例如👩‍👩‍👧‍👧，在韩国市场，即便是高风险股票的投资组合🧑🏿‍🔧，在2000年至2016年期间也亏损了大约70%。在这项研究中，我们构建了一个低风险的投资组合𓀎，该投资组合利用Black–Litterman框架来应对韩国市场的低风险异象。我们使用三种机器学习预测模型和传统的时间序列模型来预测韩国股票价格指数200 (KOSPI 200)中列出的资产的波动性🔈，并选择表现最好的一个。然后👩🏻‍🦼‍➡️，我们使用该模型将资产分为高风险组和低风险组，并创建一个反映投资者观点（低风险股票优于高风险股票）的“Black–Litterman”投资组合👨。实验表明，反映市场均衡投资组合中低风险的观点可以提高盈利能力，并且这种观点在市场投资组合中占主导地位。

Exploiting the low-risk anomaly using machine learning to enhance the Black–Litterman framework: Evidence from South Korea

Sujin Pyo (Seoul National University, South Korea), Jaewook Lee (Seoul National University, South Korea)

ABSTRACT

Many studies have revealed that global financial markets are experiencing low-risk anomalies. In the Korean market, for example, even the portfolios of high-risk stocks recorded a loss of about 70% between 2000 and 2016. In this study, we construct a low-risk portfolio that responds to low-risk anomalies in the Korean market using the Black–Litterman framework. We use three machine-learning predictive and traditional time-series models to predict the volatility of assets listed in the Korean Stock Price Index 200 (KOSPI 200) and select the best-performing one. Then, we use the model to classify assets into high- and low-risk groups and create a Black–Litterman portfolio that reflects the investor's view where low-risk stocks outperform high-risk stocks. The experiment shows that reflecting the low-risk view in the market equilibrium portfolio improves profitability and that this view dominates the market portfolio.

原文链接：https://www.sciencedirect.com/science/article/pii/S0927538X18301239

翻译🏋🏽：阙江静