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IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Chen, Yi Zhou, Aimin East China Normal Univ Shanghai Key Lab Multidimens Informat Proc Dept Comp Sci & Technol 3663 North Zhongshan Rd Shanghai Peoples R China

ISBN: (纸本)9781728121536

portfolio optimization is an important financial problem, which involves an optimal allocation of finite capital to a series of assets to achieve an acceptable trade-off between profit and risk in a given investment period. In this paper, an extended Markowitz's mean-variance portfolio optimization model, which is converted as a constrained multi-objective problem, is studied. Since this model involves both discrete and continuous variables, a multi-dimensional mapping coding scheme (MDM) has been adopted to convert discrete variables to continuous ones. Although the basic MDM is effective for dealing with the constrained multi-objective portfolio optimization problems, it sometimes prefers to choose some balanced investments, in which the allocation of funds for each selected asset is very similar. This may result in a focus on the low-risk and low-yield solutions. To solve this problem, an improved multi-dimensional mapping coding scheme is proposed in this paper. This new coding scheme is integrated into the decomposition based multi-objective evolutionary algorithm (MOEA/D). The algorithm is then applied to some test data, with the asset size ranging from 31 to 255, and the experimental results have indicated that the improved MDM coding scheme can significantly improve the performance comparing to the basic MDM coding scheme.

关键词： MOEA/D multi-objective portfolio optimization coding scheme

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Solving multi-objective portfolio optimization problem using invasive weed optimization

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SWARM AND EVOLUTIONARY COMPUTATION 2016年 28卷 42-57页

作者： Pouya, Amir Rezaei Solimanpur, Maghsud Rezaee, Mustafa Jahangoshai Urmia Univ Technol Fac Ind Engn Orumiyeh Iran Urmia Univ Fac Engn Orumiyeh Iran

portfolio optimization is one of the important issues for effective and economic investment. There is plenty of research in the literature addressing this issue. Most of these pieces of research attempt to make the Markowitz's primary portfolio selection model more realistic or seek to solve the model for obtaining fairly optimum portfolios. In this paper, PIE criterion and Experts' Recommendations on Market Sectors have been added to the primary Markowitz mean-variance model as two objectives. The P/E ratio is one of the important criteria for investment in the stock market, which captures the current expectations of the market activists about different companies. Experts' Recommendations for different Market Sectors, on the other hand, captures the experts' predictions about the future of the stock market. There are many solving methods for the portfolio optimization problem, but almost none of them investigates Invasive Weed optimization algorithm (IWO). In this research, the proposed multi-objective portfolio selection model has been transformed into a single-objective programming model using fuzzy normalization and uniform design method. Some guidelines are given for parameter setting in the proposed IWO algorithm. The model is then applied to monthly data of top 50 companies of Tehran Stock Exchange Market in 2013. The proposed model is then solved by three methods: (1) the proposed IWO algorithm, (2) the Particle Swarm optimization algorithm (PSO), and (3) the Reduced Gradient Method (RGM). The non-dominated solutions of these algorithms are compared with each other using Data Envelopment Analysis (DEA). According to the comparisons, it can be concluded that IWO and PSO algorithms have the same performance in most important criteria, but IWO algorithm has better solving time than PSO algorithm and better performance in dominating inefficient solutions, and PSO algorithm has better results in total violation of constraints. (C) 2016 Elsevier B.V. All rights re

关键词： multi-objective portfolio optimization Invasive weed optimization Particle swarm optimization Reduced gradient method Data envelopment analysis Uniform design method

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multi-objective and Financial portfolio optimization of p-Persistent Carrier Sense multiple Access Protocols with multi-Packet Reception 30

Multi-Objective and Financial Portfolio Optimization of <i>p...

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30th Euro Mini-Conference on optimization in the Natural Sciences (EmC-ONS)

作者： Samano-Robles, Ramiro Gameiro, Atilio Polytech Inst Porto CISTER INESC TEC ISEP Oporto Portugal Inst Telecomunicacoes P-3810193 Aveiro Portugal

