Evolutionary Multi-objective Portfolio Optimization in Practical Context

This paper addresses evolutionary multi-objective portfolio optimization in the practical context by incorporating realistic constraints into the problem model and preference criterion into the optimization search process. The former is essential to enhance the realism of the classical mean-variance model proposed by Harry Markowitz, since portfolio managers often face a number of realistic constraints arising from business and industry regulations, while the latter reflects the fact that portfolio managers are ultimately interested in specific regions or points along the efficient frontier during the actual execution of their investment orders. For the former, this paper proposes an order-based representation that can be easily extended to handle various realistic constraints like floor and ceiling constraints and cardinality constraint. An experimental study, based on benchmark problems obtained from the OR-library, demonstrates its capability to attain a better approximation of the efficient frontier in terms of proximity and diversity with respect to other conventional representations. The experimental results also illustrated its viability and practicality in handling the various realistic constraints. A simple strategy to incorporate preferences into the multi-objective optimization process is highlighted and the experimental study demonstrates its capability in driving the evolutionary search towards specific regions of the efficient frontier.

A Novel Smart Beta Optimization Based on Probabilistic Forecast

Rule-based portfolio construction strategies are rising as investmentdemand grows, and smart beta strategies are becoming a trend amonginstitutional investors. Smart beta strategies have high transparency, lowmanagement costs, and better long-term performance, but are at the risk ofsevere short-term declines due to a lack of Risk Control tools. Although thereare some methods to use historical volatility for Risk Control, it is still difficultto adapt to the rapid switch of market styles. How to strengthen the RiskControl management of the portfolio while maintaining the original advantagesof smart beta has become a new issue of concern in the industry. Thispaper demonstrates the scientific validity of using a probability prediction forposition optimization through an optimization theory and proposes a novelnatural gradient boosting (NGBoost)-based portfolio optimization method,which predicts stock prices and their probability distributions based on non-Bayesian methods and maximizes the Sharpe ratio expectation of positionoptimization. This paper validates the effectiveness and practicality of themodel by using the Chinese stock market, and the experimental results showthat the proposed method in this paper can reduce the volatility by 0.08 andincrease the expected portfolio cumulative return (reaching a maximum of67.1%) compared with the mainstream methods in the industry.

A novel hybrid algorithm based on a harmony search and artificial bee colony for solving a portfolio optimization problem using a mean-semi variance approach

Portfolio selection is one of the major capital allocation and budgeting issues in financial management, and a variety of models have been presented for optimal selection. Semi-variance is usually considered as a risk factor in drawing up an efficient frontier and the optimal portfolio. Since semi-variance offers a better estimation of the actual risk portfolio, it was used as a measure to approximate the risk of investment in this work. The optimal portfolio selection is one of the non-deterministic polynomial(NP)-hard problems that have not been presented in an exact algorithm, which can solve this problem in a polynomial time. Meta-heuristic algorithms are usually used to solve such problems. A novel hybrid harmony search and artificial bee colony algorithm and its application were introduced in order to draw efficient frontier portfolios. Computational results show that this algorithm is more successful than the harmony search method and genetic algorithm. In addition, it is more accurate in finding optimal solutions at all levels of risk and return.

A branch-and-bound algorithm for discrete multi-factor portfolio optimization model

In this paper, a new branch-and-bound algorithm based on the Lagrangian dual relaxation and continuous relaxation is proposed for discrete multi-factor portfolio selection model with roundlot restriction in financial optimization. This discrete portfolio model is of integer quadratic programming problems. The separable structure of the model is investigated by using Lagrangian relaxation and dual search. Computational results show that the algorithm is capable of solving real-world portfolio problems with data from US stock market and randomly generated test problems with up to 120 securities.

An improved portfolio optimization model for oil and gas investment selection

For oil company decision-makers,the principal concern is how to allocate their limited resources into the most valuable opportunities.Recently a new management philosophy,＂Beyond NPV＂,has received more and more international attention.Economists and senior executives are seeking effective alternative analysis approaches for traditional technical and economic evaluation methods.The improved portfolio optimization model presented in this article represents an applicable technique beyond NPV for doing capital budgeting.In this proposed model,not only can oil company executives achieve trade-offs between returns and risks to their risk tolerance,but they can also employ an ＂operational premium＂ to distinguish their ability to improve the performance of the underlying projects.A simulation study based on 19 overseas upstream assets owned by a large oil company in China is conducted to compare optimized utility with non-optimized utility.The simulation results show that the petroleum optimization model including ＂operational premium＂ is more in line with the rational investors＇ demand.

