Article
Management
Stefano Nasini, Martine Labbe, Luce Brotcorne
Summary: This paper introduces a novel optimization framework for multi-market portfolio management, where the market-wise portfolio selection is delegated to specialized affiliates. The problem is characterized as a single-leader-multi-follower game, with theoretical insights and numerical solution approaches provided. The study shows that the problem is NP-Hard, and proposes a decomposition procedure and strong valid inequalities to improve computational efficiency.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2022)
Article
Computer Science, Artificial Intelligence
Zhao-Rong Lai, Cheng Li, Xiaotian Wu, Quanlong Guan, Liangda Fang
Summary: In this study, a novel multitrend conditional value at risk (MT-CVaR) method is proposed for portfolio optimization, incorporating multiple trends and their influences to achieve state-of-the-art investing performance and risk management.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Business, Finance
Luis Chavez-Bedoya, Francisco Rosales
Summary: This document presents the various advantages of using portfolio rules composed by linear combinations of orthogonal components derived from the optimal solution to a linearly constrained mean-variance portfolio optimization problem. The authors argue that this approach improves the tractability of out-of-sample performance measure and the identification of risk sources in the portfolio. They propose new correction schemes based on shrinkage factors to enhance out-of-sample performance and study its behavior as the sample size and the number of assets increase. Furthermore, the results are compared with the three-fund rules of Kan and Zhou (2007) to highlight the benefits of using orthogonal portfolio rules.
INTERNATIONAL REVIEW OF ECONOMICS & FINANCE
(2022)
Article
Business, Finance
Luis Chavez-Bedoya, Francisco Rosales
Summary: This document provides a theoretical analysis of the effects of redundant constraints on the out-of-sample performance of optimal MV portfolios, showing that adding any set of redundant linear constraints can improve the out-of-sample performance of the plug-in estimator of the optimal MV portfolio. The study also illustrates a trade-off between diversification and estimation risk when risky assets are equally correlated and identically distributed, with the allocation of estimation risk across portfolios forming the optimal solution changing dramatically in terms of number of assets and correlations.
INTERNATIONAL REVIEW OF FINANCIAL ANALYSIS
(2021)
Article
Computer Science, Information Systems
Jose Almeida, Joao Soares, Fernando Lezama, Zita Vale
Summary: This paper proposes a risk-based optimization approach for centralized day-ahead energy resource management (ERM) considering extreme events. The risk-averse strategy implements the conditional value-at-risk (CVaR) method to account for worst-case scenario costs. The case study shows that the risk-averse strategy increases operational costs but reduces worst-case scenario costs, providing safer and more robust solutions.
Article
Business, Finance
Daniel Borer, Devmali Perera, Fitriya Fauzi, Trinh Nguyen Chau
Summary: Since their emergence in the late 1980s, the Value at Risk models have become the global standard in risk management and forecasting. However, concerns have been raised about their effectiveness in managing risk during global financial crises and some attribute financial contagion to Value at Risk practices. In this study, a new measure called Value at Risk elasticity is proposed and it proves to be effective in predicting asset losses during global crises. Based on the findings, including Value at Risk elasticity as an index is recommended for better risk management in portfolios.
INTERNATIONAL REVIEW OF FINANCIAL ANALYSIS
(2023)
Article
Mathematics, Interdisciplinary Applications
Yuyuan Liu, Linjie Liu, Ruqiang Guo, Liang Zhang
Summary: This study introduces the conditional investment strategy and government management into a repeated trust game, and finds that the introduction of conditional investment strategy and incentives can promote the evolution of trust. Additionally, as punishment intensity increases, trust can emerge and investment behavior can be maintained. These theoretical results are further verified by numerical simulations.
CHAOS SOLITONS & FRACTALS
(2023)
Article
Green & Sustainable Science & Technology
Mingming Zhang, Yamei Tang, Liyun Liu, Dequn Zhou
Summary: In the investment decision-making of electric power enterprises, selecting a portfolio of different power generation technologies and adapting to changes in policy scenarios can determine the optimal investment strategy by using real option and portfolio optimization models to ensure expected value while reducing risks and increasing the share of renewable energy power generation.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2022)
Article
Public, Environmental & Occupational Health
Xiwen Bai, Jasmine Siu Lee Lam
Summary: This article presents a study on risk estimation using the value-at-risk (VaR) method. It proposes a copula-based GARCH model to estimate the joint multivariate distribution, which can capture the VaR more successfully. The results have significant implications for shipowners in decision making and risk management.
Article
Operations Research & Management Science
Avinash N. Madavan, Subhonmesh Bose
Summary: A first-order primal-dual subgradient method is studied for optimizing risk-constrained risk-penalized problems using the popular CVaR measure. The algorithm achieves approximately feasible and optimal points within K iterations with constant step-size, where eta increases with tunable risk parameters of CVaR. The computational cost of risk aversion is characterized by the growth in eta, and no prior bounds are needed for dual variables due to a simple modification in the algorithm structure.
JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS
(2021)
Article
Business, Finance
Paolo Capelli, Federica Ielasi, Angeloantonio Russo
Summary: This study proposes a new risk metric called VaRESG that combines traditional market risk measure with ESG factors. The empirical application shows that VaRESG has the predictive power to reduce unexpected losses, especially under stress conditions. This is the first attempt in the financial literature to effectively integrate ESG risks into the VaR measure to predict expected losses of an equity portfolio.
FINANCE RESEARCH LETTERS
(2023)
Article
Business, Finance
Mikica Drenovak, Vladimir Rankovic, Branko Urosevic, Ranko Jelic
Summary: We have developed a novel approach to optimize Mean-Max Drawdown using buy-and-hold portfolios. By utilizing a multi-objective evolutionary algorithm on a sample of S&P 100 constituents, our optimization procedure consistently provides portfolios with better trade-offs than relevant benchmarks, regardless of selected subsamples and market conditions. The superiority of our method is particularly evident in periods of reversing market trends.
FINANCE RESEARCH LETTERS
(2022)
Article
Computer Science, Artificial Intelligence
Qiqi Li, Zhongfeng Qin, Yingchen Yan
Summary: This paper discusses a hybrid portfolio optimization problem involving mature and newly listed securities. It introduces uncertain random variables and tail value-at-risk (TVaR) to measure risk, proving mathematical properties of TVaR and providing numerical algorithms and models for optimization. A numerical example is conducted to demonstrate the method's application.
Article
Business, Finance
Dejan Zivkov, Slavica Manic, Jasmina Duraskovic, Marina Gajic-Glamoclija
Summary: This paper minimizes the risk of Brent oil in a multivariate portfolio by analyzing variance, parametric VaR, and semiparametric VaR. The results show that ASEAN stock indexes are better hedges for oil in terms of minimum variance and VaR. However, portfolios with non-ASEAN indexes have slightly lower modified VaR. When a 50 percent constraint is applied to Brent in the portfolios, the portfolios with non-ASEAN indexes perform better in terms of risk minimization.
BORSA ISTANBUL REVIEW
(2022)
Article
Energy & Fuels
Ling Li, Guopeng Hu
Summary: The accurate measurement and management of energy risk are crucial for economic development and energy security of all countries. The existing literature often uses Value at Risk (VaR) for measurement, but VaR does not satisfy the subadditivity axiom, leading to inaccurate calculation. In this paper, we propose using Conditional VaR (CVaR) under the nonparametric kernel framework to measure energy risk, and the empirical results demonstrate its effectiveness.
FRONTIERS IN ENERGY RESEARCH
(2022)
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)