Article
Mathematics, Interdisciplinary Applications
Xianping Wu, Weiping Wu, Lin Yu
Summary: This paper studies the problem of multi-period mean-variance (MV) asset-liability portfolio management, where the portfolio is constructed by risky assets and liability. The impact of general correlation is taken into consideration, extending the classical multi-period MVAL models. The authors derive explicit portfolio policies and the MV efficient frontier for this problem, and provide a numerical example to illustrate the efficiency of the proposed solution scheme.
JOURNAL OF SYSTEMS SCIENCE & COMPLEXITY
(2023)
Article
Operations Research & Management Science
Xiangyu Cui, Jianjun Gao, Yun Shi
Summary: This paper explores the multi-period mean-variance portfolio optimization problem with proportional management fees, deriving the semi-analytical optimal portfolio policy using stochastic dynamic programming. The results help clarify the benefits and costs of adopting such dynamic portfolio policy with management fees.
OPERATIONAL RESEARCH
(2021)
Article
Mathematics
Francisco Fernandez-Navarro, Luisa Martinez-Nieto, Mariano Carbonero-Ruz, Teresa Montero-Romero
Summary: This paper introduces the mean-variance (MV) portfolio and mean squared variance (MSV) portfolio methods, and proposes a mixed-integer linear programming (MILP) reformation for the non-convex QP problem, as well as a data-driven method for determining the optimal value of the hyper-parameter. Empirical tests show that the MSV portfolio exhibits competitive performance in most problems.
Article
Engineering, Multidisciplinary
Zhongming Wu, Kexin Sun
Summary: A new distributionally robust mean-variance model is proposed in this study to solve the multi-period portfolio selection problem, utilizing the Wasserstein metric to capture the uncertainty of returns. The model is transformed into a tractable convex problem using duality theory, and the radius of the Wasserstein ball is estimated using a nonparametric bootstrap method. Analyzing the in-sample data indicates that the portfolio's return and risk are relatively insensitive to parameter values. A series of out-of-sample experiments demonstrate that the proposed model outperforms other models in terms of final wealth, standard deviation, and Sharpe ratio.
APPLIED MATHEMATICAL MODELLING
(2023)
Article
Computer Science, Artificial Intelligence
Javier Perez-Rodriguez, Francisco Fernandez-Navarro, Thomas Ashley
Summary: This paper presents a novel ensemble learning framework inspired by modern portfolio optimization to address regression problems. The framework considers the error predictions of the base learners, the variability in their predictions, and the covariance among their predictions' errors to determine the ensemble's weights. Four potential instantiations are provided, where the first two models have non-negativity constraints and are solved with the active set algorithm, while the second two models have convex quadratic programming problems and are solved by the Lagrangian procedure. Extensive experiments with regression datasets confirm that the ensemble framework yields the best overall performance compared to other state-of-the-art methods.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Operations Research & Management Science
Zhongming Wu, Guoyu Xie, Zhili Ge, Valentina De Simone
Summary: This paper explores optimal strategies for long-term investment by allocating wealth among a finite number of assets over multiple periods. A new portfolio optimization framework is developed based on the Markowitz mean-variance philosophy, which can produce sparse portfolios. The sparsity of the portfolio is characterized by nonconvex penalties at each period and across periods. A generalized alternating direction method of multipliers is proposed for the constructed nonconvex and nonsmooth constrained model, and its global convergence to a stationary point is theoretically guaranteed. Moreover, numerical experiments on practical datasets demonstrate the effectiveness and advantage of the proposed model and solving method.
ANNALS OF OPERATIONS RESEARCH
(2023)
Article
Computer Science, Software Engineering
Alex Dunbar, Saumya Sinha, Andrew J. Schaefer
Summary: This paper presents continuous, convex hull, and Lagrangian relaxations for multiobjective integer programs (MOIPs) and examines their relationships. It shows that the Lagrangian relaxation can provide tighter bounds than the convex hull relaxation. Additionally, it generalizes the integer programming value function to MOIPs and defines set-valued and vector-valued superadditive duals.
MATHEMATICAL PROGRAMMING
(2023)
Article
Management
N. Meade, J. E. Beasley, C. J. Adcock
Summary: In this study, a new methodology is proposed to identify the consistency region in the risk-expected return space, where ex-post performance matches ex-ante estimates. By improving the accuracy of ex-ante estimates using the developed Berkowitz statistic, we demonstrate the superior performance of investment strategies based on consistency rather than efficiency.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2021)
Article
Operations Research & Management Science
N. Dinh, M. A. Goberna, M. A. Lopez-Cerda, M. Volle
Summary: In this paper, a suitable Lagrangian-Haar dual problem for each convex optimization problem is introduced, which is associated with infinitely many constraints indexed by the set T and a given non-empty family H of finite subsets of T. necessary and sufficient conditions for H-reducibility are obtained. Special attention is given to linear optimization, both infinite and semi-infinite, as well as convex problems with a countable family of constraints. Results on zero H-duality gap and on H-(stable) strong duality are provided.