Take Bitcoin into your portfolio:a novel ensemble portfolio optimization framework for broad commodity assets

The emergence and growing popularity of Bitcoins have attracted the attention of the financial world.However,few empirical studies have considered the inclusion of the newly emerged commodity asset in the global commodity market.It is of great importance for investors and policymakers to take advantage of this asset and its potential benefits by incorporating it as a part of the broad commodity trading portfolio.In this study,we propose a novel ensemble portfolio optimization(NEPO)framework utilized for broad commodity assets,which integrates a hybrid variational mode decomposition-bidirectional long short-term memory deep learning model for future returns forecast and a reinforcement learning-based model for optimizing the asset weight allocation.Our empirical results indicate that the NEPO framework could effectively improve the prediction accuracy and trend prediction ability across various commodity assets from different sectors.In addition,it could effectively incorporate Bitcoins into the asset pool and achieve better financial performance compared to traditional asset allocation strategies,commodity funds,and indices.

Portfolio optimization by improved NSGA-II and SPEA 2 based on different risk measures

In this study,we analyze three portfolio selection strategies for loss-averse investors:semi-variance,conditional value-at-risk,and a combination of both risk measures.Moreover,we propose a novel version of the non-dominated sorting genetic algorithm II and of the strength Pareto evolutionary algorithm 2 to tackle this optimization problem.The effectiveness of these algorithms is compared with two alternatives from the literature from five publicly available datasets.The computational results indicate that the proposed algorithms in this study outperform the others for all the examined performance metrics.Moreover,they are able to approximate the Pareto front even in cases in which all the other approaches fail.

The Constrained Mean-Semivariance Portfolio Optimization Problem with the Support of a Novel Multiobjective Evolutionary Algorithm

The paper addresses the constrained mean-semivariance portfolio optimization problem with the support of a novel multi-objective evolutionary algorithm (n-MOEA). The use of semivariance as the risk quantification measure and the real world constraints imposed to the model make the problem difficult to be solved with exact methods. Thanks to the exploratory mechanism, n-MOEA concentrates the search effort where is needed more and provides a well formed efficient frontier with the solutions spread across the whole frontier. We also provide evidence for the robustness of the produced non-dominated solutions by carrying out, out-of-sample testing during both bull and bear market conditions on FTSE-100.

Portfolio optimization of credit risky bonds: a semi-Markov process approach

This article presents a semi-Markov process based approach to optimally select a portfolio consisting of credit risky bonds.The criteria to optimize the credit portfolio is based on l_(∞)-norm risk measure and the proposed optimization model is formulated as a linear programming problem.The input parameters to the optimization model are rate of returns of bonds which are obtained using credit ratings assuming that credit ratings of bonds follow a semi-Markov process.Modeling credit ratings by semi-Markov processes has several advantages over Markov chain models,i.e.,it addresses the ageing effect present in the credit rating dynamics.The transition probability matrices generated by semi-Markov process and initial credit ratings are used to generate rate of returns of bonds.The empirical performance of the proposed model is analyzed using the real data.Further,comparison of the proposed approach with the Markov chain approach is performed by obtaining the efficient frontiers for the two models.

Matrix decomposition and Lagrangian dual method for discrete portfolio optimization under concave transaction costs

In this paper, the discrete mean-variance model is considered for portfolio selection under concave transaction costs. By using the Cholesky decomposition technique, the convariance matrix to obtain a separable mixed integer nonlinear optimization problem is decomposed. A brand-and-bound algorithm based on Lagrangian relaxation is then proposed. Computational results are reported for test problems with the data randomly generated and those from the US stock market.

Application of Interval Valued Fuzzy Linear Programming for Stock Portfolio Optimization

In this paper, based on existing results, decision making about portfolio investment schemes is discussed, ordering method of fuzzy numbers of interval value is shown, corresponding auxiliary models are established and solutions are provided with theories of fuzzy mathematics, optimization theory and numerical calculation, etc. Then it applies software programming to solve the portfolio investment situation between investors in savings and four securities according to the established models. The result shows that investors can choose the risk coefficient that they can bear to reach the maximum value of expected returns. The greater the risk coefficient, the greater the income, the smaller the risk coefficient and the smaller the income. Investors can determine their own portfolio strategy according to their own conditions in order to meet their own interests.

An Alternative Method of Stochastic Optimization: The Portfolio Model

We provide a new simple approach to stochastic dynamic optimization. In doing so, we derive the existing (standard) results using a far simpler technique than the duality and the variational methods.

