Strategic Bayesian Asset Allocation
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Strategic asset allocation requires an investor to select stocks from a given basket of assets. Bayesian regularization is shown to not only provide stock selection but also optimal sequential portfolio weights. The perspective of the investor is to maximize alpha risk-adjusted returns relative to a benchmark index. Incorporating investor preferences with regularization is related to the approach of Black (1992) and Puelz (2015). Tailored MCMC algorithms are developed to calculate portfolio weights and perform selection. We illustrate our methodology with an application to stock selection from the SP100, and the top fifty holdings of Renaissance Technologies and Viking Global hedge fund portfolios. Finally, we conclude with directions for future research.
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