optimization: A case study of Istanbul Stock Exchange

While investors used to create their portfolios according to traditional portfolio theory in the past, today modern portfolio approach is widely preferred. The basis of the modern portfolio theory was suggested by Harry Markowitz with the mean variance model. A greater number of securities in a portfolio is difficult to manage and has an increased transaction cost. Therefore, the number of securities in the portfolio should be restricted. The problem of portfolio optimization with cardinality constraints is NP-Hard. Meta-heuristic methods are generally preferred to solve since problems in this class are difficult to be solved with exact solution algorithms within acceptable times. In this study, a particle swarm optimization algorithm has been adapted to solve the portfolio optimization problem and applied to Istanbul Stock Exchange. The experiments show that while in low risk levels it is required to invest into more number of assets in order to converge unconstrained efficient frontier, as risk level increases the number of assets to be held is decreased

portfolio optimization

One of the most studied variant of portfolio optimization problems is with cardinality constraints that transform classical mean-variance model from a convex quadratic programming problem into a mixed integer quadratic programming problem which brings the problem to the class of NP-Complete problems. Therefore, the computational complexity is significantly increased since cardinality constraints have a direct influence on the portfolio size. In order to overcome arising computational difficulties, for solving this problem, researchers have focused on investigating efficient solution algorithms such as metaheuristic algorithms since exact techniques may be inadequate to find an optimal solution in a reasonable time and are computationally ineffective when applied to large-scale problems. In this paper, our purpose is to present an efficient solution approach based on an artificial bee colony algorithm with feasibility enforcement and infeasibility toleration procedures for solving cardinality constrained portfolio optimization problem. Computational results confirm the effectiveness of the solution methodology. (C) 2017 Elsevier Ltd. All rights reserved

On teaching assistant-task assignment problem: A case study

Teaching assistants (TAs), together with the senior academic staff, are the centerpiece of university education. TAs are primarily graduate students and they undertake many of the academic and administrative tasks. These tasks are assigned at the beginning of each semester and the objective is to make fair assignments so that the loads are distributed evenly in accordance with requests of the professors and assistants. In this study, a goal programming (GP) model is developed for task assignment of the TAs in an industrial engineering department. While the rules that must be strictly met (e.g., assigning every task to an assistant) are formulated as hard constraints, fair distribution of the loads are modeled as soft constraints. Penalties for deviation from the soft constraints are determined by the Analytic Hierarchy Process (AHP). The proposed GP model avoids assigning the same TA to the same task in several consecutive academic years, i.e., sticking of a task to a TA. We show that the proposed formulation generates better schedules than the previously used ad hoc method with a much less effort. (C) 2014 Elsevier Ltd. All rights reserved