This paper provides a simulation-based approach to optimal portfolio selection. We take a Bayesian approach as it naturally accounts for estimation risk, i.e., parameter uncertainty, learning of state variables and models, and can incorporate prior beliefs about future return distributions. We specifically highlight two implementations with great potential in portfolio selection. First, for com...

We propose a method for optimal portfolio selection using a Bayesian framework that addresses two major shortcomings of the Markowitz approach: the ability to handle higher moments and estimation error. We employ the skew normal distribution which has many attractive features for modeling multivariate returns. Our results suggest that it is important to incorporate higher order moments in portf...

We introduce a decision support framework for the research and development (R&D) portfolio selection problem faced by a major U.S. semiconductor manufacturer. R&D portfolio selection is of critical importance to high-tech operations such as semiconductor and pharmaceutical, as it determines the blend of technological development the firm must invest in its R&D resources. This R&D investment lea...

A new minimax model on optimal portfolio selection with uncertainty of both randomness and estimation in inputs is established and the corresponding optimal portfolio is derived analytically. Based on this result, a sufficient condition for the existence and uniqueness of a nonnegative equilibrium price system under which the total demand and supply of each asset are equal is provided and an ex...

In recent years optimal portfolio selection strategies for sequential investment have been shown to exist. Although their asymptotical optimality is well established, finite sample properties do need the adjustment of parameters that depend on dimensionality and scale. In this paper we introduce some nearest neighbor based portfolio selectors that solve these problems, and we show that they are...

This paper focuses on a continuous-time dynamic mean-variance portfolio selection problem of defined-contribution pension funds with stochastic salary, whose risk comes from both financial market and nonfinancial market. By constructing a special Riccati equation as a continuous (actually a viscosity) solution to the HJB equation, we obtain an explicit closed form solution for the optimal inves...

This paper introduces an application of financial risk management methods to the deregulated electricity markets. A framework for the Monte Carlo performance simulation of a power portfolio is presented. The optimal portfolio selection problem is addressed and a numerical method is implemented. Numerical results of simulation and optimization are presented in the Nordic electricity market. The ...

Stochastic linear quadratic portfolio selection problem for relative return process is to maximize the expected linear quadratic function of the relative return process. We introduce the benchmark process and define the relative return process as quotient which is obtained when the wealth process is divided by the benchmark process. We derive the optimal portfolio in closed form via investigati...

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 results derived not only in terms of the standard mean-variance objective, but also in terms of two of the most popular risk measures, mean-VaR and mean-CVaR developed recently. In addition, we review optimal estima...