Robust optimization (RO) is a young and active research field that has been mainly developed in the last 15 years. RO techniques are very useful for practice and not difficult to understand for practitioners. It is therefore remarkable that real-life applications of RO are still lagging behind;...

This paper introduces the MCTS algorithm to the financial world and focuses on solving significant multi-period financial planning models by combining a Monte Carlo Tree Search algorithm with a deep neural network. The MCTS provides an advanced start for the neural network so that the combined...

Adjustable robust optimization (ARO) is a technique to solve dynamic (multistage) optimization problems. In ARO, the decision in each stage is a function of the information accumulated from the previous periods on the values of the uncertain parameters. This information, however, is often...

We introduce a novel scheme based on a blending of Fourier-Motzkin elimination (FME) and adjustable robust optimization techniques to compute the maximum volume inscribed ellipsoid (MVE) in a polytopic projection. It is well-known that deriving an explicit description of a projected polytope is...

We develop scenario-based stochastic programming models for hedging the risks of international portfolios using options. The models provide an increasing level of integration in managing market and foreign exchange (FX) risks. We start with a single-stage model with currency options for...

This paper proposes a unified approach to creating investment strategies with various desirable properties for investors.Particularly, we provide a new interpretation and the resulting formulations for state space models to attain our investment objectives, which are possibly specified as...

This paper proposes a novel scheme for achieving high investment performances with Mean-Variance (MV) portfolios. As is well-known, MV portfolio performances largely depend on the quality of estimates of parameters, namely expected returns and covariance matrices. Particularly, easily...

The Black Litterman (BL) model for portfolio optimization combines investors' expectations with the Markowitz framework. The BL model is designed for investors with private information or with knowledge of market behavior. In this paper I propose a method where investors' expectations are based...