This paper reviews the literature on Bayesian portfolio analysis. Information about events,
macro conditions, asset pricing theories, and security-driving forces can serve as useful …

The modern portfolio theory pioneered by Markowitz (1952) is widely used in practice and
extensively taught to MBAs. However, the estimated Markowitz portfolio rule and most of its …

In this paper we provide a survey of recent contributions to robust portfolio strategies from
operations research and finance to the theory of portfolio selection. Our survey covers …

Abstract We extend the Black-Litterman (BL) approach to incorporate tail dependency in
portfolio optimization and estimate the posterior joint distribution of returns using vine …

This paper proposes a way to allow Bayesian priors to reflect the objectives of an economic
problem. That is, we impose priors on the solution to the problem rather than on the primitive …

Robo-Advisors has been growing attraction from the financial industry for offering financial
services by using algorithms and acting as like human advisors to support investors making …

Portfolio optimization is the basic quantitative approach for finding optimal portfolio weights.
It has become increasingly important as portfolio construction involves more and more data …

The authors propose a general framework referred to as Black–Litterman–Bayes (BLB) for
constructing optimal portfolios for factor-based investing. In the spirit of the classical Black …

This paper explores an explainable AI model in the financial industry. Macroeconomic and
market data serve as inputs of Hierarchical Clustering to distinguish among different …

Recent research advocates volatility diversification for long equity investors. It can even be
justified when short-term expected returns are highly negative, but only when its equilibrium …