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

Risk factor investing has gained popularity among academic researchers and market practitioners over the past few decades, while focusing on factors alone may be not able 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 market, factors, and portfolios via constructing factor-blending portfolios using different optimization strategies. We analyze Betas of factors against the market and Betas of portfolios against factors to explain implied patterns. S&P 500 and PAAIX (PIMCO all asset fund institutional class) are chosen as benchmarks, and the period of our dataset is from January 2018 to December 2022. Overall, maximize Sharpe ratio optimization (MSRO) outperforms other portfolios and its risk sensitivity stays at an acceptable level.