Portfolio optimization emerged with the seminal paper of Markowitz (1952). The original mean-variance framework is appealing because it is very efficient from a computational point of view. However, it also has one well-established failing since it can lead to portfolios that are not optimal...

In this paper the set of all second-order stochastic dominance (SSD) efficient portfolios is characterized by using a series of mixed-integer linear constrains. Our derivation employs a combination of the first-order conditions of the utility maximization problem together with a judicious use of...

We study the continuous time portfolio selection problem over a finite horizon for an investor who maximizes the expected utility of terminal wealth and faces transaction costs. The portfolio consists of a risk-free asset, and a risky asset whose price is modeled as a geometric Brownian motion....

This paper presents an efficient algorithm for computing the allocation weights of the risk parity portfolio (or the more general risk budget portfolio) based on Newton's method. The algorithm is provably convergent, and in dimension < 1000 requires on average less than 5 iterations

The purpose of this note is to present a further reduction of the model presented by Konno and Yamazaki (1991). In their paper the number of nonzero assets in the optimal solution is bounded by the number of model rows, 2T + 2, where T is the number of time periods (assuming no upper limit on...

We investigate the time-consistent mean-variance (MV) portfolio optimization problem under a realistic context that involves the simultaneous application of different types of investment constraints and modelling assumptions, for which a closed-form solution is not known to exist. We develop an...

We develop a novel Mean-Max Drawdown portfolio optimization approach using buy-and-hold portfolios. The optimization is performed utilizing a multi-objective evolutionary algorithm on a sample of S&P 100 constituents. Our optimization procedure provides portfolios with better Mean-Max Drawdown...