ISBN: (纸本)9783319203522;9783319203515

This paper revisits the study of wireless carrier-sense multiple access (CSMA) protocols enabled with multi-packet reception (MPR) capabilities. This study employs a new paradigm in the literature of random access based on multi-objective and financial portfolio optimization tools. Under this new optimization framework, each packet transmission is regarded not only as a network resource, but also as a financial asset with different values of return and risk (or variance of the return). The objective of this network-financial optimization is to find the transmission policy that simultaneously optimizes network metrics (such as throughput and efficient power consumption), as well as economic metrics (such as fairness, return and risk). Two transmission models are considered for performance evaluation: a Bernoulli transmission model that facilitates analytic derivations, and a Markov model that considers the backlog states of the network and that facilitates dynamic stability analysis. This work is focused on the characterization of the boundary (envelope) or the Pareto optimal frontier of different types of trade-off performance region. These regions include the conventional throughput and stability regions, as well as new trade-off regions such as sum-throughput vs. fairness, sum-throughput vs. power consumption, and return vs. risk. Fairness is evaluated by means of the Gini-index, which is used in the field of economics to measure population income inequality. Transmit power is directly linked to the global transmission attempt rate. In scenarios with weak MPR capabilities, the system has problems in achieving simultaneously good values of fairness and high values of sum-throughput. This is because of an underlying non-convex throughput region which is typical of protocols dominated by unresolvable collisions. On the contrary, in scenarios with strong MPR capabilities, good fairness, higher energy consumption efficiency, and high sum-throughput performances can be

关键词： S-ALOHA Random access multi-objective portfolio optimization Pareto optimal trade-off curve

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multi-objective portfolio optimization for Spectrum Selection in Cognitive Radio Systems

Multi-Objective Portfolio Optimization for Spectrum Selectio...

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77th IEEE Vehicular Technology Conference (VTC)

作者： Samano-Robles, Ramiro Gameiro, Atilio Inst Telecommun P-3810193 Aveiro Portugal

ISBN: (纸本)9781467363372

The use of cognitive radio technology will allow for adaptive access to licensed and unlicensed portions of the spectrum. This, in turn, will improve end-user satisfaction and will partially alleviate the bandwidth scarcity problem. However, different parts of the spectrum not only have different network (e.g. radio and load) conditions, but also different licensing and/or billing agreements. Therefore, in order for the operators to obtain the major profit and the most efficient use of radio resources, their allocation must now target both technical and economic performance metrics. This problem can be conveniently formulated as a multi-objective portfolio optimization problem. This paper aims to adapt the concepts behind multi-objective portfolio optimization, commonly used in the areas of economics and finance theory, to the particular case of spectrum selection in cognitive radio. The idea is to investigate the advantages and potential interactions between technical and economic approaches. This paper proposes a framework for such coexistence and provides simulation results considering the Pareto optimal trade-off region obtained when using a multi-objective function based on the average return and risk per-Hz of allocated spectrum. The results show that network and economic metrics can be conveniently maximized inside the Pareto optimal trade-off region given an appropriate balance between average return and the allowed risk of primary and secondary transmissions, where risk is directly associated with the interference created from secondary to primary users.

关键词： Cognitive radio multi-objective portfolio optimization spectrum aggregation/selection

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Intelligent ESG portfolio optimization: A multi-objective AI-driven framework for sustainable investments in the Indian stock market

Sustainable Futures

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Sustainable Futures 2025年 9卷

作者： Apurv Gaurav Kripamay Baishnab Piyush Kumar Singh Mechanical Engineering Department Indian Institute of Technology Kharagpur West Bengal 721302 India Centre for Rural Development and Innovative Sustainable Technologies Indian Institute of Technology Kharagpur West Bengal 721302 India

Environmental, Social, and Governance (ESG) portfolio management impacts the investor in Sustainable and Socially Responsible Investments (SRIs). However, existing frameworks often fail to integrate artificial intelligence (AI) to optimize ESG portfolios, particularly in the Indian stock market. They are lacking in terms of achieving higher efficiency in predictive returns and less efficient in integrating ESG scores with portfolio optimization and subsequently rebalancing the optimum portfolio returns. Hence, this study proposes a three-stage AI-driven framework to effectively manage ESG portfolios while maximizing predictive accuracy and investment efficiency. It demonstrates on 30 randomly selected ESG-ranked Indian stocks from diverse sets, and simulates a retail portfolio scenario by leveraging advanced machine learning and optimization techniques. In first stage, a multivariate Bidirectional Long Short-Term Memory (MBi-LSTM) network is utilized to enhance return prediction accuracy, capturing the market’s nonlinear dynamics. In the second stage, portfolio allocation is optimized by balancing risk, return, and ESG scores using the Non-dominated Sorting Genetic (NSG) algorithm. In the final stage, dynamic portfolio rebalancing is performed using the Actor-Critic Reinforcement Learning (RL) algorithm by integrating ESG scores. For predictive modeling, MBi-LSTM showed the lowest errors in all the parameters. The NSG-enabled portfolio allocation method achieved 68.58 % higher similarity to actual returns than the Mean-Variance Markowitz (MVM) model while maximizing the ESG-Sortino Ratio. In portfolio rebalancing, it outperformed traditional agent-based methods, with returns higher by 120 %, 235 %, and 101 % in average, maximum, and minimum returns, respectively. The effectiveness of the demonstrated multi-objective AI-driven framework is showing in every stage validates against the conventional evaluation metrics, showcasing its superior performance and practical v

