Konstantinos Metaxiotis

Multiobjective Evolutionary Algorithms for Portfolio Management: A comprehensive literature review

In this paper we provide a review of the current state of research on Portfolio Management with the support of Multiobjective Evolutionary Algorithms (MOEAs). Second we present a methodological framework for conducting a comprehensive literature review on the Multiobjective Evolutionary Algorithms (MOEAs) for the Portfolio Management. Third, we use this framework to gain an understanding of the current state of the MOEAs for the Portfolio Management research field and fourth, based on the literature review, we identify areas of concern with regard to MOEAs for the Portfolio Management research field.

A new Probe Guided Mutation operator and its application for solving the cardinality constrained portfolio optimization problem

Abstract This paper revisits the classical Polynomial Mutation (PLM) operator and proposes a new probe guided version of the PLM operator designed to be used in conjunction with Multiobjective Evolutionary Algorithms (MOEAs). The proposed Probe Guided Mutation (PGM) operator is validated by using data sets from six different stock markets. The performance of the proposed PGM operator is assessed in comparison with the one of the classical PLM with the assistance of the Non-dominated Sorting Genetic Algorithm II (NSGAII) and the Strength Pareto Evolutionary Algorithm 2 (SPEA2). The evaluation of the performance is based on three performance metrics, namely Hypervolume, Spread and Epsilon indicator. The experimental results reveal that the proposed PGM operator outperforms with confidence the performance of the classical PLM operator for all performance metrics when applied to the solution of the cardinality constrained portfolio optimization problem (CCPOP). We also calculate the True Efficient Frontier (TEF) of the CCPOP by formulating the CCPOP as a Mixed Integer Quadratic Program (MIQP) and we compare the relevant results with the approximate efficient frontiers that are generated by the proposed PGM operator. The results confirm that the PGM operator generates near optimal solutions that lie very close or in certain cases overlap with the TEF.

Efficient Portfolio Construction with the Use of Multiobjective Evolutionary Algorithms: Best Practices and Performance Metrics

This paper provides a systematic study of the technologies and algorithms associated with the implementation of multiobjective evolutionary algorithms (MOEAs) for the solution of the portfolio optimization problem. Based on the examination of the state-of-the art we provide the best practices for dealing with the complexities of the constrained portfolio optimization problem (CPOP). In particular, rigorous algorithmic and technical treatment is provided for the efficient incorporation of a wide range of real-world constraints into the MOEAs. Moreover, we address special configuration issues related to the application of MOEAs for solving the CPOP. Finally, by examining the state-of-the-art we identify the most appropriate performance metrics for the evaluation of the relevant results from the implementation of the MOEAs to the solution of the CPOP.

An Elitist Polynomial Mutation Operator for Improved Performance of MOEAs in Computer Networks

Polynomial mutation has been utilized in evolutionary optimization algorithms as a variation operator. In previous work on the use of evolutionary algorithms for solving multiobjective problems, two versions of polynomial mutations were introduced. In this study we will examine the latest version of polynomial mutation, the highly disruptive, which has been utilised in the latest version of NSGA-II. This paper proposes an elitist version of the highly disruptive polynomial mutation. The experimental results show that the proposed elitist polynomial mutation outperforms the existing mutation mechanism when applied in a well known evolutionary multiobjective algorithm (NSGA-II) in terms of hypervolume, spread of solutions and epsilon performance indicator.

A fitness guided mutation operator for improved performance of MOEAs

In this paper we present a new fitness guided version of the classical polynomial mutation operator. The experimental results show that the proposed fitness guided polynomial mutation (FGPLM) operator outperforms the classical polynomial mutation operator when applied in Non-dominated Sorting Genetic Algorithm II (NSGAII) in a number of performance measures that evaluate the proximity of the solutions to the Pareto front.

Examining the effect of different configuration issues of the multiobjective evolutionary algorithms on the efficient frontier formulation for the constrained portfolio optimization problem

This article examines the effect of different configuration issues of the Multiobjective Evolutionary Algorithms on the efficient frontier formulation for the constrained portfolio optimization problem. We present the most popular techniques for dealing with the complexities of the constrained portfolio optimization problem and experimentally analyse their strengths and weaknesses. In particular, we examine the efficient incorporation of complex real world constraints into the Multiobjective Evolutionary Algorithms and their corresponding effect on the efficient frontier formulation for the portfolio optimization problem. Moreover, we examine various constraint-handling approaches for the constrained portfolio optimization problem such as penalty functions and reparation operators and we draw conclusions about the efficacy of the examined approaches. We also examine the effect on the efficient frontier formulation by the application of different genetic operators and the relevant results are analysed. Finally, we address issues related with the various performance metrics that are applied for the evaluation of the derived solutions.

An Experimental Analysis of a New Interval-Based Mutation Operator

In this paper, we present a novel Interval-Based Mutation (IBMU) operator. The proposed mutation operator is performing coarse-grained search at initial stage in order to speed up convergence toward more promising regions of the search landscape. Then, more fine-grained search is performed in order to guide the solutions towards the Pareto front. Computational experiments indicate that the proposed mutation operator performs better than conventional approaches for solving several well-known benchmarking problems.

Handling the complexities of the multi-constrained portfolio optimization problem with the support of a novel MOEA

The incorporation of additional constraints to the basic mean–variance (MV) model adds realism to the model, but simultaneously makes the problem difficult to be solved with exact approaches. In this paper we address the challenges that have arisen by the multi-constrained portfolio optimization problem with the assistance of a novel specially engineered multi-objective evolutionary algorithm (MOEA). The proposed algorithm incorporates a new efficient representation scheme and specially designed mutation and recombination operators alongside with efficient algorithmic approaches for the correct incorporation of complex real-world constraints into the MV model. We test the algorithm’s performance in comparison with two well-known MOEAs by using a wide range of test problems up to 1317 stocks. For all examined cases the proposed algorithm outperforms the other two MOEAs in terms of performance and processing speed.

Supporting Business Decisions Through Standard Forecasting Practices: A Case Study in a Greek Tobacco Distribution Company

Sudden market changes have made traditional approaches on forecasting obsolete. Usually, managers put a great deal of trust into their own forecasting expertise, bypassing standard practices of forecasting. A leading company in Greek tobacco distribution market adopted successfully a new forecasting model that can produce accurate forecasts for a wide range of industrial processes, by simply including in the forecasting process a set of standard, well known but usually overlooked practices. The purpose of this paper is to introduce this simple forecasting model and to demonstrate the potential benefit from the adoption of some classic practices in the business forecasting process. The SLR model, which was initially used in the tobacco distribution company, was used as a benchmark in order to test the validity of our approach. The development of such models can provide a useful input to both marketing and operations planning in future.

The Current State of ERP Systems in Banking Sector

Abstract —Enterprise resource planning (ERP) systems integrate the organizations business functions allowing efficient information sharing across all business divisions. Through the information sharing is achieved not only better coordination but also faster and more efficient adjustment to the potential risks and business opportunities alike. This paper examines the particularities of ERP systems implementation and operation for the banking sector by considering a wide range of sources such as journal and conference papers, empirical studies and reports. Finally, through the thorough examination of the available literature, we draw conclusions about the effect by the implementation of ERP systems in the banking sector.