Portfolio optimization is an activity for balancing return and risk. In this paper, we used mean-variance (M-V) portfolio models with buy-in threshold and cardinality constraints. This model can be formulated as a mixed integer nonlinear programming...
This paper investigates performance attribution measures as a basis for constraining portfolio optimization. We employ optimizations that minimize conditional value-at-risk and investigate two performance attributes, asset allocation (AA) and the sel...
Portfolio optimization is fundamental to modern finance, enabling investors to construct allocations that balance risk and return while satisfying practical constraints. When transaction costs and cardinality limits are incorporated, the problem beco...
This research develops an original algorithm for rich portfolio optimization (ARPO), considering more realistic constraints than those usually analyzed in the literature. Using a matheuristic framework that combines an iterated local search metaheuri...
Bond ETF rebalancing is difficult to describe with return and risk objectives alone, because a portfolio that looks attractive on paper may still be impractical if it requires large and unstable trades. This paper proposes a topology-aware multi-obje...
Sustainable economic decision making increasingly requires robust methodologies capable of withstanding deep uncertainty, particularly in volatile financial and resource-constrained environments. This paper introduces a unified optimization framework...
Large-scale constrained multi-objective optimization problems (LCMOPs) pose significant challenges due to the curse of dimensionality, complex constraint landscapes, and high computational overhead. In time-sensitive scenarios, existing large-scale c...
Portfolio optimization is one of the problems most frequently encountered by financial practitioners. The main goal of this paper is to fill a gap in the literature by providing a well-documented, step-by-step open-source implementation of Critical L...
This paper revisits the dynamic MV portfolio selection problem with cone constraints in continuous-time. We first reformulate our constrained MV portfolio selection model into a special constrained LQ optimal control model and develop the optimal por...
We consider the problem of portfolio optimization with a correlation constraint. The framework is the multi-period stochastic financial market setting with one tradable stock, stochastic income, and a non-tradable index. The correlation constraint is...
This study investigates the performance of Bitcoin as a diversifier under different constraining portfolio optimization frameworks. The study employs different constraining optimization frameworks that seek to maximize risk-adjusted returns (Sharpe r...
Portfolio optimization is a mathematical formulation whose objective is to maximize returns while minimizing risks. A great deal of improvement in portfolio optimization models has been made, including the addition of practical constraints. As the nu...
This paper addresses the problem of constructing optimal equity portfolios under volatile market conditions by minimizing realized volatility—an alternative risk quantifier that more accurately captures short-term market fluctuations than tradi...
This paper presents the study of multi-objective optimization of a pharmaceutical portfolio when both cost and return values are uncertain. Decision makers in the pharmaceutical industry encounter several challenges in deciding the optimal selection...
Portfolio management based on mean-variance portfolio optimization is subject to different sources of uncertainty. In addition to those related to the quality of parameter estimates used in the optimization process, investors face a portfolio impleme...
In the paper, we introduce a multi-objective scenario-based optimization approach for chance-constrained portfolio selection problems. More specifically, a modified version of the normal constraint method is implemented with a global solver in order...
In this paper we formulate the Risk Management Control problem in the interest rate area as a constrained stochastic portfolio optimization problem. The utility that we use can be any continuous function and based on the viscosity theory, the unique...
In modern financial markets, decision-makers increasingly rely on quantitative methods to navigate complex trade-offs among multiple, often conflicting objectives. This paper addresses constrained multi-objective optimization (MOO) with an applicatio...
Enhanced indexation is an active portfolio management strategy aimed to find a portfolio outperforming a market index. To ensure stable returns and to avoid extreme losses, a sensible enhanced indexation model should be sustainable, where the paramet...
Traditional mean–variance portfolio optimization proves inadequate for cryptocurrency markets, where extreme volatility, fat-tailed return distributions, and unstable correlation structures undermine the validity of variance as a comprehensive...
Accurately predicting asset returns remains a central challenge in finance, with significant implications for portfolio optimization and risk management. In response to the challenge, this study evaluates the predictive performance of machine learnin...
In many real-world domains, optimization problems involve both quantitative objectives and qualitative criteria that cannot be directly formulated as mathematical functions. While recent research has integrated large language models (LLMs) into optim...
Commodity price co-movements significantly impact investment decisions. High correlations constrain portfolio diversification and limit risk mitigation potential. While international markets often exhibit strong price linkages, understanding national...
This paper introduces a two-phase decision support system based on information theory and financial practices to assist investors in solving cardinality-constrained portfolio optimization problems. Firstly, the approach employs a stock-picking proced...
Mean-variance optimization often leads to unreasonable asset allocations. This problem has forced scholars and practitioners alike to introduce portfolio constraints. The scope of our study is to verify which type of constraint is more suitable for a...