关键词： multi-objective portfolio optimization Predictive modeling ESG score Dynamic rebalancing Actor-critic reinforcement learning, Indian stock market G11 G12 G170 G410 O310 O320 O350

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Dry bulk shipping investments: shaping investor preferences and risk-return balance

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MARITIME ECONOMICS & LOGISTICS 2025年 1-19页

作者： Chen, Shuiyang Haralambides, Hercules Qu, Chenrui Meng, Bin Kuang, Haibo Dalian Maritime Univ Sch Maritime Econ & Management 1 Linghai Rd Dalian Liaoning Peoples R China Dalian Maritime Univ Collaborat Innovat Ctr Transport Studies Dalian Peoples R China Natl & Kapodistrian Univ Athens Athens Greece

Dry bulk shipping plays an irreplaceable role in global trade but investments in the sector are characterized by high risk and uncertainty. This paper-a sequel to our similar earlier work on tanker investments-develops a multi-objective portfolio optimization model based on stable distributions and investor preferences, referred to as the Mean-VaR-Scale-Skewness-Shannon Entropy-Gini Index (MVSS-SG) framework. The objective is to maximize returns while controlling for risk, according to investor risk preferences. We look into investments in the three most representative types of bulk carriers, i.e., the Capesize, Panamax, and Supramax ships. To shape investor preferences and their risk-return balance, the model considers five objectives: returns, risk, scale, skewness, and diversification. All indices display the typical characteristics of financial time series, with sharp peaks and heavy tails. Key findings show that constraints based on skewness and entropy improve returns, with Shannon entropy having a stronger impact on them, and the Gini index being better in mitigating risk. Sensitivity analysis shows that higher entropy preferences improve significantly both returns and diversification. The model achieves substantial risk reduction for Capesize vessels (up to 91%) and performs well in terms of cumulative returns, ranging from 526.64 to 736.96% over the 2005-2024 period. We also observe significant declines in cumulative returns during five global events-the 2009 financial crisis, Brexit, the China-US trade war, the COVID-19 pandemic, and the Russia-Ukraine war. This research contributes to the theory and practice of bulk carrier investments by offering an adaptive and customizable approach that integrates risk, returns, and investor preferences. This supports more informed investment decisions in dynamic market conditions.

关键词： Dry bulk shipping Shipping investment Risk management multi-objective portfolio optimization Stable distributions Investor profiles

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Credibilistic portfolio optimization with higher-order moments using coherent triangular fuzzy numbers

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APPLIED SOFT COMPUTING 2024年 151卷

作者： Mandal, Pawan Kumar Thakur, Manoj Mittal, Garima Indian Inst Technol Mandi Mandi 175075 Himachal Prades India Indian Inst Management Lucknow Lucknow 226013 Uttar Pradesh India

Financial portfolio formation is usually a multi-objective decision-making problem concerning return and risk on the investment. In this study, we make use of an extension of regular triangular fuzzy numbers, known as coherent triangular fuzzy numbers, to describe portfolio returns in a credibility-based framework. The paper's novelty lies in proposing and deriving a crisp equivalent for computing the coherent triangular fuzzy number-based credibilistic semivariance, credibilistic skewness, and credibilistic semikurtosis. Utilizing these analytical expressions, along with additional formulations found in existing literature, we present three multi -objective portfolio optimization models involving the practical constraints related to investment decisions. All the proposed analytical expressions, when used with the returns of the portfolio as a whole, help to overcome the computationally expensive process of simulating results using the returns of individual assets. The proposed models differ with respect to different risk measures, viz. semivariance, Mean-Absolute-Semi -Deviation (MASD), and Conditional Value-at-Risk (CVaR). These models are solved using an adaptation of an efficient multi-objective Genetic Algorithm (MOGA) specifically designed to solve portfolio optimization problems with practical constraints. Data from the National Stock Exchange (NSE) in Mumbai, India and the New York Stock Exchange (NYSE) in New York, USA, are used to demonstrate the effectiveness of the proposed portfolio optimization models and solution methodology. All the proposed models are compared with respect to each other and the benchmarks considered in this study to bring out the performance stability.

