Results 11 to 20 of about 4,935,448 (269)
Distributionally Robust Portfolio Optimization [PDF]
2019 IEEE 58th Conference on Decision and Control (CDC), 2019 In this paper we consider the problem of portfolio optimization involving uncertainty in the probability distribution of the assets returns. Starting with an estimate of the mean and covariance matrix of the returns of the assets, we define a class of admissible distributions for the returns and show that optimizing the worst-case risk of loss can be ...
I E, Bardakci, C M, Lagoa
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Benchmarking the performance of portfolio optimization with QAOA [PDF]
Quantum Information Processing, 2022 We present a detailed study of portfolio optimization using different versions of the quantum approximate optimization algorithm (QAOA). For a given list of assets, the portfolio optimization problem is formulated as quadratic binary optimization ...
Sebastian Brandhofer +8 more
semanticscholar +1 more source
MetaTrader: An Reinforcement Learning Approach Integrating Diverse Policies for Portfolio Optimization [PDF]
International Conference on Information and Knowledge Management, 2022 Portfolio management is a fundamental problem in finance. It involves periodic reallocations of assets to maximize the expected returns within an appropriate level of risk exposure.
Hui Niu, Siyuan Li, Jian Li
semanticscholar +1 more source
Procedia Economics and Finance, 2015 The problem of investing money is common to citizens, families and companies. In this chapter, we introduce the decision framework of the portfolio selection problem in general terms. We describe the basic concepts of financial assets, capital to invest, performance (rate of return) and risk (measure of dispersion) possibly with the use of examples ...
Frajtova-Michalikova, Katarina +2 more
+5 more sources
Multi-Guide Set-Based Particle Swarm Optimization for Multi-Objective Portfolio Optimization
Algorithms, 2023 Portfolio optimization is a multi-objective optimization problem (MOOP) with risk and profit, or some form of the two, as competing objectives. Single-objective portfolio optimization requires a trade-off coefficient to be specified in order to balance ...
Kyle Erwin, Andries Engelbrecht
doaj +1 more source
Multi-period portfolio optimization using model predictive control with mean-variance and risk parity frameworks [PDF]
European Journal of Operational Research, 2021 We employ model predictive control for a multi-period portfolio optimization problem. In addition to the mean-variance objective, we construct a portfolio whose allocation is given by model predictive control with a riskparity objective, and provide a ...
Xiaoyue Li, A. Uysal, J. Mulvey
semanticscholar +1 more source
Portfolio optimization with digitized counterdiabatic quantum algorithms [PDF]
Physical Review Research, 2021 We consider digitized-counterdiabatic quantum computing as an advanced paradigm to approach quantum advantage for industrial applications in the NISQ era.
N. N. Hegade +5 more
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Meta-heuristics for portfolio optimization
Soft Computing - A Fusion of Foundations, Methodologies and Applications, 2023 Portfolio optimization has been studied extensively by researchers in computer science and finance, with new and novel work frequently published. Traditional methods, such as quadratic programming, are not computationally effective for solving complex ...
Kyle Erwin, A. Engelbrecht
semanticscholar +1 more source
Possibility theory for multiobjective fuzzy random portfolio optimization [PDF]
Decision Science Letters, 2014 The problem of portfolio optimization is a standard problem in financial world and it has received tremendous attentions. Portfolio optimization plays essential role in determining portfolio strategies for investors.
Mir Ehsan Hesam Sadati +2 more
doaj +1 more source
Dynamic portfolio optimization with inverse covariance clustering [PDF]
Expert systems with applications, 2021 Market conditions change continuously. However, in portfolio's investment strategies, it is hard to account for this intrinsic non-stationarity. In this paper, we propose to address this issue by using the Inverse Covariance Clustering (ICC) method to ...
Yuanrong Wang, T. Aste
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