Portfolio Selection by Maximizing Various Risk-Adjusted Return Ratios via Convex Reformulations

In this article, the classic portfolio selection problem is reformulated as nine convex optimization problems to maximize nine risk-adjusted performance indexes based on nine different risk measures in Markowitz's return-risk framework. The exact convex reformulations facilitate a decision maker to optimize portfolios efficiently by maximizing one of the nine risk-adjusted performance criteria using widely available convex optimization problem solvers, without compromising the portfolio optimality. The superior performances of the proposed approaches to the state-of-the-art methods, in terms of out-of-sample risk-adjusted returns, annualized returns, and portfolio sparsity, are demonstrated through extensive experimentation on 13 datasets from major world stock markets.