In the existing literature, the value-at-risk (VaR) is one of the most representative downside risk measures due to its wide spectra of applications in practice. In this paper, we investigate the dynamic mean-VaR portfolio selection formulation, while the state-of-the-art has only witnessed...

We investigate a discrete-time mean-risk portfolio selection problem, where risk is measured by the conditional value-at-risk (CVaR). By embedding this time-inconsistent problem into a family of expected utility maximization problems with a piecewise linear utility function, we solve the problem...

We examine how the evidence of mean-reversion in stock returns affects dynamic trading behavior for investors with prospect-theory preferences. Particular attention is paid to the trading incentives created by the interaction between prospect-theory preferences and mean-reverting return...

Any robo-advisor needs to decide on a framework to model the preferences of its investors over uncertain outcomes. As of today, most robo-advisors model their investors as mean-variance optimizers. While the mean-variance framework is intuitive and optimal investment strategies have been derived...

Different risk measures emphasize different aspects of a random loss. If we examine the investment performance according to different spectra of the risk measures, any policy generated from a mean-risk portfolio model with a sole risk measure may not be a good choice. We study in this paper the...

As the dynamic mean-variance portfolio selection formulation does not satisfy the principle of optimality of dynamic programming, phenomena of time inconsistency occur, i.e., investors may have incentives to deviate from the pre-committed optimal mean-variance portfolio policy during the...

As the skewed return distribution is a prominent feature in nonlinear portfolio selection problems which involve derivative assets with nonlinear payoff structures, Value-at-Risk (VaR) is particularly suitable to serve as a risk measure in nonlinear portfolio selection. Unfortunately, the...

We develop in this paper a novel portfolio selection framework with a feature of double robustness in both return distribution modeling and portfolio optimization. While predicting the future return distributions always represents the most compelling challenge in investment, any underlying...