Weekly macro-financial and financial market data, combined with machine learning methods, offer new possibilities for identifying latent economic states in real time, but the portfolio value of regime detection depends critically on how detected stat...
Many data analysis problems can be modeled as a constrained optimization problem characterized by nonsmooth functionals, often because of the presence of
Cloud integration can be economically attractive yet operationally unsuitable when dependent services exchange traffic over constrained network paths. This study develops a network-aware extension of a time-dependent, three-objective integration mode...
We study an infinite-horizon consumption–investment problem in which an investor endogenously manages a consumption comfort zone above a fixed subsistence benchmark. Consumption can move freely within the prevailing admissible interval, while u...
The mining industry generates substantial volumes of operational waste, including lubricant oils, lead-acid batteries, steel scrap, and end-of-life tires (ELTs). Their valorization is increasingly required under Extended Producer Responsibility (EPR)...
Disruptions to high-speed rail (HSR) can cause passenger-service losses across cities, while air alternatives depend on transfers and residual capacity. This study evaluates functional robustness in an HSR-centered rail–air network and selects...
Practitioners choosing among risk measures for portfolio construction face a real question: does the choice of risk proxy—variance, mean absolute deviation (MAD), interquartile range (IQR), or a wider central-quantile measure—actually cha...
Artificial intelligence (AI) and machine learning (ML) are increasingly important in financial technology (FinTech) applications involving large datasets, uncertainty, and complex decision-making. First, this paper presents a review of AI- and ML-bas...
Glycolic acid (GA) is an important platform chemical with applications in biodegradable polymers, cosmetics, and pharmaceutical synthesis; however, traditional petrochemical routes raise environmental concerns, and current microbial production platfo...
As global decarbonization accelerates, the environmental and economic viability of hydrogen production largely depends on the evolving electricity supply mix. This study focused on alkaline water electrolysis (AWE) to identify the key factors affecti...
In research of a better description for information uncertainty, Z-numbers, which are related to both the objective information and the subjective criticism, were first conceptualized by Zadeh. Because of its neologism, there have been multitudinous...
To ensure that limited domestic resources are invested in the most effective interventions, immunization programs in low- and middle-income countries (LMICs) must prioritize a growing number of new vaccines while considering opportunities to optimize...
Climate change caused by greenhouse gas emissions is of critical concern to international shipping. A large portfolio of mitigation measures has been developed to mitigate ship gas emissions by reducing ship energy consumption but is constrained by p...
Variable renewable energy sources and cross-zonal trades stress transmission grids, pushing them toward thermal limits. This systematic review, reported in accordance with PRISMA 2020, examines how demand-side response (DSR) can provide relief at the...
This study provides fully mathematically and economically feasible solutions to estimating implied correlation matrices in equity markets. Factor analysis is combined with option data to receive ex ante beliefs for cross-sectional correlations. Neces...
This follow-up scientific case study builds on prior research to explore the computational challenges of applying quantum algorithms to financial asset management, focusing specifically on solving the graph-cut problem for investment recommendation....
To address the limitations of traditional investment management in volatile markets, this study develops an automated decision-making system for Taiwan Exchange-Traded Funds (ETFs) based on Bidirectional Long Short-Term Memory (Bi-LSTM) networks. The...
Digital transformation in higher education has increasingly shifted from a technology-centered agenda toward a multidimensional institutional process involving governance, quality assurance, process redesign, and data-driven decision-making. This stu...
This comprehensive review maps how China’s demand-side resources are aggregated, bid into markets, and monetized at the grid edge. We synthesize original studies and pilots to compare edge architectures for local estimation and privacy-preservi...
Effective management of organic wastes is essential for green and low-carbon development. Conventional technologies, including incineration, pyrolysis, hydrothermal carbonization (HTC), gasification, anaerobic digestion (AD), and composting, have sup...
Smart city governance increasingly relies on AI-enabled planning systems, digital twins, vulnerability scoring tools, and capital investment platforms to allocate climate-resilient housing and infrastructure investments. Yet existing smart-urbanism a...
Accurate conditional volatility forecasting is essential for risk management, asset pricing, and portfolio optimization. Despite the widespread use of GARCH family models and the proliferation of deep learning architectures, a methodological gap pers...
Credit risk assessment under severe class imbalance requires both structured imbalance correction and principled decision rules, yet most studies treat these as independent steps. This study develops a general integrated three-layer framework for cre...
Methane emissions from dairy production constitute a significant share of agricultural greenhouse gas emissions in Poland and represent a key challenge under EU climate policy and the Common Agricultural Policy (CAP). This study evaluates dairy farme...
Mission-critical Internet of Things (MC-IoT) applications, such as remote surgery, autonomous drone swarms, industrial safety monitoring, etc., have ultra-stringent requirements for reliability (99.999%), latency (sub-millisecond) and inter-slice iso...
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