21 papers · ranked by Valyu relevance
Nebojsa Bacanin, Milan Tuba
Portfolio optimization (selection) problem is an important and hard optimization problem that, with the addition of necessary realistic constraints, becomes computationally intractable. Nature-inspired metaheuristics are appropriate for solving such problems; however, literature review shows that there are very few…
Bhathiya Divelgama, Nancy Asare Nyarko, Naa Sackley Dromo Aryee, Abootaleb Shirvani + 1 more
Investing in Asian markets through exchange-traded funds (ETFs) provides investors with access to rapidly expanding economies and valuable diversification opportunities. This study examines the advantages and challenges of investing in Asian ETFs by conducting comprehensive risk assessments, portfolio analyses, and…
Łukasz Grodzki, Mateusz Slysz, Grzegorz Waligóra, Zhian Jia + 1 more
Quantum computing offers new possibilities for solving combinatorial optimization problems with rapidly growing search spaces. Among emerging hardware platforms, photonic quantum computers based on boson sampling provide a promising approach for sampling-based optimization methods. In this work, we investigate the…
Rahenda Khodier, Ahmed Radi, Basel Ayman, Mohamed Gheith
Financial Portfolio Optimization Problem (FPOP) is a cornerstone in quantitative investing and financial engineering, focusing on optimizing assets allocation to balance risk and expected return, a concept evolving since Harry Markowitz’s 1952 Mean-Variance model. This paper introduces a novel meta-heuristic approach…
Moein Owhadi-Kareshk, Pierre Boulanger
—Portfolio diversification is one of the most effective ways to minimize investment risk. Individuals and fund managers aim to create a portfolio of assets that not only have high returns but are also uncorrelated. This goal can be achieved by comparing the historical performance, fundamentals, predictions, news…
Kamer Ali Yüksel
This paper proposes a novel meta-learning approach to optimize a robust portfolio ensemble. The method uses a deep generative model to generate diverse and high-quality sub-portfolios combined to form the ensemble portfolio. The generative model consists of a convolutional layer, a stateful LSTM module, and a dense…
AmirMohammad Larni-Fooeik, Seyed Jafar Sadjadi, Emran Mohammadi, Shazia Rehman
'Shazia Rehman'] Portfolio optimization involves finding the ideal combination of securities and shares to reduce risk and increase profit in an investment. To assess the impact of risk in portfolio optimization, we utilize a significant volatility risk measure series. Behavioral finance biases play a critical role in…
Vassilios Papathanakos
| 1 | Introduction | | 1 | | --- | --- | --- | --- | | 2 | Fixed universe | | 2 | | | 2.1 Model and basic definitions | | 2 | | | 2.2 | Extremal portfolios | 4 | | | 2.3 Efficient frontier | | 5 | | | 2.4 Risk-adjusted return | | 7 | | | 2.5 | Below the efficient frontier | 7 | | | 2.6 | Volatility-stabilized markets |…
Bing Zhang
Traditional portfolio optimization methods face significant limitations in capturing complex asset relationships and adapting to dynamic market conditions. This paper proposes a novel graph attention-based heterogeneous multi-agent deep reinforcement learning framework that addresses these challenges through innovative…
Hang Kin Poon
Portfolio optimization is a critical area in finance, aiming to maximize returns while minimizing risk. Metaheuristic algorithms were shown to solve complex optimization problems efficiently, with Genetic Algorithms and Particle Swarm Optimization being among the most popular methods. This paper introduces an…
Yiqian Zhou, Weinan Chen, Deqin Lin
In the financial industry, it is of great significance to study the multiobjective portfolio optimization for obtaining a reasonable investment strategy. This paper designs the financial portfolio scheme based on the multiobjective optimization algorithm that is based on the framework of the NSGA-II algorithm. In order…
Daniele G. Gioia, Jacopo Fior, Luca Cagliero
Driving the decisions of stock market investors is among the most challenging financial research problems. Markowitz’s approach to portfolio selection models stock profitability and risk level through a mean-variance model, which involves estimating a very large number of parameters. In addition to requiring…
Authors not listed
We develop a comprehensive theoretical framework for quantum-enhanced risk modeling in financial systems, establishing mathematical foundations for representing and computing risk factors using quantum states and operations. The theory begins by formulating portfolio risk as quantum observables, where correlations…
Masoud Fekri, Babak Barazandeh
Optimal capital allocation between different assets is an important financial problem, which is generally framed as the portfolio optimization problem. General models include the single-period and multi-period cases. The traditional Mean-Variance model introduced by Harry Markowitz has been the basis of many models…
Jon Ahlinder, Patrik Waldmann
Optimum contribution selection (OCS) balances genetic gain and inbreeding by optimizing parental contributions to the next generation, but current implementations rely on point estimates of breeding values that discard the uncertainty inherent in genetic evaluations. We introduce CVaR-OCS, a novel formulation that…
Giovanni Giunta, Filipe Tostevin, Sorin Tănase-Nicola, Ulrich Gerland
Given a limited number of molecular components, cells face various allocation problems demanding decisions on how to distribute their resources. For instance, cells decide which enzymes to produce at what quantity, but also where to position them. Here we focus on the spatial allocation problem of how to distribute…
Lokendra S. Thakur, Nilanchali Singh, Gurpreet Bharj
Polygenic Risk Scores (PRS) are emerging tools for predicting an individual’s genetic risk for complex diseases. However, their usefulness in clinical practice remains limited because most existing models are based on data from people of European ancestry, leading to reduced accuracy and stability in other populations.…
S. Cavallero, A. Rousselot, R. Pugatch, L. Dinis + 1 more
We study a generalization of Kelly’s horse model to situations where gambling on horses other than the winning horse does not lead to a complete loss of the investment. In such a case, the odds matrix is non-diagonal, a case which is of special interest for biological applications. We derive a trade-off for this model…
Andre KY Low, Flore Mekki-Berrada, Aleksandr Ostudin, Jiaxun Xie + 7 more
The development of automated high-throughput experimental platforms has enabled fast sampling of high-dimensional decision spaces. To reach target properties efficiently, these platforms are increasingly paired with intelligent experimental design. When solving optimization problems, Bayesian-based optimizers are often…
Shriprakash Sinha
It is widely known that the sensitivity analysis plays a major role in computing the strength of the influence of involved factors in any phenomena under investigation. When applied to expression profiles of various intra/extracellular factors that form an integral part of a signaling pathway, the variance and density…
Authors not listed
Solving optimization problems, especially for nonlinear and constrained systems, is a challenge. Decades of specialized algorithms have been developed for general and special cases of root finding, minimization (including constraints), for parameter estimation, and mapping connected spaces. These approaches typically…