A note on evolutionary stochastic portfolio optimization and probabilistic constraints

In this note, we extend an evolutionary stochastic portfolio optimization framework to include probabilistic constraints. Both the stochastic programming-based modeling environment as well as the evolutionary optimization environment are ideally suited for an integration of various types of probabilistic constraints. We show an approach on how to integrate these constraints. Numerical results using recent financial data substantiate the applicability of the presented approach

A memetic algorithm for cardinality-constrained portfolio optimization with transaction costs

This is the author’s version of a work that was accepted for publication in Applied Soft Computing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Applied Soft Computing, Vol 36 (2015) DOI 10.1016/j.asoc.2015.06.053A memetic approach that combines a genetic algorithm (GA) and quadratic programming is used to address the problem of optimal portfolio selection with cardinality constraints and piecewise linear transaction costs. The framework used is an extension of the standard Markowitz mean–variance model that incorporates realistic constraints, such as upper and lower bounds for investment in individual assets and/or groups of assets, and minimum trading restrictions. The inclusion of constraints that limit the number of assets in the final portfolio and piecewise linear transaction costs transforms the selection of optimal portfolios into a mixed-integer quadratic problem, which cannot be solved by standard optimization techniques. We propose to use a genetic algorithm in which the candidate portfolios are encoded using a set representation to handle the combinatorial aspect of the optimization problem. Besides specifying which assets are included in the portfolio, this representation includes attributes that encode the trading operation (sell/hold/buy) performed when the portfolio is rebalanced. The results of this hybrid method are benchmarked against a range of investment strategies (passive management, the equally weighted portfolio, the minimum variance portfolio, optimal portfolios without cardinality constraints, ignoring transaction costs or obtained with L1 regularization) using publicly available data. The transaction costs and the cardinality constraints provide regularization mechanisms that generally improve the out-of-sample performance of the selected portfolios

Hybrid Representations for Composition Optimization and Parallelizing MOEAs

We present a hybrid EA representation suitable to optimize composition optimization problems ranging from optimizing recipes for catalytic materials to cardinality constrained portfolio selection. On several problem instances we can show that this new representation performs better than standard repair mechanisms with Lamarckism. Additionally, we investigate the a clustering based parallelization scheme for MOEAs. We prove that typical "divide and conquer\u27\u27 approaches are not suitable for the standard test functions like ZDT 1-6. Therefore, we suggest a new test function based on the portfolio selection problem and prove the feasibility of "divide and conquer\u27\u27 approaches on this test function

A survey on portfolio optimisation with metaheuristics.

A portfolio optimisation problem involves allocation of investment to a number of different assets to maximize return and minimize risk in a given investment period. The selected assets in a portfolio not only collectively contribute to its return but also interactively define its risk as usually measured by a portfolio variance. This presents a combinatorial optimisation problem that involves selection of both a number of assets as well as its quantity (weight or proportion or units). The problem is extremely complex due to a large number of selectable assets. Furthermore, the problem is dynamic and stochastic in nature with a number of constraints presenting a complex model which is difficult to solve for exact solution. In the last decade research publications have reported the applications of metaheuristic-based optimisation methods with some success., This paper presents a review of these reported models, optimisation problem formulations and metaheuristic approaches for portfolio optimisation

An intelligent manufacturing system for heat treatment scheduling

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This research is focused on the integration problem of process planning and scheduling in steel heat treatment operations environment using artificial intelligent techniques that are capable of dealing with such problems. This work addresses the issues involved in developing a suitable methodology for scheduling heat treatment operations of steel. Several intelligent algorithms have been developed for these propose namely, Genetic Algorithm (GA), Sexual Genetic Algorithm (SGA), Genetic Algorithm with Chromosome differentiation (GACD), Age Genetic Algorithm (AGA), and Mimetic Genetic Algorithm (MGA). These algorithms have been employed to develop an efficient intelligent algorithm using Algorithm Portfolio methodology. After that all the algorithms have been tested on two types of scheduling benchmarks. To apply these algorithms on heat treatment scheduling, a furnace model is developed for optimisation proposes. Furthermore, a system that is capable of selecting the optimal heat treatment regime is developed so the required metal properties can be achieved with the least energy consumption and the shortest time using Neuro-Fuzzy (NF) and Particle Swarm Optimisation (PSO) methodologies. Based on this system, PSO is used to optimise the heat treatment process by selecting different heat treatment conditions. The selected conditions are evaluated so the best selection can be identified. This work addresses the issues involved in developing a suitable methodology for developing an NF system and PSO for mechanical properties of the steel. Using the optimisers, furnace model and heat treatment system model, the intelligent system model is developed and implemented successfully. The results of this system were exciting and the optimisers were working correctly

Multiobjective Algorithms with Resampling for Portfolio Optimization

Constrained financial portfolio optimization is a challenging domain where the use of multiobjective evolutionary algorithms has been thriving over the last few years. One of the major issues related to this problem is the dependence of the results on a set of parameters. Given the nature of financial prediction, these figures are often inaccurate, which results in unreliable estimates for the efficient frontier. In this paper we introduce a resampling mechanism that deals with uncertainty in the parameters and results in efficient frontiers that are more robust. We test this idea on real data using four multiobjective optimization algorithms (NSGA-II, GDE3, SMPSO and SPEA2). The results show that resampling significantly increases the reliability of the resulting portfolios

Flexible resources allocation techniques: characteristics and modelling

At the interface between engineering, economics, social sciences and humanities, industrial engineering aims to provide answers to various sectors of business problems. One of these problems is the adjustment between the workload needed by the work to be realised and the availability of the company resources. The objective of this work is to help to find a methodology for the allocation of flexible human resources in industrial activities planning and scheduling. This model takes into account two levers of flexibility, one related to the working time modulation, and the other to the varieties of tasks that can be performed by a given resource (multi–skilled actor). On the one hand, multi–skilled actors will help to guide the various choices of the allocation to appreciate the impact of these choices on the tasks durations. On the other hand, the working time modulation that allows actors to have a work planning varying according to the workload which the company has to face

An Evolutionary Optimization Approach to Risk Parity Portfolio Selection

In this paper we present an evolutionary optimization approach to solve the risk parity portfolio selection problem. While there exist convex optimization approaches to solve this problem when long-only portfolios are considered, the optimization problem becomes non-trivial in the long-short case. To solve this problem, we propose a genetic algorithm as well as a local search heuristic. This algorithmic framework is able to compute solutions successfully. Numerical results using real-world data substantiate the practicability of the approach presented in this paper