Duality for robust hedging with proportional transaction costs of path dependent European options is obtained in a discrete time financial market with one risky asset. Investor’s portfolio consists of a dynamically traded stock and a static position in vanilla options which can be exercised at maturity. Only stock trading is subject to proportional transaction costs. The main theorem is duality...

Portfolio selection problem deals with how to form a satisfying portfolio, taking into account the uncertainty involved in the behavior of the financial markets. Markowitz (1952) established the relationship between the mean and variance of the investment in the framework of risk-return trade-off. Since then a variety of enlarged and improved models have been developed in several directions. So...

We develop an optimization method for constructing investment portfolios that dominate a given benchmark portfolio in terms of third-degree stochastic dominance. Our approach relies on the properties of the semivariance function, a refinement of an existing ‘super-convex’ dominance condition and quadratic constrained programming. We apply our method to historical stock market data using an indu...

One of the popular methods for optimizing combinational problems such as portfolio selection problem is swarmbased methods. In this paper, we have proposed an approach based on Quantum-Behaved Particle Swarm Optimization (QPSO) for the portfolio selection problem. The particle swarm optimization (PSO) is a well-known population-based swarm intelligence algorithm. QPSO is also proposed by combin...

In single-period portfolio optimization several facets of the problem may influence the goodness of the portfolios selected. Despite that, some of these facets are frequently ignored when the optimization problem is solved. In this thesis, we aim at investigating the impact of these facets on the optimization problem and on the performances of the portfolios selected. Firstly, we consider the p...

As the number of publicly traded companies as well as the amount of their financial data grows rapidly and improvements in hardware infrastructure and information processing technologies enable high-speed processing of large amounts of data, it is highly desired to have tracking, analysis, and eventually stock selections automated. Machine learning has already attained an important place in tra...

In the traditional portfolio model, investors calculate the expected return of assets and the covariance matrix for optimal asset allocation. This paper divides market sentiment period into three states and selectes the securities in the Chinese stock market to construct portfolios. We implement both the Fama-French five-factor model and the robust median covariance matrix approach for predicti...

This paper presents a closed-form solution to the portfolio optimization problem where an agent wishes to maximize expected terminal wealth, trading continuously between a risk-free bond and a risky stock following Stressed-Beta dynamics specified in Fouque and Tashman (2010). The agent has a finite horizon and a utility of the Constant Relative Risk Aversion type. The model for stock dynamics ...

We examine the issue of possible portfolio diversification benefits into seven Middle-Eastern and North African (MENA) stock markets. We take the standpoint of the world investor and we construct portfolios in international and local currencies based on five optimization models and two risk measures. We then compare the portfolio out-of-sample performance based on Sharpe and Sortino ratios thro...

Algorithm portfolios represent a strategy of composing multiple heuristic algorithms, each suited to a different class of problems, within a single general solver that will choose the best suited algorithm for each input. This approach recently gained popularity especially for solving combinatoric problems, but optimization applications are still emerging. The COCO platform [6] [5] of the BBOB ...