关键词： Coherent triangular fuzzy numbers Higher-order moments multi-objective portfolio optimization multi-objective genetic algorithm (MOGA) K-medoids clustering

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multi-objective heuristic algorithms for practical portfolio optimization and rebalancing with transaction cost

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APPLIED SOFT COMPUTING 2018年 67卷 865-894页

作者： Meghwani, Suraj S. Thakur, Manoj Indian Inst Technol Mandi Suran Himachal Prades India

portfolio optimization is the process of allocating capital among a universe of assets to achieve better risk-return trade-off. Due to the dynamic nature of financial markets, the portfolio needs to be rebalanced to retain the desired risk-return characteristics. The process of rebalancing requires buying or selling of assets that incur transaction costs. This study proposes a tri-objective portfolio optimization model with risk, return and transaction cost as the objectives. Various practical constraints like cardinality, self-financing, quantity, pre-assignment and cost related constraints are included in the proposed model. Three popular risk measures namely variance, Value-At-Risk (VaR) and Conditional Value-At-Risk (CVaR) are studied in the proposed work. The emphasis of the study is on handling equality constraints like self-financing constraint and the constraints arising from the inclusion of transaction cost models using multi-objective evolutionary algorithms (MOEAs). A novel repair algorithm is proposed that can effectively handle equality constraints without any requirement of any constraint handling technique. The proposed repair algorithm is suitable for a larger class of separable transaction cost model. The theoretical proof is given to ensure the validity of our claim. To verify the effectiveness of the proposed approach three algorithms from different multi-objective evolutionary frameworks are adapted and compared. In empirical study, we discuss the performances of algorithms over both in-sample and out-sample data. (C) 2017 Elsevier B.V. All rights reserved.

关键词： multi-objective portfolio optimization Cardinality constrained portfolio problem Transaction cost Repair mechanism portfolio rebalancing Value-At-Risk Conditional Value-At-Risk

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multi-objective Beetle Swarmoptimization for portfolio Selection 1

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15th International Conference on Knowledge Science, Engineering, and Management (KSEM)

作者： Yan, Tan Sichuan Univ Coll Comp Sci Chengdu Peoples R China

ISBN: (数字)9783031109867

ISBN: (纸本)9783031109867;9783031109850

The multi-objective portfolio optimization is regarded as an multi-objective optimization problem which is complicated and hard to find a satisfactory solution in a limited time. It is more complex and difficult to use the conventional method to solve this problem. In this paper, we first establish a mean-CVaR-entropy model with transaction costs and investment weight restrictions, and then propose a multi-objective optimization Algorithm for BeetleSearch(MOBSO), a meta-heuristic optimization algorithm, and a variant of Beetle Antennae Search (BAS) algorithm, which is applied into portfolio optimization to solve this constraint multi-objective optimization problem. Finally, we use the 20 stocksfrom January 2017 to December 2021 in the US stock market to do case studyand compare the results with other meta-heuristic optimization algorithms. It shows that the MOBSO outperforms swarm algorithms such as the particle swarm optimization (PSO) and the genetic algorithm(GA).

关键词： multi-objective portfolio optimization Beetle Antennae Search (BAS) Particle Swarm optimization (PSO) Meta-heuristic optimization Finance problem

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Joint Spectrum Selection and Radio Resource Management based on multi-objective portfolio optimization for Cognitive Radio Networks

Joint Spectrum Selection and Radio Resource Management based...

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1st International Conference on Future Generation Communication Technologies (FGCT)

作者： Samano-Robles, Ramiro Gameiro, Atilio Inst Telecomunicacoes P-3810193 Aveiro Portugal

ISBN: (纸本)9781467358613;9781467358590

The advent of cognitive radio technology will enable user terminals with adaptive access to licensed and unlicensed portions of the spectrum. This will improve end-user satisfaction and will partially alleviate the bandwidth scarcity problem. However, different parts of the spectrum not only have different network (e.g. radio and load) conditions, but also different licensing and/or billing agreements. Therefore, in order for the operators to obtain the major profit and the most efficient use of radio resources, their allocation must now target both technical and economic performance metrics. This problem can be conveniently formulated as a multi-objective portfolio optimization problem, which has been studied in detail in the fields of economics and finance theory. This paper presents a joint spectrum selection and radio resource management algorithm for cognitive radio networks based on the optimization of a multi-objective portfolio function based on return and risk values per bit/second/Hz of transmitted information in primary and secondary frequency bands. Our approach intends to obtain the most convenient use of radio resources that simultaneously maximizes network and economic performance indicators. System-level simulation results suggest that an optimum throughput solution can be reached inside the Pareto optimal trade-off performance region of the problem given an appropriate trade-off between risk and return objective functions.

关键词： Cognitive radio multi-objective portfolio optimization spectrum aggregation/selection

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