  Mean-variance analysis is widely used in portfolio management to identify the
best portfolio that makes an optimal trade-off between expected return and
volatility. Yet, this method has its limitations, notably its vulnerability to
estimation errors and its reliance on historical data. While shrinkage
estimators and factor models have been introduced to improve estimation
accuracy through bias-variance trade-offs, and the Black-Litterman model has
been developed to integrate investor opinions, a unified framework combining
three approaches has been lacking. Our study debuts a Bayesian blueprint that
fuses shrinkage estimation with view inclusion, conceptualizing both as
Bayesian updates. This model is then applied within the context of the
Fama-French approach factor models, thereby integrating the advantages of each
methodology. Finally, through a comprehensive empirical study in the US equity
market spanning a decade, we show that the model outperforms both the simple
$1/N$ portfolio and the optimal portfolios based on sample estimators.