Article
Operations Research & Management Science
Fabian Flores-Bazan, Filip Thiele
Summary: This paper provides sufficient conditions for the lower semicontinuity of the value function at 0, under quasiconvexity assumptions on f and g(i). The paper also discusses the existence of points where the value is achieved and the implications of zero duality gap. The results presented in this paper offer more information and applicability compared to existing literature.
JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS
(2022)
Article
Computer Science, Interdisciplinary Applications
Justo Puerto, Federica Ricca, Moises Rodriguez-Madrena, Andrea Scozzari
Summary: This paper proposes a new filtering method based on Quadratic Programming to address the issues of the classical Markowitz Mean-Variance Optimization model in real financial datasets. Experimental analysis shows that this method outperforms Random Matrix Theory and Power Mapping strategy. Additionally, a heuristic procedure is proposed to solve the computational burden increase for large datasets effectively.
COMPUTERS & OPERATIONS RESEARCH
(2022)
Article
Thermodynamics
Yunlin Wu, Lei Huang, Hui Jiang
Summary: This article introduces a method for managing investment risk and returns in the Chinese new-energy market. The study constructs a comprehensive portfolio model that modifies covariance matrix estimation using a factor structure with latent factors and considers transaction costs. The empirical results show that this model outperforms other models in controlling risk and improving portfolio returns.
Article
Management
Nathan Phelps, Adam Metzler
Summary: The efficient frontier allows investors to maximize returns for a given risk level. Cardinality constrained efficient frontiers (CCEFs) impose an upper bound on the number of assets in the portfolio. A new algorithm was developed to find CCEFs, which performs well but struggles with certain situations involving bonds and equities. We modified the algorithm to improve CCEFs, although this comes with longer runtimes.
JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
(2023)
Article
Business, Finance
Darko B. Vukovic, Moinak Maiti, Michael Frommel
Summary: This study proposes and tests a portfolio selection model with inflation allocation lines for corresponding capital allocation line and utilities in several crisis scenarios. The model is based on Markowitz's mean-variance theory, with modification of Tobin's portfolio utility function and Sharpe's portfolio theory. The results show that under conditions of low and moderate inflation, investors choose an optimal portfolio that generates the highest real returns, while in the case of severe recession, investors select a minimum variance portfolio.
FINANCE RESEARCH LETTERS
(2022)
Article
Business, Finance
Junyoung Byun, Hyungjin Ko, Jaewook Lee
Summary: Privacy protection in the financial sector has become a pressing issue following strict regulations like GDPR. To address the privacy risk in robo-advisors, a new framework using homomorphic encryption (HE) to encrypt individual risk aversion is proposed. The model incorporates an HE-friendly method for constrained optimization to find an optimal solution for mean-variance quadratic programming with inequality constraints. Empirical evaluation reveals that the model can approximate optimal solutions with an acceptable level of accuracy loss and privacy preservation cost, and that the number of assets and correlation between assets impact the accuracy loss.
FINANCE RESEARCH LETTERS
(2023)
Article
Management
Moris S. Strub, Duan Li
OPERATIONS RESEARCH
(2020)
Article
Operations Research & Management Science
Moris S. Strub, Duan Li
OPERATIONS RESEARCH LETTERS
(2020)
Article
Economics
Youcheng Lou, Moris S. Strub, Duan Li, Shouyang Wang
Summary: The study found that the wealth growth and inequality of individual investors are influenced by the reference point determined by social comparison. Without social interactions, wealth grows at a common rate and inequality increases, but with solely social interactions in determining the reference point, there could be high wealth growth and reduced inequality simultaneously. Increasing social interactions benefits both wealth growth and inequality reduction in the general case with personal and social components in the reference point.
JOURNAL OF ECONOMIC DYNAMICS & CONTROL
(2021)
Article
Management
Yun Shi, Duan Li, Xiangyu Cui
Summary: The dynamic mean-variance formulation in the market lacks time consistency in efficiency. A new policy, called the TCIE policy, is developed to address the truncated time-horizon problem and avoid irrational investment behaviors, achieving better overall investment performance compared to existing policies.
JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
(2023)
Article
Operations Research & Management Science
Xiangyu Cui, Duan Li, Yun Shi, Mingjia Zhu
Summary: In a multiperiod mean-variance framework, the issue of time inconsistency in investor decision-making is addressed. Current approaches either assume no self-control capability or assume sufficient self-control capability. However, in reality, investors often have limited self-control ability. This study formulates the problem as a planner-middleman-doer game and derives the explicit expression for equilibrium strategy.
OPTIMIZATION LETTERS
(2023)
Article
Operations Research & Management Science
Xiang-Yu Cui, Duan Li, Xiao Qiao, Moris S. Strub
Summary: This article proposes a novel dynamic asset allocation framework based on a family of mean-variance-induced utility functions, which can alleviate the non-monotonicity and time-inconsistency problems of mean-variance optimization. The framework differs from mean-variance analysis by allowing different treatment of upside and downside deviations from a target wealth level, resulting in a different characterization of investment outcomes. It retains the intuitive explanation of the investment objective and easily computed optimal strategy, while providing a semi-analytical solution for the optimal trading strategy.
JOURNAL OF THE OPERATIONS RESEARCH SOCIETY OF CHINA
(2022)
Article
Operations Research & Management Science
Rujun Jiang, Duan Li
Summary: This paper considers the generalization of the extended trust region subproblem and derives sufficient conditions for guaranteeing the exactness of the SDP relaxation.
MATHEMATICS OF OPERATIONS RESEARCH
(2023)
Article
Automation & Control Systems
Man-Fai Leung, Jun Wang, Duan Li
Summary: This article discusses decentralized robust portfolio optimization based on multiagent systems, formulating it as two distributed minimax optimization problems. It demonstrates the efficacy of cooperative-competitive multiagent systems in reaching consensus on stock prices and convergence in investment allocations. Experimental results with stock data from four major markets support the effectiveness of multiagent systems for decentralized robust portfolio optimization.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Economics
Yuanyuan Chen, Qi Wu, Duan LI
Summary: We propose a counter-cyclical initial margin model for option portfolios that explores intrinsic netting and provides a constant upper bound for maximum potential loss. We compare our model with other existing models for netting efficiency and procyclical property. Using real examples, we quantify the additional margins needed for full counter-cyclicality and offer a flexible approach to balance risk-sensitivity and counter-cyclicality if required.
JOURNAL OF ECONOMIC DYNAMICS & CONTROL
(2023)
Article
Business, Finance
Hezhi Luo, Yuanyuan Chen, Xianye Zhang, Duan Li, Huixian Wu
Summary: We investigate the optimal portfolio deleveraging (OPD) problem with permanent and temporary price impacts. We propose a successive convex optimization (SCO) approach and a global algorithm for solving the OPD problem. The numerical experiments demonstrate the effectiveness of our proposed algorithms.
MATHEMATICAL FINANCE
(2023)
Article
Computer Science, Interdisciplinary Applications
Hezhi Luo, Xiaodong Ding, Jiming Peng, Rujun Jiang, Duan Li
Summary: This paper addresses the worst-case linear optimization problem with uncertainties on the right-hand side of the constraints, proving its NP-hardness and proposing an algorithm that combines optimization techniques to find globally optimal solutions. The article also introduces a finite branch-and-bound algorithm for a specific instance of the problem and demonstrates the effectiveness of the proposed algorithms through numerical experiments on medium and large-scale instances.
