基于Black-Litterman模型的因子投资组合

Constructing factor-bearing portfolios based on the Black-Litterman model

近年来,风险因子投资受到学术研究人员和市场从业者的青睐,但仅关注因子既无法充分反映投资者的观点,也不足以生成有竞争力的投资组合.Black-Litterman模型同时考虑了市场状况和投资者的主观判断,有更高的灵活性.本文采用不同的优化策略来构建含因子的混合投资组合,探索市场、因子和投资组合之间的关系.通过分析因子关于市场的Beta值以及投资组合关于因子的Beta值来解释隐含模式.文中选择S&P 500和PAAIX作为市场基准数据集,选择的时间区间为2018年1月至2022年12月.总体而言,最大化Sharpe比率优化优于其他投资组合,且其风险敏感性保持在可接受的水平.

Risk factor investing has gained popularity among academic researchers and market practitioners over the past few decades.However,focusing solely on factors may not be sufficient to merge investor views and generate competitive portfolios.The Black-Litterman model combines market status and subjective judgment,allowing freedom in views.In this study,we explore the relationship among the market,factors,and portfolios via constructing factor-blending portfolios using different optimization strategies.We analyze the Betas of factors against the market and the Betas of portfolios against factors to explain implied patterns.S&P 500 and PAAIX from January 2018 to December 2022 are chosen as benchmarks.Overall,the maximized Sharpe ratio optimization outperforms other portfolios,and its risk sensitivity remains at an acceptable level.