Stressed portfolio optimization with semiparametric method

Tail risk is a classic topic in stressed portfolio optimization to treat unprecedented risks,while the traditional mean–variance approach may fail to perform well.This study proposes an innovative semiparametric method consisting of two modeling components:the nonparametric estimation and copula method for each marginal distribution of the portfolio and their joint distribution,respectively.We then focus on the optimal weights of the stressed portfolio and its optimal scale beyond the Gaussian restriction.Empirical studies include statistical estimation for the semiparametric method,risk measure minimization for optimal weights,and value measure maximization for the optimal scale to enlarge the investment.From the outputs of short-term and long-term data analysis,optimal stressed portfolios demonstrate the advantages of model flexibility to account for tail risk over the traditional mean–variance method.

Optimization of Pension Asset Portfolio in Nigeria with Contributors’ Specified Return Rate

This work focuses on the optimization of investment contributions of pension asset with a view to improving contributors’ participation in achieving better return on investment (RoI) of their funds. We viewed some new regulations on Nigeria’s Contributory Pension Scheme” (CPS) from amended legislation of 2014, some of which are yet to be implemented when their regulations are approved. A mathematical model involving 5 variables, 5 inequality constraints covering regulatory limitations and limitation on scarce resource known as Asset Under Management (AUM), suggested and mathematically shown to be possible through “maximization of return irrespective of risk” while obeying all regulatory controls as our constraints optimized. Optimized portfolio using MatLab shows that the portfolio representing AES 2013 portfolio with a deficit growth of 15.75 m representing 3.27% less than the portfolio’s full growth potential within defined assumptions would have been averted if contributors actually set their targets and investment managers optimize from forecasts of future prices using trend analysis.

A Decision Support System for Energy Trading and Portfolio Optimization

In the new competitive environment of the electricity market, risk analysis is a powerful tool to guide investors under both contract uncertainties and energy prices of the spot market. Moreover, simulation of spot price scenarios and evaluation of energy contracts performance, are also necessary to the decision maker, and in particular to the trader to foresee opportunities and possible threats in the trading activity. In this context, computational systems that allow what-if analysis, involving simulation of spot price, contract portfolio optimization and risk evaluation are rather important. This paper proposes a decision support system not only for solving the problem of contracts portfolio optimization, by using linear programming, but also to execute risks analysis of the contracts portfolio performance, with VaR and CVaR metrics. Realistic tests have demonstrated the efficiency of this system.

OPTIMAL CONTROL OF MARKOVIAN SWITCHING SYSTEMS WITH APPLICATIONS TO PORTFOLIO DECISIONS UNDER INFLATION

This article is concerned with a class of control systems with Markovian switching, in which an It5 formula for Markov-modulated processes is derived. Moreover, an optimal control law satisfying the generalized Hamilton-Jacobi-Bellman （HJB） equation with Markovian switching is characterized. Then, through the generalized HJB equation, we study an optimal consumption and portfolio problem with the financial markets of Markovian switching and inflation. Thus, we deduce the optimal policies and show that a modified Mutual Fund Theorem consisting of three funds holds. Finally, for the CRRA utility function, we explicitly give the optimal consumption and portfolio policies. Numerical examples are included to illustrate the obtained results.

Portfolio optimization based on network centralities:Which centrality is better for asset selection during global crises?

We construct correlation-based networks linking 86 assets(stock indices,bond indices,foreign exchange rates,commodity futures,and cryptocurrencies)and analyze the impact of asset selection on portfolio optimization using different centrality measures(including degree,eigenvector,eccentricity,betweenness,PageRank,and hybrid centralities).In times of a global crisis,peripheral assets located in cross-market networks are more suitable for investment.By comparing portfolio performance based on different centrality measures,we find that(i)hybrid,eigenvector,and PageRank centralities can best improve portfolio performance;(ii)degree centrality is suitable for larger portfolios;and(iii)eccentricity and betweenness centralities are unsuitable for network optimization portfolios.In response,we explain them based on the construction principle of centrality measures.Additionally,our optimal portfolios suggest that investors pay more attention to the role of emerging countries,which are less exposed to external shocks and whose financial markets are more likely to remain stable.

A DIRECT METHOD IN OPTIMAL PORTFOLIO AND CONSUMPTION CHOICE

In this paper we use a direct method to solve the optimal portfolio andconsumption choice problem in the security market for a specific case,in which theutility function is of a given homogenous form, i.e. the so-called CRRA case. The ideacomes from the completion technique ever used in LQ optimal control.

IMPROVED GENETIC ALGORITHM TO OPTIMAL PORTFOLIO WITH RISK CONTROL

A modified model of optimal investment port folio in a random market with risk constraints is presented. An improved genetic algorithm (GA) is proposed to solve this nonlinear optimal problem. The numerical simulation of a large-scale investment combination for Shanghai stock market shows that GA has the advantage of faster convergence and wider adaptability than traditional optimization algorithm. This result alsodemonstrates that the improved GA performs better than the basic GA.