INFORMS JOURNAL ON COMPUTING
(2021)
Article
Mathematics, Applied
Rujun Jiang, Duan Li
SIAM JOURNAL ON OPTIMIZATION
(2020)
Article
Economics
Moris S. Strub, Duan Li, Xiangyu Cui, Jianjun Gao
JOURNAL OF ECONOMIC DYNAMICS & CONTROL
(2019)
Article
Economics
Youcheng Lou, Sahar Parsa, Debraj Ray, Duan Li, Shouyang Wang
JOURNAL OF ECONOMIC THEORY
(2019)
Article
Mathematics, Applied
Rujun Jiang, Duan Li
SIAM JOURNAL ON OPTIMIZATION
(2019)
Review
Management
Vinicius N. Motta, Miguel F. Anjos, Michel Gendreau
Summary: This survey presents a review of optimization approaches for the integration of demand response in power systems planning and highlights important future research directions.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Philipp Schulze, Armin Scholl, Rico Walter
Summary: This paper proposes an improved branch-and-bound algorithm, R-SALSA, for solving the simple assembly line balancing problem, which performs well in balancing workloads and providing initial solutions.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Roshan Mahes, Michel Mandjes, Marko Boon, Peter Taylor
Summary: This paper discusses appointment scheduling and presents a phase-type-based approach to handle variations in service times. Numerical experiments with dynamic scheduling demonstrate the benefits of rescheduling.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Oleg S. Pianykh, Sebastian Perez, Chengzhao Richard Zhang
Summary: Efficient scheduling is crucial for optimizing resource allocation and system performance. This study focuses on critical utilization and efficient scheduling in discrete scheduling systems, and compares the results with classical queueing theory.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Review
Management
Hamed Jahani, Babak Abbasi, Jiuh-Biing Sheu, Walid Klibi
Summary: Supply chain network design is a large and growing area of research. This study comprehensively surveys and analyzes articles published from 2008 to 2021 to detect and report financial perspectives in SCND models. The study also identifies research gaps and offers future research directions.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Patrick Healy, Nicolas Jozefowiez, Pierre Laroche, Franc Marchetti, Sebastien Martin, Zsuzsanna Roka
Summary: The Connected Max-k-Cut Problem is an extension of the well-known Max-Cut Problem, where the objective is to partition a graph into k connected subgraphs by maximizing the cost of inter-partition edges. The researchers propose a new integer linear program and a branch-and-cut algorithm for this problem, and also use graph isomorphism to structure the instances and facilitate their resolution. Extensive computational experiments show that, if k > 2, their approach outperforms existing algorithms in terms of quality.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Victor J. Espana, Juan Aparicio, Xavier Barber, Miriam Esteve
Summary: This paper introduces a new methodology based on the machine learning technique MARS for estimating production functions that satisfy classical production theory axioms. The new approach overcomes the overfitting problem of DEA through generalized cross-validation and demonstrates better performance in reducing mean squared error and bias compared to DEA and C2NLS methods.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Stefano Nasini, Rabia Nessah
Summary: In this paper, the authors investigate the impact of time flexibility in job scheduling, showing that it can significantly affect operators' ability to solve the problem efficiently. They propose a new methodology based on convex quadratic programming approaches that allows for optimal solutions in large-scale instances.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Zhiqiang Liao, Sheng Dai, Timo Kuosmanen
Summary: Nonparametric regression subject to convexity or concavity constraints is gaining popularity in various fields. The conventional convex regression method often suffers from overfitting and outliers. This paper proposes the convex support vector regression method to address these issues and demonstrates its advantages in prediction accuracy and robustness through numerical experiments.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Kuo-Hao Chang, Ying-Zheng Wu, Wen-Ray Su, Lee-Yaw Lin
Summary: The damage and destruction caused by earthquakes necessitates the evacuation of affected populations. Simulation models, such as the Stochastic Pedestrian Cell Transmission Model (SPCTM), can be utilized to enhance disaster and evacuation management. The analysis of SPCTM provides insights for government officials to formulate effective evacuation strategies.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Qinghua Wu, Mu He, Jin-Kao Hao, Yongliang Lu
Summary: This paper studies a variant of the orienteering problem known as the clustered orienteering problem. In this problem, customers are grouped into clusters and a profit is associated with each cluster, collected only when all customers in the cluster are served. The proposed evolutionary algorithm, incorporating a backbone-based crossover operator and a destroy-and-repair mutation operator, outperforms existing algorithms on benchmark instances and sets new records on some instances. It also demonstrates scalability on large instances and has shown superiority over three state-of-the-art COP algorithms. The algorithm is also successfully applied to a dynamic version of the COP considering stochastic travel time.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Bjorn Bokelmann, Stefan Lessmann
Summary: Estimating treatment effects is an important task for data analysts, and uplift models provide support for efficient allocation of treatments. However, evaluating uplift models is challenging due to variance issues. This paper theoretically analyzes the variance of uplift evaluation metrics, proposes variance reduction methods based on statistical adjustment, and demonstrates their benefits on simulated and real-world data.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Congzheng Liu, Wenqi Zhu
Summary: This paper proposes a feature-based non-parametric approach to minimizing the conditional value-at-risk in the newsvendor problem. The method is able to handle both linear and nonlinear profits without prior knowledge of the demand distribution. Results from numerical and real-life experiments demonstrate the robustness and effectiveness of the approach.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Laszlo Csato
Summary: This paper compares the performance of the eigenvalue method and the row geometric mean as two weighting procedures. Through numerical experiments, it is found that the priorities derived from the two eigenvectors in the eigenvalue method do not always agree, while the row geometric mean serves as a compromise between them.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Guowei Dou, Tsan-Ming Choi
Summary: This study investigates the impact of channel relationships between manufacturers on government policies and explores the effectiveness of positive incentives versus taxes in increasing social welfare. The findings suggest that competition may be more effective in improving sustainability and social welfare. Additionally, government incentives for green technology may not necessarily enhance sustainability.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
