An exact algorithm for small-cardinality constrained portfolio optimisation

Graham, David I.; Craven, Matthew J.

doi:10.1080/01605682.2020.1718019

Cited by 14 publications

(8 citation statements)

References 11 publications

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“…The dataset is obtained from the Economatica

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platform. It is noteworthy that small portfolios are common in the literature, as in Dombrovskii and Obyedko, 20 Dombrovskii and Obyedko, 21 Dombrovskii and Obyedko, 22 Dombrovskii, Obyedko, and Samorodova, 9 Hanaoka et al, 35 Barroso et al, 36 Graham and Craven, 37 and Miroforidis 38 …”

Section: Numerical Experimentsmentioning

confidence: 99%

“…The dataset is obtained from the Economatica © platform. It is noteworthy that small portfolios are common in the literature, as in Dombrovskii and Obyedko, 20 Dombrovskii and Obyedko, 21 Dombrovskii and Obyedko, 22 Dombrovskii, Obyedko, and Samorodova, 9 Hanaoka et al, 35 Barroso et al, 36 Graham and Craven, 37 and Miroforidis. 38 In order to evaluate the proposed strategies, we consider a daily price dataset with 2726 days from a dataset that comprehends the period from 2011 to 2021.…”

Section: Numerical Experimentsmentioning

confidence: 99%

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Investment portfolio tracking using model predictive control

Melo

Cardoso

Jesus

et al. 2022

Optim Control Appl Methods

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The composition of an investment portfolio aiming to increase the financial returns while reducing the exposure to risk is a topic of growing interest in the world. In this direction, we propose a model predictive control (MPC) strategy in order to optimize the investment portfolio selection taking into account the tracking of a reference portfolio with desired return. In addition, an analysis comparing different sizes of the prediction horizon according to VPH-MPC (Varying Predictive Horizon-MPC) and FPH-MPC (Fixed Predictive Horizon-MPC) algorithms is conducted. Finally, we propose an optimal control problem using the tracking error as a function of loss of CVaR (Conditional Value at Risk) measurement. Numerical experiments are run based on Brazilian Stock Exchange data. The experimental results are compared with the Markowitz portfolio optimization model, a conventional tracking strategy, and benchmarks from the Brazilian financial market. This comparison indicates a good tracking performance obtained by the proposed MPC in the two versions while satisfying the constraints.

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“…The dataset is obtained from the Economatica

{}^{\copyright }

Section: Numerical Experimentsmentioning

confidence: 99%

Section: Numerical Experimentsmentioning

confidence: 99%

Investment portfolio tracking using model predictive control

Melo

Cardoso

Jesus

et al. 2022

Optim Control Appl Methods

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show abstract

“…Their complex collective behavior and their equilibrium properties describe magnetism in solid-state systems 1 , phase transitions in spin glasses 2 , quantum chromodynamics (QCD) 3 , and quantum and classical computation 4 . Spin models are also pivotal in combinatorial optimization 5 , with applications in machine learning 6 , traffic and portfolio optimization 7 , markets and finance 8 , biology and life science 9 , artificial intelligence 10 , protein folding 11 , epidemic spreading 12 , bioinformatic 13 , and material engineering 14 .…”

Section: Introductionmentioning

confidence: 99%

“…Complex optimization problems permeate science and society and are crucial to machine learning [1], traffic and portfolio optimization [2], markets and finance [3], life science [4], bioinformatics [5], protein folding [6], and epidemic spreading [7]. Finding efficient ways to tackle such problems means determining the optimal configuration of a massive amount of degrees of freedom interacting in a highly nontrivial way.…”

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confidence: 99%

Multidimensional hyperspin machine

Strinati

Conti

2022

Nat Commun

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From condensed matter to quantum chromodynamics, multidimensional spins are a fundamental paradigm, with a pivotal role in combinatorial optimization and machine learning. Machines formed by coupled parametric oscillators can simulate spin models, but only for Ising or low-dimensional spins. Currently, machines implementing arbitrary dimensions remain a challenge. Here, we introduce and validate a hyperspin machine to simulate multidimensional continuous spin models. We realize high-dimensional spins by pumping groups of parametric oscillators, and show that the hyperspin machine finds to a very good approximation the ground state of complex graphs. The hyperspin machine can interpolate between different dimensions by tuning the coupling topology, a strategy that we call “dimensional annealing”. When interpolating between the XY and the Ising model, the dimensional annealing substantially increases the success probability compared to conventional Ising simulators. Hyperspin machines are a new computational model for combinatorial optimization. They can be realized by off-the-shelf hardware for ultrafast, large-scale applications in classical and quantum computing, condensed-matter physics, and fundamental studies.

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“…Their complex collective behavior and their equilibrium properties describe magnetism in solid-state systems [1], phase transitions in spin glasses [2], quantum chromodynamics (QCD) [3], and quantum and classical computation [4]. Spin models are also pivotal in combinatorial optimization [5], with applications in machine learning [6], traffic and portfolio optimization [7], markets and finance [8], biology and life science [9], artificial intelligence [10], protein folding [11], epidemic spreading [12], bioinformatic [13], and material engineering [14].…”

mentioning

confidence: 99%

Multidimensional hyperspin machine

Strinati¹,

Conti²

2022

Preprint

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From condensed matter to quantum chromodynamics, multidimensional spins are a fundamental paradigm, with a pivotal role in combinatorial optimization and machine learning. Machines formed by coupled parametric oscillators can simulate spin models, but only for Ising or low-dimensional spins. Currently, machines implementing arbitrary dimensions remain a challenge. Here, we introduce and validate a hyperspin machine to simulate multidimensional continuous spin models. We realize high-dimensional spins by pumping groups of parametric oscillators, and study NP-hard graphs of hyperspins. The hyperspin machine can interpolate between different dimensions by tuning the coupling topology, a strategy that we call "dimensional annealing". When interpolating between the XY and the Ising model, the dimensional annealing impressively increases the success probability compared to conventional Ising simulators. Hyperspin machines are a new computational model for combinatorial optimization. They can be realized by off-the-shelf hardware for ultrafast, large-scale applications in classical and quantum computing, condensed-matter physics, and fundamental studies.

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An exact algorithm for small-cardinality constrained portfolio optimisation

Cited by 14 publications

References 11 publications

Investment portfolio tracking using model predictive control

Investment portfolio tracking using model predictive control

Multidimensional hyperspin machine

Multidimensional hyperspin machine

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