Portfolio optimisation for a Fund of Hedge Funds (“FoHF”) has to address the asymmetric, non-Gaussian nature of the underlying returns distributions. Furthermore, the objective functions and constraints are not necessarily convex or even smooth. Therefore traditional portfolio optimisation...

Portfolio optimization is one of the problems most frequently encountered by financial practitioners. To our knowledge, the Critical Line Algorithm (CLA) is the only algorithm specifically designed for inequality-constrained portfolio optimization problems, which guarantees that the exact...

Introduction -- Primer of convex analysis -- Variational analysis -- Linear and nonlinear optimization problems -- Probability and Statistics -- Fundamental modeling questions in stochastic programming -- Adjusting to uncertainty: modeling recourse -- Probability constraints -- Proximal point...

An optimal portfolio with the highest possible Sharpe ratio plays an important role for capital allocation and performance evaluation. This paper introduces a simple algorithm for finding the Sharpe-optimal portfolio without solving a non-linear problem. The results are tested on S&P 100...

An analogue can be made between: (a) the slow pace at which species adapt to an environment, which often results in the emergence of a new distinct species out of a once homogeneous genetic pool, and (b) the slow changes that take place over time within a fund, mutating its investment style. A...

This paper examines the determinants of herding at both stock and individual investor levels and studies the portfolio performance of herd vs. non-herd portfolios using machine learning algorithms. The disposition effect and the attention effect seem to explain herding behavior at the stock...

A fast method based on coordinate-wise descent algorithms is developed to solve portfolio optimization problems in which asset weights are constrained by Lq norms for 1=q=2. The method is first applied to solve a minimum variance portfolio (mvp) optimization problem in which asset weights are...

Many optimization problems that arise in practice can be reduced to the problem of computing the projection of a given vector in a Euclidean space onto the simplicial cone generated by a set of linearly independent vectors. For example, the well-known problem in finance of determining the...

We propose a novel linear approximation of expected utility. The approximation guides us as we transfer the traditional quadratic dependence of third-order stochastic dominance (TSD) into an equivalent linear system. The finding also shows a dual relationship between traditional low partial...